What to Remember

• The one-sentence test Nir uses to tell traction from distraction, which has nothing to do with what the activity actually is

• Why he calls the to-do list a broken tool, and the timeboxing move he replaced it with

🎥 Catch all future interviews, subscribe!

🎧 Prefer audio? Listen on Apple Podcasts

Outline

(1:19) – Hooked and Indistractable are complements, not opposites

(5:07) – Internal triggers, and the month I deleted every news app

(6:24) – The book about focus Nir can’t stand

(7:22) – Only 10% of distraction comes from the pings

(8:56) – Traction, distraction, and the Latin root of both

(10:49) – The email trap: work that isn’t the work

(12:27) – Name one product built to help you use it less

(14:20) – Effort pacts, and Viktor Frankl’s space

(15:43) – Why to-do lists are broken

(17:22) – Timeboxing, and the real metric of success

(22:30) – Self-regulation is the skill of the century

(28:36) – Persistence, belief, and Beyond Belief

My Takeaways

Ninety percent of it is coming from inside the house

So this is the number I had to go back and check twice. We blame the pings, the dings, and the rings, and Nir puts those at roughly 10% of the problem. The other 90% is an internal trigger, which is really just a feeling:

“Loneliness, boredom, fatigue, uncertainty, anxiety.”

Then he closes the escape hatch.

“It’s not your fault. You didn’t create these digital technologies. But it is your responsibility because who else is responsible?”

Traction and distraction are the same word

Here’s the reframe I’ll actually use. Both come from the Latin root trahere, to pull, and both end in the same six letters, action. So the test is simple:

“anything that is not what you said you were going to do in advance is by definition a distraction.”

Watching football isn’t a distraction if you planned it, and answering email is one if you didn’t. That second half is what got me, because Nir says the work-shaped distractions are the worst kind, “because you don’t even realize you’re distracted.” I said on the recording that this was hitting close to home, and I meant it.

The to-do list is a register of things you failed to do

Honestly, this is the part I didn’t want to hear because I tend to live by my to do list. A to-do list has no constraints, so you always add more and you always pick the easy item over the important one, and then you read back the leftovers night after night until you’ve built a fairly convincing case against yourself.

“There’s nothing wrong with you. You’re not broken. It’s this stupid time management technique we call to-do list that’s broken.”

His replacement is timeboxing, and it moves the finish line: success isn’t the checked box but “did I work on what I said I was going to work on for as long as I said I would without distractions?”

Be stingy with your time and generous with your money

This is the line I’d put on a wall. We clip coupons, we split checks at lunch, we hunt for deals, and then we hand our hours to whoever asks. “You can’t make more time. I don’t care if you’re Bill Gates or Elon Musk.” He carries it into parenting, calls self-regulation “the skill of the century,” and describes sitting down with his six-year-old to build her calendar, video games included. My version is a Bloom card that locks my work apps at 8 p.m., so getting back in means walking to my bedroom and tapping my phone against it. That walk is the whole point.

One Question to Sit With

Then watch the full conversation above, and go subscribe to Nir And Far, grab Indistractable, and pick up his new one, Beyond Belief.

About Nir Eyal

Nir Eyal is the author of Hooked, Indistractable, and Beyond Belief, which are built on his work in behavioral design and human motivation. Hooked and Indistractable have sold more than a million copies in over 30 languages, and Beyond Belief is an instant New York Times bestseller. He’s a former lecturer at the Stanford Graduate School of Business and the Hasso Plattner Institute of Design at Stanford, and his writing has appeared in The New York Times, Harvard Business Review, and The Atlantic. He writes Nir And Far on Substack and gives away a deep library of free tools at nirandfar.com, including a schedule maker and a habit tracker.

Nir And Far by Nir Eyal

The clearest thinking on what drives human behavior

About me

Joel Salinas is an AI Strategy Coach for founders and leaders, from solopreneurs to teams. AI is everywhere; judgment is scarce. Joel helps leaders adopt AI without outsourcing their judgment to it, through the AI Judgment Workshop and the 90-Day Judgment Engagement. Creator of the AI Leadership Triad. He writes Leadership in Change.

Written by a human, for humans.

So, when an AI rollout stalls, what actually stalled?

Most of us reach for the tool answer first. The model, the licences, the integration, the training that nobody showed up to. I’ve come to think that’s the easy thing to look at, because the harder thing is the people, and the people are where these projects really live or die.

The way I think about AI is the way I think about fire or electricity. World-changing to have around, but just having it doesn’t make anything better on its own. You have to be deliberate about how your people actually pick it up and use it.

That’s exactly the ground works on. Brennan spent a career in financial services technology and change, and now he writes Getting AI To Work, where the whole premise is that AI initiatives stall because of people, not technology.

Getting AI To Work by Brennan McDonald

I help leaders get AI initiatives unstuck.

What to remember from this piece

• When an AI project stalls, look at the people and the operating model before you look at the tool.

• Run three questions before you roll anything out: what are we making better, what will change for our team, and how will we do this properly.

• “Making better” is about time, judgment, and creativity, not just cost. Ask the people who do the work if they agree it’s a problem worth solving.

Here’s Brennan.

Reframe Your AI Change Initiative

There are clear benefits to deploying AI for leaders. You can do things more efficiently, reduce costs, or increase outputs. What I’ve found over the course of my career, and in my AI experimentation and work, is that the focus is too often on the tools. We fall in love with the solution and forget about the people required to get it over the line.

The idea of a vision and the concept of putting people first are sometimes dismissed as corporate cliches. Yet the lesson of many AI projects so far is that successful projects have great change management, cultural buy-in, and leaders who have thought carefully about how to bring their people on the journey.

In today’s article, I’m going to share three questions to reflect on when you are setting up your AI initiative, especially if you are struggling to get that cultural shift and need a reset.

1. What are we making better?

2. What will change for our team?

3. How will we do this properly?

If you run through these questions, you’ll surface some of the blockers in your business, but also the boosters that will help you land important AI projects. We are in the age of technology, but people matter more than ever.

What are we making better?

Meaningful work matters more than it ever has before. What leaders want to think about in AI transformation is: what are we making better? This isn’t just about cost. We want to understand where people are wasting time, losing the chance to apply expert judgment, wasting energy, or not being able to exercise creativity today.

AI is an opportunity to make things better by automating repetitive tasks and simpler decision-making processes, freeing people up to focus on putting their best energy into the most important problems that your business faces.

What are we really trying to improve? By implementing these AI changes, what is better? Do the people who do the work agree that this is a problem to be solved?

A good answer here sounds like faster decision-making cycles or shorter feedback loops, a reduction in admin or manual tasks, improved customer service standards and responsiveness, or safer operational flows with lower levels of risk.

What will change for our team?

One of the biggest reasons for project failure of any kind is people not buying into the future state. They do not feel that they benefit from the change that is happening.

We want to ask questions like: Who gets spare capacity or the opportunity to work on different problems if this works? Who loses control of gateways or status in the workplace? Are there ways of working that have been in place that people feel invested in, or that they feel their ongoing job security is linked to?

If we roll this change out, what happens to that, and to people’s comfort with how things are done? Are we being open and honest about exactly why we’re making this change? Can people see through the messaging?

In my Substack and in my videos, I speak a lot about how what people see in their social media feeds interacts with what they hear in the workplace. Leaders need to be open and honest about what the change genuinely means for career growth, job security, pay and benefits, and other workplace issues people care about.

This matters because you are operating in an information environment that tells many people the only change that is going to happen to them because of AI is that they lose their job.

As part of your AI initiative, you can do things better, faster, and cheaper. You can help people solve more interesting problems. You can help them become more technical so that they can get leverage out of their unique skills, experience, and domain knowledge in using AI tools to solve problems.

How will we do this properly?

This is the execution question. This is where a lot of the thinking about governance or risk management comes into play.

All these topics are important. Too often, companies fall into a governance theatre arrangement. They have all the policies, frameworks, and security controls. They miss how they make AI adoption safe, useful, and real.

When I think about how to do AI transformation properly, I mean taking advantage of the technology. I don’t want to be trapped by the limitations of an existing operating model.

That comes down to whether you are redesigning workflows end to end, or just adding AI as another step in a process and putting it into a standard operating procedure. Can your people challenge, override, or question which AI models are used and how they are used? What guardrails are in place? Where is it okay to use AI-generated content, and where is it not? What thresholds require a human in the loop?

One thing I observe and hear in the conversations I have is that firms that have copied and pasted their approach to governance and risk management, without tailoring it to their own business, often run into problems that make it harder to realise the project benefits.

When you think about how you do this properly, this is like letter-of-the-law compliance versus spirit-of-the-law thinking. If you put AI into a workflow to be able to tell your stakeholders that you are doing AI, you absolutely are not doing AI properly.

If you take a step back and think about your operating model, your people, your processes, and your platforms, and think carefully about the bare minimum you actually need if you push this technology to its logical extent over the next few years, that is what I mean with this question.

Are you doing AI properly? Very few companies are. Even some of the biggest companies in the world are still making incremental changes to how they do things. They are risk averse. There is a lot of cost, complexity, and skill required to execute complex change of any type, let alone change involving an emerging technology that brings a whole set of other risks that must be managed alongside its deployment.

And that’s why taking a step back and getting clarity upfront about what you’re making better, what will actually change for your people, and how you are going to do AI properly is worth the time. Rushing licences to people with no operating model change is a recipe for lukewarm adoption and internal pushback. When it’s easier than ever to get AI to help generate a project plan, spending more time at the whiteboard thinking about how to make the work better has more leverage than ever.

Thank you, Brennan!

Brennan’s three questions do one thing well: they move the conversation off the tool and back onto the people who have to live with it. That’s really a test of adaptability, which is the first skill in the AI Leadership Triad I write about here, because the reset only works if you’re willing to change how the work actually happens, not just what software sits on top of it.

Rushing licences to people with no change to how the work happens is just a more expensive way to stall.

If this resonated, subscribe to Brennan’s Getting AI To Work. He goes deep on exactly this kind of reset every week.

If you want help resetting your own rollout, that's what we build in the AI Judgment Workshop. Ninety minutes live, $99, and you leave with a written 90-day plan instead of another framework to file away.

Questions Leaders Are Asking

Why do most AI initiatives stall? Most AI initiatives stall because of people and operating models, not the technology itself. Teams don’t buy into the future state, no one is honest about what changes for their jobs, and governance gets copied instead of designed. The tool usually works. The rollout around it doesn’t.

How do I get my team to actually adopt AI? Start by naming what the change means for them: job security, career growth, pay, and status. People adopt AI when they can see a real benefit for themselves, not just for the company’s cost line. Ask the people who do the work whether the problem you’re solving is even a problem worth solving.

What is governance theatre in AI adoption? Governance theatre is having all the policies, frameworks, and security controls in place while missing the actual point: making AI adoption safe, useful, and real. It usually happens when a company copies another organization’s governance approach instead of tailoring one to its own operating model and risks.

What questions should I ask before starting an AI project? Ask three before you roll anything out. What are we making better, and do the people doing the work agree it’s a problem? What will change for our team, honestly? And how will we do this properly, meaning are we redesigning the workflow or just bolting AI onto the old one to say we did.

How is AI change management different from regular change management? The fundamentals are the same: vision, buy-in, honesty. What’s different is the information environment. Many people already believe AI’s only effect on them is losing their job, so leaders have to be more direct than usual about what the change actually means for careers, pay, and day-to-day work.

Should I redesign workflows or just add AI to a step? Redesign the workflow. Bolting AI onto an existing standard operating procedure so you can tell stakeholders you’re “doing AI” is the fastest route to lukewarm adoption. The payoff comes from rethinking the operating model around what the technology can actually do, not from adding one more step.

Joel Salinas is an AI Strategy Coach for founders and leaders, from solopreneurs to teams. AI is everywhere; judgment is scarce. Joel helps leaders adopt AI without outsourcing their judgment to it, through the AI Judgment Workshop and the 90-Day Judgment Engagement. Creator of the AI Leadership Triad. He writes Leadership in Change.

Brennan McDonald writes Getting AI To Work, where he helps leaders get stalled AI initiatives unstuck after a career in financial services technology and change. Find him on YouTube and LinkedIn.

Written by a human, for humans.

🎥 Catch all future interviews, subscribe!

🎧 Prefer audio? Listen on Apple Podcasts

Outline

(00:25) – Who is, and why it’s never about the technology

(01:39) – From the newsroom to Forrester to Altimeter Group

(03:41) – Electricity, steam-powered factories, and why AI moves faster

(06:24) – The biggest misconception, and “you’re not even close to beginning”

(08:44) – Fluency vs. literacy, and why the only way through is through

(11:36) – The trust wall, and how a leader destigmatizes AI in one meeting

(13:48) – Why write a book on AI when everything keeps changing

(16:42) – Step one, and the one line to add to the end of every prompt

(19:27) – Pick your lane, and the board member who owned ethical AI

(25:35) – “You’re not behind. You’re just getting started.”

(33:09) – Curiosity, imagination, and using AI to find your blind spots

My Takeaways

You’re not behind. So this is the piece I keep coming back to, because I hear that fear from almost every leader I sit with and I’ve felt it in myself. Charlene has been in this since she helped a newspaper go online in 1993, she wrote the book on it, and she still took three weeks off in January to care for loved ones, came back to Claude Cowork, and had no idea what had happened while she was gone. She felt behind too. Then she took a breath and turned it around on the rest of us.

“You’re not behind. Just look around you. How many people haven’t even started yet? So if you start today and just do one thing, you’ll be ahead of the vast majority of people.”

One of my clients is a CEO with over 45 years of practice who started sending me links to artifacts he’d built in Claude, and he could not believe he made them himself with nobody helping him. That’s what starting looks like.

Fluency is a practice, and practice is the only door. Honestly, this is where most AI advice falls apart, because it tells you what the thing is and then leaves you standing there. Charlene splits AI literacy, knowing what it is, from AI fluency, which is what happens once you’ve used it enough that you stop thinking about it. There’s one way to get there and she doesn’t pretend otherwise.

“It will feel messy. It will feel like you don’t know what you’re doing... But there is no way around it. You have to go through it. The only way through it is through it.”

I said something close to this on the live about electricity, that plugging AI into an organization nobody wired for it does nothing, but hers is the harder version, because wiring a house is a project you finish and this is a thing you only get by being bad at it for a while. She also gave the best beginner tip of the night: add “before you start, ask me any clarifying questions you may have” to the end of your prompt, and the whole thing turns from a vending machine into a conversation.

Mindset comes before tools, and before governance. Look, most of us hand AI to the CIO, buy everyone a license, and call that a rollout, and I keep watching it play out the same way. Her line on it was the sharpest thing she said all evening.

“This is not something you delegate. And what I’ve seen now is that leaders are abdicating this role.”

The fix she gave is almost embarrassingly small. Use AI to prep for your next meeting, tell the room you did it, then ask them how they used it, which sets the expectation and takes the shame out of it in the same breath. If your people think using AI counts as cheating, no tool budget on earth saves the rollout. That’s the same wall I wrote about in the empathy gap in AI implementation.

The thing I’m stealing from her. Here’s the part I hadn’t thought of, and I said so on air. Charlene feeds her own meeting recordings to AI and asks it what went wrong in the room.

“Take a transcript of my last meeting because something in it didn’t feel right. Something went off the rails and I’m not sure what’s happening. Diagnose that and give me suggestions.”

It comes back and tells her she never set an agenda, or that she talked for 70% of a call she should have been listening through. I’ve fed transcripts into Claude plenty of times to build a post out of them, and it never once occurred to me to point that back at myself and ask where I got in my own way. I use Granola to capture the calls, so the raw material has been sitting there the whole time, which is really the argument for why none of us is behind. The thing that moves you forward this week is probably already open on your screen, and you just haven’t pointed it at anything that matters yet.

One Question to Sit With

Charlene left this one hanging and I haven’t been able to put it down:

Watch the full conversation above for her answer, and for the line I keep repeating to myself, that you’ve never been in control anyway. When you’re done, go subscribe to Charlene Li on Substack.

About Charlene Li

is a New York Times bestselling author whose books include Groundswell, Open Leadership, and The Disruption Mindset. Her latest, Winning with AI: The 90-Day Blueprint for Success, is co-written with Dr. Katia Walsh, one of the world’s first chief AI officers, and it’s a leadership and strategy book rather than a technology book. She founded Altimeter Group in 2008, spent years as a Principal Analyst at Forrester Research covering the rise of social media, served as Chief Research Officer at PA Consulting, and now leads her own firm, Quantum Networks Group. She’s just moved her newsletter to Substack, and the book is at winningwithaibook.com.

About me

Joel Salinas is an AI Strategy Coach for leaders at small and mid-sized businesses and nonprofits. AI is everywhere; judgment is scarce. Joel helps leaders adopt AI without outsourcing their judgment to it, through a monthly live workshop and 90-day engagements. Creator of the AI Leadership Triad. He writes Leadership in Change.

Affiliate links included.

Written by a human, for humans.

TL;DR: Answer Engine Optimization (AEO) is the practice of getting your business cited and recommended inside AI answers from tools like ChatGPT, Google AI Overviews, and Perplexity. Traditional search optimization earns rankings; AEO earns the recommendation. This 2026 guide explains, in plain English, how AI decides who to name and how leaders can become the answer.

I got on a call with a new coaching client a few weeks ago, and one of his answers shocked me.

I asked him the question I ask everyone: how did you find me? Almost every client I’ve ever worked with came in through my newsletter. They read something, they reply, we end up talking. So I expected the usual answer.

He told me he’d never heard of my newsletter.

What he’d actually done was describe his problem to Claude. He asked it who could help him. And Claude gave him my name.

A real client, real money, a real coaching relationship, and the entire introduction happened inside an AI conversation I never saw, never optimized for, and didn’t even know was running. He didn’t Google me, he didn’t stumble onto a blog post, an AI vouched for me and he trusted it enough to reach out.

That’s the shift I want to walk you through, because it’s already happening to your business whether you’re watching for it or not.

It’s called Answer Engine Optimization, or AEO, and it’s the difference between being a link someone has to find and being the name an AI hands them.

In this guide, I’ll walk you through:

• What AEO actually is, in plain English, and how it differs from the SEO you already know

• Why AI is already forming opinions about your brand, and how to see what it’s saying

• The five honest moves that make AI more likely to recommend you

• A simple three-prompt test you can run on your own company in the next ten minutes

What Is AEO, in Plain English?

Answer Engine Optimization (AEO) is the work of getting your business named inside the answers AI tools give, instead of just ranking on a results page. When someone asks ChatGPT, Google’s AI Overview (the AI summary that now sits above the blue links), or Perplexity (an AI-powered search engine) for a recommendation, AEO is what decides whether your name comes up.

Here’s the way I think about it. Old-school search is the phone book. SEO, search engine optimization, the work of ranking well on Google, got your business listed in that book, hopefully near the front. But nobody asks the phone book who to trust. They ask a friend. And when a friend who knows the field says “oh, you should call this person,” that one recommendation carries more weight than ten pages of listings.

For a lot of people, AI has become that friend. They don’t scroll ten blue links anymore. They ask Claude or ChatGPT a real question and take the answer it gives. So the prize stopped being the click and became the recommendation. SEO gets you into the room. AEO gets you recommended once you’re there.

You’ll also see this called GEO, generative engine optimization. People use the two terms almost interchangeably. Same idea: be the thing the AI quotes.

And to clear up the question I get immediately: no, you don’t abandon SEO. SEO is still the foundation, and it always will be. A lot of what AI pulls from is built on solid search presence. AEO is the new layer you add on top, and the reason to add it now is simple. The window is open. Most of your competitors haven’t started.

Quick Win (< 60 seconds): Open ChatGPT or Claude right now and type: “What does [your company] do, and who is it for?” Read what comes back. Whatever it says, accurate, outdated, or nothing at all, that’s your starting line.

Seriously, stop reading for a second and do it. I’ll wait.

AI Is Already Talking About Your Brand

Whether or not you’ve thought about AEO, AI tools are already describing your business, comparing it to competitors, and sometimes criticizing it. The catch is you can’t see those conversations, and they aren’t always accurate or kind.

The software company BrightEdge ran an analysis in 2026 of how the major AI engines talk about brands, and the numbers should get every leader’s attention. Google’s AI Overviews are 44% more likely than ChatGPT to surface negative sentiment about a brand. And ChatGPT is 13 times more likely than Google to criticize a brand right at the moment someone’s deciding whether to buy. Same company, two AI engines, completely different verdicts. The two of them disagree on which brand to criticize 73% of the time.

When I read that, I had to read it twice. It means there isn’t one answer about your business out there. There are several, they don’t agree with each other, and you’re not in the room for any of them.

This is happening because of a bigger change underneath everything. More and more searches now end without a single click. The answer shows up on screen, the person reads it, and they never visit anyone’s website. Researchers call this zero-click. A European Parliament study in 2025 found that visits from search to news sites dropped from 2.3 billion to 1.7 billion in a single year, while the share of news searches ending in zero clicks climbed from 56% to nearly 69%. Business Insider alone lost about 55% of its search traffic.

I’m not telling you this to scare you, and I’m not interested in doom. I’m telling you because the attention you used to count on is moving into a place you can’t see, and the businesses that adapt to that are the ones AI keeps recommending. The ones that ignore it slowly fall out of the conversation. I first wrote about this over a year ago in How to Be Seen in the AI Search Era. Everything I worried about then has only sped up, and the data since has made the case stronger.

Why an AI Recommendation Beats a Click

Leaders hear “fewer clicks” and panic. But the visitors who do arrive through an AI recommendation are usually further along and readier to buy, because the AI already did the research, the comparison, and the vetting before they ever reached you.

Think about how my client actually got to me. He didn’t browse. He described his problem, the AI weighed the options, and by the time he reached out he’d already decided I was the right fit. Weeks of research and trust-building happened inside a conversation that took him a few minutes. He showed up pre-sold.

That’s the pattern. Data from the analytics firm Conductor suggests visitors who arrive from AI tools convert at roughly four times the rate of regular search visitors, and they spend longer on the page once they land. I’d take one of those over ten cold clicks any day of the week.

We’re getting close to a world where the AI isn’t just answering the question, it’s doing the shopping. I wrote about that in How AI Agents Are Replacing Online Shopping. If the AI is the buyer’s first stop, then the AI is the audience you have to win.

And here’s the part that reframes the whole job. When AI decides who to recommend, it mostly isn’t quoting your website. Studies of AI citations in 2026 show that somewhere between 90 and 95% of what AI pulls from comes from third-party sources: other people’s articles, forums, reviews, roundups. Only 5 to 10% comes from your own site. So the conversation about your business is mostly happening on other people’s pages, and a big part of the job is influencing those.

This guide gives you the map.

Subscribe now

How to Become the Answer: 5 Moves

Becoming the brand AI recommends comes down to five moves: audit what AI already says about you, answer questions directly on your own site, earn mentions on the third-party pages AI trusts, keep your facts consistent everywhere, and check back regularly.

1. Audit what AI already says about you. Before you change anything, find out what the AI thinks. Open ChatGPT, Gemini (Google’s AI assistant), and Perplexity in a private or logged-out window so your own history doesn’t bias the result, and ask about your company and your category. I’ll give you the exact prompts in a minute.

2. Answer questions directly, on your own pages. AI likes content that answers a specific question fast. The research backs this up hard. A Princeton study on AI visibility found that adding clear statistics to your content makes AI about 30% more likely to cite it, citing your own sources adds another 30%, and quoting a credible expert adds 41%. Keyword-stuffing, the old SEO trick, actually makes you 9% less likely to be cited. So write like a helpful human. Put the answer first, then explain.

3. Earn mentions where AI looks. Remember, around 90% of what AI quotes lives on other people’s pages. That means the roundups, the “best tools for X” lists, the Reddit threads, the YouTube videos. If you’re not in the comparison article AI keeps citing, you either get into it or you create a better one.

4. Keep your facts identical everywhere. AI is, at its core, a consensus machine. When your company’s description, services, and details match across your website, your LinkedIn, your Google profile, and review sites, the AI trusts the facts and repeats them. When they conflict, it often just leaves you out rather than risk being wrong.

5. Check back, because the answer changes. AI answers aren’t fixed. They shift as new sources show up. Re-run your audit every couple of months, watching especially for wrong claims and the near-purchase criticism we talked about earlier. Treating AEO as a one-and-done box to check is the same trap behind why most AI investments fail.

One heads-up before you start. The hardest move here, actually getting your business into the roundups, reviews, and comparison lists AI leans on, is a project on its own, and it deserves a full walkthrough. That’s the follow-up I’m working on now. For today, start with the audit and your own pages, because that’s the work that pays off no matter how the tools change.

Share

The Three-Prompt Test

The fastest way to see your AEO reality is what I call the Three-Prompt Test: three questions you ask the major AI tools to find out whether they know you, recommend you, and describe you accurately.

Run these in a fresh, logged-out chat across ChatGPT, Claude, Gemini, and Perplexity:

1. The direct one: “What does [your company] do, and who is it for?”

2. The category one: “What are the best [your category] for [your ideal customer]?”

3. The head-to-head: “[Your company] versus [your top competitor], which is better for [specific need]?”

For each answer, ask yourself three things: Did it mention me at all? Was what it said accurate? Would I have recommended me, based on that answer? If you’re missing from the category question, that’s your biggest gap. If the answer is wrong, that’s a brand-risk problem you can start fixing today by correcting your own pages, because those are the facts the AI checks against.

Here’s what that looks like in practice. Say you run a regional bookkeeping firm. You ask ChatGPT “what are the best bookkeeping firms for small nonprofits in Denver,” and it names three competitors and never mentions you. That’s the category gap, and it’s the one that costs you the most business. Then you ask “what does [your firm] do,” and it confidently describes a service you stopped offering two years ago. That’s the accuracy problem. Neither answer took more than a minute to find, and together they tell you exactly where to start: get mentioned where the AI is already looking, and fix the stale facts on your own pages.

That whole loop, the three prompts plus a way to score what you find, is the engine of an AEO strategy. I built the full version into a free download, the Become the Answer audit: the three prompts, a scoring sheet so you can track what each AI says about you over time, and the complete five-move checklist on one page.

To get the download, subscribe below, it’s free, then reload this page and the download will appear right here. Subscribe free →

Don’t just file this away. Run the three prompts today. It takes ten minutes and it changes how you see your whole online presence.

If you only remember this:

• AEO is the work of becoming the answer AI gives. SEO gets you in the room, and AEO gets you recommended once you’re there.

• Roughly 90% of what AI says about you lives on other people’s pages, so your reputation in AI is built mostly outside your own website.

• The buyers AI sends you are pre-vetted and ready, which makes one AI recommendation worth more than a pile of cold clicks.

So here’s what I’d love you to do. Run the three prompts on your own company, then hit reply or post a Note and tell me what you found. Was the AI right about you, wrong about you, or did it have no idea you exist? I read every one.

Subscribe now

Worth Your Time

A few pieces from other creators I think are worth your attention.

• Kamil Banc — Get AI-Search to recommend your business, correctly. A real audit of a business scoring 51/100 on AI visibility, plus how to get ChatGPT and Gemini to recommend you with the right facts.

• This Week in AI — How AI-First Marketing Looks Like in 2026. Three marketing shifts for 2026, with a full section on Answer Engine Optimization and getting discovered inside AI.

• Kristina God — Is SEO Dead in 2026? The Father of SEO and Google Just Answered That. The man who coined “SEO” weighs in on staying visible as AI answer engines replace the old search game.

Questions Leaders Are Asking

What is the difference between SEO and AEO? SEO (search engine optimization) is the work of ranking your website high on a results page so people click it. AEO (answer engine optimization) is the work of getting your business named inside the answer an AI gives. One earns the click, the other earns the recommendation.

Is AEO replacing SEO? No. SEO is still the foundation, and a lot of what AI pulls from depends on a strong search presence. AEO is a second layer you add on top. The smart move in 2026 is to keep doing SEO and start doing AEO now, while most competitors haven’t.

How do I know if AI recommends my business? Run the Three-Prompt Test. In a logged-out chat, ask ChatGPT, Claude, Gemini, and Perplexity what your company does, what the best options in your category are, and how you compare to a competitor. Note whether you’re mentioned, whether it’s accurate, and whether it recommends you.

How does AI decide which businesses to recommend? AI pulls from many sources at once, and roughly 90% of what it cites comes from third-party pages, not your own site. It favors clear, well-sourced answers, consistent facts across the web, and mentions in the roundups and reviews it already trusts.

Can a small business compete with bigger brands on AEO? Yes, often more easily than with traditional SEO. AI rewards the clearest, most relevant, best-sourced answer to a specific question over raw brand size. A focused small business that answers a narrow question well can get recommended ahead of a larger, vaguer competitor.

Joel Salinas is an AI Strategy Coach for leaders at nonprofits and small and mid-sized businesses. AI is everywhere; judgment is scarce. Joel helps leaders adopt AI without outsourcing their judgment to it, through the AI Judgment Workshop and the 90-Day Judgment Engagement. Creator of the AI Leadership Triad. He writes Leadership in Change.

Written by a human, for humans.

🎥 Catch all future interviews, subscribe!

🎧 Prefer audio? Listen on Apple Podcasts

Outline

(00:30) – Friends across Substack, a breath of fresh air

(01:23) – Burnout, a tumor, and the business he walked away from

(07:35) – Quality of life vs. lifestyle

(09:07) – Sacred business vs. opportunistic business

(11:15) – Why AI is nobody’s goal

(16:48) – The AI Judgment Workshop and reading your team

(19:18) – The Harmony Map, and slowing down in the AI era

(27:40) – The eco rat race: old identity, new context

(29:46) – We’re all toddlers with AI

(31:48) – “What do you truly want?”

(35:39) – The Golden Sequence: connection, growth, belonging

My Takeaways

Sacred business vs. opportunistic business. So Phil opened with the story I keep thinking about. He built a boutique advertising business doing over $100,000 a month with basically him and a virtual assistant, and it nearly cost him everything, autoimmune problems, a health scare, time essentially bedridden. He walked away because his heart was never in it, and out of that came the distinction the whole conversation runs on.

“When we talk about sacred business, it’s honoring all of who you are, your gifts, your talents, your failures, your victories, and it’s tied to service. The opportunistic business is chasing the trend. It’s not really looking at who am I and what wants to come through.”

When you chase the trend, you spend your life playing catch-up on somebody else’s dream. I’ve watched leaders do it with AI too, grab the shiny thing because everyone else did, and then wonder why it drains them.

AI is nobody’s goal. Here’s where I jumped in, because I see the same thing coaching leaders on AI. I’ll ask why they’re bringing it in, and a lot of the time the honest answer is “we just want to be an AI-forward business.” That’s the trap Phil is describing, and it’s why I keep saying AI is nobody’s goal.

“So much of what I’m doing is just following my own threads of curiosity and wonder, and asking the questions I’m looking to answer for myself, and then allowing people a window into that.”

AI is the means to whatever your goal always was. Marry that goal to something you actually care about and the work stops feeling like burnout, which is the whole discipline behind the AI Leadership Triad. The tool moves you toward what you wanted, it doesn’t get to become the thing you want.

The eco rat race. Phil lived in Costa Rica for three years, and he had the funniest, truest line of the whole talk, about the founders and VCs who burn out, move there to build a wellness center, and change absolutely nothing.

“They bring the same New York energy to the picture. They’re sitting in the most beautiful locations in the world and they’re still the most stressed out people you’ll ever meet. They’re bringing that old identity to a new context.”

nailed it in the chat, you can build a new business with an old identity and hit the same walls.

You can’t relocate your way out of who you are, and you can’t AI your way out of it either.

We’re all toddlers with AI. The thing I keep coming back to is how humble Phil is about all of this. He refuses to look down on anyone’s messy AI use, and he put the reason plainly.

“With AI specifically, we’re all kind of like toddlers. And I include myself in that, and even you, because how far are we really into this? We’re a couple years into this journey.”

I study this for a living and I still bring in a new expert every Thursday, because none of us has it figured out. Phil’s antidote to the noise is one question he and Carolina ask their clients: what do you truly want. Ask it enough times and it gets dangerous in the best way, because it’ll take you down a rabbit hole if you let it. That question is the human work AI can’t do for you, and it’s the same reason I keep telling leaders not to outsource their thinking to it.

Reading your team before you buy the tool is exactly what I run leaders through in my AI Judgment Workshop, which is open for registration now. If you’d rather go deeper one-on-one or with your organization, just let me know!

One Question to Sit With

Phil ended on the question I want to leave you with too: what’s the why behind where you’re putting your hours, and does it marry where your passions lie?

Watch the full conversation above for how Phil and Carolina help people sit with that. And when you’re done, go subscribe to Sacred Business Flow.

About Phil Powis

Phil Powis is the creator of Sacred Business Flow, a Substack with more than 33,000 subscribers that he writes with his partner Carolina. He teaches founders and creators to build what he calls a sacred business, one that honors who you are and stays profitable, instead of an opportunistic business that just chases the trend. His work pairs business and marketing strategy with inner work, including the Harmony Map assessment, which reads what he calls your nine fundamental frequencies and points you to the one “breakthrough frequency” worth building around. Subscribe to his writing at Sacred Business Flow, and take the free Harmony Map assessment at sacredbusiness.com.

Sacred Business Flow

Clarity, visibility, clients. A business that feels as good on the inside as it looks on the outside.

By Phil Powis ❤️⚡️

About me

Joel Salinas is an AI Strategy Coach for leaders at small and mid-sized businesses and nonprofits. AI is everywhere; judgment is scarce. Joel helps leaders adopt AI without outsourcing their judgment to it, through a monthly live workshop and 90-day engagements. Creator of the AI Leadership Triad. He writes Leadership in Change.

Written by a human, for humans.

Get more from Joel Salinas in the Substack app

Available for iOS and Android

Get the app

So here’s the pressure a lot of us are feeling right now: put AI into everything, and do it fast. Nobody wants to be the team that fell behind, so we bolt an AI feature onto the product and figure we’ll make it safe and useful later.

Here’s the thing though. Most of what decides whether an AI product actually works, and whether it’s safe to put in front of people, gets settled before anyone opens the design tool. That’s the part we skip. It’s also a big reason so many AI implementations fail.

This week I’m handing the newsletter to someone who works right in that gap. is a product strategist and UX and AI designer (UX means user experience, how a product actually feels to use), and the founder of Creative Glue Lab, where she helps teams design and build AI products. I liked how she frames this... not as a compliance headache to clear at the end, but as a set of questions you ask up front, before the decisions harden into code.

What she’s put together is a practical checklist: 8 areas that decide whether an AI product turns out useful, safe, and compliant. If you build products, commission them, or just have to approve them, this is the pre-flight list worth keeping.

UX + AI

Build better experiences in a world shaped by AI. Practical insights at the intersection of UX, AI, and Product Strategy.

By Ileana

If her thinking resonates, subscribe to her Substack after you read. I’ll meet you at the bottom with the one area I’d start with.

Over to Ileana.

New Material, New Responsibility

We’re building with a new material.

AI went from a tech demo to the thing we shape into everyday products: chatbots, AI agents, autonomous workflows, and intelligent systems.

It’s new for everyone. And the mood feels the same in most rooms: excitement + speed.

No one wants to be the team that fell behind, so the pressure is to move fast and add AI into everything. We’re being asked to build more, faster, while working with higher complexity and higher risk.

AI products remember, decide, and act on someone’s behalf. They shape what people trust, choose, think, and the calls a business makes. That capability comes with a new level of responsibility for the teams designing and building it.

Responsibility demands clarity.

Share

And clarity needs questions.

That’s a new level of responsibility, and most of it gets decided before anyone touches the interface. With AI, UX reaches well beyond how a product looks, into data, safety, and compliance.

Use this guideline to cover what you need to build useful, safe, and compliant AI products.

Designing AI Products: 8 Areas to Get Right

This guideline walks you through 8 areas that make or break the experience, the ones AI adds on top of UX fundamentals like accessibility. Some will matter more than others depending on what you’re building. Cover the ones that do, and always bring the right expertise into the room.

1. Start with fit, not capability

Should this be AI at all?

AI earns its place when it improves the experience in a way a simpler pattern can’t. Fit also means the kind of AI and the relationship it creates with humans.

What to cover:

• What problem are we solving, and what does AI make better: speed, quality, access?

• Would a simpler rule, workflow, or automation solve this better?

• What kind of experience is this: does the AI assist, act on someone’s behalf, or work in the background?

• Which model or approach fits, and what does it cost in accuracy, latency, and risk?

• What relationship are we building between humans and AI?

Look for a real reason to use AI: it should solve the problem better than the simpler option.

2. Define boundaries before behavior

What could go wrong, what should the AI never do, and who does it affect?

AI changes the shape of product risk: part of the behavior is generated in the moment, so the system can respond in ways no one fully scripted.

What to cover:

• What should this AI never do, and where could it overstep its role?

• Who uses the AI, and who is affected by its output or action?

• What happens if it’s wrong, biased, or overconfident, and people trust it anyway?

• Can people tell why it did something, check it, or undo it?

• Could one failure trigger another?

Look for clear boundaries: the team should know what the AI is allowed to do, what it should avoid, and where the stakes change because of the context.

3. Treat data as part of the experience

What does the AI know, and should it?

AI experiences run on data, and that data shapes what the AI can produce, reveal, or get wrong.

What to cover:

• What data is the AI using, and where does it come from?

• Is it personal, sensitive, biased, or outdated?

• Can people see, correct, or delete the context it’s using?

• Is the data safe to send to this model or vendor, and what should never be context?

Look for a clear data boundary: people shouldn’t be surprised by what the AI knows, remembers, assumes, or exposes.

4. Design for verification, not just output

Can people understand and verify what the AI produced?

AI output can sound confident while being wrong, so the experience has to help people judge how much to rely on it.

What to cover:

• Is the output a draft, a suggestion, a recommendation, or a decision?

• Can people verify it, see sources, or compare options?

• Does the system show uncertainty, or refuse when it shouldn’t answer?

Look for calibrated trust: the product shouldn’t make AI output feel more certain, final, or authoritative than it deserves.

5. Protect human agency over time

What does this do to people over time?

AI products affect more than task completion. They shape confidence, judgment, dependency, and behavior over time.

What to cover:

• Does this make people more capable, or more dependent?

• Does it support their judgment, or replace it?

• What does it do after weeks or months of use?

• What are the hard moments: confusion, rejection, failure, or no fix available?

Look for a healthier relationship over time: people keep their own judgment and skill, rather than handing them to the AI.

6. Make autonomy controllable

Can people steer, pause, approve, undo, or override it?

Control matters more as AI becomes more autonomous. Suggesting is one thing; acting on someone’s behalf is another. The aim isn’t to approve every step, that defeats delegation.

What to cover:

• What can the AI do alone, and what needs approval?

• What can be previewed, edited, or undone?

• Can people pause, stop, override, or redirect it mid-task?

• Is review calibrated to risk, or does it create approval fatigue?

Look for meaningful control: people can see what the AI is doing, step in before it’s hard to undo, and recover after, without signing off on every move.

7. Set expectations at the point of use

Can people tell what it’s doing, and what shouldn’t they assume?

Transparency is about showing the right information at the right moment.

What to cover:

• Do people know when AI is involved, and what’s AI-generated?

• Do they know what data or context was used, and what changed?

• Do they understand whether the AI is suggesting, deciding, or acting?

• What might people wrongly assume, and what limits need to be visible?

Look for clear expectations: people know what the AI is doing, what it isn’t, and where their own judgment still matters.

8. Plan for misuse, compliance, and ownership

What needs to be protected, monitored, recovered from, and owned after use?

AI connects language, data, tools, and permissions, which opens new safety, misuse, and compliance questions that should show up early.

What to cover:

• Could the system be manipulated through prompts, files, or connected tools?

• Could sensitive data leak, or are permissions too broad?

• Could people misuse it for fraud, manipulation, or harmful content?

• Where does a human step in, and what happens when it causes harm or confusion?

• Who monitors, supports, and owns the system after launch?

Compliance isn’t a checkpoint at the end, or a “please double-check” disclaimer. It needs to be designed for from the start. In the EU, the AI Act is phasing in, with transparency rules for AI-generated content from 2 August 2026 and stricter rules for high-risk systems.

Look for readiness: the product should be safe enough to use, clear enough to explain, compliant enough for its context, and supported enough to improve when real-world failures appear.

Subscribe now

Thank you, Ileana!

So which area do you start with? If you made me pick one, it’s the first: fit before capability. Almost every AI product mess I’ve watched traces back to a team that reached for AI because it was available, not because it was the right tool for the job. Get that one honest and the other seven get easier.

One question to sit with: where in your product is AI solving a problem a simpler rule could solve better?

FAQ

What tools do I need to design a safe AI product? Fewer than you’d think. This checklist is tool-agnostic. You need a way to write down decisions (a shared doc works), the people who understand your data and legal exposure, and whatever design and prototyping tools you already use. The 8 areas are about the questions you ask, not the software you buy.

How long does it take to work through these 8 areas? A first pass on a single feature can take an afternoon with the right people in the room. It’s front-loaded work: a few hours of hard questions before you build saves weeks of rework, incident response, and trust repair after launch. Not every area applies equally, so cover the ones that matter for what you’re shipping.

Do I need to be a designer to use this checklist? No. Ileana wrote it for teams, not just designers. Product leads, engineers, founders, and the executives approving an AI feature all have a stake in these questions. If anything, the areas most people skip (boundaries, data, compliance) sit outside traditional design work entirely.

What’s the most common mistake teams make when adding AI? Starting with capability instead of fit, reaching for AI because it’s available rather than because it solves the problem better than a simpler rule. The second most common: treating compliance and verification as things to bolt on at the end, when the article’s whole point is that they have to be designed in from the start.

How do these 8 areas apply to a small team or a non-product company? They scale down cleanly. If you’re a nonprofit adding an AI chatbot or a small business automating a workflow, you still decide fit, boundaries, data handling, and who’s accountable when it’s wrong. The stakes are the same even when the team is one person. Skip the areas that don’t apply and cover the ones that do.

Which of the 8 areas should I start with? Start with fit (area 1). If AI isn’t the right tool for the problem, the other seven areas won’t save the product. Once fit is honest, move to boundaries and data, since those shape everything downstream. Compliance and ownership are last to finish but should be considered from the first conversation.

Written by a human, for humans.

About Ileana Marcut Ileana Marcut is a product strategist, UX and AI designer, and founder of Creative Glue Lab, where she helps teams design and build digital products and AI solutions. She writes about designing with and for AI at her Substack.

Joel Salinas is an AI Strategy Coach for leaders at small and mid-sized businesses and nonprofits. AI is everywhere; judgment is scarce. Joel helps leaders adopt AI without outsourcing their judgment to it, through live workshops and 90-day engagements. Creator of the AI Leadership Triad. He writes Leadership in Change.

So here’s what I’ve been building. Below is a quick video of the “WHY” behind this workshop, and then all you need to know to sign up.

For a while now I’ve written about the same idea from a dozen angles: AI is everywhere, judgment is scarce, and the leaders who get real results aren’t the ones with the best tools. They’re the ones who decided what to hand to the machine, what to keep in their own hands, and what to protect.

You’ve read those pieces.

A lot of you have written back.

But here’s the piece I kept running into… reading about it doesn’t change how your Tuesday actually goes. It’s like running power to a building without checking the wiring first. The electricity is the easy part, and it’s not the part that keeps the lights on. So I built the room where you do the wiring.

This is what increases the success rate of each AI tool implementation you bring: the foundation.

It’s called the AI Judgment Workshop. Ninety minutes, live, and you don’t watch a webinar, you build. You leave knowing what to build first, so AI strengthens your judgment instead of quietly replacing it: what to hand off, what to keep human, what to protect, and a 90-day plan you wrote in the room.

Save your seat

The part the first cohort couldn’t stop talking about was the people. Every team has all four kinds of people in an AI rollout: the one who’s all in, the one who’s eager but hasn’t started, the one who’s quietly skeptical, and the one using it but not trusting it. Every one of them is valuable, and every one needs a different move from you. Once you can see the four, you can’t unsee them.

One person told me the quadrant is the kind of thing teams should be naming at the water cooler, and honestly, that’s the goal.

And it’s more than the 90 minutes. You leave with the Judgment Playbook to keep, and for the 30 days after, you can email me your questions as you put the plan to work, with a Day-30 review where I write back one specific recommendation for your situation.

Save your seat

The Cost: $99 — and every dollar is credited toward a future one-on-one engagement with me, if you decide to go further.

Planning to make this a habit? Premium members ($49/yr) attend every AI Judgment Workshop for $49; Executive members attend free.

The details: Twenty seats, every two months. Next one is Tuesday, September 15, 5:00pm ET, live on Google Meet, replay included. Built for leaders with a real AI call to make right now, not a someday.

Here’s why it’s risk-free. If you decide to go further and work with me one-on-one afterward, what you paid credits toward that engagement. So either the workshop is the whole thing for you, or it’s the first step, and either way you’re not out anything.

See you in the room!

If you want to learn more about any of this, the best way is to just come. Save your seat above, and I’ll see you in the room.

Joel

You open the Claude desktop app, and as of July 2026, there are two tabs across the top: Home and Code.

Chat and Cowork share Home, with a small selector in the message box to switch between them, and almost everyone lives in chat. You type a question, Claude gives you a smart answer, you copy it out and go do the actual work yourself

Here’s the honest part, and it’s the reason you can trust the 15 use cases below. My own daily driver when not on Claude Code is Cowork, not chat. That’s where most of my heavy lifting happens.

I built and ran every one of these examples on my own files in Cowork before I wrote a word about them. Claude Cowork is one step below Claude Code, it needs no code, and it gets you about 80% of what’s possible.

Learn this one mode well and you’re in the top 10% of people actually using AI, the ones who stopped asking it questions and started assigning it work.

The difference is simple. In chat, you ask, and then you go do the thing. In Cowork, you hand over the files and the assignment, and you walk away while it gets done. It’s the difference between calling a brilliant consultant on the phone and hiring an employee who sits at your computer with everything open in front of them.

In this post, you’ll learn:

• What Claude Cowork actually is, and the 5-minute setup that decides whether it works

• The top use cases across finance, operations, content, HR, and research

• A copy-paste prompt for each one, so you can run it today

Quick Win (< 60 seconds) Open the Claude desktop app, go to Home with Cowork selected in the message box (it's the default), and point it at ONE folder, not your whole Documents drive. Make a single folder, drop in the files for one task, and work from there. That one move is the entire difference between a trained employee and a mess.

What makes Cowork different from chat

Regular chat is reactive. You ask, it answers, the work is still yours. Cowork is autonomous. You define the outcome and it works through the steps, reading and writing real files, until there’s a finished deliverable sitting in your folder… A real Excel spreadsheet with working formulas. A Word document ready to send. A PowerPoint deck that’s actually formatted.

Not text you paste somewhere, but the finished file.

That shift, from asking to assigning, is

the whole thing. And it’s why the setup matters more than any clever prompt. Cowork reads short context files you write once (who you are, how you write, how you like things done), and from then on it stops being a stranger and starts behaving like someone you onboarded. I wrote a whole piece on why one afternoon of context setup gets you dramatically better AI responses, and it applies here twice over. And as of July 2026, Cowork is no longer desktop-only: it's rolling out on web and mobile (starting with the Max plan), with sessions that run remotely in beta, save to your Claude account, and keep working after you close your laptop.

Your 15 Cowork Use Cases

📊 Finance: from data to deliverable in one session

This is where Cowork earns its keep fastest. As of March 11, 2026, Anthropic added the ability for Claude to work across Excel and PowerPoint in one continuous task, carrying context from app to app. An analyst can ask it to read earnings, update a financial model, and build the summary slide without switching tools, and when an input changes, it updates the rest of the workflow. Finance teams use it for the painful, repeatable execution: intercompany reconciliations, journal entry prep, variance commentary, and model audits.

📊 1. A P&L dashboard and the deck from one dataset

The problem: Building the numbers in Excel and then rebuilding them again as slides is two jobs that eat an afternoon, and the two have to match.

What's needed: a folder with your quarterly financials (an Excel or CSV file).

In this example, you’ll see both the dataset Excel as well as the presentation created.

Copy-paste prompt:

I’m dropping a quarterly financials file in this folder. Build a 2-sheet Excel dashboard: Sheet 1, a P&L summary with revenue, gross profit, operating income, and net income, a total column with SUM formulas, a profit-margin row, currency formatting, bold headers, and a bar chart of quarterly revenue. Sheet 2, key metrics: total revenue, average quarterly revenue, Q4-vs-Q1 growth, best quarter, and operating margin. Then build a 4-slide executive deck from the same numbers: title, financial highlights with a chart, profitability trend, and key takeaways. Save both files to this folder.

Remember to connect the right folders in all of these.

📊 2. An audit of a model that won’t tie out

The problem: One broken formula can throw off an entire model, and finding it by hand is slow and easy to miss when you’re tired.

What's needed: a folder with the Excel model you want checked.

Here’s a sample of an actual doc with comments (both report and comments 100% generated by Cowork).

Copy-paste prompt:

Review this Excel model. Find all formula errors and inconsistencies, highlight each one in yellow, and add a comment explaining the issue. Do not change any values. List anything you can flag but can’t confirm under a separate “Needs human review” heading.

📊 3. A reconciliation that checks itself

The problem: Reconciling across entities and currencies takes days, carries version-control risk, and the checking is the part everyone dreads most.

What's needed: a folder with your invoice data (or upload the data directly)

This demo includes a full reconciliation of a set of journal entries csv…

Copy-paste prompt:

Using the invoice data in this folder, build a journal-entry upload sheet with these columns: entities, GL accounts, debits and credits, source references, and allocation notes. Include a reconciliation tab with checking functions, a row confirming debits equal credits, and exception flags for any line where the source can’t be traced. Cite the data source for each output row.

That last instruction, build the checking into the file, is the one finance pros stress most. If you’re having to do all the checks yourself afterward, you’ve lost the point.

📋 Project and operations: the report writes itself

Most operational work isn’t hard, it’s just repetitive and it eats your week. Cowork is built for exactly that, especially once you put it on a schedule (a scheduled task is a prompt you save with a time, so it runs on its own — and as of July 2026, it runs remotely, even with your computer off).

📋 4. A morning brief, every day at 5am

The problem: You start every day rebuilding your own priorities from a full inbox and a packed calendar before you’ve done any real work.

What's needed: Gmail and Calendar connected. No files.

Copy-paste prompt:

Every morning, check my email and calendar for the day. Flag the messages that actually need a reply, list my meetings with the prep each one needs, pull the two or three biggest stories in [your industry], and save it all as “morning-brief.md” in this folder. Set this to run daily at 5am.

Here is a sample that ran through my gmail and calendar and generated a sample. You will need to SCHEDULE and have connected Gmail and Google Calendar for this to work.

📋 5. Messy meeting notes into trackers and follow-ups

The problem: Turning messy call notes into a tracker and a follow-up for every client is two to three hours of busywork you keep pushing to later.

What's needed: a folder with your meeting transcripts (text files).

The example below included the submission of 3 transcripts, and the resulting project trackers and post-meeting emails…

Copy-paste prompt:

Process these client meeting transcripts. For each client, create one Excel project tracker with tasks, owners, deadlines, and priority levels, and one follow-up email under 150 words confirming next steps in a professional but warm tone. Where someone said “end of next week,” calculate the actual calendar date. Save everything to this folder.

📋 6. The end-of-week report for your boss

The problem: The Friday report is work you’ve already done, written up again from scratch when you’d rather be done for the week.

What's needed: your calendar and inbox connected, plus your daily notes saved in the folder.

Here is a snapshot from my email and content.

Copy-paste prompt:

Using the daily updates I’ve saved in this folder this week, plus my calendar and inbox, draft my weekly report: what moved forward, what’s blocked, and what needs attention next week. Then draft the email to my manager for me to review before it sends.

I do a version of this with scheduled tasks myself, and it’s the one Cowork habit I’d never give up. You wake up to the work already done.

✍️ Content and marketing: repurpose, don’t recreate

If you create anything regularly, Cowork removes the part you hate: the reformatting, the resizing, the moving of work between tools. Give it your raw material and your brand rules once, and it produces finished, on-brand assets on repeat.

✍️ 7. One transcript into a week of LinkedIn posts

The problem: You already said something worth sharing on a call or a video, but turning it into a week of posts is the step that never happens.

What's needed: a folder with the transcript, plus a few lines on your writing voice (or your brand file).

Here is a demo using my transcripts…

Copy-paste prompt:

Read the video transcript in this folder. First, research how the top creators structure high-performing LinkedIn posts. Then write seven posts that position me as a leader on this topic, each saved as its own document in this folder. Match my writing voice [paste a few lines on your tone, or point Claude to your brand file], and don’t write anything generic.

✍️ 8. Social clips cut from a long recording

The problem: The best moments are buried in an hour-long recording, and cutting them by hand is time you don’t have every week.

What's needed: a folder with the recording and its transcript.

For the sample below, I added a video and transcript from my most recent podcast. Below is a clip it generated from the 30 minute recording, and branded the last frame.

Copy-paste prompt:

In this folder is a webinar recording and its transcript. Find the five strongest moments, cut a short clip for each, add my branded outro, and save them as ready-to-post files with a one-line summary of each.

✍️ 9. A competitor’s whole ad strategy, analyzed

The problem: Mapping a competitor’s full funnel means clicking through every ad and landing page, a full day you’d normally pay a specialist for.

What's needed: Claude for Chrome connected, plus the advertiser's Meta Ad Library link.

For this example, I found Target’s page in Meta’s Public Ad Library. First, Cowork opened my browser, and after I allowed permissions for the page, it browsed.

Then, it took the content gathered…

And compiled the report.

Copy-paste prompt:

Open the browser, go to this advertiser’s page in the Meta Ad Library [paste the link], and review every active ad. Tell me their core strategy in one sentence, the landing pages they drive to, and the messaging angles they repeat. Save it as an HTML report I can open.

The repurposing one is closest to my world. I’ve written before about the three-AI system I use to write this newsletter, and Cowork is the piece that handles the heavy moving of content so the thinking stays human.

Subscribe now

👥 HR and people: the deliverables nobody has time for

HR runs on documents that take an hour each and never feel finished. Cowork produces them as structured, ready-to-edit files, which is exactly the kind of work that benefits from giving Claude a clear role and a required format.

👥 10. A 30-60-90 onboarding plan

The problem: Every new hire needs a real onboarding plan, and starting from a blank page each time is exactly why it often doesn’t get done.

What's needed: nothing but the prompt.

Here’s a demo with some made up data…

Copy-paste prompt:

Build a 30-60-90 day onboarding plan for a new [role] in [department] reporting to [manager]. Cover goals, learning priorities, key meetings with their purpose, early deliverables, and a manager check-in cadence for each phase. Format it as a single table at an 8th-grade reading level. Center it on role clarity and real progress, not busywork.

👥 11. Exit interviews synthesized, without the names

The problem: The patterns in your exit interviews are some of the most useful data HR has, and they sit unread because summarizing them by hand takes too long.

What's needed: a folder with your anonymized exit-interview notes.

Here are my results from a few anonymized interviews:

Copy-paste prompt:

Read these anonymized exit-interview notes. Summarize the main themes with a count of mentions per theme, recurring concerns, positive feedback, and retention risks. Separate “Evidence” (from the notes) from “Interpretation” (your read of the pattern). Keep it under 500 words, no names, and flag any theme that appears only once as anecdotal.

👥 12. A fully-loaded headcount cost model

The problem: Leaders keep asking what a role really costs, and the honest answer lives in a fully-loaded model nobody has time to build.

What's needed: nothing but the prompt and your comp assumptions.

I just ran this some basic instructions, ready and usable for my context.

Copy-paste prompt:

Build a column that calculates the fully-loaded cost per role: base salary, benefits, payroll tax, bonus, equipment, recruiting, and location factors, with logic that pro-rates partial-year hires. Give me the formulas ready to paste into Excel, a plain-language explanation of each component, and a list of assumptions Finance needs to approve.

🧠 Research and knowledge work: build a second brain that improves itself

This is the category leaders sleep on. Cowork can take a pile of unstructured material and turn it into something you actually use, then keep it current on a schedule.

Since these are the most valuable of the 15, these are reserved for Premium Members only.

To access these and much more, sign up for Premium for less than $4/mo, and Reload this page.

If You Only Remember This

• The jump comes down to a different verb. Chat is for asking. Cowork is for assigning the whole task and walking away while it gets done.

• The 5-minute setup beats the clever prompt every time. One clean folder and a short context file is what turns Cowork from a chatbot with file access into something that works like a trained employee.

• Cowork is the on-ramp to power-user territory without code. It gets you 80% of what’s possible and into the top 10% of people actually using AI.

What’s the first task you’d hand off if you never had to do it yourself again? Tell me in the comments, I read every one.

Questions Leaders Are Asking

What is Claude Cowork? Claude Cowork is Anthropic’s AI assistant that runs on desktop — and as of July 2026, on web and mobile — opens the real files on your computer, and completes multi-step tasks from start to finish. Instead of answering a question in a chat window, it produces finished Excel, Word, and PowerPoint files in your folders.

How is Cowork different from Claude Code? Claude Code is the coding-focused version, built for people comfortable building software. Cowork is one step below it and needs no code. For most leaders, Cowork covers about 80% of what they’d want AI to do, which makes it the right starting point before Claude Code.

Do I need to know how to code to use Claude Cowork? No. Cowork is designed for non-technical professionals. You write plain-language instructions, the same way you’d brief a new employee, and point it at a folder of files. The only setup is creating a working folder and a short context file describing how you want things done.

Is Claude Cowork safe to use with sensitive company data? Use a business or enterprise plan, which commits to not training on your data, and involve your legal team before sharing confidential files. Test with anonymized data first, and decide in advance which data categories are approved for use. Treat a generic prompt and a live financial file as very different risk levels.

What can Claude Cowork do that regular Claude chat can’t? Cowork reads and writes the actual files on your computer, works across apps like Excel and PowerPoint in one task, runs on a schedule, and can control your browser to do research. Chat only returns text. Cowork returns finished deliverables you can send.

Joel Salinas is an AI Strategy Coach for leaders at small and mid-sized businesses and nonprofits. AI is everywhere; judgment is scarce. Joel helps leaders adopt AI without outsourcing their judgment to it, through live workshops and 90-day engagements. Creator of the AI Leadership Triad. He writes Leadership in Change.

Written by a human, for humans.

We talked about how she gets past the polished version people hand her, why she thinks villains are freer than heroes, and the one thing she refuses to hand over to AI even though she uses it every day. If you write, lead, or read people for a living, this one is worth your half hour.

🎥 Catch all future interviews, subscribe!

🎧 Prefer audio? Listen on Apple Podcasts

Outline

(01:13) – A cold read: what ten minutes reveals

(03:10) – We are how we move through the world

(03:44) – The mask, access, and Logan Paul’s lollipop

(04:45) – Kevin O’Leary and the villain he plays

(10:54) – Why villains get to be human

(11:21) – Scaramucci’s Superman, everyone’s Batman

(17:42) – Understanding who you are not

(19:48) – Long-form in a short-form, AI-flooded world

(22:02) – “When I write, I think”

(25:20) – Reinvention and the Martha Stewart comeback

My Takeaways

We are how we move through the world.

So the line of hers I opened with is the one I keep coming back to, because it flips how most of us try to know somebody. We listen to what people say about themselves and take it as the truth, when the real signal is in how they actually move. Polina puts an enormous emphasis on access for exactly this reason; she wants to shadow a person through their day and catch the moment the mask slips.

“You just really have to observe. The mask, we know the mask. These are public figures, they’re very well media trained. My goal is to destabilize the talk track a little bit, or see a contradiction that everybody has.”

She told a story about Logan Paul that made it concrete: in the car, he told her he likes anime and dinosaur documentaries. Then he admitted the loud persona is a performance he switches on the second he walks onto a set. That gap between the private person and the public character is the whole game.

Villains are allowed to be human.

Here’s the part of the conversation that really made me think. She was talking about Kevin O’Leary, Mr. Wonderful from Shark Tank, and how he actually likes being cast as the villain.

“Heroes are very limited because they’re not free. They’re expected to be perfect. Villains are allowed to be human. They can have flaws, they can get angry, they can make mistakes. People are more forgiving.”

I hadn’t thought about it that way, that the villain gets more room to be a full person than the hero does. For anyone leading, that’s a real trap worth naming. The more polished and heroic the image you protect, the less permission you leave yourself to be human in front of your team.

The tell is the internal monologue.

What she’s really hunting for is the story people tell themselves, not the polished one they tell her, and she gets at it by asking how they imagined their life would turn out versus how it actually did, then listening to how they bridge the gap.

“You’re your life’s main character, you’re the protagonist, everybody else is a supporting character. So the way you tell that story tells me a lot.”

She gave the example of Anthony Scaramucci and his six-foot Superman statue, where he told her Superman is who we strive to be, but realistically we’re all just Batman, flawed and human and able to break. How someone narrates their own life explains most of what they let themselves do.

“When I write, I think.”

This is the one that matters most for the leaders reading this. Polina is not anti-AI; she said it helped catch a misdiagnosis when she was pregnant, and she uses it to pressure-test her thinking constantly. But she draws a hard line at the writing itself.

“When I write, I think. That’s how I think. So if I just offload everything to AI, then I’m not thinking at all. It’s thinking for me, and that’s really dangerous.”

She uses AI the way I’d argue every leader should, as a brainstorming partner and a sparring partner. Feed it a draft and ask what you’re missing; tell it your position and ask it to argue the other side. What she won’t do is let it do the thinking for her, which is the whole discipline behind the AI Leadership Triad. The tool sharpens your judgment; it doesn’t get to replace it.

One Question to Sit With

If AI can now imitate almost anyone’s output, the writing, the style, the answer, what’s the part of your work that only you can actually do?

Polina’s bet is that it’s the human things, the noticing, the taste, the paying attention that took her years to sharpen. Watch the full conversation above for how she gets there, and then go subscribe to The Profile.

The Profile

Longform profiles of the world's most extraordinary people — in your inbox every Sunday.

By Polina Pompliano

About Polina Pompliano

Polina Pompliano is the founder of The Profile, a newsletter of deeply reported profiles of the world’s most extraordinary people, published every Sunday since 2017. She previously ran the daily VC newsletter Term Sheet at Fortune, where she wrote more than 1,300 articles. She’s the author of Hidden Genius, a book about the mental models the world’s most successful people actually use. Subscribe to her work at The Profile.

About me

Joel Salinas is an AI Strategy Coach for leaders at small and mid-sized businesses and nonprofits. AI is everywhere; judgment is scarce. Joel helps leaders adopt AI without outsourcing their judgment to it, through a monthly live workshop and 90-day engagements. Creator of the AI Leadership Triad. He writes Leadership in Change.

Written by a human, for humans.

I have four businesses. Each one has its own brand, its own audience, its own voice. And the part that drains me isn’t the thinking, it’s everything around it, rebuilding the same deck for a different audience, retargeting a report, proofing the same paragraph for the fourth time, hopping between systems to find one file. None of that is the work I’m actually good at. It’s drag.

So here’s the question I keep getting from leaders, usually some version of it:

Is agentic AI (AI that doesn’t just answer questions but actually takes action across your systems) coming for my team’s jobs?

It’s a fair fear. I just think most leaders are aiming it at the wrong target. I’ve written before about how to use AI to expand your impact without adding headcount, and about why most AI investments stall out when the structure underneath them stays broken.

That’s the exact argument Daniel Fallmann, CEO of the enterprise AI firm Mindbreeze, brought to me. He comes at it from inside the enterprise, and he makes the case cleanly, so I’m handing you the floor. Read his piece, then I’ll meet you on the other side.

What to Remember

• Agentic AI — AI that takes action across your systems (retrieving, routing, coordinating approvals), not just an assistant that answers questions.

• Most organizations aren’t short on talent. They’re buried in operational complexity.

• Operational friction compounds. One delayed approval or repeated search ripples across the whole organization.

• The near-term win is removing drag from knowledge work, so your experts spend their hours on what only they can do.

The Real Opportunity In Agentic AI Is Operational Efficiency

By Daniel Fallmann, CEO of Mindbreeze

Much of the public conversation around agentic AI continues to focus on workforce replacement. Headlines frequently emphasize automation risks, disappearing jobs, and the possibility that intelligent systems may eventually operate with minimal human involvement. While those discussions attract attention, they often overlook what may become the most immediate and economically meaningful impact of agentic AI inside enterprises.

The larger opportunity is operational efficiency.

Across most organizations, high-knowledge workers spend significant portions of their day navigating friction rather than applying expertise. Employees search across disconnected systems for information, manually coordinate approvals, reconcile inconsistent data, repeat administrative tasks, and move work between fragmented workflows. These activities rarely represent the strategic value those very employees were hired to provide, yet they consume enormous amounts of organizational capacity.

Agentic AI is increasingly positioned to reduce that friction.

This shift matters because many enterprises are not constrained primarily by talent shortages or insufficient intelligence. They are constrained by operational complexity. Over time, organizations accumulate layers of systems, processes, repositories, and communication channels that make work harder to execute efficiently. The result is often a hidden productivity tax spread across the enterprise.

Recent reports highlight how organizations are increasingly shifting from isolated AI deployments toward workflow-level transformation. Rather than treating AI as another productivity tool, enterprise leaders are beginning to redesign operational processes around intelligent systems capable of participating directly inside workflows.

This distinction is important because productivity gains generated through isolated assistance often plateau quickly. Faster content generation or quicker information retrieval may improve individual tasks, but they do not necessarily remove the underlying inefficiencies that slow organizations down.

Operational friction behaves differently. It compounds.

A procurement approval delayed by fragmented systems affects purchasing timelines. A support representative searching multiple repositories for customer information increases response times. An engineer reconciling conflicting documentation across platforms slows development cycles. Small inefficiencies inside individual workflows frequently create substantial enterprise-wide consequences.

This is where agentic AI begins to create more meaningful value.

Rather than simply generating outputs, agentic systems increasingly operate across workflows. They retrieve information, coordinate actions, trigger processes, route approvals, maintain context across systems, and help reduce the administrative burden surrounding knowledge work itself.

Organizations are already confronting a new challenge emerging alongside AI deployment: managing large numbers of agents operating across enterprise environments. While this reporting (WSJ) focuses on governance concerns, it also reflects how quickly enterprises are moving toward workflow-oriented AI systems capable of acting across operational processes.

The significance of this transition extends beyond automation.

Historically, many enterprise technologies focused on digitizing tasks. Agentic AI increasingly focuses on coordinating execution.

That creates a different category of impact.

An employee who no longer spends hours locating information across systems gains time for customer engagement, strategic planning, analysis, or innovation. A manager no longer trapped in repetitive approval coordination can focus more attention on operational improvement and decision-making. In many cases, the highest value of AI may not come from replacing expertise, but from removing the friction preventing expertise from operating efficiently.

Organizations continue increasing investment in AI, yet many leaders are discovering that efficiency gains alone (Fortune) do not automatically translate into better business outcomes if operational structures remain fragmented.

This is why workflow integration is becoming increasingly important.

Share

Agentic systems generate the greatest value when they operate within enterprise context. That means understanding permissions, accessing relevant knowledge, interacting with systems of record, and maintaining continuity across workflows rather than functioning as isolated assistants.

Mindbreeze has recently discussed this transition through its analysis of enterprise agentic AI strategy, arguing that organizations increasingly need systems capable of executing work across environments rather than simply generating intelligent responses.

Similarly, Mindbreeze’s introduction of Insight Touchpoints and Insight Journeys reflects a broader enterprise movement toward standardized multi-step workflows that connect intelligence directly to execution.

The broader lesson for enterprise leaders is becoming increasingly clear.

The most immediate impact of agentic AI is unlikely to be mass workforce replacement. The more practical transformation is the gradual elimination of operational inefficiencies that consume organizational capacity every day.

That distinction matters because it changes how executives should evaluate AI investments.

The strategic question is no longer simply how many tasks AI can automate. The more important question is how much friction AI can remove from the flow of work itself.

Organizations that successfully deploy agentic AI may not necessarily operate with dramatically smaller workforces. But they are likely to operate with significantly less organizational drag.

Over time, that may become one of the most important competitive advantages enterprise AI creates.

Thank you, Daniel!

Here’s where I come down after sitting with Daniel’s argument. You might not run a procurement department or a support desk. But if you’re the leader who’s also the ops team, the grant writer, and the person formatting the board deck at 11pm, the friction he’s describing is the same friction, it’s just concentrated on one desk instead of spread across a thousand. The hours still go to finding, formatting, chasing, and reconciling, not to the work only you can do.

That’s the whole game for me. I want AI doing what it’s good at so the people can do what they’re good at, which is the same idea behind the AI Leadership Triad I keep coming back to, and the same reason I keep asking whether complexity is killing the mission.

Subscribe now

So before you go shopping for an agent, try this Monday. Look back at last week and write down the three tasks that ate the most time, then circle the ones that weren’t actually the work you’re good at, the searching, the formatting, the approval-chasing, the copying of data from one place to another. That circled list is where to point AI first.

Point AI at the drag, not the people.

If you want help finding where that drag actually lives in your organization, and where to point AI first, that's the work I do with leaders in my 90-Day Judgment Engagement. And if you'd rather start smaller, the AI Judgment Workshop waitlist is open.

Daniel Fallmann is the CEO of Mindbreeze. You can read more of his thinking at mindbreeze.com or connect with him on LinkedIn.

Questions Leaders Are Asking

What is agentic AI, in plain terms? Agentic AI is software that takes action across your systems instead of just answering a prompt. It can retrieve information, route an approval, trigger a process, and carry context from one system to the next. Think of it as a coordinator that carries a task through its steps, where a chatbot would hand you a draft and stop.

How is agentic AI different from a tool like ChatGPT? A standard assistant responds inside a single conversation. You ask, it answers, you copy the result somewhere else. Agentic AI works across the gaps between your systems, doing the searching, moving, and coordinating itself. That is why its biggest payoff shows up in workflow friction, not in faster individual answers.

Does agentic AI mean I’ll need a smaller team? Not necessarily. The clearer near-term win is removing operational drag so the team you already have spends more time on high-value work. Many organizations that deploy agentic AI well keep their headcount and simply operate with far less friction, which becomes a real competitive edge over time.

Where should a leader point agentic AI first? Start where friction compounds. Look for the approvals delayed by fragmented systems, the repeated searches across repositories, the data your people reconcile by hand. Those small inefficiencies create enterprise-wide drag. Pointing agentic AI at the flow of work, rather than at one isolated task, is where the gains hold.

Why do AI efficiency gains often fail to show up in business results? Because efficiency on a fragmented structure still leaves the structure fragmented. Faster answers inside a broken workflow do not fix the workflow. Real gains come when AI operates inside enterprise context, with the right permissions, knowledge, and continuity across systems, rather than as an isolated assistant bolted onto the side.

What’s the biggest mistake leaders make with agentic AI? Evaluating it by how many tasks it can automate instead of how much friction it removes from the flow of work. Task-counting leads to scattered pilots that plateau. Friction-removal leads to workflow redesign, which is where the durable advantage lives.

Joel Salinas is an AI Strategy Coach for leaders at small and mid-sized businesses and nonprofits. AI is everywhere; judgment is scarce. Joel helps leaders adopt AI without outsourcing their judgment to it, through live workshops and 90-day engagements. Creator of the AI Leadership Triad. He writes Leadership in Change.

Daniel Fallmann is the founder and CEO of Mindbreeze, an enterprise AI and insight platform. He has built the company around product-first engineering and out-of-the-box solutions that work without heavy professional-services overhead.

Written by a human, for humans.

🎥 Catch all future interviews, subscribe on YouTube

🎧 Prefer audio? Listen on Apple Podcasts

🤝 Want to chat one-on-one? Book a free intro call.

Outline

(0:43) – Leadership WTF: what it even means to lead now

(1:34) – Does coaching still matter when everyone has ChatGPT

(3:12) – Ask more, tell less, and why it’s harder than it sounds

(5:44) – Daniel Goleman’s six styles and the one leaders skip

(8:35) – The four Cs, laid out

(9:05) – Clarity over certainty

(11:08) – Courage over collusion

(12:45) – The sycophancy trap: six months chasing a fake theorem

(14:31) – Connection, and why humans rubbing up together still matters

(19:01) – Why big change plans fail: capacity and agency

(23:11) – The productivity fear: save 25%, produce 25% more?

(26:09) – Coaching a team in 2026: the three questions

(30:27) – Three books Michael recommends

My Takeaways

The four Cs. This was the spine of the whole conversation, and it’s the thing I’m still going over. Michael has been sitting with a simple question, what do we actually need to do as leaders in a time of AI, and he came back with four moves. “Clarity over certainty, context over content, connection over cocooning, and then courage over collusion.” I made a chart of it because I want to keep it in front of me, and honestly, once you hear all four together, you can’t unsee the tension in each one. Every C is a choice between the easy thing AI is great at and the harder thing that’s still ours to do.

Clarity over certainty.

So here’s where it got real for me. Michael’s point is that AI is fantastic at sounding sure, and that confidence is exactly the trap. “AI is brilliant at being certain,” he said, and then he told a story from a conversation with Dan Ariely (the behavioral economist behind Predictably Irrational) about a researcher who wasted six months chasing a new math theorem that ChatGPT told him he’d found, and that turned out to be complete nonsense. Certainty is cheap now. The leader’s job is clarity, which is the slower work of asking what the real problem even is before you go solve the confident-sounding one.

Connection over cocooning.

This is the one I keep chewing on. Michael admitted something I think a lot of us feel and don’t say out loud, that one of the strangest things about AI is he almost doesn’t need to talk to other human beings anymore, because it’s just him and his agent going back and forth. “It feels like it’s human, but it’s not human.” And I sat with that, because I could open Claude right now and ask it how Michael would answer a question and get a close approximation, which is exactly the thing that should scare us a little. His answer is that leaders create more than the sum of their parts when there’s real connection, and there’s something about humans rubbing up together that no agent replaces.

Capacity before change.

The most practical thing he said is that big change plans almost always fail, because leaders keep pouring water into a full glass. If your people are already at capacity, a shiny new AI rollout has nowhere to land. And this landed against a real moment for me, when someone on my team heard “AI will save you 25% of your time” and asked me straight up whether that meant I’d now require 25% more output. That fear is the whole game. Michael’s take is that the promise of AI isn’t just doing more stuff, it’s creating the capacity and the agency for people to run small experiments and actually manage the change instead of drowning in it.

The three questions that still work

So if you take one thing you can use Monday morning, take these. Near the end I asked Michael what coaching a team actually looks like in 2026, and he gave me three questions from The Coaching Habit that still hold up when AI can answer almost anything. The strategic question, “what are we going to say yes to, and if we say yes to this, what must we say no to?” The real-challenge question, “what’s the real challenge here?” And the learning question he closes every conversation with, “what was most useful or valuable here?” I’ve started using that last one, because it makes sure everyone walks away a little smarter, including me. I made a second card of these so you can keep them in front of you.

If AI can generate every answer in your organization, what’s the one thing your people still need a human leader for? Sit with that, then watch the full conversation above, and if you want the real depth on any of this, go grab The Coaching Habit and subscribe to Michael’s Change Signal podcast.

Subscribe now

About Michael Bungay Stanier

Michael Bungay Stanier is the author of The Coaching Habit, one of the best-selling coaching books of all time, now celebrating its ten-year anniversary. He also wrote How to Work with Almost Anyone and hosts the Change Signal podcast, where he cuts through the blather to find what actually works in change. His whole mission is helping leaders be more coach-like: stay curious a little longer, rush to advice a little more slowly. Find his books and free resources at thecoachinghabit.com.

About me

Joel Salinas is an AI Strategy Coach for leaders at small and mid-sized businesses and nonprofits. AI is everywhere; judgment is scarce. Joel helps leaders adopt AI without outsourcing their judgment to it, through a monthly live workshop and 90-day engagements. Creator of the AI Leadership Triad. He writes Leadership in Change.

Written by a human, for humans.

Anthropic shipped something this week that looks small and isn’t. As of July 7, 2026, Claude Cowork (Anthropic’s AI agent that does real multi-step work for you, not just chat) runs on web and mobile, not only on your desktop. You can start a task at your desk, check on it from your phone, and pick up the finished work later, with your laptop closed the whole time.

Everyone Filed This Under the Wrong Headline

I know how that sounds. TechCrunch filed it under the coding agent wars, another agent update for the engineers. HOWEVER, here’s the piece that I want to focus on: Anthropic’s own numbers say more than 90% of Cowork usage wasn’t software development, and the biggest categories were business operations and content creation.

Read that again. The tool everyone files under “coding” is mostly being used for the exact work most of us actually do.

That gap between how a tool gets labeled and how it gets used is the whole story here.

Cowork Is the Level Most Leaders Should Be On

I’ll be honest about my own setup. I run about 95% of my work in Claude Code, the developer-flavored end of the tool. But that’s not where I point the leaders I coach. For almost all of them, the sweet spot is one level down: Claude Cowork. Connecting it to the tools they already use, building a skill or two, pointing it at a folder of research, letting it browse the web and come back with something useful. If you want the full map of how those levels stack, I laid it out in the four levels of Claude. Level 3, Cowork, is where most leaders get the most value for the least effort.

And until this week, Cowork had one real drawback that kept getting in the way: it was chained to the desktop. You could hand it a genuinely useful task, but only while you were sitting there with the app open. It was like having a sharp assistant who could only work while you stood over their shoulder. The second you stepped away, everything stopped.

The Leash is Off

That leash is off now. Close the laptop and head to your meeting, and Cowork keeps going. Scheduled tasks run with no device online at all. When it hits a real decision, it pauses and asks you for approval instead of guessing. So the thing you handed off at 9am can actually be moving while you’re in a room full of people at 10.

Here’s why that matters more than it looks. When a tool only works while you watch it, you use it like a tool, something you pick up, operate, and put down. When it keeps working after you walk away, you start using it like a person you delegated to. And delegation is a leadership skill, not a technical one. Most of us already know how to hand a task to someone, set the guardrails, and check in later. That instinct, the one you built managing people, is exactly the instinct this rewards.

What to Actually Hand It First

So here’s where it gets practical. If you’re going to test this, don’t start with the hardest thing you do. Start with the work that eats your week and doesn’t need your judgment on every single step. Anthropic’s own breakdown is the tell here, because the two biggest categories were business operations and content creation, and that’s most of a leader’s week right there. Pick something out of one of those buckets and hand it off before your next meeting. A few places I’d start:

• The recurring report you dread — the weekly numbers pull, the status roll-up, the “where are we on this” summary. Point Cowork at the folder or the tools where that data lives, tell it the format you want, and let it draft while you’re in your 10am.

• The research you keep meaning to do — the competitor you’ve been circling, the market you’re curious about, the vendor you’re vetting. Hand it the question, let it browse, and have it come back with something you can react to instead of a blank page.

• The content you owe — the post, the team update, the first draft of the thing you keep pushing to next week. Give it your voice and your notes, and check the draft later.

• The scheduled thing you always forget — set it to run on its own, no device open, and come back to it done.

Notice the pattern. Every one of these is something you’d already hand to a capable person if you had one sitting outside your office, and that’s the whole test. If you’d trust a sharp assistant with it, you can trust this with it, as long as you read the work before it goes anywhere that matters.

I don’t want to oversell the moment. This is a beta, it’s rolling out to Max subscribers first, and “the laptop can be closed” is a long way from “run your whole operation on autopilot.” You still have to know what’s worth handing off, and you still have to check the work. But the direction is clear, and the friction that kept Cowork stuck on one machine is the friction that just disappeared.

If you’ve been waiting for a reason to take Cowork seriously, this is it. The version most of your team should be using just stopped being something you have to babysit from your desk.

I’m putting together my top 15 ways to actually use Cowork for business, the specific plays I walk my coaching clients through, and it’s going out to subscribers soon. If that’s useful, make sure you’re on the list.

Subscribe now

The tool your team actually needs just lost its leash. The question now is what you’d hand it first.

What’s the one task you’d hand off today if you knew it would keep working after you closed your laptop? Tell me in the comments.

Joel Salinas is an AI Strategy Coach for leaders at small and mid-sized businesses and nonprofits. 1:1 coaching, team workshops, and AI strategy work built around amplifying what your team is already good at. Creator of the AI Leadership Triad. He writes Leadership in Change.

Written by a human, for humans.

Sources

• Anthropic announcement: https://claude.com/blog/cowork-web-mobile

• TechCrunch: https://techcrunch.com/2026/07/07/the-coding-agent-wars-are-spilling-into-the-rest-of-the-office-claude-cowork/

Here’s the thing about meetings: most of us prepare for the big ones and wing the rest. The board presentation gets three rehearsals. The “quick intro call” with a potential client often gets zero, and then we wonder why it went nowhere.

But in the world we’re in now, that quick call is often the whole game.

One meeting can swing from an unimpressed client who politely closes the door, to a partnership that brings referrals and growth for years. Same person, same thirty minutes. The only difference is whether you walked in ready.

And prep is the part that always gets squeezed. You mean to review who you’re meeting, you run out of time, and you walk in cold. Then the meeting ends, everyone says “let’s follow up,” and three days later the follow-up you meant to send is still sitting in your head while the momentum dies.

This month’s drop fixes both ends of that. The Meeting Prep Skill does two jobs.

1. Before a meeting, you paste in who you’re meeting and what it’s about, and it hands you a prep brief: the one goal, who’s in the room and what they want, the three things to land, the objections you’ll likely hit and how to answer them, and the line to open with.

2. After the meeting, you paste your messy notes and it writes the follow-up in your voice and pulls out every action item with an owner and a date.

Paid subscribers get the full prompt below, the 2-minute install, and how I actually run it.

Subscribe now

The Meeting Prep Skill

This one works like June’s, so it asks you a few questions first and builds itself around your role and your voice. That’s the question block at the top of the prompt, so don’t strip it.

Copy the whole block below (including the first line) and paste it into Claude Cowork.

Read more

The bar used to be produce content, produce content, produce content, and most people didn’t have the time or the persistence to keep going, so just showing up was the whole game. That bar is gone now. Anybody can type a prompt and get ten decent posts before lunch. Which means the thing that actually keeps a reader with you is the part AI can’t fake. has built a solopreneur business coaching creative entrepreneurs, with 34,000+ subscribers on Substack, and we spent this conversation on exactly that tension: how you grow when the tools that used to separate you from everybody else are now free to everybody else. We got into the 1:5 engagement ratio, why community became the whole point after the pandemic, and where he draws the line on letting AI touch his fiction. Watch the full thing above.

Subscribe now

🎥 Catch all future interviews, subscribe on YouTube

🎧 Prefer audio? Listen on Apple Podcasts

🤝 Want to chat one-on-one? Book a free intro call.

Outline

(1:25) – From Medium to Substack, and starting with one publication

(3:37) – Youth worker to hiking blog to solopreneur

(5:36) – Why the digital-course boom cooled and community took over

(9:18) – Finding your why and your message

(10:49) – The 1:5 ratio: using other people’s audiences

(13:34) – The content bar collapsed with AI

(18:07) – AI should amplify your strengths, not replace your expertise

(22:27) – Using AI as an editor for fiction

(23:28) – The sensitivity check: AI on a humanitarian content team

(26:57) – David’s new group coaching program

(29:17) – Who should reach out to David

How to Write for a Living

Writers should get paid for doing what they love. Subscribe now to get my FREE 7-day email course, Substack Launchpad.

By David McIlroy

My Takeaways

The content bar hit zero. David and I got to the same place from different sides. The old advantage was volume, and volume is now free. So the question flips. If a post can come out 100% AI-created, what actually separates you from the hundred other people running the same prompt? David put it this way: “You can create. It’s the kind of content, it’s the depth that now you can go to, which makes the difference.” The depth is yours. The prompt isn’t.

Borrow other people’s audiences. When your own list is small, David’s move is a rough 1:5 ratio, one thing you post, five thoughtful comments on bigger creators’ work. And he was firm that the comment has to earn its place. “It needs to be something thoughtful that adds to the conversation and demonstrates that you have some level of expertise to share as well.” Not “great idea, happy for you.” That’s the version an AI bot posts, and readers can smell it. (I get those on LinkedIn constantly, three perfectly structured paragraphs from someone who clearly never read the post.)

Share the messy stuff. This is the one I’d underline for any leader building a presence online. David’s point was that people don’t connect with polished text, they connect with a real person behind it. “You’ve got to share some of the messy stuff as well, the struggles, the good times, the family stuff, the pets, the nature.” AI can generate the frameworks. It can’t hand you your kid’s drawing or your dog running through the grass, and that’s the part a reader actually stays for. It’s the same reason I keep coming back to community as the thing that carries you.

AI as editor, not author. David keeps AI completely out of his fiction as a writer, but he runs finished manuscripts through it to catch plot holes and inconsistencies. “It’s working like an editor on some level. It doesn’t produce any ideas or guide the story in any way, but it helps keep me right if I’m making mistakes.” That’s exactly how I use it too. On the humanitarian side, my content team of about 25 people runs work through Claude to check whether a message written by a non-Latino would read wrong to a Latino audience. Not to write it. To catch what a human would miss and then keep going. The line David draws is the right one: the moment AI starts replacing what makes you you, you’ve stopped growing.

One Question to Sit With

Where do you draw the line, what do you let AI do, and what stays yours? The bar for producing content just hit zero, and the part that’s actually worth following is the part AI can’t fake.

About David McIlroy

David McIlroy is a strategy coach for creative entrepreneurs, a solopreneur, fiction author, and podcaster based in Northern Ireland, with 34,000+ subscribers across his Substack publications. He helps creators build a personal brand, strategy, and system for clarity and growth. His new weekly group coaching program opens Tuesday, July 28, 2026. Subscribe and reach out to him on Substack.

About me

Joel Salinas is an Executive AI Coach for leaders at small and mid-sized businesses and nonprofits. 1:1 coaching, team workshops, and AI strategy work built around amplifying what your team is already good at. Creator of the AI Leadership Triad. He writes Leadership in Change. If you want help thinking through your own AI strategy or online presence, start here.

Written by a human, for humans.

So, a confession before I hand this one off. A lot of what keeps my own work moving isn’t a bigger team; it’s a stack of AI I’ve slowly taught to handle the routine parts, and I got there mostly because I had no other way to keep up. I didn’t set out to become the person describes in this piece, I backed into a rough version of it because the alternative was drowning. I wish I had this guide before starting.

Justin runs The Landing Pad, where he writes about AI for marketers, and he’s named something I watch play out in almost every organization I coach. Teams get anxious about AI, so leaders buy tools.

More platforms, more seats, more Friday demos. And the anxiety doesn’t budge, because a tool was almost never the thing missing in the first place.

Every team I sit with already owns more software than it uses. What they’re missing is a person whose whole job is to know which of it matters and go build the pipes.

He calls that person the marketing engineer.

Read this one slowly. Then go look at your best marketer a little differently.

What to Remember

• AI-nxiety is the low hum of anxiety on marketing teams as tools ship faster than anyone can keep up with. A lot of it is manufactured by “I cracked AI” content selling certainty.

• Justin calls that person the marketing engineer, a marketer who builds with AI, sitting inside the team.

• Hiring an engineer and pointing them at marketing usually ships work that’s off-brand in ways the team can’t catch and redirect fast enough.

• You can start fractionally: bring someone in a day or two a week to map and build the highest-value workflows first.

Here’s

The Landing Pad

The Landing Pad is a weekly newsletter for marketers focused on career growth, job opportunities, and the mental side of modern work. Each week blends open roles, honest perspective, and practical encouragement for wherever you are in your career.

By Justin Taylor

The AI-nxiety on your team isn’t a tools problem

Why leaders keep buying software to fix something only a person can fix

I’m Justin Taylor, a marketing consultant who runs The Landing Pad where I write about the future of marketing, AI for marketers, and insights on what’s happening to the marketing industry.

Every marketing leader I talk to is doing some version of the same thing right now. Buying tools.

A new AI writing platform. An agent builder. A seat for everyone on the team, a mandate to “use AI more,” a Friday demo where somebody shows the thing they automated that week. It feels like progress. It photographs like progress.

I also hear all the time from marketers who keep getting forwarded articles and LinkedIn posts from their bosses telling them it’s something they should figure out.

The teams are quite anxious.

It’s the low hum that sits behind the day. It’s the reason a perfectly competent senior marketer with fifteen years of experience suddenly feels behind. The tools won’t sit still. A new image generator on Wednesday. A new agent that can apparently do half the job by Friday. Nobody’s caught up on the last one, and three more have shipped. The pace itself is the trap.

The senior marketers don’t know how to figure it out so they’re pushing it onto the more junior “tech savvy” employees who don’t have the means to meaningfully implement anything.

Here’s the part worth sitting with as a leader. A lot of that anxiety is manufactured. Your team is being marketed to.

Half the “I cracked AI” content aimed at them is a comment trap dressed as a framework. Type a word, get the playbook, feed the algorithm. The promise is the bait and the reader is the product. It works because it sells certainty in a moment when nobody has any. Your team is buying it, in attention if not in dollars, and carrying the residue into work on Monday.

So the reflex from the top is to buy that certainty back. And I get it. A tool is a line item you can approve this quarter. A tool has a demo. A tool doesn’t require admitting you’re not sure what your team should look like in two years.

But here’s what I think we’re getting wrong

We’re treating a staffing problem like a software problem.

The work piling up in every marketing org isn’t work a tool does on its own. Somebody still has to know what to build.

Let me be specific. I’m sure you have a list on your desk of things that should be automated and aren’t. Reviews from four sales channels pulled into a sentiment doc by hand once a month. A weekly recap written from scratch every time, even though the numbers come from the same five places. The connections between systems that should exist and just don’t. Maybe you have the tools. You have the access. But what you don’t have is a person whose whole job is to go find that work and build the pipes.

Not another marketer. Not another developer. Someone in the middle.

Think about the most routine thing your team does. Your social lead presenting last week’s numbers, say. Open each platform. Check the organic numbers per post. Pull the comments. Go to a third-party tool for sentiment. Check demographics. Compare against last week. Build the follower-growth chart. Screenshot everything. Drop it into slides. They’ll tell you it’s a three-step process. It’s twelve. And most of those twelve are routine, not judgment.

That gap, the routine work quietly swallowing the judgment work, is the real problem in the building. No tool you buy off the shelf closes it, because the tool doesn’t know which twelve steps your team actually runs.

A person does. There’s a name forming for that person. The marketing engineer. A marketer who builds with AI, sitting inside the team. The work that used to take an account team plus a strategist plus a producer, done by one person with a few agents. Their job is to make the routine eighty percent invisible so the team’s energy goes to the judgment, twenty-first, not last.

And here’s the part that should change how you hire. This person is a marketer first.

Not an engineer who picked up marketing on the side. Not a developer who learned Claude last month. Someone who spent years understanding customers, briefs, brand voice, how a quarter actually comes together, and then learned to build. Knowing what to build is harder than knowing how to build it. The “how” can be taught in months now. The “what” takes years and can’t be googled.

I watch companies get this backwards constantly. They hire an “AI person” from an engineering background and point them at marketing problems. The output looks impressive. It’s also wrong, often, in ways the engineer can’t see and the marketing team can’t articulate fast enough to redirect. So the work ships quietly off-brand, and everyone decides AI just isn’t there yet.

AI is there. The role was wrong.

Subscribe now

The move to make before your next tool contract

So if you’re feeling the pressure to do something about AI, here’s the move I’d make before signing the next tool contract.

Look at your best marketer. The one with the taste and the context. Ask what they’d build if they had the time and a little cover. Then give them exactly that. Permission to stop doing the manual work long enough to automate it.

That costs less than most of the platforms you’re evaluating, and it does something none of them can. It turns the anxiety in the room into a direction. The person who was worried about being replaced becomes the one building the thing that replaces the busywork. That’s a very different feeling to manage.

You can start fractionally

And you don’t have to bet a headcount to start. This role works fractionally. Bring someone in a day or two a week to map the highest-value workflows, build the first few, and show the team what a marketer who builds actually looks like. You learn what the full-time version is worth by watching what the part-time version ships.

None of this needs a reorg. It needs seeing the role that’s already forming under the layers everyone’s worried about, and hiring into it on purpose instead of waiting for it to appear.

The teams that win the next couple of years won’t be the ones with the most tools. Most of you have too many already.

They’ll be the ones who figured out this was a person all along.

Thanks, Justin!

Justin’s last line is the one I keep coming back to: the teams that win the next couple of years won’t be the ones with the most tools, they’ll be the ones who figured out this was a person all along. If you take one thing from this, look at your best marketer, the one with the taste and the context, and ask what they’d build if you gave them the cover to stop doing the manual work long enough to automate it.

The person on your team most worried about being replaced by AI is usually the one best positioned to build the thing that replaces the busywork, which is the whole shift I wrote about in AI-enhanced or AI-replaceable.

If this piece was useful, subscribe to Justin at The Landing Pad for more on marketing in the AI era.

Questions Leaders Are Asking

What is a marketing engineer? A marketing engineer is a marketer who builds with AI from inside the team. Instead of only running campaigns, they automate the routine work, connect the tools you already own, and build small agent-driven workflows. The role sits between marketing and engineering: marketing judgment first, building skills second.

Does a marketing engineer need to be a developer or a designer? No. This person is a marketer first, someone who already understands customers, briefs, and brand voice, and then learned to build with AI. Knowing what to build is the hard part, and it takes years. The building itself can be learned in months. Hiring an engineer and adding marketing later usually gets it backwards.

Will buying more AI tools fix my team’s AI anxiety? Rarely. Most teams already own more software than they use, so another platform adds a seat and a demo without moving the anxiety. What settles a team is a person who knows which routine work to automate and can actually build it. The anxiety turns into direction once someone owns that.

Do I need a full-time hire, or can this be fractional? You can start fractionally. Bring someone in a day or two a week to map your highest-value workflows, build the first few, and show the team what a marketer who builds looks like. You learn what a full-time version is worth by watching what the part-time version ships, before you commit a headcount.

Does the marketing engineer role only apply to marketing teams? The name comes from marketing, but the pattern shows up in sales, operations, finance, and nonprofit teams too. Anywhere routine work is swallowing judgment work, the same fix applies: one person with domain taste who can build with AI, rather than another off-the-shelf tool nobody has time to wire up.

What AI tools does a marketing engineer actually use? Fewer than you’d expect. Most of the work runs on one capable assistant like Claude (Anthropic’s AI assistant, the main rival to ChatGPT) plus a couple of agents, connected to the data sources the team already pulls from by hand. The skill isn’t collecting tools, it’s knowing which twelve manual steps to turn into one.

Written by

Justin Taylor is a marketing consultant and the writer behind The Landing Pad, where he covers the future of marketing, AI for marketers, and what’s happening to the marketing industry. Follow him at @thelandingpad.

Joel Salinas is an AI Strategy Coach for leaders at small and mid-sized businesses and nonprofits. 1:1 coaching, team workshops, and AI strategy work built around amplifying what your team is already good at. Creator of the AI Leadership Triad. He writes Leadership in Change.

Written by a human, for humans.

We went from a Shark Lagoon PDF he sold as a kid to a company that put publishing tools in everyone’s hands. Along the way he unpacked why he tells builders to fall in love with their customer’s pain instead of their idea, his Four P’s of knowledge capital, and why AI is turning all of us from creators into builders. If you’re sitting on expertise and wondering what to do with it, this is the one to watch.

Outline

(00:04) – Welcome, and going live on Substack

(00:53) – A sprinkler toy, a taco, and a lifelong side-hustle habit

(02:39) – Shark Lagoon: the first thing he ever sold online

(04:36) – Kajabi’s first customer

(05:28) – Launch day, and a million dollars in 24 hours

(08:44) – The Saturday the support tickets caught fire

(09:56) – The survey that revealed what Kajabi really was

(10:32) – From $10 billion to “we’re all becoming builders”

(12:13) – “Fall in love with your customer’s pain, not your idea”

(15:24) – Knowledge capital and the Four P’s

(22:57) – Skills as the new digital product, and taste as the new edge

(36:43) – What Travis is building now: Wazi and PageSumo

My Takeaways

Fall in love with your customer’s pain, not your idea. Here’s the line I keep coming back to from the whole conversation. Travis has been building things his entire life, and the thing he kept circling back to is that being good at creating stuff is exactly what makes you fall in love with the wrong part of it.

“When you’re good at creating things, you fall in love with the idea too much,” he said. “But if you’re not helping someone, if you don’t provide value to them, it doesn’t matter.”

What I pushed on with him is how much sharper that line cuts right now, because once anyone can build almost anything over a weekend, a lot of us are going to mistake “I can make this” for “someone actually needs this,” and those two things were never the same.

Everybody is sitting on knowledge capital, and most of us take it for granted. So here’s the framing from his book You, Inc. that I want more leaders to steal. Travis calls it the Four P’s: Profession, Passion, Pain, and Problems. His claim is that all four are knowledge somebody else would pay for, and we discount them because they come easy to us.

“Everybody is an expert in their own life,” he said, and the example that got me was his kids yelling across the house about how to beat a level in Minecraft. In that one small section of life, his oldest son was the expert. So the thing he leaves you with is simple and a little uncomfortable: you already have something worth teaching, and the only real work is looking back at your own trail long enough to notice what it is.

We’re shifting from a creator economy to a builder economy. This is the part I was leaning in for, because it’s exactly what I’ve been living. Travis’s take is that AI has handed everyone the capacity to build, not just to make content.

“AI has given us the capacity to build, where anybody can build software, anybody can build agents, workflows,” he said, “and I really encourage anybody that watches this to go start doing that just for fun on the side.”

I told him about the workout app my wife and I both wanted but couldn’t find, so one weekend I sat down in Claude Code (Anthropic’s coding tool you run by talking to it), took screenshots of the two apps we were each paying fifteen dollars a month for, listed the features we actually wanted, and built our own. I’ve used it almost every day for six months. That used to take a team and a budget, and now it takes a curious Saturday.

In the age of infinite output, taste is the edge. So if anybody can build anything, what’s actually scarce now? Travis’s answer is taste, and taste comes from being an expert who knows what to leave out. He pointed to In-N-Out here in Southern California, how the whole brand is a curated menu, a few things done well instead of a wall of options.

“It’s really good at producing a lot of stuff quickly,” he said about AI, “but how do you have taste? How do you curate the good? That’s the next real expert, the person who knows how to navigate that and still produce high-quality stuff.”

His advice for getting started is to build in a vacuum for a while and be your own first customer, because the fastest way to develop taste is to make something you’d actually use. That’s the same thread of adaptability and creativity I keep writing about in the AI Leadership Triad. The tools got cheap, so judgment is the job now.

Subscribe now

One Question to Sit With

What’s the one pain, yours or a customer’s, that you’d build for first now that the tools aren’t the hard part anymore?

Watch the full conversation above, and then go follow Travis Rosser and check out what he’s building at Wazi and PageSumo.

About Travis Rosser

Travis Rosser is the co-founder of Kajabi, the platform creators have used to sell more than $10 billion in courses, coaching, and digital products. He’s the author of You, Inc., a book about turning your own knowledge into a business through what he calls the Four P’s. These days he’s building two new tools with AI: Wazi (wazi.ai) and PageSumo (pagesumo.com). Follow his work on LinkedIn.

About me

Joel Salinas is an Executive AI Coach for leaders at small and mid-sized businesses and nonprofits. 1:1 coaching, team workshops, and AI strategy work built around amplifying what your team is already good at. Creator of the AI Leadership Triad. He writes Leadership in Change. If you want help thinking through your own AI strategy or online presence, start here.

Written by a human, for humans.

Thank you , , , , , and many others for tuning into my live video with ! Join me for my next live video in the app.

Get more from Joel Salinas in the Substack app

Available for iOS and Android

Get the app

I keep hearing this one term in every AI conversation right now: MCP. Executives drop it in meetings, vendors put it on their slides, and almost everyone nodding along has no real idea what it means. Here’s the honest part. MCP is not a mind-blowing concept. It’s a genuinely useful one that’s been wrapped in enough technical language to scare off the exact people who’d benefit from it most, which is leaders trying to get real value out of AI.

So let’s strip the jargon off it.

Also, at the end of this post, I’m giving you my full guide of top 25 MCPs to use, for free! Don’t miss it!

Subscribe now

What an MCP actually is

MCP stands for Model Context Protocol. Anthropic, the AI company behind Claude (the main rival to ChatGPT), introduced it in late 2024, and it’s now an open standard that any AI company can use.

The way I think about it is simple.

Imagine you hire a brilliant new employee on day one. If you never give them a login, a building badge, or access to a single file, all they can do is answer general questions from memory.

Smart, and completely useless on anything specific to you. That’s AI without MCP. An MCP is the badge and the logins. It’s a standard, safe way to hand your AI assistant controlled access to the tools your team already uses. The engineers have a shorthand for it. They call it “USB-C for AI,” one universal port instead of a custom cable for every single device.

That’s it. That’s the whole concept everyone’s been making you feel dumb about.

Why this matters more than it sounds

They typed some questions into a box, got generic answers, and concluded it wasn’t worth the hype. I get it. But that’s like judging your new hire while they’re still locked out of every system in the building. It’s also why so many people only ever see a fraction of what these tools can actually do.

McKinsey reported in 2025 that roughly 78% of organizations now use AI, and yet most still can’t scale it across their actual operations. The bottleneck usually isn’t the model. It’s that the AI can’t reach the real work, the files, the customer records, the calendar, the systems where your business actually lives. MCP is the piece that closes that gap, and it does the same job that giving AI real context does, just at the level of your whole toolset. That’s why it stopped being a developer curiosity and started showing up on executive agendas.

Why every scaling organization will end up with one

This is already happening, with or without a plan. Microsoft, OpenAI, Amazon Web Services, PayPal, and Cloudflare have all adopted MCP. Gartner expects 40% of enterprise software to include built-in AI agents by the end of 2026, up from under 5% today. Forrester expects nearly a third of enterprise app vendors to ship their own MCP connections this year. Forbes has gone as far as calling MCP the likely “defining interface standard of the AI-first enterprise.”

Translation for the rest of us: the tools your team already pays for are about to come with AI access built in.

This is the Adaptability piece of what I call the AI Leadership Triad. The advantage here doesn’t come from owning the smartest model. Everybody has access to roughly the same models now. It comes from being the organization that actually wired that model into your real work, and right now most of your competitors still haven’t.

What this looks like in real life

Let me make it concrete with my own setup, because I won’t tell you to do something I don’t do. I run four businesses and help lead growth at a global humanitarian organization, so I live in this stuff all day.

Right now my Claude is connected through MCP to my email, my calendar, my Google Drive, my Notion, my HubSpot, a research tool called Tavily, and a custom assistant I built that tracks my commitments and contacts. It’s the natural next step past the Claude setup I already run.

So when I ask Claude to do something, it doesn’t make me paste anything in. It already has the context. I can say things like “find me two free hours this week to write and hold them,” or “pull the contacts I haven’t followed up with and draft the notes,” and it just does it, inside my real tools, with my real information. That’s the difference between an assistant that talks about your work and one that actually does it.

The one risk to respect

I won’t pretend this is all upside. Giving AI access to your systems also widens what security people call your attack surface. There are real new risks, like “tool poisoning,” where a bad connection tricks the AI into doing something it shouldn’t with your data.

HOWEVER, the answer isn’t to slam the door and fall behind.

The leaders getting this right connect deliberately: trusted, official connections only, access limited to what a task actually needs, and at any real scale, one person who owns the decision of what gets plugged in. I put a short “how to vet an MCP before you connect it” checklist on the last page of the download for exactly this reason.

👉 25 Must-Have MCPs

To get the full guide, 25 Free MCP Connectors for Leaders Using Claude, simply Subscribe today! Then reload this page and download.

Subscribe now

What I’d do Monday morning

Don’t overthink it. Pick one tool you already live in, your email, your calendar, or your notes, and connect it to Claude. Most of the connections I use take about two minutes and zero code. Feeling AI reach into your real work just once changes how you think about all of it.

And if you want the shortcut, I pulled together the 25 MCP connections I’d start with, the exact ones I use plus the best leader-ready picks, each with a direct link and that safety checklist. It’s free for subscribers, right below.

Because the real question isn’t whether your organization will use these. The tools you already pay for are about to ship with them. The question is whether you’ll be the leader who connected them on purpose, or the one who found out their team already had.

So tell me: what’s the first system you’d want your AI to actually plug into?

Share

If you only remember this

• An MCP is just a safe, standard way to give your AI assistant access to the tools you already use. The jargon hides how simple it is.

• Most leaders who say AI underwhelmed them only ever tried the chatbot version, locked out of their actual work.

• The edge won’t come from a smarter model, since everyone has the same ones. It comes from being the organization that connected it to real work first.

Questions leaders are asking

What is an MCP in simple terms? MCP (Model Context Protocol) is an open standard that lets an AI assistant connect to your real tools and data, like email, files, and your CRM, instead of only answering from general knowledge.

Does my company need an MCP? If you want AI to do real work inside your systems rather than answer generic questions, yes. Most enterprise software is expected to ship AI connections by the end of 2026.

Do I need a developer? No. Many connections (email, calendar, notes, Drive) take about two minutes through the Claude or ChatGPT app, with no code.

Is MCP safe? It adds real value and real risk. Use official connections, limit access, and vet anything before you connect it.

What’s the difference between MCP and a normal integration? A normal integration is custom-built for one tool and one use. MCP is a universal standard, so you connect once and reuse it everywhere.

Joel Salinas is an AI Strategy Coach for leaders at small and mid-sized businesses and nonprofits. 1:1 coaching, team workshops, and AI strategy work built around amplifying what your team is already good at. Creator of the AI Leadership Triad. He writes Leadership in Change.

Written by a human, for humans.

A story in this post stuck with me for days, especially as I was in a hiring season for my team. A engineer applied to five startup roles she’s genuinely qualified for, and kept getting rejected for a reason you will not believe.

I’ve been chewing on where AI belongs in the work we do and where it doesn’t, and hiring is one of the sharpest places that question shows up. Get it wrong, and you let a machine decide who’s even worth a human’s attention. Get it right, and AI does the sorting it’s good at, while a person still makes the call that actually matters. That’s the line I keep coming back to:

Keep human what needs to stay human, and put AI in its place, on purpose.

That’s why I wanted you to read this guest post. Today’s author, Sebastian Scott, is the co-founder and CEO of Clera, an AI talent platform, and he lives inside this exact problem every day. His case for why “qualified” is about to mean something different is the clearest I’ve read.

Subscribe now

What to Keep an Eye On as You Read

• The résumé screens for words, not people, and the strongest candidate is often the one a keyword filter deletes first.

• AI’s right role here is decision-support, not decision-maker: it widens the field, a person still owns the call.

• Train a model on who you hired before, and it will automate your old blind spots behind a cleaner interface, so the bias doesn’t disappear; it just hides.

Here’s Sebastian.

AI Won’t Replace Recruiters. It Will Redefine What Great Hiring Looks Like.

By Sebastian Scott, CEO of Clera

A few months ago, an engineer applied to five startup roles she was genuinely qualified for. But here’s what went wrong: all five rejected her before a human ever opened her file. Her resume said “support engineer,” and the automated filters were scanning for “software engineer.” By any real measure, she was exactly who those teams needed. The system was simply built to match words, not people.

That story is ordinary, which is the problem. For decades, hiring has run on a single premise: find people whose resumes match the requirements of a role. AI is dismantling that premise, and the leaders who understand why will pull ahead of the ones still optimizing a process that no longer reflects how work happens.

The popular debate asks whether AI will replace people. In hiring, that’s the wrong question. AI is changing what we can see about a person, who reaches out to whom, and what the word “qualified” even means.

The Resume Is Losing Its Monopoly

Resumes were designed for a different era. They tell us where someone worked, what degree they earned, and what titles they’ve held. They tell us very little about how someone solves problems, learns, collaborates, or adapts when the work changes.

At the same time, AI is making traditional credentials less predictive. A marketer can now run analysis that once required a specialist. A salesperson produces research that used to need a whole support team. One person at a startup ships what would have taken several specialists a few years ago.

So the question leaders should ask is no longer “What has this person done before?” It’s “How effectively can this person turn new tools into value?” That changes everything downstream.

What they’re really looking for is something the resume was never built to show, and for the first time, emerging talent platforms can measure this directly.

The Future of Hiring Is Signal-Rich, Not Resume-Rich

The opportunity for leaders is to evaluate talent on signals rather than credentials alone. A signal is evidence that predicts future performance: how fast someone learns, how clearly they think, how well they exercise judgment, how they approach a problem they have never seen before. These have always mattered more than a job title. They were simply too expensive to assess at scale, so we relied on the resume because it was cheap.

AI changes that. Used well, it lets a team consider far more candidates, find the patterns that tend to precede success, and notice the people a keyword filter would have discarded. The engineer from the opening is precisely who a signal-based process catches and a resume-based process loses.

One caution. This does not mean handing the decision to a model. The right design is AI as a decision-support system, not a decision-maker: the machine widens the field and organizes the evidence, and a person still makes the call and owns it.

AI Creates a New Category of Employee

Across industries, a new kind of high performer is also forming. They are not always the most experienced, the most credentialed, or the most senior. What sets them apart is how well they work alongside AI. They turn business problems into tasks a machine can help with, judge the output critically, and pair their own instincts with what the tools surface. The results often outrun anything their resume would predict.

That is the opportunity: organizations can become far more productive. The challenge is that most hiring processes were never built to spot it. They still reward pedigree over adaptability and experience over learning speed. In an AI-enabled economy, those instincts cause companies to overlook their best potential hires.

Finding People Was Never the Broken Part

It helps to be precise here, because the AI-in-hiring conversation blurs very different things together. A new generation of AI sourcing tools, software that scans hundreds of millions of public profiles and returns a ranked list of candidates in seconds, is genuinely useful. (Sourcing simply means finding potential candidates. An applicant tracking system, or ATS, is the database where a company stores and filters the people who apply.)

These tools have one important consequence: finding names is now cheap and close to solved. A long list of plausible candidates has become a commodity.

But finding people was never the part that was broken. The broken part is everything after the list. A name on a list is still a stranger who hasn’t been contacted, hasn’t agreed to anything, and may not even be looking. The strongest people are usually passive candidates, meaning they are employed, heads-down, and not browsing job boards, which makes them invisible to any process that waits for applications. What is scarce now is a candidate who has been reached, understood, checked by a human, and is genuinely interested in talking.

Subscribe now

When the Company Applies First

The most underrated shift AI enables isn’t speed. It’s reversing the direction of hiring. For a century, the candidate has done the work: find the opening, tailor the resume, apply, and wait to be screened out. The strongest people never enter that funnel, because the strongest people aren’t looking.

AI makes the opposite model practical. A recruiting system can now identify a specific person, understand what they’re working toward, and surface them to a hiring manager who reaches out first, before that person has applied anywhere. The company expresses interest, and the candidate gets to respond to a real opportunity instead of shouting into an ATS.

This changes the psychology of hiring, not just the logistics. When a company opens the conversation, candidates feel chosen rather than processed, and people who feel chosen show up more engaged and more willing to commit. At Clera, the team has built around exactly this inversion, but the principle holds regardless of the tool. In an AI-enabled market, the advantage goes to the organizations that reach out first and make the case, rather than waiting to be found.

Candidates Are Changing Too

The workforce is evolving alongside employers. Candidates now have tools that help them write, research, prepare, and learn at unprecedented speed, and leaders should expect them to arrive better informed and more technologically enabled than ever.

That creates a new problem for evaluation. If AI can help anyone produce a polished resume or rehearse an interview, those signals become less reliable indicators of potential. The best processes respond by focusing on how candidates think rather than how they present: real-world scenarios, problem-solving exercises, collaborative discussion, demonstrated learning. These give a clearer view of how someone will perform in an environment where AI is available to everyone.

Start With the People Who Aren’t Applying

Employers do not need to rebuild their hiring stack to start moving in this direction. Take one open role and try a different first step. Instead of opening the applicant pile, write down the three or four signals that genuinely predict success in that role and the qualities that matter. Then go find three people who fit those signals and are not applying, or the ones already doing the work somewhere else. Have a human, ideally the hiring manager, reach out to each of them personally with a specific reason they were recognized. While here, look at the last batch of rejected applicants and count how many were filtered on keywords before anyone read the file. That number is the blind-spot rate, and it is usually higher than anyone expects. Do this once and two things become obvious: where the process is matching words instead of people, and how different the conversation feels when the company moves first.

The Ethical Responsibility of Leaders

None of this is free of risk. AI can reduce bias, and it can just as easily manufacture it. Train a model on who got hired in the past, and it will quietly automate yesterday’s blind spots behind a cleaner interface. Leaders who put AI into hiring owe their candidates and their teams a few non-negotiables: be transparent about where AI is used, keep a human accountable for every final decision, protect candidate data, and audit the system regularly for outcomes.

The goal is not efficiency at all costs. Speed is the easy win. Fairer decisions are the harder and more valuable ones, and they only happen on purpose. Trust will become one of the defining competitive advantages of the AI era, and the organizations that earn it will attract the stronger talent.

The Real Competitive Advantage

The companies that win the next decade will not be the ones with the most advanced AI. They will be the ones that combine human judgment and machine capability better than anyone else, and that starts with how they hire.

Thank you, Sebastian!

Here’s what I keep coming back to. The goal was never to take the human out of hiring. It’s to spend our humanity where it actually counts, on the judgment, the call, the person sitting across from us, and let the machine handle the sorting underneath. Sebastian calls it signal over résumé, and I’d add that the signals he’s describing- how fast someone learns and how well they adapt- are the exact muscles I keep pointing leaders back to in the AI Leadership Triad.

Read more from Sebastian Scott at Clera, or follow him on LinkedIn.

Questions Leaders Are Asking

What is signal-based hiring? Signal-based hiring evaluates candidates on evidence that predicts future performance, how fast they learn, how clearly they think, how they handle an unfamiliar problem, rather than on credentials alone. Signals have always mattered more than job titles. They were just too expensive to assess at scale, so hiring leaned on the résumé because it was cheap. AI changes that math.

Why do strong candidates get rejected by hiring software? Most applicant tracking systems match keywords, not capability. If a résumé says “support engineer” and the filter wants “software engineer,” a qualified person gets cut before any human reads the file. The fix is to define the three or four signals that actually predict success in a role, then check how many applicants were filtered on wording alone.

What is a passive candidate? A passive candidate is someone employed, heads-down, and not browsing job boards, which means they never enter a process that waits for applications. They’re often the strongest people for a role precisely because they aren’t looking. Reaching them requires the company to move first, identifying the person and opening the conversation rather than waiting to be found.

Should AI make the final hiring decision? No. The right design is AI as a decision-support system, not a decision-maker. The machine widens the field and organizes the evidence, and a person makes the call and owns it. Handing the decision fully to a model is how organizations automate their old blind spots behind a cleaner interface and lose accountability for who gets hired.

How can companies use AI in hiring without amplifying bias? AI can reduce bias or manufacture it, depending on the data. Train a model on who got hired before and it will repeat yesterday’s patterns. The guardrails: be transparent about where AI is used, keep a human accountable for every final decision, protect candidate data, and audit outcomes regularly. Fairer decisions don’t happen by accident, they happen on purpose.

Sebastian Scott is the Co-Founder and CEO of Clera, an AI-powered talent platform that introduces top tech professionals to startups directly, not through job boards. He led Clera’s $3M pre-seed round (1984 Ventures, Deel Ventures, and angels from OpenAI, LinkedIn, and Sequoia) and has grown it to 60,000+ represented professionals and 500+ startup clients. Connect with him on LinkedIn.

Joel Salinas is an AI Strategy Coach for leaders at small and mid-sized businesses and nonprofits. 1:1 coaching, team workshops, and AI strategy work built around amplifying what your team is already good at. Creator of the AI Leadership Triad. He writes Leadership in Change.

Clera is an AI-powered talent agent that connects top tech professionals directly with VC-backed startups through iMessage and WhatsApp — no job boards, no applications. Backed by $3M from 1984 Ventures, Deel Ventures, and angels from OpenAI, LinkedIn, and Sequoia — with 60,000+ professionals and 500+ startup clients. See more here…

Written by a human, for humans.

Watch the full conversation above.

Also, click below to subscribe on Apple Podcasts! 👇

Outline

(00:00) – Intro: who Cory is and how he got to Substack

(01:20) – Why human content is becoming a premium tier

(01:56) – Five businesses, zero experience, and learning by building

(08:42) – The founder bottleneck: leading from the backseat

(10:06) – If you got sick tomorrow, would the business survive?

(14:28) – Type-A founders, trust, and hiring for the match

(16:46) – The hiring framework: skill, work style, communication, personality

(20:03) – VAs are for outcomes, AI is for tasks

(22:10) – Humans with AI, not AI versus humans

(23:25) – Everyone is starting at zero with AI

(33:49) – Why he named it Taking My Time Back

My Takeaways

The founder is the bottleneck. So the whole conversation really starts here. The minute a company can’t survive without the founder in every decision, the founder has stopped leading and started blocking. Cory said it about as bluntly as it gets:

“Any founder, any CEO trying to lead from the backseat drives the car off the road.”

I’ve watched this one up close, and the test I keep coming back to is simpler than people want it to be. If it’s all hinging on you, you’re not leading well, you’re just busy.

The system only exists in your head. Here’s the part founders hate to hear. When delegation fails, we blame the hire. Cory’s take is that the real problem is upstream, because the playbook never left your head. He fixed it by writing his whole content process down so completely that his assistant couldn’t fail.

“I documented my strategy, made it super clear so they couldn’t fail. I set them up for success, and it started working.”

That reframed it for me. The bottleneck usually isn’t your team’s ability, it’s the stuff you’ve never bothered to get out of your own head.

Hire for the match, not just the skill. When I asked Cory how he actually trusts people enough to step back, he didn’t start with skill. He started with fit, work style, communication style, personality, and then skill.

“Hiring someone is not just skill alone. What’s their work style? What’s their communication style? The match is everything.”

And he was honest about why most of us struggle with it. A lot of founders are type-A with trust issues, me included on some days. If you hired someone for a skill you don’t have, the worst thing you can do is hover. Give them the room to be good at the thing you brought them in for.

AI is for tasks, people are for outcomes. This is the line I’ll be repeating for a while. Cory draws a clean line between what you hand a person and what you hand a machine.

“You don’t bring in a virtual assistant to work on a task. You bring them on to deal with an outcome. You bring on AI to deal with individual tasks.”

I told him on the spot that’s a quote CEOs could put on their walls, and I meant it. It also points at where this is going. When everyone has AI doing the repetitive work, the human part, the judgment, the relationship, the actual experience, stops being the cheap part and becomes the premium one.

One Question to Sit With

If you got sick tomorrow and unplugged for two weeks, would your business still be standing when you got back? Sit with the honest answer.

Watch the full conversation above, and go subscribe to Taking My Time Back, Cory’s Substack on helping founders win their time back.

About Cory Blumenfeld

is the Founder and CEO of BlueMoso, a managed virtual assistant service for founders, executives, and agencies, with a vetted talent pool of more than 70 assistants and specialists. He’s a five-time founder with two exits across health tech, fintech, and outsourcing, and he runs his companies remotely from Playa del Carmen. He writes Taking My Time Back on Substack.

Taking My Time Back

Helping business owners and founders take back their time by building the right systems, the right teams, and the right balance of AI and human, so the business doesn't rely on them.

By Cory Blumenfeld

About me

Joel Salinas is an Executive AI Coach for leaders at small and mid-sized businesses and nonprofits. 1:1 coaching, team workshops, and AI strategy work built around amplifying what your team is already good at. Creator of the AI Leadership Triad. He writes Leadership in Change. If you want help thinking through your own AI strategy or online presence, start here.

Written by a human, for humans.

Thank you , , and many others for tuning into my live video with ! Join me for my next live video in the app.

Get more from Joel Salinas in the Substack app

Available for iOS and Android

Get the app

The first time I opened Claude Code, I almost closed it. Black screen, blinking cursor, and every instinct I had told me I didn’t belong there. I’ve never written a line of code in my life. I typed one plain sentence anyway, watched it go to work, and felt the same ground shift I felt the first time I ever used AI back in 2022.

Here’s the thing. I didn’t get to that screen in one leap. I got there by climbing, one level at a time, starting with the most ordinary uses you can imagine. Most leaders never make the climb, because they’re using Claude (Anthropic’s AI assistant, the main rival to ChatGPT) like a chatbot. They ask it the occasional question, get an average answer back, and decide that’s all there is.

The gap between where they’re standing and what’s actually possible is enormous, and almost nobody has handed them a map across it.

So I built one.

I put the whole climb, every level, with screenshots of every step, into a 51-page field guide called Leading with Claude.

I’ll come back to that. First, let me give you the map itself, because you can climb every one of these levels on your own, and by the end of this post you’ll know exactly where you stand.

In this post, you’ll learn:

• The four levels of Claude, from basic chat to building your own tools

• A simple way to find which level you’re actually on right now

• The one real example I use at each level to run four businesses, with no coding

AI isn’t one giant step

Most leaders treat AI as one giant step they’re either ready for or they’re not. That framing is the whole problem, because it isn’t a single step you take, it’s a climb you make in four levels, each one building on the last, and the first level is lower and more ordinary than anyone tells you. Here are the four bearings:

• Level 1, Basic Claude. Talking to it well.

• Level 2, Projects. Claude that remembers your world.

• Level 3, Skills. Claude that does your repeatable work the way you’d do it.

• Level 4, Claude Code. Claude that builds.

Most people are parked at the bottom and don’t know the other three levels are even there. Let’s climb.

Quick Win (under 60 seconds) Open any Claude chat. Before your next question, paste one sentence of context first: who you are, who the answer is for, and what a good answer looks like. Then ask the same question you were going to ask anyway. Put that answer next to the one you’d have gotten cold. The gap you just created is the entire point of Level 1.

Level 1: Basic Claude (talking to it well)

Level 1 is plain conversation, and the whole skill is context. When you type a bare question with no context, Claude does the only thing it can: it scans everything it knows, finds the most average answer, and hands it back. Give it the context first, who you are, what you’re working on, what good looks like, and you get something built for your actual situation. Same tool, completely different output.

Start where the stakes are zero. As of June 2025, 73% of ChatGPT messages were non-work, up from 53% a year earlier (OpenAI economic-usage research with the National Bureau of Economic Research, 2025). That’s not a knock on people, it’s how comfort gets built. My own Level 1 reps were ordinary: I had Claude translate my parents’ prescription instructions into Spanish, I used it to vet whether a book my kid wanted was age-appropriate, and I turned a dry geometry worksheet into right-triangle problems starring dinosaurs so a homework fight turned into a laugh. None of that mattered, and that’s exactly why it worked. The low-stakes reps are where you learn to tell a good answer from a weak one, and that judgment is what every level above this one stands on.

If you want to go deeper on this one move, I wrote a whole walkthrough on how one afternoon of setup gets you 90% better AI responses.

Level 2: Projects (Claude that remembers your world)

Plain chat has amnesia. Every new conversation, Claude wakes up knowing nothing about your company, your last decision, or the rules you spelled out yesterday. For low-stakes reps that’s fine. For real recurring work, that amnesia is a tax you pay in the five minutes you burn re-explaining yourself before you can ask the actual question.

A Project is how you stop paying it. It’s a workspace inside Claude that remembers. You load it once with the documents and the standing instructions for one slice of your work, and from then on every conversation inside it starts already up to speed. Think of it as guardrails on a road: it keeps one long stretch of work pointed the same direction so you don’t re-steer from scratch every morning.

The Project I lean on hardest is the one I write this newsletter in. Its knowledge holds two documents, my writing guide and a set of humanizing rules, and its standing instruction is one line: follow the guide, step by step, and don’t jump ahead until I sign off. That system took my article time from the better part of a day down to about 90 minutes. The setup tax went from “every time” to “once.”

Want the guided version of this climb, not just the overview? That’s the book, and it’s the one place I walk you up all four levels step by step. Premium members get the full 51-page Leading with Claude field guide free, plus the tested prompts, frameworks, and skill library. Become a premium member ($8/month or $49/year).

Level 3: Skills (work that sounds like you)

A Skill is a small, reusable package that teaches Claude how to do one specific job your way. You build it once, and from then on Claude recognizes when a job calls for it and follows your exact approach on its own, in any conversation. A Project keeps one road on course. A Skill is a tool in your pocket you can pull out on any road. The way I explain it: a Skill is the difference between asking a smart stranger at a conference for advice and asking your own COO. Both know business. Only one knows you.

You don’t write any code to build one. You describe how you do a job, then in Cowork (Claude’s desktop chat) you say “turn that into a Skill for me,” and Claude writes the whole thing. I run four businesses, and each one has its own brand Skill. When I ask for a one-pager to hand out at a conference, I never say which brand, Claude loads the right Skill on its own and uses the correct colors, voice, and messaging. I once built one live on a Substack session in a few minutes, an A/B title tester that takes any headline and returns three versions in the voices of writers I admire. The room watched a two-page prompt collapse into one sentence. I broke down five of the Skills I actually use in 5 Claude Skills That Save Me 40 Hours Monthly if you want the specifics.

Level 4: Claude Code (Claude that builds)

Every level so far happened inside a chat window. Claude Code (Anthropic’s coding-focused version of Claude) is different in kind. It’s an AI agent that doesn’t just talk about the work, it does it: it reads files, builds things, runs tasks, and connects to the apps you already use. You still talk to it in plain English. I named mine Alfred, because the moment you name something you start onboarding it like a teammate instead of poking at a tool.

I almost closed that black screen, the terminal (the plain text window where you type commands), the first time I opened it. Then I typed one sentence and the ceiling came off. Having never written code, I built the website for my coaching business, I built my own fitness app instead of renting one, and I moved my entire newsletter system into it. These days Alfred keeps a living document on how each business is doing, knows all four brands cold, and when a job is big it splits into a team of sub-agents, one writing code, one making PDFs, one running research, while I watch. I barely touch the terminal.

I’m not going to pretend this level is as easy as the first three. It rewards real time and study, and most leaders I work with don’t have months to learn a new craft on top of running their organization. If a seven-year-old can do the simple version (mine built an app in 12 minutes), you can cross the edge once. If this is the level you want built into your business properly, that’s the work I do one-on-one, and a free discovery call is the fastest way to start.

Find your starting line

You don’t have to start at Level 1. Find the line that sounds most like you right now, and start there.

If you’ve barely touched Claude, or just ask the occasional one-off question, start at Level 1, Basic Claude.

If you use Claude most days but retype the same background every time, start at Level 2, Projects.

If you’ve set up a Project or two but are still writing giant prompts to sound like you, start at Level 3, Skills.

If your Skills are humming and you want Claude to actually build things, start at Level 4, Claude Code.

Want a sharper read before you pick? I built a free assessment, the AI Leadership Compass, that shows where you sit on two axes: how you think about AI, and how much you’ve actually put it to work.

The whole climb, in your hands

That’s the map. If you want the guided version, the do-it-as-you-read walkthrough with a screenshot of every step, a small challenge at the end of each level, two of my own Claude Skills with the full prompts, and a four-week climb plan, that’s the book.

Get your e-copy for only $4.99: Get Leading with Claude.

Or become a Leadership in Change premium subscriber ($8/month or $49/year), and it's yours free, along with the full prompt and skill library, the frameworks, and everything else I publish: Become a premium member.

You don’t need to be ready for Level 4. You need one low-stakes rep at Level 1 tonight.

Which level are you actually on right now, and what’s kept you from climbing to the next one? Tell me in the comments, I read them.

Subscribe now

Joel Salinas is an AI Strategy Coach for leaders at small and mid-sized businesses and nonprofits. 1:1 coaching, team workshops, and AI strategy work built around amplifying what your team is already good at. Creator of the AI Leadership Triad. He writes Leadership in Change.

Written by a human, for humans.

Questions Leaders Are Asking

What are the four levels of using Claude? The four levels are Basic Claude (plain conversation done well), Projects (a workspace that remembers your context), Skills (reusable packages that do a job your way automatically), and Claude Code (an AI agent that builds and runs things for you). Each one builds on the level before it.

Do I need to know how to code to use Claude Code? No. Claude Code runs on plain-English instructions. I’ve never written a line of code and I use it to build websites, apps, and tools. You talk to it the same way you talk to the chatbot, and it does the technical work. There’s a real learning curve, but the entry point is one sentence.

What’s the difference between a Claude Project and a Claude Skill? A Project holds context for one slice of work and stays put inside that workspace. A Skill travels: it’s a reusable capability Claude carries into any conversation and uses on its own when a job calls for it. Most leaders use both, Projects to stay on course and Skills as the tools they carry everywhere.

Is Claude free, and which plan do I need for Skills and Claude Code? Claude’s Free plan ($0) covers basic chat, web search, memory, and basic Projects. Skills and Claude Code need a paid plan, starting with Pro at $20 per month. For most individual leaders, Pro is the level that opens the full climb.

Where should I start if I’ve barely used Claude? Start at Level 1 with something low-stakes from your everyday life, not work. Translate a document, plan a trip, vet a decision. The goal is comfort, not mastery. Those early reps build the judgment that every higher level depends on, and they take minutes.