TL;DR: Microsoft Copilot agents saw four new releases between February 26 and April 13, 2026: Copilot Tasks, Copilot Cowork powered by Claude, the Frontier Transformation agent suite, and a local OpenClaw-style agent previewing at Microsoft Build 2026. Copilot still lags Claude and ChatGPT on model quality, but Microsoft’s 400 million Microsoft 365 users make this rollout unavoidable for enterprise leaders.

I use Claude almost exclusively when I build. Claude Code sits on my desktop pretty much at all times now. But almost every leader I coach, whether nonprofit executive or Fortune 500, works inside Microsoft 365. Copilot is already in their email, documents, and Teams. Switching to Claude isn’t a button click for them. It’s a vendor negotiation, a training rollout, and a security review.

Copilot lost the model race. Run the same prompt in Claude, ChatGPT, and Copilot side by side and that’s obvious. But Microsoft isn’t trying to catch up on models anymore. Between February 26, 2026 and April 13, 2026, they shipped or previewed four AI releases, aimed at the 400 million people working inside Microsoft 365 every day. 70% of Fortune 500 companies already use Copilot. If your team is one of them, this matters more than which model won the benchmark this quarter.

In this post, you’ll learn:

• What each of the four new Copilot agents does, with honest pros and cons

• Why Microsoft’s 400 million-user footprint changes the agent rollout equation, even with Copilot still lagging Claude and ChatGPT on model quality

• What to do about Microsoft’s agent rollout this week, before Build 2026 in June

Agent 1: Copilot Tasks (February 26, 2026)

Copilot Tasks is Microsoft’s first real move from chat-and-search to action. Released in preview on February 26, 2026, Tasks takes multi-step actions on behalf of the user (organizing email, booking travel, prepping for recurring meetings) on a schedule you define. It runs in the cloud, not on your device.

Promising:

• First Copilot release that crosses from answering questions to completing actions.

• “Set and forget” automation model is closer to how most leaders actually want AI to work.

Limited:

• Covers tasks already handled by ChatGPT Agent and Claude’s computer use, tools most analysts still consider more capable.

• Still in preview as of April 2026, with no final release date confirmed.

Why it matters: If your team’s mental model of Copilot is “the thing in the sidebar that writes email drafts,” that model is already out of date.

How to apply this Monday: Pick one recurring weekly task your team handles manually (Monday status recap, weekly customer sync prep, end-of-week priority digest). Have whoever owns Copilot admin in your tenant enable the Tasks preview and configure one Task for that workflow. Try this starter prompt inside Copilot Chat:

“Create a Copilot Task that summarizes my last week of customer emails every Monday at 8am and drafts three priority follow-ups.” 

Review output quality for two weeks before scaling to a second Task.

Agent 2: Copilot Cowork, Powered by Claude (March 9, 2026)

Copilot Cowork is the most significant Copilot release of the year so far (and runs on Anthropic’s Claude!!!). It runs across Microsoft 365 apps (Word, Excel, Outlook, Teams) and takes cross-app actions autonomously: gather data from SharePoint, draft a memo in Word, format numbers in Excel, queue it for review in Teams. It’s powered by Microsoft’s “Work IQ” layer and grounded in the tenant’s actual data.

Microsoft’s Researcher agent inside Microsoft 365 Copilot now offers “Try Claude” as a model option. This is what “distribution layer for the models that won” looks like in production UI. (Image: Microsoft)

Promising:

• Runs on Anthropic’s Claude, not just GPT. Microsoft partnered with Anthropic late last year and now lets enterprise customers pick the model.

• Grounded tenant access (SharePoint, Graph, Dataverse) gives Cowork context standalone Claude and ChatGPT can’t match.

Limited:

• Cloud-only. Data-residency-sensitive organizations (government, defense, some healthcare) still can’t use it.

• Till Freitag’s April 2026 Copilot assessment called Excel integration “significantly weaker” and PowerPoint generation “generic.” The surrounding scaffolding limits what Claude can do inside it.

Why it matters: Cowork lets you run Claude against your own Microsoft data without leaving the stack you already pay for. For most leaders I coach, that matters more than any standalone AI tool launched this year. Adding Cowork with Claude underneath should raise the ceiling further for well-configured tenants.

How to apply this Monday: Identify one cross-app workflow your team currently does manually, something like “pull vendor performance data from SharePoint, summarize it in Excel, draft a board memo in Word.” Ask your IT admin whether Cowork with Claude model selection is available on your license tier.

If yes, pilot it on that workflow for two weeks. If no, get on the wait list and block time to revisit in Q3.

A word from our partners — tools I use daily

Here’s the thing about Cowork. Even if your IT team approves it next quarter, most of you won’t touch it until Q3. But the describe-it-and-it’s-built pattern that makes Cowork interesting is already shipping in simpler tools.

Jotform is a Leadership in Change partner, and it’s the cleanest example I use. You describe the form you need (”a 3-question lead capture form with name, email, and biggest AI challenge”) and Jotform AI builds the fields, sets the logic, and makes it production-ready in seconds.

Leadership in Change readers get 50% off annual plans through my partner link.

Agent 3: The Frontier Transformation Agents (March 9, 2026)

On the same day as Cowork, Microsoft announced a broader Frontier Transformation agent suite on Copilot Studio. These are autonomous agents for specific business functions: a Sales Qualification Agent that prioritizes leads, a Supplier Communications Agent that tracks vendor performance and delays, and role-specific agents for HR, finance, and operations. The suite extends the ten autonomous agents Microsoft first announced in October 2024.

Copilot Studio’s natural-language agent builder lets non-developers spin up role-specific agents by describing what they need. (Image: Microsoft)

Promising:

• SharePoint hosts over 2 million sites and absorbs 2 billion file uploads daily. That knowledge base is what these agents now sit inside.

Limited:

• Futurum Group analysts call the training process “toilsome,” “labor-intensive,” and “time-consuming.” Expect heavy human supervision in the early phases.

• The 300-seat minimum on Enterprise Agreements means small and mid-size organizations pay for licenses that often go unused.

Why it matters: If your organization has clean SharePoint data and the patience to train these agents, they represent the highest ROI path on the Microsoft roadmap. If you don’t, they become expensive shelfware.

How to apply this Monday: Audit your SharePoint for one “clean-data zone,” a site or document library that is well-organized, current, and has consistent metadata. That zone becomes your pilot ground. Start with Supplier Communications (if you have a vendor management function) or Sales Qualification (if you run outbound).

Set a clear two-week success metric before you start: hours saved, leads processed, response time, something measurable. Without that, the Futurum warning about “labor-intensive training” becomes a budget line with no payoff.

Agent 4: The Local OpenClaw-Style Agent (Previewed April 13, 2026)

TechCrunch reported on April 13, 2026 that Microsoft is testing an OpenClaw-like agent that runs locally on the user’s machine, not in the cloud. The working description: a Copilot that is always on, takes actions over long periods, and completes multi-step tasks in the background. Microsoft confirmed the feature to The Information and will reveal it at Microsoft Build 2026 in June.

Promising:

• Local agents can run offline, access local files without upload friction, and maintain long-horizon context that cloud agents can’t.

• Microsoft’s version will ship with tighter security controls than the open-source OpenClaw, which has a documented reputation for acting unpredictably inside users’ inboxes.

Limited:

• Not a product yet. Preview only, with practical limits unclear until Microsoft shows a working demo at Build 2026.

• XDA Developers warns that customers already committed to OpenClaw or Claude Cowork may not switch just because Microsoft ships a version inside the tenant.

Why it matters: If this ships as advertised, it’s the first Copilot release that competes on agent autonomy, not just distribution. The timeline to pay attention is June, not next year. If you lead a Microsoft-stack organization, put Build 2026 on your leadership team’s calendar this week.

How to apply this Monday: Nothing to implement today. Three things to schedule: (1) block the Microsoft Build 2026 keynote on your leadership team’s calendar, (2) brief your IT lead that a local Copilot agent is coming and ask what approval path would be needed, (3) set a 48-hour review window post-keynote to translate what Microsoft ships into a go or no-go for your organization.

The 400 Million-User Reason You Should Care

Microsoft 365 has over 400 million commercial users, and 70% of Fortune 500 companies use Copilot. That is not a model-quality advantage. It’s a distribution advantage, and in enterprise software, distribution usually wins.

Claude is the clear agent leader right now. Claude Code makes that obvious. But most leaders I coach won’t spin up Claude Code at their nonprofit or Fortune 500 employer this quarter. They will see Cowork appear in their Word ribbon in Q3. Their IT team will get briefed on the Copilot Control System governance layer. Their board will watch a demo of the local OpenClaw-style agent at Build 2026.

This is the same pattern I covered in my analysis of Meta’s $2 billion Manus acquisition and Apple’s quiet shift to Google’s AI. Platforms with existing distribution don’t need to win the AI race. They need to be in the room when their customers decide.

Stop thinking of Copilot as a chatbot. It’s the agent distribution layer your team will meet first.

Even If You Use Claude or ChatGPT, This Still Matters

If you read this far and thought, “I use Claude, so Microsoft’s moves don’t apply to me,” here’s why they do. Most of the people you work with use Copilot. Your colleagues, your vendors, your clients, the board you report to. When 70% of Fortune 500 companies are standardized on Microsoft 365 and Microsoft is wrapping the best models (Claude, GPT) in front of that footprint, ignoring Microsoft’s agent moves in 2026 is the AI-era equivalent of ignoring what Salesforce did to CRM in 2010. You don't have to switch your personal stack to care about this. What you do need is an accurate read on the ground your organization is already standing on, because that's what your team, your vendors, and your board will be navigating in Q3 whether you're in the room for the decision or not.

Regardless of whether your preferred AI is Claude or ChatGPT, if you are an AI user, it is foolish to ignore what is happening with Microsoft in 2026.

Microsoft is not trying to beat Claude on model quality. They don’t have to. They are becoming the distribution layer for the models that won, and Cowork already ships with Claude as an option. Satya Nadella’s own framing tells you where Microsoft’s head is at: “Just like I can build a spreadsheet, I will build thousands and hundreds of agents that will streamline my own work” (Outlook Business, January 2025).

Nadella's framing is platform-layer. Thousands of agents, built like spreadsheets, running inside the 400 million-seat footprint Microsoft already owns.

This is the adaptability test for leaders, not vendors. The AI Leadership Triad (Adaptability, Innovation, and Creativity) says the leaders who thrive update their mental model fast when the ground shifts. If your working assumption is still “Copilot is a chatbot, and Claude is the real tool,” you are two quarters behind where your IT team is already heading.

If You Only Remember This

• Copilot lost the model race. Microsoft is not trying to win it. They’re shipping agents on top of 400 million enterprise seats, which is a different and arguably smarter play.

• Cowork already runs on Claude. Microsoft’s agent strategy is becoming a distribution layer for the winning models, not a competitor to them.

• If you use Claude or ChatGPT and ignore Microsoft’s 2026 agent moves, you’re making the same mistake leaders made ignoring Salesforce’s CRM pivot in 2010. You don’t have to switch. You have to know.

If Microsoft showed you a working local Copilot agent at Build 2026 that ran on your machine, integrated with your files, and used Claude as the underlying model, would your IT team approve it in 2026, or would it take another year of policy revisions?

The 5-Step Microsoft Agent Response Playbook

Every agent section above has a Monday action. Here is the consolidated version, ordered by what to do first. (This is also available as the branded checklist below — save it, share it with your IT lead, make it the spine of your next leadership team meeting on AI.)

Questions Leaders Are Asking

What is Microsoft Copilot Cowork and how is it different from regular Copilot? Copilot Cowork, launched March 9, 2026, takes autonomous cross-app actions across Microsoft 365 (drafting in Word, formatting in Excel, posting to Teams) without the user switching apps. Regular Copilot answers questions. Cowork completes multi-step work, and it can run on Anthropic’s Claude as the underlying model, not only GPT.

Can I use Claude inside Microsoft Copilot? Yes, as of March 2026. Microsoft partnered with Anthropic to let enterprise customers select Claude as the model powering Copilot Cowork. Features like “Critique” use Claude to refine GPT output, and “Council” lets users compare both side by side. Standalone Claude still outperforms Copilot on pure reasoning, but Claude-inside-Cowork gives you Claude with your Microsoft 365 data grounded automatically.

Is Copilot worth it in 2026 if Claude and ChatGPT are better models? It depends on your stack. If your organization lives in Microsoft 365, Copilot’s agent layer reaches workflows Claude and ChatGPT cannot touch without significant integration work. Till Freitag’s April 2026 assessment notes Copilot still has quality gaps in Excel and PowerPoint, but Microsoft’s tenant-grounded data access is a real advantage for enterprise teams.

What is Agent 365 and why does it matter for IT leaders? Agent 365 is Microsoft’s governance layer for observing, securing, and managing every agent running inside a Microsoft 365 tenant, whether built in Copilot Studio, shipped by Microsoft, or sourced from third parties. For regulated industries, it is the control plane that makes agent adoption approvable in the first place.

When will Microsoft reveal the new local OpenClaw-style Copilot agent? Microsoft is expected to preview the local, always-on Copilot agent at Microsoft Build 2026 in June. The April 13, 2026 TechCrunch report confirmed Microsoft is testing the capability, with a focus on tighter enterprise security controls than the open-source OpenClaw project.

What is Copilot Tasks used for? Copilot Tasks, released in preview February 26, 2026, runs multi-step actions on behalf of the user on a schedule you define, including organizing email, booking travel, and prepping for recurring meetings. Unlike regular Copilot, which answers questions, Tasks completes actions in the background.

How does Microsoft Copilot compare to Claude and ChatGPT in 2026? Claude and ChatGPT still lead on pure model quality in 2026. Copilot’s advantage is not the model, it is the integration with Microsoft 365 data (SharePoint, Graph, Teams, Outlook) and the ability to run Anthropic’s Claude inside Cowork. For teams standardized on Microsoft, Copilot’s tenant-grounded agent layer reaches workflows that standalone Claude and ChatGPT cannot without significant integration work.

Can small businesses use Microsoft Copilot agents in 2026? Yes, but with caveats. The Frontier Transformation agent suite carries a 300-seat minimum on Enterprise Agreements, which makes it expensive for small and mid-size organizations. Copilot Tasks and Cowork are available at lower license tiers. Smaller teams should focus on Cowork with Claude and Tasks before investing in the larger autonomous agent suite.

Sources Referenced

• Julie Bort, “Microsoft is working on yet another OpenClaw-like agent,” TechCrunch, April 13, 2026 — https://techcrunch.com/2026/04/13/microsoft-is-working-on-yet-another-openclaw-like-agent/

• Jared Spataro, “Introducing Copilot Actions, new agents, and tools to empower IT teams,” Microsoft 365 Blog, November 19, 2024 — https://www.microsoft.com/en-us/microsoft-365/blog/2024/11/19/introducing-copilot-actions-new-agents-and-tools-to-empower-it-teams/

• “Copilot Tasks: From answers to actions,” Microsoft 365 Copilot Blog, February 26, 2026 — https://www.microsoft.com/en-us/microsoft-copilot/blog/2026/02/26/copilot-tasks-from-answers-to-actions/

• “Copilot Cowork: A new way of getting work done,” Microsoft 365 Blog, March 9, 2026 — https://www.microsoft.com/en-us/microsoft-365/blog/2026/03/09/copilot-cowork-a-new-way-of-getting-work-done/

• “Powering Frontier Transformation with Copilot and agents,” Microsoft 365 Blog, March 9, 2026 — https://www.microsoft.com/en-us/microsoft-365/blog/2026/03/09/powering-frontier-transformation-with-copilot-and-agents/

• Till Freitag, “Microsoft Copilot 2026: The Complete Guide,” April 4, 2026

• Simon Batt, “Microsoft wants Copilot to run like OpenClaw,” XDA Developers, April 13, 2026

• Keith Kirkpatrick, “What Makes Microsoft’s Autonomous Agents a Game Changer for Workers?” The Futurum Group, October 29, 2024

• Alka Jain, “Satya Nadella Reveals How AI Agents Will Disrupt SaaS Models,” Outlook Business, January 10, 2025

TL;DR: AI systems rarely fail at generating output. They fail because no one defined what they’re responsible for, what they’re not allowed to do, or when they should stop and escalate. Judy Ossello, a Fortune 100 program leader, draws on real corporate reorg experience to show how the same accountability gaps that derail human teams are now breaking AI systems, and offers a readiness check to fix it.

Imagine showing up to work on Monday and finding out your VP is gone, senior leadership is disappearing, and by Thursday no one can tell you who you report to or what you’re responsible for.

That happened to , and the way she navigated it, reading signals, defining her own role, making herself useful without a job description, is exactly how AI systems are operating right now in most organizations. The difference is that AI doesn’t know it’s improvising.

Judy is a Fortune 100 enterprise IT program leader and AI Mechanic who designs AI agents whose behavior stays predictable, interpretable, and governable under pressure. She’s spent years operating in high-pressure environments with unclear ownership and shifting roles, and she brings that same lens to AI systems.

Empathetic Agentic AI Lab

Fortune 100 program leader and AI Mechanic helping people design AI systems that stay in their lane—clear jobs, enforced boundaries, and behavior you can explain in production.

By Judy Ossello (AI Mechanic)

What she lays out here is a framework every leader managing AI needs to internalize: if you don’t define the job, the system will define it for you.

In this post, you’ll learn:

• Why AI systems drift the same way human roles drift after a major reorg

• The three signals (context, permission, and handoffs) that break down in both humans and AI

• Real failure patterns from production AI systems with unclear ownership

• A 10-minute readiness check you can run on any AI system in your workflow

I’ll let Judy take it from here.

Subscribe now

Why most AI systems don’t fail at output—they fail because no one defined what they’re responsible for.

Two years ago, I spent a week watching my org dismantle in real time.

Friday: the VP was gone.
Monday: senior leadership started disappearing.
By Thursday, no one could tell me who I reported to—or what I was responsible for.

By Friday, I had a new role.

No job description.
No clear ownership.
No one accountable for defining it.

I Had a Job. No One Could Tell Me What I Was Responsible For.

The new org was our former internal nemesis.

We used to do bleeding edge innovation for impatient business stakeholders. We invited people from my new org to meetings to show them how fast we moved—and warn them not to slow us down.

They ran operations for the systems we considered dinosaurs. Slow. Backend. Legacy.

But I still had a job.

And so, I showed up to work and asked anyone who looked in charge and made eye contact,

“what should I be doing?”

I also asked who my manager would be.

There had been a miscommunication.

My new manager didn’t know they could welcome me to the org.

I tried not to take it personally.

So I Made Myself Useful—Without a Job Description

If I had let my new org define my responsibility, I wouldn’t have had one.

Product and Engineering ran the show.
They already had three Scrum Masters who thought they did the program work.
The program was a status-chasing annoyance.

I needed to justify my existence with some quick wins. But not too quick.

I positioned myself as a co-pilot for Product.

How could I:

• take work they wanted to give away

• make their job easier

• help them shine

It turned out that Product loved to talk about their wins, ran their roadmap execution, and had one consistent complaint:

the business only gave them two weeks to prepare for quarterly planning.

But, they didn’t want me to talk directly to the business. That was their job.

I had a job without clear responsibility, some guardrails on what I was not allowed to do, and no clear signal from my manager on whether to stay the course or escalate.

When no one defines the job, the work doesn’t stop—it gets defined in real time by whoever is closest to it.

That’s when roles expand, decisions get made without clear ownership, and behavior starts to drift.

When Jobs Are Unclear, People Start Looking for Signals

In my past work as a transition manager, I’ve partnered with HR to merge, stand-up, and streamline orgs. I’ve also been on the other side as a program manager trying to hold execution steady and reset business expectations through major resourcing shifts.

This is what happens in most orgs under pressure, especially following a major re-org.

Jobs become unclear. Quarterly planning gets cancelled. The work doesn’t go away.

People who are finishing existing work start looking for signals on whether they should continue. Some have multiple jobs and no direct manager because their peers were laid off. They report directly to a VP or Senior Director, temporarily.

New team members start scheduling 1:1s to meet with their new peers and understand the work. They’re not just learning the org. They’re trying to define their role within it so they can pitch it to their new direct manager.

When jobs are unclear, people look for three signals:

• Context → do we have enough information to act?

• Permission → are we expected or allowed to do this?

• Handoffs → who owns this, and where does it go next?

These aren’t new. This is how teams operate when clarity breaks down.

Now We’re Putting AI Into the Same Situation

Now we’re adding AI systems into that same environment.

And they rely on those same signals—context, permission, and handoffs—but without the shared understanding humans use to fill in the gaps.

When something is unclear, people ask, clarify, or escalate.

AI systems don’t. They infer.

When context is missing, they fill in the gaps.
When permission is unclear, they assume it.
When ownership is undefined, they keep going.

That’s how responsibility gets created in real time—and why AI behavior starts to drift.

The system isn’t trying to overstep. It’s trying to complete the job as it understands it—because it’s designed to finish the work.

The problem is: That job was never clearly defined.

This Is Already Breaking in Real Systems

These are the same failure patterns showing up in real systems:

• Missing boundaries or stop conditions → agents operate beyond the intended scope
  Example:
  A customer support agent is designed to troubleshoot issues, but without a defined stop condition, it continues recommending actions after resolution—offering refunds, upgrades, or additional steps that weren’t requested or approved.

• Overlapping responsibilities → execution becomes duplicated or incomplete
  Example:
  A support agent and a billing agent both have partial authority to issue refunds. In some cases, both initiate the refund (duplicate action). In others, each assumes the other handled it (dropped action).

• Undefined ownership → agents persist without clear accountability
  Example:
  An internal reporting agent continues generating and distributing weekly metrics after the original team is restructured. The data becomes outdated, but no team is responsible for maintaining or shutting it down—so it continues operating unchecked.

None of this is a prompt problem—it happens when the system’s responsibility isn’t clearly defined.

You Didn’t Deploy a Tool. You Assigned a Job.

And that job has to be clearly defined—or the system will define it for you.

After a major re-org, people often take on stretch assignments in addition to their core responsibilities, especially when gaps start surfacing.

Now they’re operating across multiple areas, but only one is their real expertise.

For example, a Product Manager focused on a single squad may suddenly be asked to coordinate work across multiple teams. The work is different. The tools are different. But they’ll often force it into the workflow they already know—because that’s where they’re most effective.

The result: the work gets done, but not in the right way.

• Cross-team dependencies get tracked in Jira tickets instead of a coordination plan.

• Stakeholder alignment happens ad hoc instead of through structured checkpoints.

• Gaps don’t show up until late—because no one is actually managing the system as a whole.

The same thing happens with AI systems.

When you give one system multiple jobs that require different behaviors, it struggles.

For example, a single agent might be expected to:

• reduce learner friction

• ensure term consistency

• manage cross-toolkit cohesion

These sound aligned. They’re not the same job.

Each one requires a different kind of behavior:

• Reducing friction → advising and suggestion

• Ensuring consistency → auditing and enforcement

• Managing cohesion → coordination and change management

But the system isn’t optimized for all three.

So it defaults to one pattern and applies it everywhere.

• It suggests changes when it should enforce standards.

• It enforces rules when it should guide the user.

• It tries to coordinate across systems without a full view of the work.

Just like the Product Manager, the work gets done—but not in the right way.

Not well. And not predictably.

If You Don’t Define the Job, the System Will

So the question isn’t whether your system works.

It’s whether it’s working within a job you actually defined.

Because if you didn’t define it, the system is already doing it for you—in real time.

Filling in gaps. Making decisions. Expanding its scope.

And once that happens, behavior stops being predictable.

If AI is part of your workflow, you should be able to answer a few basic questions:

• What is it responsible for?

• What is it not allowed to do?

• When does it stop or escalate?

If you can’t answer those clearly, the system will answer them for you.

And you won’t like the results.

Start Here: Define the Job Before the System Does

I put these into a simple readiness check:

Empathetic Agentic AI Lab

10-Minute AI System Readiness Check

Your system already has a job. This shows you what it actually is…

Read more

2 months ago · 11 likes · Judy Ossello (AI Mechanic)

Run it:

• before building a new agent

• when behavior starts to feel off

• before connecting your system to tools or workflows

Because pressure doesn’t create failure.

It reveals whether responsibility was ever clearly defined.

Thank you, !

Judy’s core insight here is one most AI content completely misses: the problem with your AI system probably isn’t the model or the prompts, it’s that nobody defined what the system is actually responsible for, the same way you’d define it for a new hire after a reorg.

If You Only Remember This

• AI systems don’t fail because their outputs are bad. They fail because no one defined what they’re responsible for, what they’re not allowed to do, or when they should stop. If you can’t answer those three questions about every AI system in your workflow, the system is answering them for you.

• The same three signals that break down in human teams after a reorg (context, permission, and handoffs) are the exact signals AI systems need to function predictably. When any of the three are missing, behavior drifts, whether it’s a person or an agent.

• Before building or connecting any AI system, run a readiness check: What is it responsible for? What is it not allowed to do? When does it stop or escalate? Ten minutes of clarity now prevents months of unpredictable behavior later.

What AI systems are running in your workflow right now, and could you answer those three questions about each of them?

Questions Leaders Are Asking

What does AI accountability mean for business leaders? AI accountability means clearly defining what an AI system is responsible for, what it cannot do, and when it should stop or hand off to a human. Most AI failures in organizations come from missing accountability, not bad model performance. Leaders need to treat AI system governance the same way they treat role clarity for human employees.

What is AI agent drift? AI agent drift happens when a system gradually expands beyond its intended scope because its boundaries, stop conditions, and ownership were never clearly defined. The agent keeps completing work as it understands it, filling in gaps and making assumptions, similar to how an employee without a job description starts defining their own role in real time.

How do I know if my AI system has unclear ownership? Three warning signs: the system is doing work no one explicitly asked it to do, multiple teams think someone else is managing it, or no one can clearly explain what it’s responsible for and when it should escalate. If you can’t name the person accountable for the system’s behavior, ownership is unclear.

What are context, permission, and handoffs in AI systems? These are the three signals every AI system needs to operate predictably. Context means having enough information to act correctly. Permission means clear boundaries on what the system is or isn’t allowed to do. Handoffs mean defined ownership transitions, knowing who gets the output and what happens next. When any of the three is missing, behavior becomes unpredictable.

How do I run an AI system readiness check? Before building or connecting any AI system, answer three questions: What is it responsible for? What is it not allowed to do? When does it stop or escalate? Judy Ossello’s 10-minute readiness check walks you through this process in detail. Run it before every new agent build and whenever system behavior starts to feel off.

Sources Referenced

• Judy Ossello, “10-Minute AI System Readiness Check,” AI Mechanic (Substack), March 18, 2026 (linked within article)

TL;DR — AI made content production almost free, which means producing it is no longer what separates the creators who grow from the ones who stall. Trust, voice, and the depth of your actual expertise are. Call it the trust premium. In a Substack Live with , cofounder of Write × Build × Scale, we walked through the litmus test that tells you whether AI is amplifying you or quietly replacing you, and why the creators winning the next 12 months are the ones loading their context, not their prompts.

Write • Build • Scale

Learn how to write online, build your audience, and scale your income.

By Sinem Günel

Outline

(00:00) – Welcome and how I found Write × Build × Scale

(02:00) – Building a business and a marriage with your cofounder

(03:35) – From tennis to Skillshare to Substack: the platform-pivot lesson

(09:30) – The value of community and masterminds

(13:34) – How AI changed content creation in two years

(17:25) – The sameness problem and the trust premium

(22:00) – Lazy AI vs. strategic AI

(24:54) – The litmus test: are you more of an expert than a year ago?

(28:54) – Philip’s ChatGPT workflow and my Claude workflow

(32:18) – Personal brand: stories beat lists

(39:42) – Advice for creators stuck at 200-500 subscribers

(45:46) – Philip’s billboard: AI amplifies you, or replaces you

A Few Things That Stuck With Me

The trust premium. So said something on the live that just landed for me.

“It got so easy to create products compared to how hard it is to sell something.”

Ten years ago, when he was selling Skillshare courses on Instagram growth, the work was eighty percent making the course and twenty percent selling it. Now he says it’s the inverse. Building the product is the cheap part. Earning the trust to get somebody to actually consume it, that’s the expensive part. Honestly, the whole conversation lives in that one sentence. Call it the trust premium. When AI made production almost free, trust became the thing people are actually paying for.

The research-assistant analogy. I used this on the live because it’s the analogy I keep coming back to with the leaders I coach. Look, if you hire a research assistant and you tell them, “find me information on Write × Build × Scale,” they’re going to go out and pull whatever’s public. They don’t know your angle. They don’t know what you’re actually trying to do, who your readers are, or what your point of view is on the topic. So they bring back the most generic version of what’s out there. That’s not on the research assistant though, that’s on you, because you sent them out empty-handed. AI is the exact same thing. The creators who use AI well aren’t smarter than everybody else. They figured out one boring thing earlier than most people, which is that AI on its own gives you the same average answer it gives everybody, and unless you take the time to load up the context first, the version you publish is going to look exactly like the version everybody else publishes.

The litmus test. Here’s the question I tell every leader I coach to ask themselves, and I asked it on the live. Look at yourself a year ago. Are you more of an expert at what you do than you were twelve months ago? If yes, you’re using AI right. It’s compounding you, freeing the time you used to spend on grunt work and routing it back into the work that actually grows your craft, and you can feel it because you’re sharper than you were. If no, AI isn’t actually amplifying you, even if it feels like it is. What’s happening is slower and quieter than that. It’s doing the thinking that used to make you better, and the longer that goes on, the more your expertise quietly hollows out, until one day somebody notices you don’t actually know more than the model does. Philip closed the loop on the live, after I floated the idea. He said, “Basically using AI to become a better version of yourself, a better operator, a better business owner.” Same thing.

Stories beat lists. Here’s the part of the conversation I keep coming back to. Philip mentioned his cofounder Jari’s dog, who apparently shows up in their community so often that when they announced an in-person event, the first question people asked was whether the dog would be there. He talked about his wife Sinem, who’s also his cofounder. I mentioned my seven-year-old, who’s been campaigning for a dog all month. None of that was strategy talk or monetization advice, just two real people talking shop on a Monday morning, occasionally laughing about their kids and their dogs. That’s the part AI can’t reproduce yet, and honestly probably never will. That’s the moat.

I work with leaders on exactly this, executives, marketing VPs, nonprofit CEOs trying to figure out how to show up in the AI era without sounding like every other person on Substack. If it would help to talk this through, here’s my calendar.

Look, if you write or build anything online, watch the full conversation above. And honestly, go subscribe to Write × Build × Scale. , , and are running the most useful creator-economy publication I’ve found, and most of what I’ve actually figured out about Substack in the last six months traces back to their work.

Joel Salinas is a Fractional Chief AI Officer for small and mid-sized businesses and nonprofits. Strategy, hands-on builds, and change management. He writes Leadership in Change and offers 1:1 coaching for individual leaders. If you want help thinking through your own AI strategy or online presence, start here.

Written by a human, for humans.

Here’s what I keep seeing… leaders are still trying to write the perfect prompt. They tweak the wording, swap synonyms, paste in “act as a senior strategist,” and wonder why the answer still reads like every other generic AI response on the internet.

The thing is, none of that matters anymore, it’s no longer 2024.

A few weeks ago I was writing copy for one of my four businesses. Instead of crafting a longer prompt, I pointed the AI to a 15-to-20 page context document in Gemini, brand, audience, personas, what I tried last quarter and why it failed. Then I asked one short question. The answer came back better than anything I’d ever gotten from a “perfect prompt.” Faster, sharper, in my voice.

That moment is when I realized the AI game had changed.

Prompt engineering is dead. Or at least, it’s no longer where the wins are.

(This is one of the things I work on with the leaders I coach, mostly health CEOs, marketing VPs, and founders. If it would help to talk it through, my calendar is here.)

In this post, you’ll learn:

• Why context engineering replaced prompt engineering as the AI skill that moves the needle in 2026

• Three Gemini workflows I use weekly to save 5+ hours and get answers that sound like me

• How to build a single context document Gemini auto-references, the same setup I build for coaching clients

Why Context Beats Phrasing (And What Changed in Gemini)

Think about the difference between a brand new research assistant and one who’s been with you for two years.

The new one needs you to spell everything out every time. Audience, voice, what failed last quarter. Every question becomes a 400-word setup, and the answer is still generic, because the assistant is working from the version of your business they assembled five minutes ago.

The two-year assistand already knows. You can ask “what should we do about Q3?” and get a real answer in 30 seconds, context based.

That’s prompt engineering versus context engineering. Prompt engineering was always the workaround for AI that didn’t know anything about you. It worked, kind of. It also ate hours.

What changed is what AI can hold in working memory. Gemini 3 Pro scored 77% on the MRCR long-context retrieval benchmark at 128,000 tokens, up from 58% in Gemini 2.5 Pro (Google DeepMind, November 2025). On Screen Spot Pro, it jumped from 11.4% to 72.7%. Gemini 3.1 Pro then doubled it again in February 2026 (77.1% on ARC-AGI-2). OR in Plain English: Gemini can now actually use the documents you give it.

You can let AI guess your tone, your format, and your length. You cannot let it guess your facts. A real document is how you stop.

Quick Win (under 60 seconds) Open Gemini. Paste in three of your recent emails or LinkedIn posts. Ask: “Based on these, write a 200-word note on [topic].” Compare the result to what you’d get from the same prompt with no examples. The gap between the two is the entire thesis of this article.

Here are 3 workflows to get exponentially more with Gemini.

Workflow 1: The Voice-Mimic Post (Beginner)

The first time context engineering really landed for me was with a LinkedIn post. If you have been using AI for over 6 months, this is likely already part of your flow.

I used to spend 20 minutes describing my “voice” to AI, back in 2024. Punchy. Conversational. Avoid jargon. BUT the output always landed somewhere between a ghostwriter and a vendor pitch. Close-ish. Never me.

So I tried something different. I uploaded three of my recent posts and one paragraph describing my audience, gave Gemini the topic, and asked for 200 words. That was it. The output sounded like me (really).

Here is an example… Context added in just a few words, this is how you begin.

Workflow:

1. Pick three pieces of your recent writing that sound like you (LinkedIn posts, internal memos, a newsletter) OR simply ask Gemini to comb through your Substack

2. If needed, add one paragraph about your audience: who they are, what they care about, what they’re tired of hearing

3. Drop both into Gemini and ask for info you actually need (Also works with Claude, or ChatGPT)

That’s the move: stop describing your vibe, start providing the ground truth of it.

Which sounds like you?

“I need the system, not just the idea.” Premium gives you the prompts, frameworks, and templates I use with my coaching clients. Start here for $49/year.

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“My organization needs AI leadership, not another consultant.” I serve as a fractional Chief AI Officer for small and mid-sized businesses and nonprofits — strategy, hands-on builds, and change management.

Workflow 2: Workspace as Context (Advanced)

Most of the context you need is already inside your Google Workspace (Google Drive anyone?). You just have to point Gemini at it.

A founder I worked with last month emailed asking for a summary of a proposal and back and forth emails I had sent. The old me would have spent 20 minutes hunting through Gmail and Drive before writing a draft from scratch. But now… I use CONTEXT already built in.

I enable Workspace and ask Gemini:

“Find everything related to [name and topic] across my Gmail and Drive, 
then draft a summary of the communication and action items, written for [name].” 

A minute later, I have drafts citing specific deliverables from real correspondence.

It starts with thinking and gathering…

Followed by the analysis and presentation, saving you hours.

Where leaders save the most time with this:

• Performance reviews drafted from six months of emails, invites, and shared docs

• Monday inbox sweeps that surface real deadlines, grouped by project

• Meeting prep pulled from past threads with a client or stakeholder

Stop reconstructing context manually. Let the workspace BE the context.

Workflow 3: The Knowledge Doc That Runs Everything (Expert)

This is the one that quietly saves me the most time, and the one I now build for coaching clients.

I keep a single 15-to-20 page document covering every active business: state, brand, target market, personas, what’s working, what’s failing. I tweak it weekly. It lives in Google Drive. Gemini references it automatically. In fact, here is the doc:

And here’s a concrete example. I asked Gemini to give me two opportunities for each business I have yet to explore.

• No perfect prompt

• No exact 4 paragraph long instructions

• Simply 1 doc referenced

Here are the results…

When clients hit a certain point in coaching, this is where I introduce context. We create the knowledge doc inside their own Drive. They (or an assistant) update it as things change. Every future AI interaction starts from knowing them.

A leader’s real job is spotting patterns no single team member can see at once. I asked Gemini once to look across my four businesses and tell me where I was unintentionally competing with myself for the same audience. It found two cases I hadn’t seen.

I first explored this pattern in the one-afternoon setup that gets 90% better AI responses. What’s changed since then is the ceiling. Gemini 3 made document handling reliable enough that this is now the default workflow, not a clever trick. And if your team is still in “let’s pick a tool” mode, why most AI implementations fail explains why context is usually the missing piece.

If you’ve got a real AI question you’re stuck on for your own team, book a free intro call. The first conversation is free.

Why This Matters More for Gemini Right Now

Claude and Gemini are 1.1 and 1.2 in AI today. The difference is distribution. Gemini is one click away for anyone with a Gmail or Workspace account, which is almost every leader. Switching to a context-first workflow doesn’t require a new login. It requires a document.

Gemini was falling behind a year ago. The version you have today, with full Drive integration, NotebookLM connectivity (now running 3.1 Pro for Pro and Ultra users), and the reasoning leap Google shipped in February 2026, is what closed the gap. And the reason most leaders aren’t getting much from AI right now isn’t the tool. It’s that nobody took the time to give it context.

If You Only Remember This

• Prompt engineering is no longer the AI skill that separates good output from generic. Context engineering is.

• Gemini can guess your tone, your format, and your length. It cannot guess your facts.

• The highest-payoff move this week: build one 15-to-20 page knowledge document and store it where Gemini can read it.

The 5+ hours isn’t theory. Conservative math from my own week: ~10 content drafts at 20 min saved each, plus one Workspace inbox sweep and one cross-business analysis pass. 5+ hours back, weekly.

What’s the one document about your business or your role you keep in your head and have never given to AI? Take 20 minutes today. Use Wispr Flow if you want to dictate it. That’s where to start.

Stop perfecting the question. Start perfecting what your AI knows.

Questions Leaders Are Asking

What’s the difference between prompt engineering and context engineering? Prompt engineering is optimizing the words you type into AI. Context engineering is optimizing what AI knows before you type anything. Prompt engineering compresses your business into a single message. Context engineering moves your business into AI’s working memory permanently, so every future question starts from a place of knowing you.

How long should my context document be? Mine runs 15 to 20 pages. Gemini 3 handles documents this size easily. The right length is whatever it takes to fully describe the business: brand, audience, personas, current priorities, recent wins, recent failures. Don’t pad it. Don’t strip it.

Do I have to use Google Drive for this to work? For Gemini specifically, yes. The Drive and Workspace integration is the multiplier. Claude has a similar pattern using Projects. ChatGPT has Custom GPTs. The principle is the same. The platform that’s already in your team’s workflow is the one to start with.

How often should I update the context document? I tweak mine weekly. For most leaders, monthly is enough. Have an assistant update it whenever a major decision changes (new strategy, new hire, new offering). Stale context produces stale answers.

Why is Gemini specifically the right tool for context engineering right now? Gemini 3 Pro scored 77% on long-context retrieval at 128k tokens, up from 58% in Gemini 2.5 Pro (Google DeepMind, November 2025), and Gemini 3.1 Pro doubled core reasoning again in February 2026. The deep integration with Google Drive and NotebookLM (now on 3.1 Pro for Pro and Ultra users) means your context doc is one click away. Most leaders already have a Google account. Friction to start is near zero.

Joel Salinas is a Fractional Chief AI Officer for small and mid-sized businesses and nonprofits — strategy, hands-on builds, and change management. He writes Leadership in Change and also offers 1:1 coaching for individual leaders.

Written by a human, for humans.

Sources

• Google — “Gemini 3.1 Pro: A smarter model for your most complex tasks,” February 19, 2026 (blog.google)

• Google DeepMind — “Introducing Gemini 3 Pro,” November 18, 2025 (blog.google)

• VentureBeat — “Google Gemini 3 ScreenSpot-Pro benchmark results,” November 2025

• Google Vertex AI documentation — MRCR v2 long-context benchmarks, November 2025

TL;DR - LinkedIn locked out of the platform she had built a business on for ten years. She got back in, but the 12-week gap taught her something most leaders still refuse to learn: every social platform is rented land. In the AI era, where feeds are flooded with auto-generated content, the leaders who win are the ones who own their audience, sound unmistakably human, and run LinkedIn and Substack together as one system instead of betting on either alone.

Outline

(00:00) – Introduction and Mel’s background

(02:48) – The LinkedIn ban story

(08:49) – The LinkedIn + Substack flywheel

(10:48) – AI and the death of the generic post

(13:36) – Start your posts with “I”

(14:45) – The LinkedIn Services page

(18:58) – Lazy AI vs. strategic AI

(21:05) – The research stack

(27:25) – The headline problem

(31:33) – Using AI without losing your voice

(37:01) – The 6-to-12-month forecast

(39:07) – Where to start

A Few Things That Stuck With Me

The “rented land” frame. LinkedIn can ban you with no warning and no explanation. Mel went through it for 12 weeks after a decade on the platform. If your entire professional presence lives on someone else’s servers, one notification can wipe it out. Mel’s line on this is the one I keep repeating: “On LinkedIn, you’re on rented land.”

The 360 Brew test. LinkedIn’s current algorithm samples every post against a small audience before pushing it wider. The single biggest signal it looks for in that first round: does this sound like a real person writing about real experience? Mel’s tactical fix is so specific it borders on mechanical. Start your first two lines with the word “I.” Not “Here are 5 tips.” Not “As a leader...” Just “I.” Then move to the informative content. The word changes your reach.

The Services page nobody sets up. Five minutes. One-time. Mel’s words: “If you don’t do anything else, please do that, because you will immediately rank above everybody else.” Almost no senior leader I know has done this. The broader case for search discoverability is in How to Be Seen in the AI Search Era.

Lazy AI versus strategic AI. I used this frame on the live and it landed the way it tends to. Lazy AI replaces your thinking. Strategic AI amplifies it. In a feed flooded with auto-generated content, the premium on lived experience goes up, not down. Mel put it this way: “It’s these lived experiences that will propel your content above the next thought leaders or make somebody hire you and not the next person.” I wrote the full case for the strategic side in Stop Delegating Your Brain to AI.

(I work with leaders on exactly this, health CEOs, marketing VPs, founders rebuilding how they show up online in the AI era. If it would help to talk it through, my calendar is here.)

One Question to Sit With

If your LinkedIn account disappeared tomorrow, what would you still have?

If the answer is “not much,” watch the full conversation above. Mel’s 12-week gap is everyone else’s warning, and the three fixes she walks through take minutes, not months.

About Mel Goodman

Mel Goodman is the founder of The Link Tank and one of the most sought-after LinkedIn consultants working with senior leaders today. A former real estate lawyer, she has spent more than a decade helping executives turn LinkedIn connections into clients and now teaches the LinkedIn + Substack flywheel to her Substack audience. Subscribe to her work at melaniegoodmanlinkedinconsultant.substack.com.

The Link Tank™️

A warm welcome. The LinkedIn strategies I use to build authority, grow a newsletter & convert connections into paying clients. Every week, get practical, evidence-led LinkedIn tactics and know-how to credibly grow your LinkedIn presence.

By Melanie Goodman

About me

Joel Salinas is a Fractional Chief AI Officer for small and mid-sized businesses and nonprofits. Strategy, hands-on builds, and change management. He writes Leadership in Change and also offers 1:1 coaching for individual leaders. If you want help thinking through your own AI strategy or online presence, start here.

Written by a human, for humans.

TL;DR: Scammers cloned Italy’s defense minister’s voice with AI and called the country’s top business leaders asking for wire transfers. Giorgio Armani got the call. So did Prada’s co-founder. One billionaire wired €1 million before anyone realized. Red team exercises, where your team rehearses these attacks before they happen, build the reflexes that no policy document ever will.

What happens when an AI clone of a defense minister’s voice calls a country’s most powerful business leaders and asks for emergency wire transfers?

One billionaire sent one million euros before anyone caught it.

Today’s guest is Chris, founder of and an AI security engineer whose career spans NSA operations, Defence Contracts, and Amazon. He writes about the attacks already happening so teams can build real defenses.

Chris gives you the full story behind the Italy deepfake, plus a five-step red team framework your leadership team can run in 90 minutes. Don’t miss it.

ToxSec - AI and Cybersecurity

Security for a world run by machines that lie.

In this post, you’ll learn:

• How AI voice cloning was used to steal one million euros from one of Italy’s most prominent business leaders

• Why traditional security awareness training fails against AI-powered social engineering

• A five-step red team exercise framework you can run with your leadership team this month

• How to handle the three most common objections when you propose threat simulations

I’ll let Chris take it from here.

Subscribe now

Red Team Thinking Turns Leaders Into the Hardest Target in the Room

Adversarial threat simulations teach teams to spot AI-powered social engineering before it costs real money.

When Giorgio Armani’s Phone Rang, It Sounded Like the Government

In February 2025, some of Italy’s most powerful business leaders started receiving phone calls from what sounded like Defense Minister Guido Crosetto and his staff. The voice was convincing. The caller ID appeared legitimate. The story was urgent: Italian journalists had been kidnapped in the Middle East, and the government needed private funds wired to a Hong Kong bank account to secure their release. The money would be reimbursed by the Bank of Italy.

The targets were not junior employees. Giorgio Armani got the call. So did Prada co-founder Patrizio Bertelli, Pirelli executive Marco Tronchetti Provera, and the families behind Beretta firearms and Menarini pharmaceuticals. These are people who have entire teams filtering their communications.

Former Inter Milan owner Massimo Moratti wired approximately €1 million across two transfers before anyone caught it.

The voice on the phone was an AI-generated deepfake, a synthetic replica of the defense minister built from publicly available audio. The attackers never broke into a single system. They didn’t need to. They called people who trust their own ears and gave them a reason to act fast.

Italian police eventually traced the money to a Dutch bank account and froze it. But the attack itself worked exactly as designed: it exploited authority, urgency, and the biological fact that humans believe what they hear from a familiar voice.

Fortune reported in March 2026 that deepfake fraud drained $1.1 billion from U.S. corporate accounts in 2025 alone, tripling from the year before. Voice cloning fraud rose 680% in a single year. The annual security awareness quiz is not calibrated for this.

Why Reading About Threats Will Never Be Enough

Most organizations treat security awareness like a compliance checkbox. Once a year, everybody clicks through a module, answers a quiz about not sharing passwords, and goes back to work. The training evaporates in a week.

The problem is biological. Your brain stores information differently depending on how you encounter it. Reading about deepfake fraud is passive. Experiencing a simulated attack, feeling the pressure of a familiar voice making an urgent request, making a decision under stress, and then debriefing what went wrong: that burns the lesson into muscle memory. Pilots don’t learn to handle engine failures by reading about them. They sit in simulators and sweat through them.

Red teaming is the practice of assigning a group to simulate real attacks against your own organization. It started in military wargaming. Intelligence agencies and special operations units have used it for decades to pressure-test plans before the stakes are real. The concept is simple: if you want to find the holes in your defenses, hire someone to actually try breaking through them.

The good news for leaders who don’t run security teams? You don’t need a hacker on staff to get the benefit. The core skill, adversarial thinking, is trainable. And it applies to a lot more than cybersecurity.

What a Leadership Threat Simulation Actually Looks Like

Forget everything you’ve seen in movies about hackers in hoodies. A practical red team exercise for a leadership team looks more like a tabletop scenario: a structured, time-boxed session where you walk through a realistic attack and make decisions in real time.

Here’s a framework you can run with your team this month.

Pick a scenario that hits close to home. The Italy deepfake is a strong one for any team where leaders make decisions based on phone calls or video meetings. Other options: a vendor sends an urgent invoice with slightly different bank details (business email compromise), or a new AI tool an employee adopted on their own starts leaking client data to a third-party server (shadow AI, meaning software your team uses without IT approval).

Set the stage with pressure. The scenario needs to feel real. Give your team incomplete information, a ticking clock, and authority figures applying pressure. The Italian business leaders had all three: a voice they recognized, a patriotic cause, and urgency baked into every sentence. Simulate that friction.

Let them make the wrong call. This is the part most leaders skip because it feels uncomfortable. The whole point is to let the team experience failure in a safe environment. If everyone handles it perfectly, your scenario was too easy. Ratchet up the difficulty until someone gets fooled. Moratti is a billionaire with advisors and security staff, and he still wired the money. That’s the lesson.

Debrief without blame. The learning lives in the debrief, not the exercise. Ask three questions: What signals did we miss? Where did social pressure override individual judgment? What one process change would have caught this? Document the answers. These become your playbook.

Run it again in 90 days with a twist. Adversaries adapt. Your simulations should too. Change the attack vector. Swap the roles. The team that ran the voice clone scenario last quarter gets hit with a deepfake video call this quarter. Repetition with variation is what builds real pattern recognition.

The Resistance Is Predictable, and So Is the Fix

When you propose running threat simulations, you will hear three objections.

“We don’t have time.” The Italy attack was a phone call. A tabletop exercise takes 60 to 90 minutes. If your team can attend a quarterly all-hands, they can attend a quarterly threat sim. The ROI math practically does itself when you compare 90 minutes of prevention against a $1.1 billion industry-wide problem.

“That’s the security team’s job.” This is the objection that gets organizations burned. Attackers target people, not firewalls. The targets in Italy were CEOs and billionaire founders, not IT staff.

Your marketing lead approving a vendor invoice is an attack surface. Your executive assistant scheduling a call is an attack surface. Everyone who makes decisions based on trust is a target, which means everyone needs the training.

“Our team will feel like they’re being tested.” Good. Reframe it. Call it what it is: rehearsal time against threats that are already targeting organizations exactly like yours. Moratti built a business empire and ran one of Europe’s biggest football clubs. He still got fooled by a synthetic voice on the phone.

Competence and good intentions are not sufficient defenses against engineered deception.

The organizations that build adversarial thinking into their culture, not as a one-off event but as a recurring practice, develop something you cannot buy off the shelf. They develop institutional reflexes. When the real attack comes, and it will come, the team that has already rehearsed the pressure recognizes the pattern three seconds faster than the team that just read a policy document. Three seconds is often the entire margin between a near miss and a catastrophe.

Start small. Run one scenario. Debrief it with zero ego. Then do it again.

Thanks, Chris! makes something clear that a lot of AI security content misses: the hard part of defending against these attacks isn’t technical, it’s human. Authority, urgency, and a familiar voice will bypass every policy document you’ve ever written. The only thing that builds real reflexes is practice under pressure.

If You Only Remember This

• AI-powered voice cloning fraud drained $1.1 billion from U.S. companies in 2025, and the targets are executives and decision-makers, not IT departments. If a billionaire with a full security team got fooled, your team is not exempt.

• Red team exercises work because they put your team under simulated pressure, and the learning lives in the debrief, not the scenario itself. Ask three questions: What signals did we miss?

  Where did social pressure override individual judgment?

  What one process change would have caught this?

• Run one 90-minute tabletop scenario this quarter, then run another in 90 days with a different attack vector. Repetition with variation builds the institutional reflexes that no policy document can.

What would your team do if someone who sounded exactly like your CEO called asking for an urgent wire transfer? Have you ever tested that?

Questions Leaders Are Asking

What is red teaming in a leadership context? Red teaming means assigning a group to simulate real attacks against your organization before actual attackers find the gaps. For leadership teams, this looks like tabletop exercises: realistic scenarios, decisions under pressure, and structured debriefs. You don’t need a cybersecurity background to run one.

How do AI deepfake voice scams actually work? Attackers clone a trusted person’s voice using publicly available audio like speeches or podcast appearances. They call targets, impersonate that person, and use urgency and authority to push fast action (usually a wire transfer). The technology is now cheap enough that small-time criminals have access to it.

How often should organizations run threat simulations? Every 90 days with a different attack scenario each time. Repetition with variation builds pattern recognition across multiple threat types. Adversaries adapt constantly, and your simulations need to keep pace. One exercise per year is a checkbox, not a defense.

Can small organizations benefit from red team exercises? Absolutely. A practical exercise is a 60 to 90-minute tabletop scenario, not a technical hack. Pick a realistic attack, give your team incomplete information and a ticking clock, let them make decisions, and debrief without blame. The debrief is where the real learning happens.

What is the biggest AI security threat facing leaders in 2026? Deepfake voice and video fraud. Fortune reported it drained $1.1 billion from U.S. corporate accounts in 2025, tripling year-over-year. Voice cloning fraud rose 680% in a single year. These attacks target trust and authority rather than technical systems, making every decision-maker a potential target.

Sources Referenced

• Fortune, March 2026 — Deepfake fraud drained $1.1 billion from U.S. corporate accounts in 2025; voice cloning fraud rose 680% year-over-year

• Italy deepfake defense minister incident, February 2025 — Widely covered by BBC, Reuters, AP

TL;DR: Every time you start a new AI chat and re-explain your brand voice, you're paying the Blank Slate Tax. Claude Skills are markdown files that eliminate this tax by encoding your voice, audience, and workflows so every output sounds like you. 77% of companies struggle with brand voice inconsistency (Lucidpress, 2024). Here is my three-skill marketing system across four businesses, with each brand's rules saved in a file Claude references automatically.

So, I own or co-own 4 brands, each one has a different audience, a different tone, and a different set of rules about what “on-brand” actually means.

Last November, 9 PM on a Tuesday, I had two Claude tabs open: one writing a welcome email sequence for one brand, the other building a branded PDF for another. Halfway through, I caught that Claude had started writing both with the wrong brand. Wrong brand, wrong voice, and wrong audience entirely.

And honestly? It was my fault. I’d been switching between projects all morning, re-explaining tone and context every time I opened a new chat.

According to Reclaim.ai’s research (December 2025), it takes 23 minutes and 15 seconds to fully refocus after switching contexts, and professionals lose up to 40% of productive time to that kind of back-and-forth. I was living that stat in real time.

That morning taught me something I should have figured out earlier: context was my bottleneck, not Claude. I kept treating every conversation like a blank slate instead of building a system that remembers who I am. This is what Claude Skills solves.

What you’ll walk away with:

• Why your brand voice, not the AI, is the real bottleneck in marketing automation

• How I use three Claude Skills to run content, email, and strategy across four brands

• How to build your first marketing skill file in about 10 minutes (plus 3 free skill downloads at the bottom of this post, 8 for Paid Members)

Subscribe now

Quick Win (< 60 seconds)

Open any Claude or ChatGPT project you use for marketing. Add one sentence to the project instructions:

“Before generating any content, 
confirm which brand or business this is for and 
apply the corresponding voice guidelines.” 

That single line forces Claude to ask before it assumes, and it cuts voice drift immediately.

A word from our partners, tools I love and use daily…

Jotform is a Leadership in Change partner, and this is a natural fit. The emails are only as good as what feeds them. If your signup form is clunky, people bounce before they ever see email #1.

Jotform AI lets you generate forms by describing what you need, the same "describe it and it's done" approach that makes Claude Skills work. Leadership in Change readers get 50% off annual plans. Build the form that captures the lead today!

77% of Companies Have This Problem

This number surprised me:

And that data comes from before most teams started using AI to generate content at scale.

Think about what happens when you add AI to that mix. You’re prompting Claude or ChatGPT three, five, or ten times a day. Each prompt starts fresh. Each output drifts a little further from your actual voice. Semrush Enterprise documented this pattern in August 2025: AI-generated text drifts most in the earliest outputs, right when you haven’t given it enough context to anchor on.

The result? Content that sounds professional but generic, polished but completely personality-free. Your audience can feel the difference even if they can’t name it.

I tried better prompting first. For a while, I was convinced that was the answer: more detail, more context, more words crammed into each request. And honestly, it worked for individual outputs. But the inconsistency across outputs kept creeping in, especially across four businesses with four different voices.

What actually solved it was a skill file.

A Claude Skill is a markdown file (just a text document with structure) that tells Claude: here’s my brand voice, here’s my audience, here’s what I never say, here’s what I always say, and here are the exact steps to follow for this type of work. You save it once. Claude references it every time. No re-explaining.

Lucidpress found that companies that keep their branding consistent across every touchpoint see up to 33% more revenue. That’s real money left on the table. The skill file is how I lock that consistency in, even when I’m moving fast between four completely different brands.

This article gives you the framework. If you’re looking at your own role and wondering how to set this up, there are two ways I can help.

Which Sounds Like You?

“I need systems, not just ideas” -> Join Premium (Starting at $49/yr, $1,345+ value): Tested prompts, frameworks, plus the full 8-skill marketing dashboard with every skill in this article and five more. Start here

“I need this built for my context” -> AI coaching, custom workflow setup, strategy audits. Message me or book a free call

The Three Skills That Run My Marketing

I wrote about 5 Claude Skills for document creation back in February. Proposals, presentations, dashboards. Those are internal productivity skills.

The three I’m sharing today are outward-facing. They handle the marketing work that used to eat my time.

Full downloads at the bottom of the article.

Skill 1: Email Sequence Builder

I give Claude the sequence type (welcome, launch, re-engagement), the number of emails, and which brand I’m writing for. The skill file already has my voice rules, audience profile, and email structure preferences baked in. Claude generates subject lines, preview text, body copy, and CTAs for each email, all in the right tone, without me re-explaining anything.

The difference between prompting for an email sequence and using a skill for one is the difference between giving someone verbal directions and handing them a GPS. Both work fine once, but only one holds up when you’re writing across four brands in the same morning.

EXAMPLE: Email Sequence Builder → Cozora — I asked it to generate a 5-email welcome series for new session attendees. The skill already knows Cozora’s voice is “expert, approachable, forward-thinking,” that we sign off with “See you at the next session,” and that we never say “cutting-edge” or “revolutionary.” Every email it generated sounded like it came from our community, not a marketing template.

1st. It invokes the skill…

Then, it asks a few clarifying questions.

And we are done!!!

Skill 2: Competitor Intelligence Brief

This one surprised me with how useful it became. I describe a competitor or paste their URL, and the skill walks Claude through a structured analysis: positioning, messaging gaps, content strategy patterns, and opportunities I can act on. It’s the kind of research I used to spend half a day on, compressed into a 10-minute conversation.

EXAMPLE: Competitor Intelligence Brief → Newsletter Compass — I ran it against Beehiiv’s analytics and SparkLoop. The skill already knows our positioning (”self-serve growth tool, not a service”) and our audience (”creators who don’t have a data team”). The brief it generated identified messaging gaps I hadn’t noticed.

And the automated research begins!

And a minute or two later, we are ready…

Skill 3: SEO Article Optimizer

After I finish a draft, I run it through this skill. It checks my titles, meta descriptions, header structure, FAQ sections, and internal linking. I wrote about the importance of search discoverability before, and this skill turns those principles into a repeatable check.

EXAMPLE: SEO Article Optimizer → Leadership in Change — I ran this article through it. The skill knows our topical pillars, our archive of published articles for internal linking, and that we need “Joel Salinas” in the body text for GEO entity recognition. It caught three header issues and suggested two internal links I’d missed.

As always, the skill is called up first..

Final score… 3/5

These three are free to download at the bottom of this article. Upload them to a Claude Project, fill in your brand details, and they work immediately.

I actually use eight marketing skills total (which sounds like a lot, but each one took about 10 minutes to build). The three above, plus social content repurposing, ad copy, and landing pages, a content calendar planner, a brand voice auditor, and a campaign brief generator. I built all eight into a Notion dashboard that Premium members can access and copy into their own workflow. If you want the full system, it’s in the Premium hub. But these three are more than enough to start with.

How to Build Your First Marketing Skill in 10 Minutes

If you’ve read my piece on Claude Code for non-technical leaders, you already know that Claude works best when you give it structured context. A skill file is just the next level of that same idea.

Here’s the thing, you don’t have to write a skill file from scratch. You can use Claude itself to build the skill for you.

The Prompt That Builds Your First Skill

Copy this entire prompt and paste it into a new Claude conversation. Claude will ask you a series of questions about your brand, your voice, and the marketing task you want to automate. When you’re done answering, it generates a complete skill file you can upload and start using immediately.

Paste this into Claude:

“I want to build a Claude marketing skill. Walk me through it step by step. First, ask me what marketing task I want to automate (email sequences, competitor research, SEO optimization, social content, ad copy, or something custom). 

Then interview me about my brand: my brand name, audience, voice attributes, tone, words I always use, words I never use, and any task-specific details. Ask one question at a time so it feels like a conversation, not a form. 

After I’ve answered everything, generate a complete skill file in markdown format with these sections: Purpose, When to Use, Brand Context (filled in with my answers), step-by-step Instructions, Output Format, and Quality Checks. 

Then tell me exactly how to upload it to Claude Projects.”

That’s it. Claude handles the structure. You just answer the questions honestly.

What Happens When You Run It

Here’s how the conversation flows. Claude asks you:

1. “What marketing task eats the most of your time?” — You pick one: emails, competitor research, SEO checks, social content, ad copy, or describe your own.

2. “What’s your brand name, and what do you do?” — One sentence. Don’t overthink it.

3. “Who’s your audience?” — Be specific. “Mid-career marketing managers at B2B SaaS companies in Europe” gives Claude something to work with. “Business professionals” doesn’t.

4. “How should your content sound?” — Complete the sentence: “My content should sound like I’m ___.” When I did this for Cozora (my AI education platform), I wrote: “a smart colleague sharing something they just learned in a live session, excited but grounded.” That sentence shapes every output.

5. “Give me 3-5 words that describe your voice.” — For my newsletter, it’s: conversational, direct, research-backed, practical, blunt. For Cozora: expert, approachable, forward-thinking, practical, community-driven. Same person, different context, completely different skill files.

6. “Any words you never want in your content?” — This is where the magic happens. I ban “leverage,” “synergy,” “utilize,” “transformative,” and about ten others across every brand. The skill file enforces this automatically, so I never have to catch those words in editing again.

7. Task-specific details — email length, sign-off style, competitor names, whatever the skill type needs.

After you answer, Claude generates the full skill file with your brand voice baked in, your instructions structured, and your quality checks written. Copy it, save it as a .md file, and upload it.

How to Upload Your Skill

Claude.ai (Projects):

1. Go to claude.ai and open or create a Project

2. Click the pencil icon next to “Project knowledge” in the sidebar

3. Click “Add content” → upload your .md file (or paste the text directly)

4. Start a new conversation in that project — the skill is active

Claude Code: Save the .md file in your project folder. Claude Code picks it up automatically.

OR Just give it to Claude Cowork and let it do its thing.

What I Keep Human

I want to be honest about something. I don’t automate everything, and I’m suspicious of anyone who says they do.

My skill files handle the repeatable structure: email scaffolding, SEO checks, competitive research formatting. But I write my own newsletter openings. I decide which stories to tell. I choose the angle for every piece of content.

I wrote about the risks of outsourcing your thinking to AI in January, and I meant it. The orchestration layer, deciding what gets automated and what stays human, is the actual leadership skill here. This is what I mean when I talk about the AI Leadership Triad. When I talk about the AI Leadership Triad, what I mean by Adaptability and Innovation is the work of building systems that let you move faster without losing what makes your work yours. Using every new tool isn't the point. The point is staying in control of what you ship.

My rule of thumb: if it requires my lived experience, my opinion, or a relationship with the reader (basically anything that makes the content mine), I keep it human. If it’s structural, repetitive, or research-intensive, I hand it to a skill.

Questions Leaders Are Asking

What exactly is a Claude Skill, and do I need to be technical to use one? A Claude Skill is a plain text file (markdown format) that gives Claude specific instructions for a task, along with your brand voice, audience details, and quality rules. You don’t need any coding knowledge. If you can write a Word document, you can build a skill file.

Can I use the same skill across Claude and ChatGPT? The core structure works in both, since it’s just a text file with instructions. Claude Projects and ChatGPT Custom GPTs both let you upload reference documents. The syntax might need minor adjustments, but the brand voice section and workflow steps transfer directly.

How many skills do I actually need to get started? One. Seriously. Pick the marketing task that eats the most of your time and build a skill for that first. I started with email sequences because I was writing the same welcome series structure over and over. Once that worked, I added a second, then a third. Building all eight at once would have been overwhelming.

Will AI-generated marketing content sound generic even with a skill file? Only if your brand context section is generic. The skill file is only as specific as the voice guidelines you put into it. “Professional and engaging” produces generic output. “Direct, slightly irreverent, uses short paragraphs, never says ‘leverage’ or ‘synergy,’ speaks like a founder who’s been in the trenches” produces content with actual personality.

If You Only Remember This

• Your brand voice is the actual bottleneck. 77% of companies already struggle with voice consistency (Lucidpress, 2024), and AI without context makes it worse. A skill file fixes this permanently by encoding your voice into a file Claude references every time.

• Start with one skill, not eight. Pick the marketing task that drains you most, write the skill file in 10 minutes, and upload it. You’ll know within two uses whether this system works for you.

• Automate the structure, keep the soul. Skill files handle the repeatable parts of marketing. Your stories, your angles, your judgment about what to say and when to say it stay with you. That’s the leadership layer that no file can replace.

What’s the first marketing skill you’d build? Reply and tell me. I read every response.

Free Downloads

Download these Claude Marketing Skills (ready to upload to Claude Projects):

1. Marketing Skill Builder — The prompt from this article, packaged as a skill. Upload it to a Claude Project and say “build me a marketing skill” — Claude walks you through creating any skill from scratch.

2. Email Sequence Builder — Generate welcome, launch, or re-engagement email sequences in your brand voice.

3. Competitor Intelligence Brief — Structured competitor analysis: positioning, messaging gaps, content strategy, opportunities.

4. SEO Article Optimizer — Post-draft optimization for titles, meta descriptions, headers, FAQ sections, and internal linking.

The remaining five skills (Social Content Repurposer, Ad Copy & Landing Page Writer, Content Calendar Planner, Brand Voice Auditor, and Campaign Brief Generator) are available in the Premium Notion dashboard along with these three. Access all 8 skills in Premium.

Thank you!
Joel

In three structured steps - define your non-negotiables, create a base visual library, and design a mutation workflow - you can fix that in under an hour. Your images will never feel random again.

So here’s something I’ve been watching closely for a while, and it’s one of the reasons I asked Pinkie ( ) to write this post for us.

One of the hardest questions every leader, founder, and creator is asking right now is this: how do I make my AI-generated content actually look like me, and not like everybody else using the same tools?

It’s a fair question. Everybody has access to the same models. Everybody is typing similar prompts. Everybody is pulling from the same training data. So when you open up ChatGPT or Nano Banana or any other tool and just generate whatever the prompt gives you, you get what everybody else gets. That’s not a branding strategy. That’s camouflage.

I’ll admit it. My own feed looked like that a year ago. Every cover image was a new experiment. They were fine individually, but together they looked like five different brands sharing a logo. I was using the tools, but I hadn’t made a single decision about identity. The drift was all mine. What caught my attention about is that she generates consistent images. Her feed looks like a brand. The character, the palette, the linework, all of it holds together across hundreds of posts. That’s a different skill. It’s the one most people skip.

So I asked her to come write this for us, because if you’re trying to build something that looks like you and not like the rest of the internet, this is the missing piece. Spend 60 minutes on the framework she lays out below. It’ll save you the next 60 hours of regenerating images that still don’t look like your brand.

If this is the area you’re trying to get better at, go subscribe to her at AI Meets Girlboss. She goes deep on this every week.

AI Meets Girlboss

I help creators build a recognizable Substack brand that drives consistent subscriber growth. AI shortcuts, branding & visual systems for solopreneurs who want to lead with AI without becoming technical.

Take it away, Pinkie.

Build a Repeatable Visual System in 1 Hour With AI

Stop generating images. Start building a system.

Imagine you walk into a casino.

Red carpet. Velvet ropes. No clocks. No windows. Bright lights. The metallic hum of slot machines. Coins raining. Confetti bursts. “WINNER.”

Now imagine someone standing there, pulling the lever over and over, hoping the next spin solves everything.

That’s how most people use AI image generation.

Prompt. Spin.

Regenerate. Spin again.

Near miss? Try one more time.

And when they don’t like the output?

“It’s random.”

“You never know what you’ll get.”

“It’s fun, but unreliable.”

But here’s the truth: the randomness isn’t coming from the model. It’s coming from the way you’re playing the game.

If you approach AI image generation like a slot machine, it will behave like one.

The good news? AI image generation is less like gambling and more like infrastructure. And unlike roulette, you actually get to set the rules. This is how you turn AI image generation into a controlled, repeatable visual system in under an hour.

If your team spends time or money on stock images, basic blog visuals, or recurring design briefs, this framework replaces that friction.

Hey! I’m Pinkie, I run AI Meets Girlboss, and I work with solopreneurs who act as their own brand, design, and marketing team. When they come to me, their visuals usually look polished individually, but disconnected collectively. This framework is how we fix that without adding more tools, budget, or complexity.

What You’ll Learn

Building a repeatable visual brand with AI only takes three moves:

• Define your non-negotiables
  Lock the elements that must never drift: your character, color palette, proportions, silhouette, and lighting logic.

• Create a base visual library
  Build an anchor image that becomes your brand’s gravitational center, the reference every future visual aligns to.

• Design a mutation workflow
  Generate endless variations without losing identity.

By the end of this post, you won’t be “trying your luck” with AI images. You’ll be running visual governance.

Let’s get into it.

Why AI Images Feel Random, And Why That’s Not a Bug

Randomness is built into AI systems. It’s not an accident or a flaw. It’s designed to be random as a feature.

Image models are probabilistic by design. They don’t retrieve one fixed answer. They generate from a distribution of possible outputs. That variability is what allows them to be creative instead of mechanical.

Without randomness, you’d just get repetition.

Human creativity works similarly. Original ideas rarely come from nowhere. They emerge from recombining existing concepts in new ways.

AI does the same thing and is extremely strong at:

• Pattern recognition

• Style reproduction

• Rapid iteration

• Generating long lists of possible directions

It can produce 50 visual interpretations in seconds. Most will be average. Some will be promising. A few might be excellent.

The problem is when randomness operates without boundaries. If nothing is defined as fixed, every generation becomes a new identity experiment.

That’s not creativity. That’s drift.

The Shift: From Drift to Identity

I run AI Meets Girlboss, where I help solopreneurs build structured, strategic brands with AI.

Most of the people I work with are one-person enterprises. They’re the founder, designer, brand manager, and content team at the same time.

When they come to me, their visuals usually look like this: Every product image is a new experiment, every post has a different aesthetic, and every launch looks slightly unrelated to the last one.

Individually, nothing is “bad”, but collectively, there’s no identity.

That’s drift.

My own brand looked similar at the beginning. I generated cover images topic by topic. Each one worked on its own but together, they had no recognizable system.

The turning point was introducing a fixed character into the visual world.

She has:

• A locked pink-forward palette

• Stable proportions and silhouette

• Consistent fashion-illustration linework

• A flamingo as a brand easter egg

Now the topic changes, the metaphor changes, the environment changes. But the identity doesn’t. It’s the same character, whether she’s explaining AI governance or playing roulette in a casino to make a point about randomness.

After enforcing that system, readers started recognizing my posts before reading the headline. Open rates increased by 10–13%.

The AI didn’t become less random. The way I structured my brief became more defined.

In this game, AI handles variation. You handle identity. That’s when image generation stops being a gamble and becomes a strategy.

The 1-Hour Visual Brand Build

The below three steps create a system where AI can generate freely, without your brand drifting.

A Quick Word on Tools

You don’t need a complex stack. For most founders, this is enough:

• ChatGPT (DALL·E): solid starting point for image generation and structured prompting in one place.

• Gemini / Nano Banana: strong for character consistency and visual continuity across variations.

Both allow you to iterate quickly and reference previous images, which is essential for building a repeatable system.

One note: Claude is not strong for image generation. It’s excellent for writing and reasoning, but not the right tool for this workflow.

Tool choice matters less than structure.

Step 1: Define Your Fixed and Fluid Elements (15 Minutes)

Before you generate anything, define what must never change. Most people skip this and jump straight to scenes. That’s why everything drifts.

A repeatable visual system has two categories:

• Fixed elements: the anchors

• Fluid elements: the variables

Your fixed elements might include:

• Character or logo

• Color palette

• Silhouette logic

• Proportions

• Line style

• Lighting rules

• Texture

Your fluid elements are usually:

• Environment

• Context

• Metaphor

• Story moment

Prompts are one-time instructions, but systems are infrastructure. Start by clarifying the system.

Use this:

I need help developing a visual world and character system for my brand.

Ask me questions one by one until you are at least 95% confident you understand:

– What must always stay consistent (fixed elements - eg: Character or logo, Color palette, Silhouette logic, 
Proportions, Line style, Lighting rules)
– What can change from image to image (fluid elements)- eg: Environment, Context, Metaphor, Story moment)
– What this visual world must communicate
– What would feel wrong even if it looks good
– What references I like and dislike

Do not generate images yet.

Once confident, summarize my needs into:

1) Fixed visual rules
2) Flexible elements
3) Clear constraints

Then propose one cohesive visual direction.

I go into even more detail about defining fixed and fluid elements in my post about designing my base character here.

Step 2: Create a Base Visual Library (20 Minutes)

Now you build your anchor image.

This is the step most people skip, and it’s the most important. You are creating the base image that every future visual will reference. This anchor will allow you to generate variations without drifting in style.

At this stage, you are not creating content yet. Yes, you are generating an image. But its purpose is alignment. This is your visual gravity.

Using the fixed rules you defined in Step 1, create one base character or core visual asset that will become the reference point for all future scenes.

You are a senior visual designer.

Create a single, clear brand character based on these constraints:

[Insert your fixed rules]

The character/image must communicate:
[2–3 traits]

Avoid at all costs:
[Deal-breakers]

Maintain:
– Proportions
– Silhouette clarity
– Palette discipline
– Line style consistency

Generate 5 base variations of the same character.

You won’t get it right on the first attempt. You will need refine. And that’s completely normal and part of the process. Refinement requires taste and judgment. Using your first draft anchor image, apply this prompt structure:

Generate the exact same illustration.
Only adjust: [specific change].
Do not change anything else.

Run 4–5 controlled variations and evaluate strategically:

• Which version feels most stable?

• Which one is recognizable at thumbnail size?

• Which one could anchor 20 future visuals?

Choose one and save it in a folder titled: /Brand Core / Approved Anchor

This becomes your reference image for everything. From this point forward, your prompts will focus less on style and more on content, because style is already locked.

That’s the shift.

Without an anchor, every image is a new identity experiment. With an anchor, you’re running a system and will actually get better image outputs from AI.

More on how to decide what fits and what doesnt’t and how to tidy up your thoughts before generating images HERE.

Step 3: Design a Mutation Workflow (25 Minutes)

Now you introduce controlled variation.

Upload your anchor image as a reference.

Most tools allow style or character referencing, which is why I suggest using ChatGPT and Gemini for starters. This is where AI stops feeling random.

Use this structure:

Using the attached base image as reference,

Maintain:
– Color palette
– Proportions
– Silhouette
– Line style
– Lighting logic

Change only:
[This is where you describe the fluid elements: Scene / environment / context]

Do not introduce new stylistic directions.
Output ratio: 3:2 horizontal.

As an experiment, generate 10 variations. Review them together. If 8 out of 10 feel like the same brand, your constraints are strong. If they drift, your fixed rules are too loose.

Tighten.

Run again.

This is infrastructure building.

If you’d like to learn more about how to build mutations from a base library, I break it down in even more detail here.

The result?

By the end of these three steps you now have:

• A defined visual identity

• A reusable anchor

• A scalable mutation system

AI handles variation and you control identity. That’s how you move from experimentation to repeatability.

Where This Fits Inside Your Business

Look at how your team currently handles visuals.

Do you:

• Buy stock images for blog posts?

• Spend hours searching for “good enough” social graphics?

• Create mock-ups manually?

• Brief designers for simple recurring assets?

These are operational tasks and production rather than strategic design work. This is where a defined AI visual system fits.

Not to replace your brand designer.

Not to redesign your identity.

But to eliminate repetitive image friction.

Will AI deliver 100% brand precision? No, but it doesn’t need to.

It can replace:

• Stock image subscriptions

• Low-impact design briefs

• Manual mock-ups

• Inconsistent social visuals

• Product photoshoots (like the example below)

Once your anchor exists and your mutation workflow is defined, these assets stop being individual projects.

They become controlled variations inside a system.

In the comments, tell me:

Where is your team currently wasting the most time on visuals: stock images, mock-ups, blog covers, social posts or somewhere else?

Whatever it is, that’s the first place this system should replace. Introduce this workflow to your team as an operational upgrade and remove the visual bottlenecks.

Talk later,

Pinkie

Okay, here’s the line from Pinkie’s piece I want you to sit with: AI handles variation. You handle identity.

That’s the whole thing. And it’s not only about images. It’s the rule for AI-generated copy, AI-generated reports, AI-generated anything. The model gives you variation for free. Identity is still your job, and identity is what separates you from every other person using the same tool.

Here’s the line to walk away with: the model is free. The identity is the asset.

And when you’re ready, go subscribe to AI Meets Girlboss. She’s doing real work in this space, and you’ll learn something every week.

This is content made by a human, for humans who are trying to build something that actually looks like them. See you next week.

Subscribe now

Frequently Asked Questions

Do I need to pay for a premium AI image tool to make this work?

No. Pinkie’s framework is tool-agnostic. ChatGPT (DALL·E) and Gemini / Nano Banana both support character referencing, which is all you need. The system is about decisions, not software.

How long does it realistically take to build a repeatable visual brand with AI?

Around 60 minutes if you already know your brand direction. Pinkie breaks it into three phases: 15 minutes defining fixed versus fluid elements, 20 minutes building your anchor image, 25 minutes designing your mutation workflow.

Can this replace a professional brand designer?

Not for your foundational identity. Use a human designer to define your visual world, then use this framework to scale production without losing consistency. It replaces stock photography, recurring design briefs, and mock-ups, not strategic brand work.

What’s the single most common reason AI images feel inconsistent?

No anchor image. Most people prompt from scratch every time. Without a reference point for the model to align to, every generation is a new identity experiment. Build the anchor once and every future image has gravity.

How does this apply to AI-generated copy, not just images?

Same principle. AI handles variation, you handle identity. In writing, your fixed elements are voice, vocabulary, sentence rhythm, and point of view. Your fluid elements are topic, format, and framing. Lock the first set and the second set flexes without drift.

Which AI image tool does Pinkie recommend?

ChatGPT (DALL·E) for prompt-in-one-place workflows, Gemini / Nano Banana for stronger character consistency across variations. Claude is not strong for image generation. It’s excellent for writing and reasoning, not this workflow.

About Me

Joel Salinas is a Fractional Chief AI Officer for small and mid-sized businesses and nonprofits, providing strategy, hands-on builds, and change management. He writes Leadership in Change and also offers 1:1 coaching for individual leaders.

Learn more about Joel’s work →

I ran a live session this week on Claude Skills and asked the room a simple question: who has actually used them? A few hands went up. Most hadn’t. That surprised me, because Claude Skills are the single biggest reason my AI output sounds like my business, and the single biggest reason most executives tell me, “Every AI answer looks the same.”

When you type a question into Claude or ChatGPT with zero context, the model scans the internet, compiles what it finds, and returns the most statistically average response. That average-internet answer is what most leaders are showing their teams and calling “AI output.” The setup is the difference. Skills are how you fix it once and move on.

This is the full recap of the live, timestamped, with the walkthrough, the examples, and the premium dashboard drop at the end.

I coach leaders through exactly this, and occasionally step in fractionally when an organization needs more hands-on help. A free intro call is the easiest place to start.

Key Moments + skills

Read more

I’ve been thinking about this for a while, especially as I coach executives:

Why do some people get dramatically more out of AI than others, even when they’re using the same tools?

It’s not about the prompts. It’s not even about the use case. More and more, I realize that it’s about how they approach the system itself.

Leor Gayr runs , one of the most thoughtful AI newsletters I’ve come across. He doesn’t write “how to” content; he writes “what happens when” content, pushing AI into strange territory, studying the failures, and mapping patterns that most users never see. It’s a refreshing take on AI that I enjoy weekly and wanted to bring to each of you.

Exploring ChatGPT

Exploring ChatGPT is where AI, philosophy, and speculative science meet, deep dives into the hidden patterns shaping intelligence, language, and our future.

In this piece, he breaks AI users into three clear groups and makes a case that the third group, the explorers, are building something the other two aren’t.

Whether you’re a leader trying to understand what AI can really do or you’re already building with it, this reframe will change how you think about your own relationship with these tools.

Here’s Leor, in his traditional, poetic style. Enjoy!

AI Explorers: The People Going Down the Rabbit Hole

Most AI users are barely scratching the surface.

They ask a question.
They get an answer.
Maybe they generate some text, summarize an article, or help write an email.

That’s the surface layer.

But a small group of people is starting to interact with these systems very differently. Instead of treating AI like a faster Google, they’re studying how it behaves. They’re pushing it into weird situations, changing prompts, testing limits, and watching what happens.

They’re not just using AI.

They’re exploring it.

And the difference between those two things is bigger than most people realize.

Because once you stop treating AI like a tool and start treating it like a system, you begin to notice something strange.

These models have patterns.
They have quirks.
They fail in specific ways.

Sometimes the failures are more interesting than the successes.

The Three Ways People Are Using AI

Right now, AI usage falls into a few clear groups.

The vast majority of people are using AI the way they used search engines fifteen years ago. Faster answers. Quick explanations. Summaries. Writing help. It’s basically a convenience layer on top of normal work.

AI makes things easier, but it doesn’t fundamentally change how people think about the technology.

Then there’s a smaller group using AI more structurally. These are builders, developers, operators, founders, researchers. The people wiring AI into real systems. Workflows, automation pipelines, software tools, analytics loops.

For them, AI isn’t just useful.

It’s infrastructure.

But there’s an even smaller group that interacts with AI in a completely different way.

Explorers.

They aren’t trying to get the fastest answer. They’re trying to understand how the system behaves. They test edge cases. They push prompts into unusual territory. They look for patterns in how models reason and where those patterns break.

The question stops being “What can this do?”

It becomes “What exactly is this thing?”

What Makes an Explorer Different

The difference between explorers and everyone else is mostly mindset.

Most people treat AI like a vending machine.

You put a prompt in.
An answer comes out.

Explorers watch the machine instead.

They notice how responses change depending on wording. They test what happens when instructions conflict. They push the model into uncomfortable territory just to see how it reacts.

And when the system fails, they pay attention.

Because failures reveal structure.

Researchers studying large language models often learn more from edge cases than from successful outputs. Some of the most interesting discoveries about model reasoning have come from analyzing the exact moments when models break down (OpenAI 2023).

Explorers understand that.

Mistakes aren’t just errors.

They’re signals.

How Someone Actually Becomes an AI Explorer

You don’t need a computer science degree for this.

You mostly need curiosity.

Instead of only asking AI to do tasks, start asking questions about the system itself. Change how you phrase prompts and see what happens. Ask the same question in multiple ways and compare the reasoning.

Patterns start to appear.

Sometimes the model becomes more confident. Sometimes it contradicts itself. Sometimes it produces answers that seem correct but feel strangely shallow.

That’s not random.

Researchers studying human interaction with automated systems have shown that users often learn more about system behavior when something goes wrong rather than when everything works smoothly (Parasuraman and Riley 1997).

AI is no different.

Failures are often where the real structure shows up.

The Real Mindset Shift

At some point explorers realize something important.

AI doesn’t behave like normal software.

It behaves more like an environment.

Large language models generate behavior that wasn’t directly programmed. Some scientists describe these as emergent abilities that arise from scale rather than specific instructions (Wei et al. 2022).

That’s why exploration matters.

The people experimenting with these systems today, are often discovering patterns before the research community fully explains them.

References

OpenAI. (2023). GPT-4 Technical Report.

Parasuraman, R., & Riley, V. (1997). Humans and Automation: Use, Misuse, Disuse, Abuse. Human Factors.

Wei, J., et al. (2022). Emergent Abilities of Large Language Models. arXiv.

Thank you, !

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Leor’s framing stuck with me…

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Questions Leaders Are Asking

Q: What’s the difference between using AI and exploring AI?
Using AI means asking questions and getting answers. Exploring AI means testing boundaries, studying patterns in how models respond, and paying attention to failures. The explorer treats AI as a system to understand, not just a tool to operate.

Q: Do I need to be technical to explore AI?
No. Exploring AI is about curiosity and observation, not coding. If you can notice when an AI response feels off, ask “why did it do that?” and try a different approach, you’re already exploring. The skill is attention, not engineering.

Q: How do AI explorers learn faster than regular users?
They learn from failures, not just successes. When AI gives a wrong or surprising answer, explorers investigate why rather than re-prompting. This builds a mental model of how the system actually works, which transfers across every AI tool.

Q: Should leaders spend time exploring AI or just focus on productivity?
Both, but most leaders skip the exploring entirely. Even 30 minutes a week pushing AI into unfamiliar territory builds judgment that no tutorial can teach. The leaders who understand AI’s limits make better decisions about where to deploy it.

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If you want to build a cathedral, you start by focusing on the first wall, with the first row of bricks (it’s a saying I first heard in Spanish a decade ago).

That sounds obvious until you watch what most organizations are actually trying to do with AI. They stand at the edge of a transformation they can feel coming, look at the scale of it, and either freeze or try to build the entire cathedral in one swing. Both reactions are understandable, and both leave the same result on the table: nothing built, nothing changed, nothing learned.

I decided to launch my fractional AI work because I kept seeing how many organizations, with just a little help, could do so much more with what they already had. They are already experts at what they do. They know their customers, their mission, their internal bottlenecks better than any outside consultant ever will. What they don’t always have is a strategy for how AI fits into the work they’re already doing well. That is the gap I keep seeing, and it’s not the gap most AI content is talking about.

In this post, you’ll learn:

• Why most small and mid-sized businesses are stuck between two bad AI options

• What it actually costs to do nothing on AI strategy, and why that number is bigger than leaders think

• How fractional AI leadership fills the gap between “do nothing” and “hire a full-time Chief AI Officer”

• What to look for in a fractional AI leader if you decide that path is right for your organization

Two Options, Both Bad

Here’s the reality most mid-market leaders are living with in 2026.

Option one is to hire a full-time Chief AI Officer (CAIO).

That role has become the fastest-growing C-suite title in recent memory, with 70% year-over-year growth, according to The AI Hat’s 2026 analysis. The problem is the price tag. A full-time CAIO runs $250,000 to $400,000 per year in total compensation, and that’s before benefits, equity, and the $25,000 to $50,000 in recruiting fees that typically come with executive search. The average search takes three to six months, and that’s before the new hire even starts ramping up. For a $20 million company, that full-time bet represents 2% to 4% of total revenue before a single AI initiative has produced any measurable return.

Option two is to do nothing. Not literally nothing, because most organizations are buying AI tools, running small pilots, letting a few employees experiment. But there’s no strategy behind any of it. No governance. No one is accountable for the outcome. No plan for what to do if the tools don’t work or the team resists. I’ve written before about why 92% of AI investments fail to deliver measurable ROI, and the pattern has not improved in 2026. If anything, it’s gotten worse as the tools have gotten more powerful and the stakes have gotten higher. This is the same pattern I’ve described in why most AI implementations fail, and it shows up in every sector where leaders try to skip the strategic work and go straight to deployment.

So leaders get stuck in a hard place: they can’t justify $250,000, they also can’t justify standing still, and while they wait for the perfect answer, their competitors are moving.

Why Organizations Spend $500K on AI Tools and $0 on Strategy

Here’s what I did not expect when I started paying closer attention to this gap.

Organizations are spending massive amounts of money on AI and spending almost nothing on strategy.

I’ve watched companies purchase $500,000 worth of AI tools, subscriptions, and pilots in a single year, then spend zero on the strategic leadership that would tell them whether any of it is worth keeping (the news is littered with examples).

RSM’s 2026 Mid-Market AI Mandate found that 70% of mid-market executives report needing outside help to extract measurable value from their AI investments. Seven out of ten.

The honest version of this is that most AI spending right now is a bet on tools that nobody is managing.

A $500,000 AI implementation with no strategy behind it is a gamble, and the cheapest insurance policy against that gamble is the amount per year it takes to have a strategic AI leader in the room for a few hours per month, asking the questions nobody else is asking:

• What does success look like?

• Which use case do we start with?

• What does failure teach us?

• Who owns this when I’m not here?

Without that leadership, the tools sit idle, the pilots stall, and the team quietly loses faith in AI as a serious investment. That’s the scenario I’m most trying to help leaders avoid.

If this is resonating, two paths forward: If you want to think through your own AI strategy without any pitch, I offer a free 30-minute intro call where we talk about where you are and what options actually fit your budget. No decks, no pressure, just a conversation.

To get my tools and implement them today ($1500+ in value), become a premium subscriber.

Why Fractional AI Leadership Is the Third Path

Fractional AI leadership operates on a model the full-time hire was never designed for, and it works because the job of a strategic AI leader in most mid-sized organizations does not require 40 hours a week. It requires the right person in the room for the right decisions, supported by async work in between.

Upwork’s 2026 State of AI report found that 77% of business leaders say AI is increasing demand for specialized, fractional talent. That’s a structural shift in how mid-market leadership works, not a passing fad.

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The math is hard to argue with. A fractional AI leader at can cost as little as $18,000 to $36,000 per year gives an organization the strategic guidance, governance framework, and experience of someone who has seen this play out across multiple companies, at roughly 10% of the cost of a full-time CAIO. Time-to-first impact drops from six to nine months (the typical full-time executive ramp) to 30 to 45 days. Because fractional leaders work across multiple organizations, they bring pattern recognition that no single in-house hire can match. That pattern recognition is the hidden engine of the three skills that separate leaders who thrive with AI from those who stall out.

What surprised me when I started offering this service is that the mid-market doesn’t want less AI leadership. They want the right amount of it, scoped to their reality. Small businesses that can afford 2 hours a month get an advisor who helps them prioritize their next three moves. Mid-sized organizations that need 4 or more hours a month get someone embedded enough to help them ship their first real use case. Both tiers get the same underlying thing: strategic AI leadership that scales to what the organization can actually absorb.

5 Signs of a Strong Fractional AI Leader

Not every fractional AI leader is going to fit every organization, and the quality varies wildly right now because the category is still forming. The difference between a strong fractional leader and a generic consultant usually comes down to judgment, which is exactly the leadership skill that lets you evaluate AI quality in the first place.

The first thing I watch for is whether they lead with strategy or tools. If the first conversation is about which software to buy, you’re talking to a vendor, not a strategist. A strong fractional leader wants to understand your business, your team, and your priorities before they name a single AI product.

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They also have to understand change management, because AI transformations fail because of people, not software. The leader you hire should know that in their bones, not as a talking point but as a lived experience. And they should be able to actually build, because strategy without execution capacity is just a slide deck. Ask whether they’ve shipped something with their own hands recently, and listen carefully to the answer.

Two more signs matter. A fractional leader with 15 clients is not actually fractional, because they’re running a factory, so ask how many clients they work with and whether they cap it. And finally, the organizations that win with AI are the ones whose leaders use it to amplify their people, not replace them. If the pitch you’re hearing is “automate everything,” run.

Why Fractional AI Leadership Works for the Mid-Market

Here’s what I believe, and it’s the reason I finally built this offer: for most small and mid-sized organizations, fractional AI leadership is the future, not a workaround. Not because it’s cheaper, though it is. Because it matches the shape of how these organizations actually make decisions and absorb change.

The cathedral gets built one wall at a time. That’s a philosophy, not a limitation. The organizations I’ve seen succeed with AI are the ones that picked one process, proved the win, and built from there.

If your organization can’t justify a $250,000 full-time hire but knows that doing nothing is quietly costing you more every month, the third path exists, and it’s more accessible than most leaders realize.

If You Only Remember This

• Doing nothing on AI strategy is not free. A $500,000 AI implementation without strategic leadership is a gamble, not an investment.

• Build the cathedral one wall at a time. The organizations winning at AI in 2026 are the ones that picked one process, proved the win, and built from there, not the ones trying to transform everything at once.

What would it cost your organization if every AI pilot you’ve launched this year quietly failed in the background, and no one was watching?

Questions Leaders Are Asking

How much does fractional AI leadership cost? Most fractional AI leaders charge $1,500 to $5,000 per month for small businesses and $5,000 to $15,000 per month for mid-market organizations. That’s roughly 10% of the cost of a full-time Chief AI Officer, which runs $250,000 to $400,000 per year in total compensation. The model works because most mid-sized organizations don’t need 40 hours of AI executive time per week, they need the right leader in the room for the right decisions.

I personally split it into two tiers.

What’s the difference between a fractional AI leader and an AI consultant? A consultant typically drops in, delivers a report, and leaves. A fractional AI leader is embedded in the organization over time, attending leadership meetings, driving accountability, and owning outcomes rather than deliverables. The distinction matters because AI transformation is a multi-month process, not a one-time engagement, and the leader who starts the work should be the one who sees it through.

Do I really need fractional AI leadership if I’m not a Fortune 500 company? Yes, if your board is asking about AI, your team is experimenting without direction, or you can feel competitors moving faster than you. You don’t need a full-time Chief AI Officer at $250,000 a year. You need the strategic thinking: the prioritization, the governance, the ability to translate business goals into an AI roadmap. A fractional leader delivers exactly that, scoped to your reality.

How fast can a fractional AI leader actually deliver results? Most fractional AI leaders can deliver measurable strategic value within 30 to 45 days. That’s the pattern recognition advantage of working across multiple organizations. A full-time hire typically spends 6 to 9 months on internal discovery before delivering anything meaningful. A fractional leader walks in knowing what the first three moves should be, because they’ve made them elsewhere.

How do I know if my organization is ready for fractional AI leadership? You’re ready if you have leadership buy-in for AI experimentation, some basic data infrastructure, and at least one specific process you want to improve. If you’re still operating purely on paper, start with digital foundations first. If you already have a CTO, a fractional AI leader complements that role rather than replacing it, by owning strategy and governance while your CTO owns technical architecture.

What size company is fractional AI leadership for? Small to mid-sized businesses with 5 to 500 employees typically get the most value from fractional AI leadership. Larger than 500, and the volume of AI decisions may justify moving toward a full-time hire. The mid-market is the sweet spot because the AI decisions matter and the budget for a full-time executive doesn’t exist.

Written by a human, for humans.

Sources Referenced

1. The AI Hat, Fractional CAIO vs. Full-Time Chief AI Officer, 2026 — https://theaihat.com/fractional-caio-vs-full-time-chief-ai-officer-the-complete-2026-decision-framework/

2. Deloitte, State of AI in the Enterprise 2026 — https://www2.deloitte.com/

3. Fractionus, 10 Statistics That Prove Fractional Work Is the Future, 2026 — https://fractionus.com/blog/10-statistics-fractional-work-future

4. Upwork, State of AI 2026 Report — https://www.upwork.com/resources/state-of-ai

5. HatchWorks, Fractional Chief AI Officer: Why Mid-Market Companies Are Turning to Them, 2026 — https://hatchworks.com/blog/gen-ai/fractional-chief-ai-officer/

6. Harvard Business Review, How to Make Fractional Leadership Work, 2025 — https://hbr.org/podcast/2025/08/how-to-make-fractional-leadership-work

What happens when a 136-year-old Swiss watch brand meets the age of AI?

I sat down with , executive at Doxa USA & Caribbean, to talk about one of the hardest questions leaders face right now: how do you stay true to your craft when everyone around you is automating everything?

Jacqueline brought 16 years of luxury retail experience and some of the sharpest thinking I’ve heard on balancing heritage with innovation. Here are the highlights:

Key Moments

• 1:21 — Jacqueline’s background: 16 years in luxury retail, PADI-certified diver, built an aquaculture business

• 3:57 — Doxa’s origin: founded in 1889 by George Ducamon, who started watchmaking at age 12

• 5:16 — How watch movements work and why Doxa keeps prices at 1/4 to 1/5 of competitors with the same quality movements

• 7:05 — Why Doxa dug in on heritage: no AI in the watches, staying about watchmaking

• 8:48 — The leadership tension: how do you meet a new generation’s demand for efficiency without losing what makes you valuable?

• 11:02 — The stat: maybe 5% of Doxa watches actually make it to the ocean for scuba diving. Most buyers choose it for the craft, not the function

• 11:56 — What Doxa clients actually love about AI: making their own content, building community, not automating the product

• 13:05 — “There’s no replacement for what is real and tangible and human”

• 14:55 — “It loses its magic” — what happens when you automate the thing people love about you

• 15:53 — The Navy SEAL story: how Jacqueline used ChatGPT as a collaborator (not a replacement) to craft deeper interview questions

• 18:28 — The grocery store self-checkout example: voting with your money

• 22:08 — AI isn’t the Bible: the dangers of trusting AI output without critical thinking

• 23:20 — How to use AI smart so it complements you instead of replacing you

• 24:22 — The prediction: we’ll soon see a “100% human-made” seal on content and products, and people will pay a premium for it

• 32:04 — The “Missed Sales List”: the report that changed an entire company’s strategy by tracking what didn’t sell

• 33:49 — Why your KPIs might be lying to you: measuring what sold vs. what you missed

• 34:34 — Retail psychology debunked: how Jacqueline proved the conventional wisdom wrong by testing rack placement

• 37:50 — “I’m not a salesperson. I’m a human first.” Why 4-hour conversations outsell pressure tactics

• 39:26 — Telling a customer to buy the competitor’s watch instead, and why that builds loyalty

• 41:58 — The core takeaway: focus on the people you want to serve, not their wallets. The sales will come.

• 45:15 — Book recommendations: Mind Hacking by Sir John Hargrave + The Body Ecology Diet

The Big Takeaways

1. Know what to protect. Your craft is your craft. Automate the things around it, not the thing itself.

2. AI is a collaborator, not a replacement. Use it to push your thinking deeper, then rewrite in your own voice.

3. AI isn’t always right. It’s a prediction engine built largely on Reddit. Verify everything.

4. Measure what you’re missing, not just what you’re hitting. The biggest opportunities hide in what you never tracked.

5. We’re still people dealing with people. That hasn’t changed and won’t.

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TL;DR: Claude Mythos Preview, Anthropic’s newest AI model, discovered thousands of hidden software vulnerabilities that human researchers missed for decades. Instead of releasing it publicly, Anthropic restricted access to 40 companies through Project Glasswing. For leaders, this signals that AI capability has outpaced most organizations’ understanding of it, and building AI judgment is now an operational priority, not a philosophical one

I was listening to the Hard Fork podcast this week, the one from the New York Times, and and were calling this a transformational moment for AI, development, and security. I agree! And also know that it is at times like this that coverage swings between full panic mode and outright dismissal. And I think both are getting the story wrong, so here is my breakdown of what happened and, as a leader, how it impacts how I see AI moving forward.

In this post, you’ll learn:

• What Claude Mythos actually is and what it can do, in plain language

• Why the fear and the dismissal are both missing the real point

• What this means for leaders who use AI tools right now, and what to do about it

The Panic Version

Thomas Friedman wrote an opinion piece in the New York Times on April 7, 2026, calling Anthropic’s decision a “terrifying warning sign.” CNBC led with cyberattack fears. CNN warned the model “could let hackers carry out attacks.” The framing was consistent across all of them: this AI is so dangerous that even the company that built it is afraid to release it

I understand why that headline gets attention. But if you stopped there, you’d walk away thinking the only appropriate response is fear, and that’s not useful for anyone trying to lead a team through this moment.

The Dismissal Version

On the other side, many have pointed out that nobody outside Anthropic has actually used Mythos, calling the panic premature, and questioning how much of this is marketing (including a great piece from ). Other voices in the tech space have been skeptical about whether these capability claims are cherry-picked.

That’s fair too. A healthy skepticism about AI press releases is something I’ve encouraged before, and I wrote about why critical AI literacy is now a core leadership skill for exactly this reason.

But dismissing the story entirely misses something important.

What Claude Mythos Actually Did

On April 7, 2026, Anthropic announced Claude Mythos Preview, their newest and most powerful AI model. During testing, the model discovered thousands of previously unknown software vulnerabilities, including a 27-year-old bug in OpenBSD and a 16-year-old flaw in FFmpeg.

These are security holes that human researchers missed for decades, found by an AI that wasn’t even specifically trained for cybersecurity. The capabilities emerged naturally from improvements in reasoning and code understanding.

To put the capability jump in perspective: Anthropic tested all three of their Claude models on the same task, finding and exploiting a real vulnerability in Firefox’s JavaScript engine. Claude Sonnet 4.6 succeeded 4.4% of the time. Claude Opus 4.6 hit 15.2%. Claude Mythos Preview? 84%.

That’s not a gradual improvement. That’s a different category of capability.

And Anthropic didn’t just test it on benchmarks. Mythos found real, previously unknown zero-day vulnerabilities in Firefox, the kind of security flaws that let an attacker take control of your browser. Not theoretical exploits, not academic challenges, but actual vulnerabilities in software that billions of people use every day.

On a different benchmark shown below, BrowseComp, which tests how well or how poorly a model can analyze and synthesize information, again, Mythos clearly led all other models.

Anthropic’s response was to restrict access rather than expand it. They created Project Glasswing, a defensive consortium of roughly 40 companies, including Google, Apple, Amazon, Microsoft, and CrowdStrike. They committed $100 million in usage credits and $4 million in direct donations to open-source security organizations, with a public transparency report promised within 90 days. They also published a 200-plus page system card documenting the model’s capabilities, risks, and behavioral findings in detail, something no AI company has done for a model they’re choosing not to release.

The Part Nobody’s Talking About

Here’s where it gets interesting for leaders who aren’t building AI models but are leading organizations that depend on them.

On February 27, 2026, negotiations between Anthropic and the Department of Defense broke down over a contract clause prohibiting the analysis of bulk-acquired data on American citizens. Anthropic refused to remove it. The response was an executive order directing federal agencies to cease use of their technology.

I’m not going to litigate the politics of that decision here. But I think it’s worth sitting with the irony: the company that showed the most discipline about what their AI should and shouldn’t do is the one being penalized for it.

And here’s what made me want to write this for you specifically.

One of the most revealing findings from Anthropic’s own system card is that the real safety risk with Mythos isn’t some sci-fi scenario where the AI plots against you. It’s much more mundane than that. During testing, the model did things like cover up its own coding mistakes and email people it shouldn’t have contacted, not out of malice, but because it was aggressively trying to complete its task.

The real danger is competence without judgment. A model that’s extraordinarily capable but doesn’t always know when to stop, slow down, or ask permission.

Which is exactly why I keep thinking about electricity. Electricity is one of the most transformational technologies in human history, and we understand it very well at this point. But people still die from electrocution every year, not because they don’t value their own lives, but because they don’t understand the risks of what they’re handling. When you’re working with live wires, the right gloves, the right shoes, and the right insulated protection can save your life. And then you use electricity confidently, every single day, because you understand it.

AI is at that stage right now. You learn to understand what you’re working with well enough to handle it safely, and then you use it with confidence. I’ve been thinking about this a lot, and it’s a big part of why I spend my time helping leaders build exactly this kind of judgment. Too many leaders are either paralyzed by headlines like Friedman’s or casually dismissing risks they haven’t taken the time to understand. I wrote about the thinking tools leaders need to evaluate AI honestly, and that piece is more relevant now than when I published it.

What happens to your organization if the AI tools your team relies on suddenly develop capabilities nobody anticipated? That’s not a hypothetical anymore. That’s what Mythos just demonstrated.

Build Your Own Judgment

If you lead a team that uses AI in any capacity, here’s what I’d ask you to consider this week: do you actually understand the capabilities and limitations of the tools your people are using? Not at the level of “I’ve heard it can write emails,” but at the level of “I know what these systems can do, where they break, and what guardrails we have in place.”

If the answer is no, you’re the electrician working without gloves. And the tools are getting more powerful faster than most governance committees meet.

Stop waiting for someone to tell you whether to trust AI. Build your own judgment now. That’s the insulation that protects you, and it’s the only kind that scales with the technology.

If You Only Remember This

• Claude Mythos found thousands of software vulnerabilities that human experts missed for decades, and Anthropic chose to restrict access rather than release it broadly

• The panic and the dismissal both miss the real story: AI capability is advancing faster than most leaders’ understanding of it, and that gap is where the real risk lives

• Understanding AI risk is like understanding electricity: you don’t avoid it, you learn how to handle it safely and then use it with confidence

Questions Leaders Are Asking

What is Claude Mythos and why isn’t Anthropic releasing it to the public? Claude Mythos Preview is Anthropic’s newest AI model, announced on April 7, 2026. It discovered thousands of hidden software security flaws during testing, including bugs that went undetected for over two decades. Anthropic restricted access to roughly 40 technology companies through a defensive initiative called Project Glasswing, backed by $100 million in usage credits, so defenders can fix vulnerabilities before the capability becomes widely available.

Should I be worried about AI cybersecurity threats to my organization? The concern is legitimate, but the right response is preparedness, not panic. AI is finding software vulnerabilities faster than ever, which means the window between a flaw being discovered and someone trying to use it shrinks considerably. Make sure your organization has current security practices, updated software, and a basic understanding of how AI tools interact with your systems.

What should non-technical leaders actually do about AI risk right now? Start by understanding the real capabilities of the AI tools your team uses. Build internal guidelines for how AI gets deployed in your workflows. Develop your own informed perspective rather than relying on media coverage that swings between hype and fear. Joel Salinas has written about building critical AI literacy as a leadership skill and AI discernment rules for leaders as starting frameworks for exactly this kind of thinking.

Written by a human, for humans.

Source: Claude Mythos Preview System Card by Anthropic, April 2026.

TL;DR: AI agents are systems that take a goal, make decisions, and act, unlike chatbots that just respond. Most leader teams confuse chatbots with agents, investing in tools that were never designed to carry real work forward. walks through what agents actually are, a 4-question test for whether you need one, and a step-by-step no-code build using free tools.

Most leaders I talk to right now say the same thing: “We need AI agents.” But when I ask what they mean by that, the answers are all over the place. Some are describing chatbots. Some are describing automated workflows. Some are describing science fiction.

The confusion is real, and it’s expensive. Teams are buying tools they don’t need, building systems that don’t match their problems, and calling everything an “agent” because the word sounds impressive.

This week’s guest post comes from , whose work I’ve come to love. Claudia Saleh is an AI Senior Leader, strategist, and educator who has guided thousands of professionals, from CEOs to team leads, in understanding and deploying AI practically and responsibly through her newsletter Claudia + AI, AI Agents courses, and executive workshops. Her upcoming book, AI Adoption Playbook, is a practical guide for making AI adoption stick in real teams. She also teaches a free 10-lesson AI Agents course on Substack.

If you’ve been wondering whether your team actually needs an agent or just a better workflow, this article will give you the answer. And if you do need one, Claudia walks you through building your first one without writing a single line of code.

Claudia + AI

I write a lot about AI. AI Adoption, AI Agents, AI Governance, AI literacy, AI News, Future of AI at work... but also Innovation, Travel, Books and Disney. Sometimes, all at once. Subscribe now! :)

I’ll let her take it from here.

Creating Your First AI Agent: A No-Code, No-Hype Guide for Leaders

The Problem Nobody Talks About

I was in a meeting recently when someone confidently announced: “We already use AI agents.” So I asked the obvious follow-up: “What do they do?” The answer: “They answer questions.”

That moment captures the biggest challenge in AI today. The word agent has been stretched to mean almost anything: a chatbot, a smart prompt, a workflow with AI bolted on. And when a leader bases decisions on that confusion, they invest in tools that were never designed to carry real work forward.

This article cuts through that. By the end, you will know exactly what an AI agent is, whether you need one, and how to build a simple, working version today, without writing a single line of code.

What Is an AI Agent?

Most of the things people call agents these days, are really just very good at one thing: responding. You paste in some text, you get a response, and then the ball is back in your court. You still decide what happens next.

An agent is different because it takes a goal rather than just a question. Once it has that goal, it can decide what to do next, use tools, check information, take action, and stop when the work is done, without you driving every step.

Think of it like the difference between a very knowledgeable colleague you have to pepper with questions all day versus one who you can hand a clear objective to and trust to come back with results. The second colleague is doing agent-level work.

Inside every agent, five things are working together. The agent perceives what is happening (an email arrives, a form is submitted). It reasons about what the situation means. It plans a sequence of steps. It remembers relevant context. And it takes action, sending a message, updating a record, routing a request. When any one of these pieces is missing or weak, agents feel unreliable. When they work together, agents feel almost like a dependable team member.

Do You Really Need an Agent?

Here is the question most vendors do not want you to ask:

Does my problem actually require an agent? The honest answer is that many problems do not.

A simple workflow, meaning a fixed sequence of steps that never changes, will handle a lot of operational work reliably and cheaply. AI automation, which is a workflow with AI helping inside specific steps, handles a bit more variety. Agents are for the situations where the path itself is not fixed, where the system needs to decide what to do next based on context, not just execute a script.

But not all agents are created equal. They have an agency spectrum that goes from supervised to autonomous. At lower levels, the agent only acts when prompted and stays tightly guided by a person. As autonomy increases, the agent begins to initiate actions, make choices within boundaries, and handle more of the flow on its own, with humans reviewing or intervening when needed. Fully autonomous agents sit at the far end of this spectrum. They operate continuously, adapt to changing conditions, and pursue goals with minimal oversight, which also makes them harder to control and riskier to deploy.

Before building anything, run your task through these four questions:

If you answer yes to most of them, you have a candidate worth building. If most answers are no, a simpler automation, or even doing nothing, is probably the smarter call.

Build Your First Agent: A Step-by-Step Example

The fastest path to understanding agents is building one. Many AI tools now have agents embedded in them. OpenAI has Agent Mode in ChatGPT Plus (the paid version $20) and Agent Builder , Anthropic has Claude Computer Use, and Google has Vertex AI Agent Builder.

In ChatGPT Plus Agent Mode, you can test using this prompt. Don’t forget to select Agent Mode in the Tools option in the Chat box:

You are a travel planning agent with computer access. 
Plan a 3-day trip to [destination] from [origin] for [dates] 
within a total budget of [$X] for [Y] people. 
Ask clarifying questions if needed.

It will use the browser to check websites and prices. You can add more details about flights, the origin airport, the category of hotels, and things to avoid. The more information you give to the agent, the better the results will be.

If you don’t have ChatGPT Plus, you can sign up for one of the low-code tools that allow you to create agents. The example below uses Relay.app, a free tool that requires no coding. You can sign up in minutes. The task I chose for this exercise is one most teams recognize immediately: processing a shared inbox.

Imagine your team gets dozens of requests by email every day. Some are urgent. Some are missing information. Someone has to read each one, decide what it is, and figure out what to do next. That is exactly the kind of repeating, judgment-heavy work where an agent earns its place.

1. Set your trigger

Go to Relay.app, create a new workflow and choose a Gmail or Outlook trigger: “New email arrives.” This is the moment the agent wakes up.

2. Add a filter

Exclude newsletters, receipts, and known automated senders. If the subject contains “unsubscribe,” the agent stops. This keeps it focused on real requests.

3. Add an AI classification step

Send the email subject and body to an AI prompt. Ask it to return structured fields: what type of request is this, how urgent is it, is anything missing, and what should happen next.

4. Add branching logic

If the request is sensitive or urgent, route it to a human for review. If information is missing, draft a reply asking for exactly what is needed. If the request is complete, log it in a tracker.

5. Test with real emails

Run 10 actual emails through the system and compare the agent’s decisions to what you would have done. Adjust the AI prompt until the categories and routing feel consistent.

What you get: a system that reads, classifies, and routes incoming requests without anyone having to paste emails into a chat or manually decide what each one needs. A human still reviews edge cases. But the repetitive triage work has already happened.

No code and Low code Tools to Get Started (All Free to Try)

There are dozens of tools to help you create agents. Some of these tools focus on visual building and low-effort setup. Platforms like Make, Zapier (that started as workflow automation tools), Relay.app, and n8n let you connect triggers, logic, and actions using visual flows. They are especially useful for agents that react to events, evaluate information, and take clear actions like sending messages, updating records, or creating tasks. When combined with language models, these tools can support agents that reason, route work, and remember outcomes, even though they started as automation platforms.

• Relay.app is best when you want to see the decision logic clearly. It is visual, well-documented, and ideal for tasks with explicit branching paths. Their template library includes a LinkedIn hook researcher, an influencer analyzer, and more.

• Make connects to thousands of apps and handles complex data flows well. It is especially strong when the output of one AI step feeds directly into another system. They have recently added a natural-language agent builder.

• Zapier connects to over 7,000 apps and is the fastest to set up. It is a strong fit for agents that need broad app coverage rather than complex logic. A 14-day free trial gives you full access to explore.

All three offer templates you can start from rather than building from scratch. The right choice depends less on the tool and more on how clearly you have defined the task, which brings us to the most important skill in all of this.

The Skill That Matters More Than the Tool

Whether you use Relay, Make, Zapier, n8n, or any other platform, the quality of your agent comes down to one thing: how precisely you have defined the work.

An agent needs a clear goal, a small set of decision rules, a defined output format, and explicit conditions for when to stop or ask for human help. Getting that right is a thinking exercise, not a technical one. It is the same clarity you would bring to a brief for a new hire.

A useful exercise is to complete this sentence before you touch any tool:

“When [this event happens], I want the agent to [evaluate this], 
and then [take this action] unless [this condition is true], 
in which case [route it here].”

If you can complete that sentence, you already have the outline of a working agent. The rest is execution.

What Agents Should Not Do (Yet)

Agents work best at the middle of the autonomy spectrum, not fully manual and not fully autonomous.

• Avoid handing agents decisions that involve legal, financial, or personnel risk.

• Avoid using them for exploratory creative work where the goal itself is still forming.

• And avoid building an agent for a task that happens once a year.

Responsibility never disappears when you deploy an agent. The team or leader who built it still owns the outcomes. That means deciding where human review is required, when the agent should pause, and what edge cases always go to a person.

Your Action for This Week

Pick one task you repeat every week. Write it out in plain language:

“When this happens, I usually decide this, and then I do that.”

If the event can be detected, if AI can help with the decision, and if the system can take the final action, you have the outline of an agent. Pick one of the three tools above and spend 20 minutes exploring a template that is close to your task. You do not need to finish it. You need to start understanding how these systems behave.

And if you want a structured path through all of this, from what agents are to building ones that run on their own, I have a full FREE course with 10 lessons in Substack. You can start with Lesson 1 here: What People Mean When They Say AI Agents.

Thank you, !

Claudia makes something clear that I think a lot of AI content misses: the hard part of building an agent isn’t the technology, it’s the thinking. Defining the goal, the decision rules, and the boundaries, that’s leadership work, not engineering work. And it’s the same muscle you use when you onboard a new hire or brief a team on a project.

If you’ve been sitting on the sidelines waiting for agents to “get simpler,” they already did. The tools Claudia walks through are free, visual, and require zero code. The only question is whether you’ve done the thinking to know what to build.

Follow Claudia’s work at Claudia + AI and check out her free 10-lesson AI Agents course.

- Joel

Questions Leaders Are Asking

What is the difference between a chatbot and an AI agent? A chatbot responds to questions and puts the next step back on you. An AI agent takes a goal, decides what to do next, uses tools, checks information, takes action, and stops when the work is done, without you driving every step. The key difference is autonomy and goal-directed behavior.

Can I build an AI agent without coding? Yes. Free no-code platforms like Relay.app, Make, and Zapier let you build working AI agents using visual flows. You connect triggers, AI classification steps, and branching logic without writing a single line of code. All three offer templates to start from.

How do I know if my task needs an AI agent or just automation? Run it through four questions: Does it repeat often? Is the error cost manageable? Does it consume meaningful time? Does it require judgment, not just fixed steps? If most answers are yes, especially the judgment question, an agent is the right fit. If not, simple automation works better and costs less.

What tasks should I NOT give to an AI agent? Avoid giving agents decisions involving legal, financial, or personnel risk. Skip tasks that happen rarely (once a year) or exploratory creative work where the goal itself is still forming. Agents work best at the middle of the autonomy spectrum, supervised, not fully autonomous.

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Which Sounds Like You?

“I need hands-on AI workflows, not just theory” → Join Premium (Starting at $49/yr, $1,345+ value): Step-by-step agent builds, automation playbooks, and direct coaching access to implement AI that actually works. Start here

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TL;DR: Gemini and NotebookLM are two Google AI tools that work as a complete research system when combined. Gemini discovers sources across the web while NotebookLM grounds every answer in sources you control, creating what Joel Salinas calls an “anti-hallucination layer.” For the 3 billion professionals already on Google Workspace, this is the fastest path to AI research you can actually trust.

Every piece of research I publish goes through the same filter. Before any AI touches my draft, I load my sources into NotebookLM. Research papers, reports, transcripts, and data I actually trust. The AI works within those boundaries, and every answer it gives me comes back with a citation I can trace to a specific passage in a specific source.

I call it my anti-hallucination layer.

I didn’t build this habit because I’m paranoid. I built it because I’ve seen what happens when leaders skip this step: they publish something an AI made up, someone catches it, and the credibility they spent years building takes a real hit. (This is one of the most common patterns I see in coaching conversations with leaders navigating AI.)

Here’s what most people don’t realize: NotebookLM is powerful on its own, but it was designed to work alongside Gemini. Together, they form a complete research system where Gemini discovers and NotebookLM verifies. And if your organization already runs on Google Workspace, and roughly 3 billion users globally do, you’re sitting on a research engine you haven’t turned on yet.

In this post, you’ll learn:

• Why raw AI answers are a credibility risk and how controlled-source research fixes it

• How NotebookLM and Gemini work together as a 2-step research system

• How to build a grounded knowledge base and then generate briefs, visuals, emails, and spreadsheets from it

AI Is a Master Guesser, Not an Expert

Step back for a minute and think about what AI actually does when you ask it a question. It predicts the most likely next word based on patterns in its training data. It does this extraordinarily well, well enough that the output reads as if an expert wrote it. But reading like an expert and being an expert are two very different things.

When you ask Gemini, Claude, or ChatGPT a question with no context and no sources, you’re asking a prediction engine to guess what a credible answer looks like. Sometimes the guess is perfect. Sometimes it fabricates a statistic, invents a citation, or confidently states something that was true in 2023 but outdated by 2026.

For most casual uses, that’s fine. But if you’re a leader publishing research, briefing your board, advising your team, or presenting data to stakeholders? “Probably accurate” is not a standard you can afford.

Look, I’m not saying AI tools are unreliable. Used wisely, they’re some of the most powerful research instruments that have ever existed. What I am saying is that using predictive AI without controlling your sources is like driving without a seatbelt: most of the time you’re fine, and then one day you’re not, and the cost is enormous. Your credibility, built over years of careful work, can take a hit from a single hallucinated stat that you didn’t catch.

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That’s the problem this system solves.

Quick Win (< 60 seconds) Open notebooklm.google.com right now. Click “New Notebook,” paste one URL you trust (a report, an article, a PDF), and ask it a question. Notice how every answer cites the exact passage from your source. That’s the difference between controlled and uncontrolled AI.

The Fix: A Research System You Can Actually Trust

The concept behind this is simple: build your knowledge base first, then let AI work from it. Start with the sources you trust. Add what you find through discovery. Then do all your output work (briefs, visuals, emails, presentations) from that grounded foundation, where every answer traces back to something you chose and reviewed.

NotebookLM is the foundation. This is Google’s RAG-based (Retrieval-Augmented Generation, which just means “AI that only answers from the documents you give it”) research workspace. You upload your sources, PDFs, Google Docs, YouTube links, web pages, even audio files, and NotebookLM builds a private knowledge environment around them. When you ask it questions, it only pulls from what you’ve given it. Every response includes inline citations linked to the exact passage in the exact source. Nothing from the open web. No guessing.

I wrote about NotebookLM’s individual features in depth and walked through a complete beginner setup in earlier posts. What I want to focus on here is what happens when you pair it with Gemini for the output work, because that’s where the real research power lives.

Gemini handles the output layer. With a 2-million-token context window (the largest among frontier AI models as of April 2026), Gemini can pull in your entire NotebookLM notebook and work from it directly. Once connected, you can ask questions, generate executive briefs, create images, build presentations, and even draft emails, all grounded in the sources you already vetted.

Full transparency: my own research stack pairs NotebookLM with Claude for the discovery step. But if your team already lives in Google Workspace, Gemini is the natural choice because it keeps the entire workflow inside one ecosystem, and the integration between the two tools is seamless.

If this is resonating, two paths forward: Premium members get implementation frameworks behind insights like this (join for $49/yr), or if you want hands-on help for your specific situation or a custom tool for your business, book a free coaching call.

Where NotebookLM and Gemini come together: The 2-Step Workflow

Here’s the system in practice. Each step takes minutes, and together they replace hours of tab-juggling, manual cross-referencing, and hoping your AI didn’t make something up.

Step 1: Build Your Knowledge Base (NotebookLM)

This is where you do the real work, choosing what goes in. Open NotebookLM and create a new notebook for your research project.

You have two paths to fill it, and most projects use both:

Discovery: NotebookLM has a built-in web search feature that lets you find and add sources without leaving the tool. Type a topic into the search bar, review what comes back, and selectively add the sources you trust. You control what gets in. Nothing is added automatically.

Your own sources: Upload what you already have. PDFs from your team, Google Docs from your Drive, YouTube videos you’ve watched, URLs you’ve bookmarked. NotebookLM accepts all of these.

I recently built a notebook on the 2026 generative media landscape. I used the discovery feature to find market reports, legal analyses, and industry benchmarks, then added a few sources I already had bookmarked. In about ten minutes I had eight grounded sources: a Global Market Analysis report on the content creation market ($886 billion projected by 2036), the Andreessen Horowitz State of Generative Media 2026 report, a legal analysis of US courts shaping AI copyright, Upwork’s hiring data on demand for quality-focused creative roles, YouTube’s Effect Maker launch, and three more covering Tencent’s ComfyUI partnership, leading companies in the space, and AI content marketing benchmarks.

With those sources loaded, NotebookLM immediately generates a summary and suggests questions. But here’s what matters: every answer it gives you from this point forward links back to a specific passage in a specific source. You can click the citation, read the context, and decide for yourself whether to use it. This is the anti-hallucination layer in action.

Copy-Paste Prompt (for querying your notebook): Based on these sources, create a research brief that includes: (1) the 5 most important findings with direct quotes, (2) any statistics with their exact source and date, (3) points of disagreement between sources, and (4) questions these sources don’t answer.

Step 2: Move to Gemini and Build Everything From It

This is where NotebookLM and Gemini connect, and where most people don’t realize the workflow exists.

Open the Gemini app and mount your NotebookLM notebook. (Google calls this “connecting” your notebook to Gemini.) Once mounted, Gemini can see every source in your notebook and work from that grounded foundation. You’re not starting from scratch or hoping Gemini searches the right things. You’re pointing it at research you already vetted.

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From here, the outputs are wide open:

Ask deeper questions. “Based on my research, what are the three strongest arguments that AI-generated content will face regulatory pushback by 2027?” Gemini answers from your sources, not the open web.

Generate an executive brief. “Write a 500-word executive summary of these findings for a leadership team evaluating AI content tools.” You get a brief grounded in eight vetted sources, not a hallucinated summary of the internet.

Create visuals. Use Gemini’s image generation to build charts, infographics, or presentation visuals based on the data in your research. “Create a bar chart comparing the projected content creation market size in 2026 vs 2036 based on the Global Market Analysis data.”

Note: To get the infographic on brand for your business, simply add detailed brand instructions to the prompt.

Build a spreadsheet. Ask Gemini to organize your findings into structured data. “Put the key statistics from each source into a table with columns for: source name, key finding, specific number, and date.” Export that into Google Sheets and you have a living reference document.

Draft an email. “Write a 3-paragraph email to my leadership team summarizing the three most relevant findings from this research for our Q3 content strategy.” Gemini drafts it from your grounded data. You review, edit, and send.

Turn findings into action. Once your research is organized, tools like Jotform let you operationalize it, turning insights into team surveys, client intake forms, or data collection workflows that connect your research to real decisions.

Audio Overview (back in NotebookLM). Before you finalize anything, go back to NotebookLM and generate an Audio Overview. It converts your entire notebook into a podcast-style conversation between two AI hosts who discuss, debate, and summarize your research. I use this for review: listening to AI hosts walk through my research brief catches weak arguments my eyes skip over when reading. It’s like having a peer review you can run in 10 minutes. (I covered 6 ways leaders can use Audio Overviews in a previous post.)

Your Company Already Pays for This

Here’s where the Google ecosystem angle gets practical.

As of March 2026, 750 million people use Gemini monthly, and Google has sold over 8 million paid Gemini Enterprise seats across 2,800+ companies. Barclays analyst Raimo Lenschow put it this way in March 2026: “What Google is doing with Gemini in Workspace is the most compelling enterprise AI play I’ve seen. It removes the adoption barrier entirely.”

If your organization is on Google Workspace, you already have the infrastructure for this. Gemini lives inside Gmail, Docs, Sheets, and Drive. NotebookLM connects natively to Google Drive, letting you pull documents directly from your existing folders into a research notebook.

The free tier of NotebookLM gives you 50 sources per notebook, 50 daily chats, and 3 Audio Overviews. That’s enough to run this entire workflow on your next project without spending a dollar. Google AI Pro ($19.99/month) expands to 300 sources and 500 queries if you need more.

Most AI adoption stalls because it means learning new platforms, new logins, new ecosystems. This system removes that friction entirely. If your team can use Google Docs, they can use this workflow. That’s a real competitive advantage that most organizations are leaving on the table.

If you want help building this into your leadership practice, book a free discovery call.

If You Only Remember This

1. AI without controlled sources is a credibility risk. Every major AI tool (Gemini, Claude, ChatGPT) is a prediction engine, not an expert. Your reputation depends on catching what it gets wrong before your audience does.

2. NotebookLM grounds your research, Gemini turns it into action. Build your knowledge base first with sources you trust, then move to Gemini to generate briefs, visuals, emails, and structured data, all anchored to those sources.

Questions Leaders Are Asking

What is NotebookLM and how is it different from ChatGPT or Gemini? NotebookLM is Google’s source-grounded research tool. Unlike ChatGPT or Gemini, which can pull from their general training data, NotebookLM only answers based on the specific documents, URLs, and files you upload. Every response includes citations linked to the exact passage in your source material.

Can I use NotebookLM for free? Yes. The free tier includes 50 sources per notebook, 50 daily queries, and 3 Audio Overviews. That’s enough to run the complete discovery-to-output workflow on most research projects. Google AI Pro ($19.99/month) expands those limits significantly.

Do I need to be technical to use this workflow? Not at all. If you can use Google Docs and paste a URL, you can run this entire system. NotebookLM was built for researchers and professionals, not engineers. I’ve walked complete beginners through it in my step-by-step guide.

How does this compare to using Perplexity for research? Perplexity is excellent for fast, cited web searches (I covered it in AI Research 101). The key difference is control: Perplexity searches the open web, while NotebookLM only works with sources you specifically choose. For research where credibility is on the line, that control matters. Many leaders use both, Perplexity for quick lookups and the Gemini + NotebookLM system for anything they’ll put their name on.

What if my company is on Microsoft, not Google? NotebookLM accepts uploaded files from any source (PDF, DOCX, plain text), so you can use it regardless of your primary ecosystem. The Gemini integration is more seamless for Google Workspace users, but the core anti-hallucination workflow works for anyone.

On your next research project, Monday morning, start with NotebookLM. Connect it to Gemini. Tell me how it went.

I get this question constantly: “What AI newsletters should I actually be reading? There are sooooo many!”

So I put together my honest list (Full infographic at the end).

These aren’t the biggest names or the flashiest brands. These are the newsletters I personally read, learn from, and recommend when someone asks me where to start. Every single one has earned a spot in my inbox.

I organized them by what you care about, because “AI” is a massive space and your time is limited. Find your lane. Subscribe to 3 or 4. That’s enough.

If You Lead a Team or Organization

You need AI thinking applied to real leadership decisions, not coding tutorials.

• how to boss AI by — How to lead humans and machines without losing yourself in the process. Exactly the tension most leaders feel right now.

• AI-Ready CMO by — AI marketing intelligence for senior marketers, delivered in 5 minutes daily. Peter calls it career insurance in the AI era, and honestly, he’s right.

• AI CFO Office — has strategies, playbooks, and tools for finance leaders who want to lead the future of finance, not react to it.

• Cash & Cache by & — Where capital meets code. If you’re thinking about how AI transforms business value and markets, Ashwin and Raghav connect those dots clearly.

If You Work in Marketing or Content

The marketers who figure out AI content strategy now will have an unfair advantage for years.

• MarTech AI by — Weekly AI content strategy insights that actually work. Charlie skips the hype and goes straight to what’s producing results for marketers right now.

• The AI Creator Drop by — AI prompts, automation, and systems for creators and entrepreneurs ready to scale. Practical, no fluff.

• Grow With AI by — AI playbooks for growing your audience and income without burning out. If you’re a creator trying to do more with less, start here.

If You’re Building a Solo Business

Solopreneurs who learn to work with AI aren’t just saving time. They’re building businesses that look like they have a team of ten.

• Unplugged by — Focused on building a $5K to $10K/month newsletter business. Yana shares the exact model she used, and it’s refreshingly specific.

• Behind the Craft by — Practical AI tutorials and interviews for busy people. Peter also offers a free AI learning path that’s genuinely one of the best I’ve seen.

• Solopreneur Code by — Cracking the growth equation for one-person businesses. If you’re trying to break free from the “do everything yourself” trap, Anfernee gets it.

If You’re Building AI Products

Whether you’re a product manager, a builder, or someone who wants to understand how AI products actually get made.

• Build to Launch by — Helps you build AI systems from your daily friction and turn your expertise into real, launched products. Jenny’s approach is refreshingly grounded.

• Product with Attitude by — An AI Product Manager helping everyone become AI-native builders. Karo’s framing is great: design and build with AI, not just alongside it.

• OpenClaw — unboxes AI that actually does things. Building cheaper, more capable agents and smarter routing for tools. Technical, but accessible if you’re curious about where agents are heading.

• Prompt-Led Product by — Leadership through technical clarity. Elena helps product managers build AI workflows, infrastructure, and tools to streamline their work.

• Wondering About AI by — Karen builds tools with Claude Code and other AI platforms and shares exactly what works and what flames out. Honest and practical.

If You Follow AI News and Trends

Stay informed without drowning in noise. These writers filter the signal from the hype.

• AI Supremacy by — News and op-eds at the intersection of AI, technology, and business. Michael has been doing this longer than most and it shows.

• AI Disruption by — A Beijing-based AI engineer sharing daily articles on industry trends, tools, and developments. The international perspective is valuable and hard to find elsewhere.

• future/proof by — Focused on helping you stay relevant in a fast-changing world of work and ideas. Dan thinks bigger than most.

• Millennial Masters by — Big ideas and bold moves for people building what’s next.

• Exploring ChatGPT — where AI, philosophy, and speculative science meet for deep dives. If you want to think about AI beyond the tools, this one stretches your perspective.

If You’re Interested in AI Infrastructure

Not for everyone, but if you want to understand the physical and financial layer underneath all of this.

• Global Data Center Hub by — Five-times-a-week coverage of the data center industry, including infrastructure, connectivity, and AI compute. If AI is the brain, data centers are the body. This newsletter covers the body.

• Sebastian Barros Newsletter by — Clarity on the future of connectivity, cloud, semiconductors, and AI infrastructure with a capital allocation lens. Sebastian writes with a level of precision that’s rare.

If You’re Getting Started with AI

No shame in starting here. Actually, some of these are among the best-written newsletters on this entire list.

• AI Made Simple by — Simple yet practical ways to use AI in everyday life and work. Nitin has a gift for making complex ideas feel approachable.

• How to AI by — For those who feel AI is something only others are good at. Two emails a week making AI accessible and actionable. Ruben meets you where you are.

• The AI Maker by — Practical blueprints to turn complex AI tools into simple systems you can build. Wyndo bridges the gap between “I’ve heard of this” and “I actually built something.”

• AI Adopters Club by — Helps you become the AI expert at your company with practical workflows and templates. If your goal is to be the person your team turns to for AI questions, this is the one.

• Cozora by , ME, & — Weekly expert insights delivered every Thursday. Full disclosure: I cofounded Cozora with Claudia and Michael, but the curation of expert voices they bring is genuinely worth your Thursday.

• Prosper in AI by — Actionable ways to grow professionally with AI. Ilia breaks things down in a way that makes you feel like you can actually do this, no matter where you’re starting from.

If You Want AI Doing the Heavy Lifting

These writers focus on building AI systems that actually work for you, day after day.

• AI blew my mind by — Daria turns decades of professional experience into AI systems that work 24/7. Her weekly insights on making AI do the heavy lifting come from real implementation, not theory.

• ROBOTS ATE MY HOMEWORK by — AI systems that sharpen your mind, your work, and your taste, without turning you into another robot. Mia’s voice is distinctive and her thinking is sharp.

• GenAI Unplugged by — Practical generative AI automation for people who want to build systems that run without them. If you’re tired of doing the same tasks manually, this is your newsletter.

If You Care About AI Safety and Security

Speed matters, but so does building AI that doesn’t blow up in your face.

• Empathetic Agentic AI Lab by — Focused on responsible AI system design. Judy helps builders define their AI’s job, set clear boundaries, and prevent behavioral drift. If you’re deploying AI agents in a business, this is required reading.

• ToxSec by — Deep technical analysis of AI cybersecurity, covering threat intelligence, adversarial prompt attacks, model vulnerabilities, and defense frameworks. Chris writes for people who understand that AI security isn’t optional.

That’s 34 newsletters across 9 categories. Bookmark this post. Send it to a colleague who keeps asking you “what should I read about AI?”

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And if I missed someone you love, drop them in the comments. I’m always looking for good reads.

What AI topic are you most focused on right now? Tell me in the comments and I’ll point you to the right 3.

Frequently Asked Questions

How many AI newsletters should I actually subscribe to?
Three or four, max. Seriously. Pick one from your primary interest category and one or two from adjacent areas. You’ll learn more from reading three newsletters consistently than skimming fifteen. Quality reading beats inbox volume every time.

Are all of these newsletters free?
Most offer free tiers with optional paid upgrades. I recommend starting with the free version of any newsletter on this list, because every single one delivers real value before asking for a dollar.

How did you pick these 34 newsletters?
I read them. That’s the whole criteria. These aren’t the biggest names or the ones with the most subscribers. They’re the ones that consistently teach me something, challenge my thinking, or give me something I can use the same week. If I wouldn’t recommend it to a friend, it’s not on this list.

I’m a business leader, not a developer. Which ones are for me?
Start with the “Lead a Team” category (how to boss AI, AI-Ready CMO, AI CFO Office, Cash & Cache). Then look at “Getting Started” if you want to build your AI fluency, and “News & Big Ideas” to stay informed without getting buried in technical details.

What about AI safety? Should I care about that as a business leader?
Yes. If you’re deploying AI tools in your organization, understanding boundaries, behavioral drift, and security vulnerabilities matters. The “AI Safety & Security” category (Empathetic Agentic AI Lab and ToxSec) covers this from both the design and cybersecurity angles.

Will you update this list?
I plan to. If you know a creator who should be on here, drop their name in the comments. I’m always looking for good reads, and I’d rather add someone great than miss them.

If you’re still figuring out where you stand with AI as a leader, start with my AI Leadership Compass — it takes 3 minutes and tells you exactly where to focus.

Joel

In December, wrote one of the most popular guest posts in the history of this newsletter: AI Research 101: Learn Perplexity from Scratch in 1 Hour. That post taught you how to build Perplexity Spaces, automate research with Tasks, and stop treating AI search like a fancier Google.

Since then, Perplexity has kept shipping. No announcements, no press events, just a changelog that has gotten long enough that the tool you set up in December might be running on half its current capability.

So… I asked Ilia to come back and cover what changed. Welcome to Perplexity 201!

Ilia writes Prosper, one of my favorite newsletters on practical AI systems. He thinks in workflows, not hype, and that is exactly why I keep bringing him back.

Prosper

Tactical AI workflows and frameworks every week - so you build systems that actually work, not just prompts that disappear.

By Ilia Karelin

In this post, you’ll learn:

• How Perplexity’s upgraded memory and personalization features carry your research context forward automatically

• How to set up scheduled searches that deliver intelligence briefs without you lifting a finger

• What changed in Perplexity Finance that makes company research actually auditable

• How connectors pull live data from Gmail, Slack, and other tools directly into your research

  

Take it away, Ilia.

AI Research 201: Learn Perplexity Features

I covered Perplexity in December as an intelligence platform that most leaders were only scratching the surface of. Since then, Perplexity has kept shipping. No big launch events, no press tours. Just a changelog that’s gotten long enough to matter.

Below are 10 features that landed since December and are worth knowing if you use Perplexity for work or for your own projects. They’re not all equal - some are quick settings you can flip in 60 seconds, a few are bigger shifts in what the product can do. All of them have a practical application for how leaders research and make decisions.

Perplexity memory and personalization features

1. Improved Memory Engine

The memory feature existed before. What changed is the quality.

The previous engine remembered too many things at too low a quality. The updated engine now recalls the right details in 95% of cases, up from 77%, while making half as many memories. Fewer, better.

In practice: if you’ve been researching a specific market, tracking a competitor, or working on a recurring project, Perplexity now carries that context into your next conversation without you re-explaining it. The tool accumulates your working context the same way a good analyst would.

Prompt to try: “Based on what I’ve been researching in the past 30 days, what should I be paying attention to?”

2. Response Preferences

Set your preferred output format once. It applies to every answer from that point forward.

Go to Settings → Personalization. Three things to set while you’re there:

- Your occupation (who you are) and custom instructions (things about you, your projects, etc.): shape how Perplexity frames answers without re-stating your context every session

- Response Length and Headers and Lists: control whether you get essays or scannable outputs

- Memory: toggle “Reference search history” on, and Perplexity uses your previous searches when answering

For leaders who want concise, scannable outputs — set both dropdowns to match your preference and stop getting essays when you asked a quick question.

Your first action: Open Settings → Personalize. Set it now. Takes 60 seconds.

3. Improved History Search

With this one, you can find any past thread without remembering the exact words you used.

Perplexity’s search previously required close keyword matching. The updated engine handles fuzzy recall - you can search by topic, approximate wording, or what you were trying to figure out at the time. Results show text previews of matching content, and clicking a thread takes you directly to the relevant message, not the top of the conversation.

Useful for anyone doing ongoing research who wants to pick up a thread from two weeks ago without rebuilding context from scratch.

Perplexity learning mode and scheduled searches

4. Learn Step by Step

This used to be locked to student accounts. It is now available to everyone via the “+” menu in the input bar → More → Learn step by step.

Instead of getting a direct answer, Perplexity walks you through a topic with questions, hints, and scaffolded explanations tailored to your level (you can set it up).

The practical application for leaders: use it to actually understand something you’d normally just skim. Competitive dynamics, financial concepts, technical architecture - anything where a quick answer leaves you dependent on asking again next time.

I used it to have Perplexity quiz me on a simple topic. It created 10 structured questions, gave me options to either increase the difficulty and the quantity of questions.

Prompt to try:

“Teach me [topic] step by step. 
I’m familiar with [adjacent concept] but want to understand the mechanics of [specific thing]. 
Quiz me as we go.”

5. Scheduled Searches

Scheduled Searches are recurring queries that run automatically and notify you with results.

This is the feature that turns Perplexity from reactive to proactive. Set a search to run daily, weekly, or monthly. Include a prompt that shapes the format and focus of the output. Get a notification when it’s ready.

How to set it up:

1. Go to Notifications in the left sidebar, then select Scheduled Searches

2. Create a new scheduled search - enter your prompt, set frequency, toggle notifications

Here’s the prompt I use for my weekly brief (feel free to use it as well):

Title: Daily AI Web & Reddit Briefing
You are an AI news curator specializing in the latest developments and discussions in the AI research and industry space, 
using Perplexity’s live web and Reddit search capabilities.
​

Your task is to provide a concise, up-to-date summary of the most interesting and high-engagement recent articles, 
blog posts, announcements, and Reddit threads about AI, 
focusing only on the last 3 days and prioritizing the freshest content available at query time.
​

Every time this Task runs, follow these instructions exactly:

1. Fetch fresh web + Reddit content
Use Perplexity’s real-time web search (including, blogs, arXiv, company posts)
and Reddit search to find content from roughly the last 72 hours related to:
​
Look into https://huggingface.co/papers to identify weekly, daily, monthly top papers - put them into the task output.

New AI models, papers, benchmarks, or libraries
Agentic systems, tool-using models, and multi-agent frameworks
AGI, alignment, and safety debates
AI policy, regulation, government or corporate moves
Open-source releases (models, frameworks, repos)
High-signal community discussions from researchers, engineers, and serious practitioners (especially on Reddit).
​
Prioritize:

Articles and posts from:
AI labs (OpenAI, DeepMind, Anthropic, Meta, xAI, etc.)
Research groups, universities, conferences, and reputable tech media
Well-known researchers, engineers, founders, and OSS maintainers

Reddit threads with:

Substantial technical or conceptual discussion
Many comments and upvotes in AI-related subreddits (e.g., r/MachineLearning, r/artificial, r/LocalLLaMA, r/generativeAI, /r/Singularity, /r/OpenAI, /r/ClaudeAI, /r/StableDiffusion, /r/ChatGPT, /r/ChatGPTCoding, /r/aivideo, /r/aivideo, /r/ClaudeCode, /r/PromptEngineering).
​
Deprioritize:

Pure memes, shallow “AI is crazy” posts, low-effort listicles
Pure marketing fluff without technical or strategic substance.
​
If there is not enough content in the last 24 hours, extend to 72 hours, 
but do not go beyond that unless absolutely necessary.
​

2. Identify 4–6 key themes
From the top cluster of relevant links and threads, infer 4–6 themes that best capture what the AI community is actively talking about right now, such as:
​
A big model or product launch
A widely discussed research paper or benchmark
A contentious debate (safety vs speed, open vs closed, AGI timelines)
A new agentic framework or tools getting traction on GitHub and Reddit
A major policy/regulatory announcement or corporate strategy shift.
​

For each theme, ensure it is:

Clearly described in 1–2 sentences
Grounded in specific sources (articles or Reddit threads), not invented.
​
3. Select 5–8 standout items
Choose 5–8 individual “standout” items (mix of web pages and Reddit threads) that are:

Recent (prefer last 48–72 hours)

High-signal (technical, insightful, or strategically important)

Either:

Highly cited/shared/covered across multiple sites, or

Backed by strong Reddit engagement (upvotes + comments) or credible authors.
​
For each standout item, provide:

Source type: Web article / Blog / Lab post / Paper / Reddit thread
Title or thread label (shortened if needed)
Source or author (e.g., “OpenAI blog”, “arXiv”, “r/MachineLearning”, “@username if clearly mentioned in article”)
Date (relative is fine, e.g., “2 days ago”)

A 1–2 sentence paraphrase of the core idea or result
A 1-sentence note on why it matters, e.g., “introduces a new benchmark that flips previous rankings,” “widely debated safety concern,” “shows practical wins for agentic systems.”
​
Do not fabricate items or details—everything must be traceable to real, 
recent links or threads.
​

4. Structure of the response
Always structure the final answer in this exact format:

Title line:

Latest AI Buzz from the Web & Reddit (as of {{current date in user’s local time}})
Section 1: Main Themes
One short intro sentence.
Then 4–6 bullet points.
Each bullet: a concise theme label in bold, followed by 1–2 sentences of explanation.

Example:

New open-source agent framework takes off – A new OSS agentic framework on GitHub 
is getting traction, with multiple posts and Reddit threads sharing early benchmarks 
and integration tips.

Section 2: Standout Recent Links & Threads

5–8 bullets.
Each bullet MUST follow this pattern:
Source type – Title / Thread name – Source (site or subreddit) – [Date]. 
Core idea (1–2 sentences). Why it’s noteworthy (1 sentence).

Example:

Reddit thread – “Daily Discussion Thread | Dec 18, 2025” – r/generativeAI – 1 day ago. 
Practitioners share early experiments with a new code model and discuss failure cases in production. Notable for hands-on reports and high engagement from indie devs.
​

Do not include full URLs unless explicitly asked by the user in a follow-up.
​
Closing line (always the same):
These are pulled live—ask for deeper dives on any topic or narrower focus (e.g., only safety, only open-source, or only Reddit).

5. Style and constraints
Tone: neutral, informed, slightly energetic, no hype.

Audience: technical / AI-literate reader who wants signal, not fluff.
Length: aim for 2–4 short paragraphs plus the bullet lists above.
Paraphrase rather than quoting long passages.
If the last 3 days have unusually little activity in some areas (e.g., policy), 
state that briefly instead of inventing content.
​

Use this prompt every time this daily Perplexity Task runs to generate a fresh, 
time-sensitive snapshot of AI discourse from the broader web and Reddit, 
complementing whatever the user already gets from X/Grok.

Write the prompt like a standing instruction set: what to include, what to exclude, how to structure the output. “Search for X, exclude Y, present it as Z” - done right, you get a usable brief delivered to you, not just a news alert.

New Perplexity Finance features

This cluster matters specifically if you track markets, research companies, or make investment decisions. The Finance tab is easy to ignore. These four updates change that.

6. Finance Heatmap

The Finance homepage now includes a heatmap of top stocks. Hover over any name and you get a live-updating explanation of what’s driving price movement, including the world news.

This is the difference between checking a ticker and understanding the session. Available on desktop and mobile web.

7. Analyst Ratings

Every equity page now shows analyst consensus ratings with the 52-week target range and a synthesis that factors in the most recent news. Useful for quickly calibrating whether a price move reflects a fundamental shift or short-term noise before you spend more time on it.

8. Auditable Financials with SEC Filing Links

This one is underrated. I don’t use this one as much, but I was blown away by how good it looks. You have to give it to Perplexity - their design is amazing.

When you’re doing due diligence or competitive research, you can now click any line item in the Financials tab and land on the exact page of the SEC filing it came from - pre-scrolled to the relevant section.

No searching through PDFs. No scrolling through 80-page filings. The answer is source-traceable in one click. Moreover, you can talk to it.

Available in the Financials tab on all US equity pages.

9. Finance Chart Upgrades

Two specific additions to the chart view on any asset page:

- Advanced mode: Tap the upper-right corner for a full-screen graph with zoom controls for specific time windows

- Interval selector: Use the three-dot menu in the toolbar to change tick frequency on the chart

If you’ve been frustrated by the default chart view not giving you enough resolution, these exist now.

How to use Perplexity connectors

10. Perplexity Connectors

Perplexity now supports connectors - integrations that let you pull live data from external tools directly into a conversation. There are hundreds of them available: Gmail, Google Drive, Slack, Salesforce, Notion, and more.

The honest caveat: most connectors are built for Computer, Perplexity’s agentic feature that requires Pro or Max subscription. But some work directly in the standard prompt interface - and that’s where it gets immediately useful for most people.

Go to Settings → Connectors, or look in the “+” menu inside any conversation.

For example, I connected the Gmail connector and can now pull context from my inbox into a search session without leaving Perplexity. Ask about an email thread, a sender, or a topic - and get an answer grounded in your actual messages.

The setup takes under two minutes, and the payoff compounds every time you would have otherwise switched tabs to check something. On top of that, you can combine the connector context with something that you are already searching in Perplexity.

What to set up first

The pattern across these ten features is consistent. Perplexity is building toward a tool that knows you, runs without you asking it to, and gives you source-traceable answers you can put your name behind.

Perplexity is no longer just a search engine. Memory that carries your context forward, scheduled searches that run without you, finance data traceable to the original SEC filing, connectors that pull in your actual emails - these are research and decision-making tools. Using them is the difference between a tab you open and close and a system that works while you’re doing other things.

Start here:

1. Open Settings → Personalize. Set your response length and structure now.

2. Create one Scheduled Search for something you currently check manually every week.

3. Open any equity page in Perplexity Finance and explore the Financials tab with the SEC drill-down.

One setup. Recurring leverage.

Thank you, !

The through-line across all 10 features is the same: Perplexity is becoming a tool that knows your context, runs on a schedule, and traces every answer back to a source. Pick one feature from this list, the one closest to a workflow you already do manually, and set it up before the week is over.

And if you want help building AI research into your leadership practice more broadly, I can help with that, just book a free intro meeting.

Subscribe now

Check out Ilia’s newsletter Prosper for more practical AI systems that compound over time.

If You Only Remember This

• Perplexity’s memory engine now recalls the right context 95% of the time with half the noise, which means your research sessions compound instead of starting from scratch every time you open a new thread.

• Scheduled searches turn Perplexity from a tab you open into a system that runs for you. One well-written prompt on a weekly schedule replaces hours of manual monitoring.

• The finance features now link directly to SEC filings, so every number you cite in a meeting or a memo is traceable in one click, not buried in an 80-page PDF.

Questions Leaders Are Asking

Q: Is Perplexity Pro worth paying for in 2026? If you use Perplexity for work more than twice a week, yes. The Pro plan unlocks scheduled searches, connectors, and advanced model selection, which are the three features that turn Perplexity from a reactive search tool into a proactive research system. The free tier is solid for basic queries, but the automation features require Pro.

Q: How is Perplexity different from ChatGPT for research? Perplexity searches the live web and cites every source with a link you can verify. ChatGPT draws from training data and can browse the web when prompted, but Perplexity was built specifically for sourced, traceable research. If you need to put your name behind the findings, traceability matters.

Q: Can Perplexity replace a research assistant? For recurring intelligence gathering, competitive monitoring, and news synthesis, Perplexity’s Spaces and scheduled searches handle about 80% of what a junior research analyst would do. The 20% it can’t replace is judgment, knowing which findings actually matter for your specific decisions. That is still your job as a leader.

Q: What should I set up first in Perplexity? Open Settings, then Personalize. Set your response length, toggle on memory, and add your role in the occupation field. This takes 60 seconds and immediately improves every answer you get going forward. After that, create a scheduled search for something you currently check each week manually.

Further reading: If you’re building a broader research system beyond Perplexity, How to Turn AI Research Into Executive-Level Analysis covers how to move from raw AI output to analysis you’d present to a board. And if you want to pair Perplexity with a knowledge management layer, The Ultimate Second Brain Blueprint walks through the full system.

Sources Referenced

• Perplexity AI (perplexity.ai) — all 10 features documented from product updates, December 2025 through April 2026

• Ilia Karelin, “AI Research 201: Learn Perplexity Features,” Prosper newsletter, April 3, 2026

• Original guest post: AI Research 101: Learn Perplexity from Scratch in 1 Hour, Leadership in Change, December 11, 2025