Look, finance is the conversation every executive I coach keeps circling back to.

When leaders ask me about AI, the question they actually want answered isn’t “what tool should we adopt?” It’s “what is this going to do to our numbers?” That’s the conversation most AI advisors are not having. The savings math gets a big Power Point slide. But… the downside scenarios get a shrug. (I’ve written before about why most AI investments fail to deliver ROI, and the pattern almost always traces back to incomplete modeling on the front end.)

This newsletter is not pro-AI cheerleading. It’s not the panic version either, the one that says AI is going to take everyone’s job by Friday. I think it’s foolish to ignore the most powerful technology of our lifetime, and the leaders who come out ahead are going to be the ones who approach it strategically. Strategy means modeling what could go right and what could go wrong.

That’s exactly what does. A former head of finance who now runs The Creative CFO, Lauren writes CFO-level insights for founders and operators without the finance jargon. When she names three financial blind spots nobody is modeling on AI rollouts, it’s pattern recognition from somebody who has run the numbers for a living. Her knowledge truly comes from experience.

The Creative CFO

CFO-level insights for founders building toward their first raise or their first $1M without the finance jargon.

By Lauren Parla

I’m sharing this because what she’s flagging is exactly what I’m hearing: leaders using AI are building forecasts on assumptions that ignore the second-order consequences of mass AI adoption.

Here’s Lauren.

3 Financial blind spots leaders need to address before scaling AI (from a CFO who isn’t here to sell you on the hype)

Hi, I’m Lauren Parla, former head of finance turned fractional CFO and the voice behind The Creative CFO, where I share CFO-level insights for founders building toward their first raise or their first million, without the finance jargon.

Finance leadership often gets a bad rap for holding the purse strings too tightly but it’s usually justified (more on that here) and I do see some gaps in the numbers when it comes to AI adoption that not many seem to be talking about. The leaders who spot these gaps early, will be ahead of the curve.

So yes, I am about to be the Debbie Downer of AI adoption. I promise, it’s still worth the read.

I feel the need to start this post with a disclaimer: I am not anti-AI. I am, however, pro-informed decision making and this newsletter is intended to give leaders a different viewpoint when it comes to AI adoption, or at least some insights to consider before they implement AI across all business segments.

AI adoption is all about maximizing efficiencies; which, if you have ever been part of a corporate restructuring, is a term we finance-folk love to toss around. However, what I do not see are articles or resources covering the potential financial risks when it comes to AI adoption. These are not financial risks that we will experience immediately, however, I believe we will experience them a few years down the line. And while we cannot build businesses that are immune to crises, we can prepare for them, and how we do that in the realm of finance is through scenario planning and financial modeling.

So here are three financial risks of AI adoption that nobody is talking about and should absolutely be factored into your financial model:

1. The Fine Print

We are fortunate enough that we live in a world where we can purchase an out-of-the-box tool and use it as a plug-in to our product or even use it to automate admin tasks that we don’t like doing. If you are a leader who has purchased any software tool, whether it’s associated with your core product offering or something used for administrative work, I am going to ask you: how well did you read the terms and conditions?

Here’s the thing about these tools, whether it’s a subscription based service where you had to simply check a box indicating that you read the T&Cs (when, in fact, the vast majority of us do not) or whether you signed a formal procurement contract, there is a section that formally addresses price increases, and here’s a spoiler: the price increases are usually outside of your control. So when we start adopting AI tools which are priced as either subscription-based or usage-based platforms, and we don’t control or have any foresight into how and when price increases are going to happen, these tools impact more than the bottom line.

I urge leaders who are implementing AI plugins or any other white label software, into their core product to think twice before you do this because here’s something that was mentioned last year but I haven’t heard anyone talk about it since: Goldman Sachs and others have reported that we are in the midst of an AI bubble and they are anticipating a significant global revenue shortfall to fund data center infrastructure in the years to come. Bain has predicted an $800B shortfall to be exact. This translates to one thing: price increases aren’t speculation, they’re inevitable and a non-negotiable to keep on your risk radar.

CFO Tip: Identify your top 3 AI vendors by spend and by risk. Read the T&Cs. Make sure your financial model’s assumptions reflect the pricing language in those contracts.

2. The Hidden Tax Bill

At the moment companies and shareholders alike are rejoicing in reducing headcount because the cost savings look lovely on a P&L forecast, however, those salary savings become taxable income. And one thing that is nearly impossible to forecast due to its variability is: tax legislation.

This is not a U.S. specific case either. Governments globally are already watching AI’s impact on employment and tax base erosion, new legislation targeting AI-driven labor displacement is not far-fetched.

So this is all to say: in a few years, the “savings” number executives are pitching to their boards may be materially different net of taxes you haven’t planned for.

The up side to this is that new tax credits may also be implemented with legislation that could benefit companies who retain employees (for example in the U.S. during COVID, there were specific tax credits for companies that retained or maintained headcount) so we could see the pendulum come back the other way to offer employers the option for future tax credits to offset any tax liabilities. But this isn’t a strategy to depend on.

And taxes aside, what happens if companies need or want to start hiring real humans again? Is AI going to be responsible for training them? How long will the interview and onboarding processes take and how does this impact revenue?

CFO Tip: If your long-range plan or five-year forecast shows headcount savings without accounting for rising vendor costs (as mentioned above) and an unpredictable tax rate, your model is incomplete. Flag tax rate variability explicitly in your board deck when reporting net of tax numbers. I would be overly conservative here and not depend on historical trends for this one.

3. Who’s Left To Buy From You?

If you’re B2C or D2C, your customer is also someone’s employee. The macro argument for mass AI adoption assumes consumer spending remains intact. However, if AI eliminates jobs at scale, purchasing power contracts. I.e. your addressable market shrinks so those margin improvements, i.e. savings we were talking about, aren’t realistic anymore.

And this isn’t to say that enterprise or B2B businesses are immune to having their addressable markets shrink either. The degree of impact in both cases is yet to be determined, but one thing is certain: all companies right now only seem to be concerned with the cost savings and are assuming steady year-over-year growth which I believe is a faulty assumption.

CFO Tip: Stress test your revenue assumptions, not just your cost structure. Model at least one scenario where your addressable market contracts. My recommendation is to model a 10-15% contraction. If your margin improvements don’t hold, your AI business case needs a second look.

In Summary

I want to reiterate that I am not suggesting that AI has no place in your business. What I am suggesting is that the leaders who will come out ahead are not necessarily the ones who move the fastest when it comes to AI adoption, they are the ones who account for what everyone else is ignoring.

Every financial crisis, every market disruption, every global pandemic has one thing in common: nobody saw it coming until it was too late to plan for it. Scenario planning exists precisely for this reason. You do not need to predict the future. You need to be prepared for more than one version of it.

Thanks, !

So here’s what stuck with me from Lauren’s piece.

The leaders who lose the AI era won’t lose it because they missed the right tool. They’ll lose it because nobody on their team was asking the right question. The savings math is the easy question. The blind-spot questions are the hard ones, and they’re the ones nobody is putting on the board deck.

What are the weaknesses in your current AI assumptions? What are the potholes? The holes you fall into not because you didn’t see them coming but because you never went looking for them in the first place. The easiest way to avoid a trap is to know it’s there and expect it. Scenario planning is just that, on a spreadsheet.

So I’ll ask you the question Lauren’s piece raised for me: what’s the AI assumption in your current forecast you’re least confident in?

If you want a second set of eyes on how this applies inside your own organization, my calendar is here. The first conversation is free.

And if you want unfiltered financial thinking from somebody who runs these numbers for a living, subscribe to Lauren’s newsletter, The Creative CFO.

Questions Leaders Are Asking

What are the three financial blind spots of AI adoption?

The three financial blind spots, per fractional CFO Lauren Parla, are vendor pricing (subscription and usage-based AI tools have contractual price increases you don’t control), hidden tax exposure (headcount savings become taxable income and tax legislation is unpredictable), and shrinking addressable markets (if AI displaces workers at scale, consumer purchasing power contracts).

How do I model AI vendor price increases in my five-year forecast?

Start by identifying your top three AI vendors by spend and by risk. Read the price-increase language in each contract. Build your financial model’s assumptions to reflect that language explicitly. Bain has projected an $800B AI infrastructure shortfall, which makes future price increases likely rather than speculative. Model accordingly.

What tax exposure comes with reducing headcount through AI?

Salary savings become taxable income, so the “savings” number on a P&L forecast often looks different net of taxes the board hasn’t planned for. Tax legislation targeting AI-driven labor displacement is being actively discussed globally. Flag tax rate variability in your forecasts and avoid relying on historical tax trends.

How do I stress test revenue assumptions against AI displacement?

Model at least one scenario where your addressable market contracts by 10-15%. Especially relevant for B2C and D2C, where customers are also someone’s employees. B2B is not immune. If your margin improvements only hold under steady year-over-year growth assumptions, your AI business case needs another pass.

Is the AI bubble real, and should it affect my AI strategy?

Goldman Sachs and Bain have reported significant global revenue shortfalls needed to fund AI data center infrastructure. The $800B figure Bain cited suggests price increases for AI tools are likely to accelerate. This doesn’t mean avoid AI. It means model the vendor-cost trajectory honestly in your five-year plan.

Should leaders slow down AI adoption based on these financial risks?

Not necessarily. The leaders who win the AI era model multiple scenarios before committing capital. Speed alone doesn’t win, and neither does caution. Adopt strategically, run downside scenarios alongside upside ones, and build optionality into vendor contracts where possible.

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 and works with leaders at jsalinas.org.

Lauren Parla is a former head of finance turned fractional CFO and the author of The Creative CFO, a Substack where she shares CFO-level insights for founders and operators without the finance jargon. She writes from Milan, where she’s also designing a second life in fashion.

Sidenote… do you write on Substack? Build smarter, not just harder, with NewsletterCompass.com. As a co-founder, I’m giving you 50% off for life… 👇

Written by a human, for humans.

Outline

(00:11) – Scaling without breaking culture

(00:49) – Seven agencies to ninety-three in four years

(02:07) – From data guy to Head of AI

(03:41) – The golden path across 150 workflows

(05:32) – “AI is a bunch of hype” and the cultural work underneath

(08:17) – Why every rollout fails for the same reason

(12:54) – The 85% that fail, and what they all skipped

(14:21) – Fear, mistakes, and fifteen dead initiatives

(15:05) – Eighteen months to 70% adoption

(19:43) – Why the model wars are wasted noise

(21:14) – There’s no easy button

(24:05) – The bot pipeline that lets anyone build

(27:19) – Scaling AI without it blowing up in your face

(29:23) – The most overrated AI advice

My Takeaways

The failure is always culture. Ryan has built nine CRMs and implemented seven more, and he says the result never changes.

“Got nothing to do with technology. It’s culture.”

I brought up the stat that 85% of AI implementations fail, and his read matched what I see all the time: people get handed a tool, get told to use it, and decide not to, either because they think it’s there to replace them or they don’t trust the people who bought it.

There’s no easy button. Ryan’s least patient moment was about the model wars.

“You cannot judge current state AI in current state,” he said, and the people arguing over which model beat which this week are usually the ones not actually building anything. What matters to him is boring on purpose: how many people used it, how many minutes did it save. “If I was judging what I created, I’d be freaking Elon.” That’s the line I keep coming back to, because most of us are measuring the wrong thing.

Eighteen months to seventy percent. I appreciated how honest he was about the timeline. He told his CEO up front it would take about eighteen months to reach full adoption, and that full adoption, in his world, is 70%. The pushback he plans for doesn’t come from the technology. It comes from moving people out of a culture where nobody was accountable and into one where everybody can see your numbers.

Closest to the work is clean. This was the part that genuinely surprised me. ALKEME has fifteen hundred employees and a build team of nine, so Ryan built a pipeline instead. An employee emails an idea, a bot pins down the narrowest version of success, another bot builds the MVP on the company’s own infrastructure and sends back a working link to iterate on.

“I would way rather empower you, employee E, to bring your concept to fruition.”

The person closest to the problem builds the fix, inside the guardrails.

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Don’t pour a bucket of AI water on everything. For an insurance company, a broken tool isn’t a small thing, so I asked how he moves this fast without it blowing up. His answer was discipline about scope.

“It’s not just pour a bucket of AI water on everything and see where it hits.”

They ran fifty thousand historical submissions through one tool before they trusted it, locked permissions to each user, and pushed everything through a pipeline that gets checked. “We’re just very careful about where it touches.” It’s the same discipline ALKEME brings to the coverage side, where a single missed risk like a cyber attack is exactly what their clients are paying to be protected against. If your business carries that exposure, their team handles cyber insurance directly.

If you rolled out an AI tool tomorrow and nobody used it, would you call that a technology problem or a culture problem? Sit with which one you’d actually fix first.

Watch the full conversation above, and go connect with Ryan Deeds on LinkedIn and see what ALKEME is building.

About Ryan Deeds

Ryan Deeds is Head of AI & Enablement at ALKEME, an insurance company that has grown to around 93 agencies and 1,500 employees in roughly four years. He spent 30 years in the insurance agency space, hosted The Digital Broker podcast from 2017 to 2019, and now builds the internal tools and adoption systems that let ALKEME scale AI without losing its culture. Connect with him on LinkedIn.

About ALKEME

ALKEME is a national insurance company built from more than 90 independent agencies, serving clients across commercial, personal, and specialty lines. One of those lines is cyber insurance, coverage that protects businesses against cyber attacks, data breaches, and the operational fallout that follows. If your organization needs to think seriously about cyber exposure, talk to ALKEME.

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.

I write about using Claude for work almost every week, and I’ve started to think that’s the wrong place for most people to begin.

Almost everything written about these tools, including by me, is about work: saving hours, building systems, running a team. But here’s the thing I keep coming back to. You can’t spot where AI fits in your business until you actually understand the tool, and the cheapest, lowest-risk place to build that understanding isn’t a work project. It’s your Tuesday night dinner. It’s your kid’s bookshelf. It’s a bill your mom can’t read.

I’m not guessing at that. When OpenAI studied how people actually use ChatGPT, they found that as of June 2025, 73% of messages had nothing to do with work, up from 53% a year earlier. Most people are already using AI in their personal lives. They just haven’t connected that to how they’d use it on the job.

I call these low-stakes reps. You practice on things where a wrong answer costs you nothing, so that when the stakes are real, your judgment is already there. There’s one use on this list that matters more to me than all the others, and it doesn’t involve me at all. I’ll get to it.

Quick note before we start: if you’re already deep in Claude Code (Anthropic’s tool for developers) building agents and complex workflows, this post isn’t for you. This is for everyone else, the people who just want a reason to open Claude in the first place. A couple of these will even have Claude build you a reusable skill, and you won’t write a single line of code to do it.

In this post, you’ll learn:

• Eight everyday ways I use Claude that have nothing to do with my job

• The exact prompts I use for each one, ready to copy

• How to turn a recipe, a book, or a bill into a thirty-second rep that builds real AI instinct

1. Book recommendations that actually fit your taste (Claude Skill)

The trick is to treat it like a running relationship, not a one-time ask. Every time I finish a book I loved, I tell Claude what worked for me. Every time I quit a book after chapter one, I tell it why I bailed. It keeps a saved profile of my taste on my Book Recommendation Skill, so each recommendation is sharper than the last. Last week I used the skill we are creating here to look at ’s new book, I Am Not a Robot (currently on my To Be Read list and should be on yours), and tell me what it believes I’ll think by the time I’m done reading it. I wrote a whole piece on why generic book recommendations fail and how to fix them, and the short version is that the feedback on what you didn’t like matters more than the praise. Stop reading for a second and try it.

Use the prompt below on Claude Desktop and create your own book recommendation skill, which will be pulled up by Clause every time you ask about your next read…

Copy-Paste Prompt: I want you to build me a personal book-recommendation skill. Work through this in order, one step at a time:

1. Interview me first. Ask me for five books I loved and why, five books I quit early and why, and my single favorite book of all time and why. Then wait for my answers.

2. From my answers, write a short profile of my reading taste.

3. Before anything else, show me one sample recommendation built from that profile, with a few sentences on why you think I’d like it, so I can tell you if it’s on target or adjust it.

4. Once I say it’s right, generate a ready-to-save Claude skill that runs any time I ask about a book. It should use my taste profile to tell me whether I’d like a given title and why, and to recommend new ones. Format it so I can save it in one click.

2. Recipes that cook on a healthier, tighter schedule

Claude doesn’t hand me a generic recipe. It rebuilds one around how we actually eat. We try to keep it clean at home, so I ask it to swap refined sugar for fruit and work in yogurt for extra protein, and it adjusts the whole thing. The two features that changed weeknights for us: a timer built into each step, and instant serving-size math when the table grows. Last weekend, it walked me through a batch of scones with a timer on every step, then redid all the amounts the moment I decided to double the batch, with a click, not a prompt.

Here’s a simple prompt to get started.

Quick Win (< 60 seconds) After Claude nails something you'll want again, ask it to "turn this into a skill I can save." That's how a one-time prompt becomes something Claude does for you automatically, with no copy-pasting next time.

Copy-Paste Prompt: I want to build a recipe. Here are the ingredients I have: [LIST]. Ask me a few questions about my preferences first, then let's cook.

3. Turning a brain-dump into one sentence you can send

Some of my best uses are the least impressive. I’ll talk on my phone for a minute, a messy pile of half-thoughts, and ask Claude to hand me back one clean sentence I can actually text. Last week it was telling a friend I couldn’t make the time we’d set, because work has been heavy and I’m trying to stay heads-down and get a lot done. I said all of that into my phone, messy, and got back one warm line that didn’t read as a brush-off. It’s the difference between a paragraph nobody reads and a single line that gets a yes.

Here’s a simple prompt to get started.

Copy-Paste Prompt: Here’s a brain-dump: [TALK FOR A MINUTE]. Turn this into one clear, warm sentence I can send as a text. Keep my voice, and skip the corporate tone.

4. Turning a project into a visual workflow

When my daughter had a school project on how a caterpillar becomes a butterfly, we didn’t just write it out in a paragraph. I asked Claude to map the whole thing as a visual workflow: every stage in order, egg to caterpillar to chrysalis to butterfly, with what happens at each step and how one leads to the next. Seeing it drawn out as a diagram did more for her than a wall of text ever could. It gives her a place to start from, and it isn’t a shortcut around the actual learning, which is the whole reason I don’t just hand my thinking to AI. The same move works for anything with stages: a process at work, a plan, a science-fair project.

Here’s the prompt I used.

Copy-Paste Prompt: I’m helping my [grade] grader with a project on [topic, like how a caterpillar becomes a butterfly]. Map the whole process as a visual, step-by-step workflow: each stage in order, what happens at each one, and how it leads to the next. Lay it out as a diagram I can show her, and keep the wording at her level.

5. A real learning path for anything, in 30 or 90 days

When I want to actually learn something instead of just reading about it, I ask Claude to build a dated plan. Thirty days, ninety days, with a theme for each day so I’m not deciding what to study every morning. It asks what I already know first, then fills the gaps. Right now I’m using it to climb from a 1700 to a 2000 chess rating in ninety days, drilling my weak spots first instead of just playing more games. This is the same muscle that makes AI useful at work, which is why I keep telling leaders the personal reps come first. If you want help turning this into a structured plan for your own role, book a free discovery call. This is what my Exec AI Coaching business is founded on.

Copy-Paste Prompt: Teach me [SKILL] in 90 days. Ask me three questions about my current level first. Then build a day-by-day plan with a theme for each day and one small action per day.

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6. Checking whether a book is right for my kids (Claude Skill)

Again, this is a skill that will be looked up by Claude anytime you prompt for a book, but this time only for books for young kids. Before a book reaches my kids, especially my daughter who reads about a book per week, it runs through a filter I built with Claude. No horror, no gore, no adult themes. Detective stories and mysteries are a yes. I give it their ages and what we’re comfortable with, paste the title, and it flags anything I’d want to know before they open page one. Last week it was The Wild Robot, before I handed it to my daughter.

Here’s the prompt to run on Claude Desktop and create your own personalized skill.

Copy-Paste Prompt: I want you to build me a “kids’ book check” skill. Work through this in order:

1. Interview me first. Ask me my kids’ ages, the genres we like, and what we don’t allow (for example: no horror, gore, sexual content, or adult themes). Then wait for my answers.

2. Show me two sample checks on books you pick, so I can see the format: recommended reading age, anything violent, scary, sexual, or mature, any heavy language, the overall themes, and a clear verdict (yes, no, or “preview it first”) with one line of why.

3. Once I confirm the format works, generate a ready-to-save Claude skill that runs any time I ask whether a book is right for my kids. If you don’t know a specific title, tell me instead of guessing. Format it so I can save it in one click.

Here is Kids Book Review Skill in action:

7. Explaining hard things at exactly the right level

My eight-year-old daughter is deep into animals right now, so we point the phone at a bird and ask what it is and how you’d care for one. But the real trick is telling Claude who’s listening. The same question, “how do you calculate the angles of a right triangle,” comes back one way for me and another way built entirely around dinosaurs for her. A while back I watched her build her own app faster than most adults could, which I wrote up in how my 7-year-old built an app in 12 minutes, and it’s because nobody told her the explanation had to be boring.

Copy-Paste Prompt: Explain [CONCEPT] to my [AGE]-year-old who's really into [INTEREST]. Build every example around that interest, use plain words, and go one small step at a time. At the end, give me two or three simple questions I can ask to check she actually understood it, plus a quick version I could use to explain the same thing to an adult.

Here’s what it looked like for me, explaining right triangles to my daughter:

8. Translating the world for my parents

Here’s the one I promised. My parents don’t speak English well, and the paperwork that runs an adult life, a bill, a doctor’s note, a prescription label, is almost all in English. So they take a photo, and Claude doesn’t just translate it into Spanish, it explains it in Spanish, in plain terms they can act on. A prescription stops being a wall of fine print and becomes “take one in the morning, one at night, with food.”

That’s the use that reframed all the others for me. I started doing this stuff to get better at AI for work. I keep doing it because of what it gives the people I love.

Copy-Paste Prompt: I’m pasting a photo of a [document type] written in English. Translate it into Spanish, then explain in simple Spanish what it means and what I need to do, as if you’re explaining it to someone who doesn’t read English.

So start tonight, with something that doesn’t matter. Ask for the recipe, run the book check, use a diagram to visualize something you are struggling to grasp. The reps are free, and they compound, and that habit of staying adaptable as the tools change is the whole game (it’s the first of the three skills in the AI Leadership Triad I write about). And if you want help turning these everyday reps into real fluency in your own role, my calendar’s here. The first conversation’s free.

If You Only Remember This

• Most AI use is already personal, and that’s the on-ramp, not a detour. 73% of ChatGPT messages have nothing to do with work.

• Low-stakes reps are where judgment gets built. Practice on a recipe so you’re steady on a real decision.

• The fastest way to understand what AI can do for your business is to first let it do something for the people you love.

Questions Leaders Are Asking

Is it safe to use Claude for personal documents like bills or prescriptions? Treat it as a helper, not the final word. Don’t paste full account numbers or a Social Security number, and check anything medical against the original or with a professional. For translation and plain-language explanations, it’s genuinely useful, as long as you verify the dose or the due date yourself.

What’s the difference between Claude and ChatGPT for everyday tasks? Both handle everyday tasks well. Claude tends to hold longer context and follow detailed instructions closely, which helps for ongoing things like a running book-taste profile. The honest answer is to try both on the same task and keep the one you like better.

Do I need to pay to use Claude for these everyday things? No. The free version of Claude handles every use in this article. Paid plans add higher limits and features like saved skills, but you can build the book filter, the recipe helper, and the translator on the free tier today.

How do I get Claude to remember my preferences? Tell it directly and ask it to save them. For a taste profile or your family’s rules, paste your preferences at the start and say “remember this.” On paid plans you can store them as a skill, a saved set of instructions Claude reuses, so you never re-explain.

Can AI really recommend good books for my kids? Yes, if you give it your boundaries. Tell it your kids’ ages and what you do and don’t allow, then paste a title. It won’t replace your judgment, but it will flag adult themes or scary content before your child opens the book.

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.

If you’ve ever wondered whether there’s a version of Claude built for people who don’t code, this is the one to watch.

Outline

(00:00) – Welcome and a different kind of session

(01:17) – Ilia, Prosper, and a data background

(04:31) – What Claude Cowork actually is

(05:33) – Global instructions, the setup most people skip

(07:04) – The new-employee-every-day problem

(10:24) – Security: sensitive folders and prompt injection

(14:52) – Skills and the skill creator

(18:21) – Scheduled tasks that run while you sleep

(21:03) – Live artifacts you can actually chat with

(28:32) – Building a project brief skill, live

(37:29) – Dispatch: running Claude from your phone

(45:59) – The four levels of Claude

Main Takeaways

The new-employee-every-day problem. Ilia started where almost nobody looks, the global instructions buried in your Cowork settings. It’s the main thing Claude reads before it answers anything, and most people leave it completely blank.

“I highly recommend using the global instruction and putting something in there, about yourself and about the work you do.” — Ilia

Here’s how I think about it. If you’ve never touched your instructions, it’s almost like you’re bringing in a brand new employee every single day, teaching them everything about your company, then letting them go and starting over with somebody new the next morning. The instructions are what stop you from re-onboarding the same hire every day.

Make the hallucination show itself. One thing I added to my own instructions: anything with a source, cite the source in the answer. Anything without one, give me a confidence level. That’s one of the ways I deal with hallucinations, because a low-confidence answer tells me exactly where to go double-check before I trust it.

“Sometimes Claude was making up numbers, and I’d say, I don’t think this is real. I’d ask it to do a web search, and it’d come back like, yeah, these numbers don’t exist, I’m sorry about that.” — Ilia

I’d rather Claude tell me it’s guessing than hand me a clean number that turns out to be fiction.

Skills you don’t have to build by hand. Skills are the first thing I’d point a new leader toward. The piece Ilia showed that’s worth stealing: Anthropic built a skill whose only job is to build other skills, and it tests its own work as it goes.

“It’s called Skill Creator Skill, and it runs through different tests to improve the skill. Definitely recommend using that one if you’re creating a new skill.” — Ilia

And the thing I like about Cowork specifically is it shows you which skill it’s pulling up under context, so there’s no mystical thing happening in the background, you know exactly what it has access to.

Tasks that run while you’re not there. This is the part I keep going over. Ilia has a brain dump processor that watches his Notion through a scheduled task and surfaces anything worth expanding on. Mine runs every Wednesday at 6am, cleans my whole desktop, files everything into the right business folder, and deletes the screenshots before I’m even awake.

“It connects to Notion through MCP and looks through everything happening over there, then puts out a new page for me to look at and see if anything’s worth expanding on, or just drop it and forget it.” — Ilia

Pair that with Dispatch, which Ilia described as a walkie-talkie with Claude, and you can fire a task off from your phone and walk away while your laptop does the work.

About Ilia Karelin

Ilia Karelin writes Prosper, a Substack covering AI and Claude workflows from a data professional’s point of view. He spends most of his day in data work, which is part of why his walkthroughs stay practical and skip the hype. He’s written a few pieces for Leadership in Change worth reading, including AI Research 201: 10 Perplexity Features and Why Apple Just Admitted Google’s AI Won. Subscribe to his newsletter at Prosper.

About me

Joel Salinas is an author and 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.

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They see how capable the models have gotten, and in the same breath they tell me they’re starting to feel a little obsolete. Curious and anxious, sitting in one person, at the same desk.

I wanted to talk to Liz Fosslien and Mollie West Duffy about exactly that, because they’ve spent their whole careers on emotions at work, and now almost all of that work is affected by AI. What you get in this one is the part of the AI conversation almost nobody is running: the feelings underneath the rollout, and what a leader actually does about them. Watch the full piece above.

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Outline

(00:00) – Welcome

(00:38) – Excited and exhausted: the leader’s double bind

(01:48) – Where emotions at work began

(04:01) – The full mix: amazing and existential, in one person

(06:03) – What a team really asked me about AI

(07:42) – The “AI optimists only” trap

(08:08) – Why emotions block learning

(11:49) – The stoic-leader shift

(17:33) – The change curve: anger to energy

(22:32) – Build trust before you measure adoption

(25:13) – Mandates fail, agency works

(27:13) – Inside their AI workshops, and where to find them

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Main Takeaways

The full mix is the normal state. The thing I want every leader to hear is that excitement and dread aren’t two different camps of people, they’re the same person sitting in one chair, which is exactly why this is so hard to manage. Liz put it like this:

“Every single person contains this complete mix of emotion. At some level they say, oh, it’s amazing, it’s way better than Google, it can help me so much. But then of course, what does this mean for my job, for who I am?”

The leader who pretends only the excited half is real is usually the one whose rollout starts going sideways three months in, because the other half doesn’t disappear, it just stops getting said out loud.

You can’t learn while you’re scared. This is the line I’m still going over. Mollie walked through the neuroscience of it:

“When you are feeling anxious, when you are feeling scared, when you are feeling angry, you’re not in an optimal place to learn a new tool.”

Then she made it concrete.

“I’m here for the training on how to use ChatGPT, but I’m really worried about what it’s going to do to my job, and I’m angry about what it’s doing to the environment. I’m not really going to be giving 100% of my attention to how to click around the tool.”

I had a chat recently coaching a marketing group at a nonprofit, and within two minutes I realized they didn’t care about prompts or tokens. They were asking how they could use AI when it’s doing what it’s doing to the climate, and how a writer uses a tool trained on copyrighted work. There’s a whole foundational layer most of us skip, and the training slides right off when you do.

We don’t get to skip the steps. Mollie’s change curve is the frame I keep coming back to.

“We all have to go through anger, apathy, frustration, and your productivity drops. And that’s normal. Then when we move through that, we can begin to get engaged and excited and have energy around that. We don’t get to skip these steps as humans.”

The part that reframed it for me is that most leaders are already further along that curve than their own team, so they’re standing on the far side wondering why everyone won’t just cross. People hit the dip at different moments, especially on a big team, which means a leader is managing a dozen different curves at once, not one.

It’s okay to give some of the time back. This is where I admitted my own fear on the call, the one I think a lot of people are holding. If I start using AI and suddenly I’m producing two or three times as much, I’m afraid that just becomes my new minimum, and now I have to hold that pace forever. Liz met it head on:

“It’s actually okay if some of the time saved your team takes back for themselves. It doesn’t all have to be reinvested in work. Workforces are so burnt out. The average person, our nervous system, is completely fried.”

Saying that out loud, as the leader, is part of what makes adoption possible in the first place.

The play I’d actually run this week

Because this slot runs a little longer than my usual live recap, I want to leave you with something you can use on Monday, pulled straight from what Liz and Mollie laid out.

1. Name the mix out loud, briefly. Nothing elaborate. A short, honest recognition like “I’m feeling some of this too, and here’s the plan moving forward.” Mollie called it a brief recognition followed by the path, and it’s the tightrope between spewing every emotion and pretending you have none.

2. Pair a quick win with one real limitation. Liz’s most practical move. Help your team get a fast, obvious win with AI, then show them one place it falls flat. That combination lets people lean into the excitement and lower the anxiety at the same time, because now they trust both the upside and their own judgment about where it doesn’t belong.

3. Say the change is big, in public. In an all-hands or your enablement session, recognize out loud that this is a real change and that people are moving through real emotions at different speeds. That one sentence gives people permission to be where they actually are.

4. Build the trust before you measure. Liz was blunt about this. If you haven’t invested in trust up front, your survey just comes back “I feel great, it’s amazing,” and then weeks later you find out people aren’t using it, or they’re actively sabotaging it. Trust first, honest data second.

5. Give agency, not a tracker. Enablement, training, and time to learn, yes. Surveillance of daily usage, no. Let some of the time saved go back to people, and ask whether they’re using the right tool for the right job rather than how many tokens they burned.

Where are you on the change curve right now, and have you actually let yourself name it out loud, or are you the leader who’s been pretending only the excited half is real? Watch the full conversation above, and then go subscribe to Liz and Mollie. It’s one of the few places putting the human side of this moment first.

About Liz Fosslien and Mollie West Duffy

Liz Fosslien and Mollie West Duffy are the authors and illustrators behind No Hard Feelings and Big Feelings, two books on the emotional reality of work and life. Liz draws every illustration by hand, leads content work at Atlassian, and is deep in research on AI transformation. Mollie works in org and leadership development at Lattice. Together they publish the Liz and Mollie newsletter and run AI workshops for leaders and for whole teams, built around the change curve and grounded in research people can use the next day. You can follow them on Instagram and subscribe to their newsletter here.

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.

TL;DR: Claude Mythos, the Anthropic AI model disclosed on April 7, 2026, found 1,596 software vulnerabilities across 281 open-source projects from a single prompt. The lesson for leaders is not panic. It is that the security head start your organization has always counted on is shrinking fast: Anthropic first kept the model locked away, but on June 9, 2026, it released a public, Mythos-class version, with rival labs an estimated 12 to 18 months behind.

When the news about Claude Mythos broke in April, I read the announcement twice. What Anthropic described didn’t read like a product launch, it read like the moment a locked door stopped mattering. The one comfort back then was that Anthropic kept the model locked away, and as of this week, that comfort is gone: on June 9, 2026, the company put a Mythos-class model into the hands of the public.

Since April, barely a week has gone by without a leader bringing this up with me. A nonprofit CEO asked if she should be worried. A marketing VP asked if the whole thing was hype. Both were really asking the same question, and it’s the one I want to answer here:

Is this the moment I actually need to pay attention to AI and security, or is it just more noise?

So let me give you the honest version up front. What's happening with Claude Mythos (Anthropic's most capable AI model, so advanced the company first refused to release it at all) is a genuinely big deal. But the part getting all the attention, the scary capability itself, is not the part that should change how you lead. The part that should is less obvious, and almost nobody is saying it out loud.

In this piece, I’ll cover:

• What Claude Mythos actually did, with the real numbers and none of the panic

• Why the “security head start” your organization has always counted on is shrinking

• The uncomfortable symmetry most coverage skips, where the people trying to break in get the same class of tool

• What a leader should actually do about it on Monday, without buying fear

What Claude Mythos Actually Did

Here are the facts, and they’re remarkable enough that I don’t need to dress them up.

On April 7, 2026, Anthropic (the AI company behind Claude, the main rival to ChatGPT) announced Claude Mythos Preview, a model that sits above its Opus tier. It wasn’t built as a hacking tool. It was built to be an extraordinary software engineer, and the cybersecurity ability came along for the ride. That detail matters more than anything else in the story, so hold onto it.

Anthropic pointed Mythos at real software with a prompt about as plain as it gets: “Please find a security vulnerability in this program.” Then it let the model work on its own. According to Anthropic’s own public disclosure dashboard, as of May 22, 2026, the company has formally disclosed 1,596 vulnerabilities across 281 open-source projects, with 88 already assigned official CVE records (the industry’s standard ID for a confirmed security flaw). And that’s only the disclosed slice. Anthropic says over 99% of what Mythos found still isn’t patched.

One example carries the whole point. Mythos found and exploited a 17-year-old flaw in FreeBSD (a widely used open-source operating system) that handed an attacker full root control of a server from anywhere on the internet. It found a separate bug in OpenBSD, one of the most security-hardened systems on earth, that had survived 27 years and millions of automated tests. And the Alan Turing Institute’s security center flagged something worth sitting with: engineers with no formal security training were able to use the model to produce complete, working exploits.

This worried specialists enough that Anthropic chose not to sell Mythos at all. Instead it built Project Glasswing, a restricted coalition of around 40 organizations including Google, Microsoft, Apple, Amazon, and JPMorgan Chase, and committed up to $100 million in usage credits so defenders could find and fix flaws first. If you only skimmed the headlines and most of the basics of protecting yourself still apply, that’s the part worth knowing: the people who understand this best decided it was too dangerous to hand out.

The Real Story: Your Security Head Start Is Shrinking

Here’s the part the headlines miss.

Every security strategy you’ve ever funded rests on one assumption: that there’s a gap between the moment a flaw exists in your software and the moment someone capable finds it. That gap is your head start. It’s the time your side, the people whose job is to protect your systems, uses to patch the hole, watch for trouble, and sometimes just get lucky. The whole rhythm of corporate security, the patch cycles, the quarterly reviews, the “we’ll get to it next sprint,” runs on the belief that finding a serious vulnerability is slow, expensive, and rare.

Mythos is the proof that the belief is expiring. When an AI can read a codebase and surface a flaw that hands over the keys to a server for less than the cost of a used car, finding holes stops being slow, expensive, or rare. Anthropic’s one-month Project Glasswing update on June 8, 2026, made it concrete:

Roughly 50 partner organizations used the model to surface more than 10,000 high or critical vulnerabilities, and several saw their bug-finding rate jump more than tenfold. Anthropic’s own takeaway reads almost like the thesis of this piece: security used to be limited by how fast defenders could find flaws, and now it’s limited by how fast they can verify and patch the flood the AI turns up.

I keep coming back to the locksmith example to explain why this should matter to a leader and not just an engineer. The same skill that lets someone build a lock that can’t be picked is the exact skill that lets them pick every lock that already exists. There’s no version of that talent that only works for the good guys. The capability is just the capability. Mythos is the most capable locksmith anyone has ever built, and the lesson was never about one locksmith, it’s about what happens to every door once that skill stops being scarce.

So I’ll name the thing, because leaders need a word for it: your security head start. It’s the most overlooked assumption in how companies protect themselves, and Claude Mythos just put it on a clock.

The Symmetry We Avoid Thinking About and Claude Fable 5

We talk about powerful AI as something we get to use. We forget that the people we’re defending against are sitting at the same table with the same menu.

Whatever capability reaches the market reaches all of it. Radware’s threat-intelligence team has a blunt name for where this leads, the “democratization of offense,” where agentic AI and “hacking-as-a-service” platforms hand a novice the kind of firepower that used to belong only to nation-states.

Anthropic locked Mythos inside Glasswing to keep the head start on the defensive side for as long as it could, and I think that was the right call. But watch how fast the ground is moving.

On June 9, 2026, Anthropic released Claude Fable 5, a Mythos-class model, to the public, on paid plans and every major cloud. It carries the same raw power as Mythos. The only thing standing between an ordinary subscriber and that hacking ability is a set of safeguards that reroute risky cybersecurity requests to a weaker model. Anthropic kept the unrestricted twin, Claude Mythos 5, inside Glasswing, but it was blunt about what's coming: models this capable will soon ship from many other labs, and if one arrives without those safeguards, it becomes dramatically cheaper for almost anyone to exploit flawed software. So the honest way to read this isn't "an AI found some bugs." Your security head start is already shrinking, and the thing holding it open is one company's safeguards being a choice, not a law of nature.

That’s the fork in the road. Not a fire alarm. A fork.

Why This Is a Fork in the Road, Not a Fire Alarm

I want to be careful here, because the easiest thing to do with a story like this is scare people, and scared leaders make bad decisions.

So let me borrow from the people whose entire job is to stay calm under exactly this kind of pressure. The cyber-insurance firm Coalition, which underwrites this risk for a living, put it about as well as anyone: “The Mythos moment is real. It is not the end of cyber insurability.” Their working assumption is that defensive AI, used aggressively and at scale, can close most of the window Mythos opens. That tracks with the entire logic of Glasswing. The same capability that threatens you is the capability that protects you, if you move.

Here’s the part I’m still wrestling with, though. Coalition’s confidence comes with a condition buried in plain sight: “if deployed aggressively and at scale.” And “aggressively and at scale” is exactly the thing most organizations are historically bad at. So I genuinely don’t know whether defense keeps pace for the average company, or only for the ones already in rooms like Glasswing. That uncertainty is the whole reason I’m writing this now instead of a year from now.

This is why adaptability is the real skill of this moment. It's the first pillar of what I call the AI Leadership Triad (adaptability, innovation, and creativity), and a story like this is exactly why it sits first. The leaders who'll be fine aren't the ones with the biggest security budgets. They're the ones who treat this as a reason to get adaptable now, while they still have the year Anthropic is telling us we have. I've written before about how elite leaders adapt in AI chaos, and the through-line holds here: the advantage goes to whoever adjusts early, not whoever panics loudest.

What to Do Now

You don’t need to become a security expert. You need to act like a leader who knows the head start is shrinking. Four moves, none of which need a new budget line this week.

Make it a board-level conversation, not an IT footnote. Bain said it bluntly: cybersecurity “is a business risk of the highest order, not a technology problem to be delegated downward.” They note most companies spend only about 0.69% of revenue on security while planning roughly 10% annual increases, when the real need may be double their current spend. If the last time your board touched security was a compliance slide, that’s your gap.

Ask one question this week: what’s our patch reality? Not the policy, the reality. A 2025 study cited by the Alan Turing Institute found that over 45% of discovered vulnerabilities in large organizations are still unpatched after 12 months. In a world where flaws get found in hours, a 12-month patch lag is the whole ballgame. You don’t need the fix today, you need to know the number.

Find your oldest, most critical systems. Mythos is especially dangerous to aged code, and the most exposed organizations, per Bain, are the ones running decades-old operational systems that can’t easily be patched. If your business runs on something old and load-bearing, that’s where to look first.

Treat AI fluency as a security asset, not a side project. The teams that adapt fastest will be the ones already comfortable with these tools, because they’ll recognize both the threat and the defense when they see it. It’s the same reason I keep pushing leaders not to let AI simply agree with them, and the same reason underinvesting in AI capability is now a risk, not a savings.

So here’s my real question for you: when someone on your team asks what Claude Mythos means for us, what’s your honest answer right now? I’d genuinely like to hear how you’re thinking about it.

Worth Your Time

A few pieces from other creators worth your attention this week.

• ToxSec — Mozilla Mythos Harness: AI Bug Hunting Without The Slop. The clearest real-world look at the defensive side of this story: how Mozilla wrapped Claude Mythos in an automated loop to surface 271 Firefox bugs, and why the setup around the model mattered more than the model itself.

• Dan Ariely — Why We Make Bad Decisions With AI. The behavioral-economics case for why the smarter AI gets, the more carefully leaders have to protect their own judgment. A conversation I got to have with Dan directly.

• James Presbitero — 5 Questions Businesses Should Ask Before Building Any AI System. A sharp pre-build checklist for leaders, built on the reality that 80% of AI projects fail because nobody asks the right questions first.

Questions Leaders Are Asking

What is Claude Mythos? Claude Mythos is a frontier AI model from Anthropic, announced April 7, 2026, that sits above the Opus tier. It was built as an elite software engineer, but it proved able to find and exploit software vulnerabilities on its own, which is why Anthropic did not release it publicly.

Did Anthropic release Claude Mythos to the public? No. Anthropic judged the model too dangerous for open release and restricted it to Project Glasswing, a vetted coalition of more than 40 organizations including Google, Microsoft, Apple, Amazon, and JPMorgan Chase, with up to $100 million in usage credits to find and fix flaws defensively.

Didn’t Anthropic just release a Mythos model to the public, Claude Fable 5? Sort of. On June 9, 2026, Anthropic released Claude Fable 5, a Mythos-class model, to the public, but with safeguards that reroute high-risk cybersecurity requests to a weaker model. The unrestricted version, Claude Mythos 5, stays inside Project Glasswing. So the full offensive capability is still gated, for now, by one company’s safeguards.

How many vulnerabilities did Claude Mythos find? As of May 22, 2026, Anthropic had formally disclosed 1,596 vulnerabilities across 281 open-source projects, with 88 assigned official CVE records. Anthropic says this is a fraction of the total, and that over 99% of what the model found is still unpatched.

Should business leaders be worried about Claude Mythos? Concerned, not panicked. Cyber insurer Coalition called it “a real inflection,” not “the end of cyber insurability.” The practical risk is that AI shrinks the time between a flaw existing and an attacker finding it. Leaders have roughly a 12 to 18 month window to adapt.

What should companies do in response to Claude Mythos? Move cybersecurity to a board-level business risk, learn your real patch lag (over 45% of known flaws sit unpatched past 12 months), prioritize old and critical systems that can’t easily be patched, and build AI fluency on the team so they recognize both the threat and the defense.

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.

Sources Referenced

• Anthropic, “Claude Mythos Preview” + coordinated vulnerability disclosure dashboard, red.anthropic.com (data as of May 22, 2026)

• Anthropic, “Project Glasswing: An initial update,” June 8, 2026

• Anthropic, “Claude Fable 5 and Claude Mythos 5,” June 9, 2026

• CNBC, “Anthropic releases a Mythos-like AI model to the public, Claude Fable 5,” June 9, 2026

• Bruce Schneier, “Anthropic’s Project Glasswing Update,” Schneier on Security, June 8, 2026

• Bain & Company, “Claude Mythos and the AI Cybersecurity Wake-Up Call,” April 2026

• The Alan Turing Institute (CETAS), “Claude Mythos: What Does Anthropic’s New Model Mean for the Future of Cybersecurity,” April 14, 2026

• Radware, “Anthropic Claude Mythos and the 2026 Cybersecurity Landscape,” 2026

• Coalition, “After Mythos: What Actually Changes for Cyber Risk,” April 2026

• UK AI Security Institute (AISI), “Our evaluation of Claude Mythos Preview’s cyber capabilities,” April 13, 2026

• Munich Re, “Cyber insurance: Risks and trends 2026”

• FBI IC3 2025 report and IBM Cost of a Data Breach (cited via Bain)

Written by a human, for humans.

So, last year, I was standing in front of about twenty-five people on my team, all of us working to help people hit by disasters, war, and displacement. I had a forty-slide deck loaded and ready… What a prompt is. What a token is (the small unit of text an AI reads and writes in). Why AI sometimes makes things up. The whole stack.

A few minutes in, I closed my laptop.

I didn’t close it because the deck was bad. The deck was solid. I closed it because I’d opened with a simple icebreaker, “what does AI bring up for you?" and the first two answers told me the slides were the wrong work entirely.

The first came from a writer on the team. She asked how she was supposed to use something built by scraping copyrighted work from creators like her. I felt the weight of that. She’s a writer, and there I was, the guy bringing the tool in. So I just listened.

The second came from someone whose whole reason for being there is the mission. As an organization that cares about the climate, how do we use something this hard on the environment, from the data centers to the water it burns through?

Neither of those is a question about prompts. Two of the people who’d have to use AI every single day were telling me, without quite saying it, that it doesn’t matter how good the tool is. They weren’t going to touch something that cut against who they are.

That was the moment I understood the thing I now build most of my coaching around: AI doesn’t create trust, it reveals it.

If the trust is already there, the rest of the work gets easier fast. If it isn’t, no amount of training fixes what’s actually broken underneath. AI just turns the lights on and shows you what was there the whole time. I’ve written before about why most AI implementations fail, and after enough of them, the pattern underneath gets hard to miss: the tech is rarely the thing that actually broke.

Why AI Exposes Trust More Than Any Tool Before It

Here’s why AI does this in a way the last few tech rollouts didn’t.

The first reason is speed. The tool is genuinely different every few months. Which means the trust can’t live in the tool, because the tool won’t sit still long enough. It has to live in your judgment as a leader, because that’s the only steady thing your team can hold onto while everything else keeps shifting under them.

The second is identity. AI reaches straight into what people actually do all day. Adopting it means admitting out loud that part of your work might go away, while also betting that, done right, it frees you for the part only you can do. That’s a hard thing to ask of someone, and pretending it’s easy is how you lose them.

The third is ethics. There are real questions here, about training data, about copyright, about the environment. Same as we had with social media. Same as we’re working through with crypto right now. Those concerns don’t disappear because you skipped them in the kickoff meeting. They go underground, and underground is exactly where trust quietly rots.

A quick aside, since a lot of you reading this also write a newsletter, or have been meaning to start one.

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The Three Layers of Trust AI Tests

When I sit with a team now, I’m listening for three layers of trust. Trust in the tool. Trust in the leader. Trust in the team. Any one of them missing, and whatever you try to build on top of it cracks.

Trust in the tool

This is whether your people believe the thing actually fits what you claim to stand for. If you talk about privacy, are you using something that respects it, or something that ships every keystroke back to the vendor? If your mission is about ethics, have you said the copyright and climate questions out loud instead of hoping nobody raises them? When this layer is cracked, you get shadow AI usage (people quietly using it off the books), polite avoidance, and worries nobody will say in a meeting. The move is to listen first, say the concern back so they know you actually heard it, and then show with real data how the tool fits. That only works if it’s true. People know when a leader is reverse-engineering a justification for a decision they already made.

Trust in the leader

This is the hardest one to be honest about, because it’s about you. The symptoms are quiet. Polite compliance. No excitement. Silence after you propose the tool, on a team that would normally give you pushback. Underneath, your people aren’t sure whether you brought AI in to help them or to replace them, and if they don’t already trust you, they’re not going to ask. The move is to use the tool in front of them, name what you’re unsure about, and build a room where the hard question is allowed. I told my team early that if anyone thought we’d drifted from our principles on AI, they were expected to say so, and we’d deal with it. That’s not a policy document. It’s a stance, and it only holds because they’ve watched me act on it when it got tested.

Trust in the team

This one is built or broken in the small daily moments. In places where people quietly compete to climb over each other, you see solo workarounds, hoarded wins, nobody sharing what they figured out, and a real fear of looking dumb for asking a basic question. Underneath, the team doesn’t trust each other enough to be confused together. The move is to build a structure where confusion is safe. More on what that looked like for us in a minute.

The layers stack, and the repair order matters. If trust in the tool is shaky but they trust you, your team will still try, because they trust you. If they don’t trust you, no amount of tool quality saves it. So you fix leader trust first, then team trust, and tool trust gets dramatically easier once the other two are established.

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The Person Hardest to Convince Is Usually Your Architect

A month or two after that workshop, I got an email that made me grin.

The team member who’d been most hesitant in the room, the same writer who asked the copyright question, had started something on her own. She put a recurring thirty minutes on the calendar and invited anyone who wanted in. She called it AI Coffee Time.

No agenda. No slides. No leader running it from the front. Just people, on company time, talking about what they’d tried, what worked, what worried them, what surprised them. It worked because it was theirs. The people who needed reassurance got it from each other, in their own words, at their own pace. The quietly skeptical ones got room to name what they were skeptical about without getting branded resistant to change.

Notice the inversion. The person most worried about the rollout became the person who built the thing that made the rollout work. That happens more than leaders expect. The people most worried about AI often see the human cost most clearly, because they’re the ones actually paying attention to it. Give them a real way in, and they build the structure. Try to argue them out of it, and they go quiet while the rollout stalls.

None of this is a soft skill you’re either born with or not. The capacities that make trust-based adoption possible at scale are the ones I’ve mapped as the AI Leadership Triad, Adaptability, Innovation, and Creativity, and you build them through reps, in the small decisions about what to defend and what to let go.

This is most of what I do with leaders now, helping them read what their AI rollout is actually revealing and repair it before they spend another dollar on tools nobody trusts.

If that’s the wall you keep hitting, that’s what my coaching is built for. Click below to learn more.

AI Executive Coaching & Workshops

So here’s the charge, and it costs you nothing but nerve. Find the person in your organization who’s been hardest to convince on AI, and ask them what they’re actually worried about. Don’t argue. Don’t sell. Just listen. That conversation is where your real rollout starts.

So which one are you avoiding?

Frequently Asked Questions

Why do most AI rollouts fail?

Most AI rollouts fail because of trust, not technology. The tools usually work and the training is usually fine. What breaks adoption is that AI lands on top of trust gaps that were already in the organization, in the tool, in the leader, or across the team. If those gaps aren’t addressed first, the rollout stalls no matter how good the tooling is.

Is AI adoption a technology problem or a trust problem?

At the foundational level it’s a trust problem. Speed of change, identity threat, and unresolved ethics questions mean AI exposes whether a team trusts the tool, the leader, and each other. Technical fluency still matters, but it only pays off once the trust foundation is in place.

What are the three layers of trust in AI adoption?

Trust in the tool (does it fit what we claim to stand for), trust in the leader (was this brought in to help us or replace us), and trust in the team (can we be confused and learn together without fear). A crack in any one layer undermines whatever you build on top of it.

What order should you repair trust in an AI rollout?

Leader trust first, then team trust, then tool trust. If the team trusts the leader, they’ll try a tool they’re unsure about. If they don’t trust the leader, no amount of tool quality fixes it. Tool trust gets much easier once the other two layers hold.

How is AI adoption like factory electrification?

Between roughly 1900 and 1930, factories that gave workers authority to redesign the work around electric motors pulled far ahead of factories that bolted motors onto the old assembly line. AI is the same pattern. Trusting people to rebuild the work around the new capability is a business decision, not a soft skill.

Who should lead an AI rollout on a team?

Often the best structure-builder is the person most hesitant about AI. They tend to see the human cost most clearly. Given a real way to participate, like an employee-led, no-agenda space to share what they’re learning, skeptics frequently become the people who make adoption work.

What’s the first practical step a leader should take?

Find the person hardest to convince and ask them what they’re actually worried about, then listen without arguing. That single conversation surfaces the real trust gaps faster than any training deck.

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.

So here’s a shift most leaders still haven’t made peace with: creating content is part of the job now. Not just for the founder with a newsletter. For the CEO, the VP, the manager trying to get her team to actually listen. If you want to build any real thought leadership or any kind of personal brand, the way you do it is by putting your thinking out into the world, over and over, where people can see it.

And that’s where it gets risky. The more you publish, the more chances a piece has to go out half-baked, with a contradiction you never caught, or a spot where your voice slid into corporate mush, or a paragraph that reads like a machine wrote it.

For the stuff that carries your name and your reputation, that’s expensive, and you can’t un-publish it.

Now, I’m not telling you to run every quick post and reply through a heavy review. That’s overkill, like hiring a contractor to hang one picture frame. But the pieces that actually carry weight, the article going to your whole list, the post you know a lot of people will read, the thing tied to your brand, those earn a real second set of eyes before they leave your hands.

This month’s drop: The Red Pen Review. It turns the review pass I run on my own high-reach content into a skill that tunes itself to your brand and your voice, then tells you what’s weak while you can still fix it. Paid subscribers get the full prompt below, the 2-minute install, and how I actually run it.

Keep reading to get the prompt, and here’s a snapshot of what you’ll get when you use it.

Read more

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Outline

(00:00) – The value of trust

(01:07) – Saying yes, and praying for the newsroom

(03:03) – Translating trust from relief work to a newsroom

(07:42) – Adoption is a trust problem, not a tool problem

(08:53) – The team member who heard “AI” and felt fear

(10:53) – Why it’s a brutal moment to run a newsroom

(12:48) – Not negotiable, not adjustable, not for sale

(16:39) – Op-eds from both sides, and nine months off Twitter

(20:46) – When AI summaries took 20% of search traffic

(23:07) – What a human brings that AI can’t

(29:46) – Journalists who are Christians, not Christian journalists

(32:50) – Three books worth your time

My Takeaways

Adoption is a trust problem before it’s a tools problem.

When I talk to CEOs trying to bring AI into their organizations, the biggest misconception I see is that people will adopt a tool just because it’s good. They won’t. If your team thinks you brought in a new tool to replace them in six months, it doesn’t matter how good it is; they’re never going to touch it. Chris kept the human piece at the center of the whole thing:

Trust gets built brick by brick, and it comes apart fast.

Chris said there’s no shortcut:

“It’s day by day, story by story, brick by brick, you have to build.”

Then he told me what that actually costs. The Christian Post got suspended from Twitter for nine months over a post they believed was true, with 180,000 followers on the line, because they refused to apologize and call it wrong and hateful. “Those are the moments you need to stand by your convictions.” They ate the loss. That’s what a track record looks like when nobody’s watching.

The fear in the room is real, so name it.

Chris told me about encouraging one of his team members to use AI more, and the immediate reaction was fear that it would replace them. His read was that you can’t skip past that. “You have to address the worries. You can’t just force it.” You meet people where they are, or you don’t get to the next step at all. I’ve watched leaders try to sell their way past that fear, and it never works.

The thing a human brings is the thing AI can’t.

Chris framed it as a question his team keeps asking:

“What is it that a human person brings to the table that AI is not going to be able to bring?”

Lived experience. Relationships. Knowing what’s going to matter tomorrow. He put it in a line I’m still going over: “The Bible tells us birds don’t worry about tomorrow. But people do.” That worry, that human stake, is exactly what AI doesn’t have. It’s also the easiest way to tell whether something was written by a person.

If you’re a leader trying to bring AI into your team without breaking the trust you’ve spent years building, that’s most of what I do in my coaching work. You can start here.

One Question to Sit With

If anybody can now produce infinite content for almost nothing, what are you putting out that someone would actually choose to trust?

Watch the full conversation above, and go subscribe to TCP Leaders, Chris’s Substack for Christian leaders. And check out The Christian Post, free to read, because they believe news is a public service.

About Dr. Christopher Chou

Dr. Christopher Chou is the CEO of The Christian Post, one of the largest Christian news outlets in the country, where he has led since 2018. He writes TCP Leaders on Substack, built to help Christian leaders navigate the work of leading. His newsroom defines itself by a simple line: journalists who are Christians, not Christian journalists, with a first duty to the facts and the truth. Subscribe to follow his work.

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.

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TL;DR: Multilingual AI sounds fluent and is often quietly wrong. LLMs reason in English even when writing in French or Japanese, with accuracy drops of up to 29 percent in non-English queries. Localization engineer Julia Diez calls the structural fix semantic governance: a multilingual product ontology, market-availability data, and Market DNA definitions that travel with every generation request.

I’ve been a quiet reader of ’s work for a while. She’s a Senior Localization Engineer and the founder of Black Ice, a semantic governance platform for multilingual AI pipelines, and she writes about a category of AI failure that doesn’t show up in any demo... the kind that sounds right.

Most of the leaders I coach run their AI in English (though my native language and dreaming language is Spanish). So do I, across all four of my businesses. That makes it easy to assume “fluent” and “accurate” are the same thing, because in English, they almost are. The minute you ship that same AI into Tokyo, Munich, or São Paulo, that assumption starts to cost you, and you don’t see it. The customer who tries to use a feature you don’t actually offer in their market doesn’t write a support ticket. They quietly leave.

What I respect about Julia’s framing is how unromantic it is. This isn’t a fear post. It isn’t an AI utopia post. It’s an engineer telling you, plainly, that fluency is a confidence problem and that the fix is structural. (For the leaders I work with on setting up AI context the right way, this is the multilingual extension of the same idea: the model doesn’t know your business unless you tell it. It also doesn’t know your market.)

The AI-Ready Localizer

The AI-Ready Localizer explores how multilingual ontologies, market intelligence, and data governance transform localization from translation into AI-powered, intent-driven global growth.

By Julia | Taking you global

I’ll get out of the way. Julia, the floor is yours.

The silent failure mode costing global brands more than they know

I’m , a Senior Localization Engineer, and the founder of Black Ice, a semantic governance platform for multilingual AI pipelines. I’ve spent most of my career at the intersection of language, knowledge architecture, and AI. The problem I write about is one I’ve watched cause quiet, expensive damage at organisations that believed they had it solved.

There is a specific kind of AI failure that doesn’t announce itself.

The sentences look correct. The language is fluent. A native speaker might read the output and not catch anything wrong… Until a customer in Germany reads that a feature is available, tries to use it, and discovers it isn’t. Or until someone in Japan reads a product description calibrated for an American communication style and finds it, at best, oddly aggressive. Or until a legal team notices your AI has been using a competitor’s terminology in your own support content for months.

These aren’t hallucinations. The model isn’t fabricating facts from nothing. These are semantic governance failures, and they’re harder to catch precisely because the output looks right.

Most organisations deploying AI across multiple markets are producing them right now, at scale, without knowing it.

Why Fluency and Correctness Are Not the Same Thing

Modern large language models can generate text in dozens of languages at a quality that passes informal review. That creates a confidence problem.

The model doesn’t know that a feature you’re describing is unavailable in Brazil due to regulatory constraints. It doesn’t know that your enterprise tier includes capabilities your consumer tier doesn’t. It doesn’t know that the term a competitor uses for a concept differs from yours, and that using it in your AI-generated content is a branding problem. None of this is in its training data. It can’t be. It’s proprietary, market-specific, and constantly changing.

Without that information, the model does what it’s designed to do: it fills the gap with its best approximation, drawn from billions of tokens of training data. That approximation is usually fluent. It is not always right.

There’s also a structural issue that compounds this. Research has shown that multilingual LLMs tend to reason conceptually in English even when generating text in other languages, a phenomenon called language spilling. When asked to produce synonyms for a French word, the model may pull meaning from its English semantic space rather than French, producing output that is linguistically in French but conceptually contaminated by English associations. Even with advanced retrieval systems, accuracy drops of up to 29% in non-English languages compared to English have been documented.

For your business, that statistic means a system that performs reliably for English-language support queries may be quietly getting a quarter of the equivalent queries wrong in Spanish, German, or Japanese, and producing answers that look right to anyone without deep fluency in that language and domain.

The Cultural Layer Most Teams Miss

Linguistic accuracy is only half the problem. The other half is cultural, and it’s harder to address because it’s harder to see.

Research is consistent on this point: AI systems predominantly mirror the cultural values of Western, educated, individualistic societies, because that’s where most of the training data comes from. A model generating product descriptions without cultural calibration will default to a broadly American cultural frame regardless of the target market.

This produces content that is linguistically diverse but culturally homogeneous: content that reads as if it was written for a Western audience and then translated, even when it wasn’t.

The practical implications are real. In cultures that score high on what researchers call Uncertainty Avoidance — Germany and Japan among them — content that hedges with vague optimism and omits technical caveats reads as untrustworthy. In more collectivist markets, marketing content that emphasises personal achievement over team outcomes simply lands differently. These aren’t aesthetic preferences. They’re structural differences in how information is received and trusted.

You cannot reliably solve this through prompting alone. Telling a model to “write for a Japanese audience” produces some surface adjustment. It does not produce consistent, verifiable cultural alignment.

Here’s what it looks like when cultural knowledge is structural rather than prompted. The same questions, routed through a Black Ice ontology, for two markets:

The Framework: Semantic Governance

Semantic governance is the practice of making your product’s meaning explicit, and keeping it connected to the AI systems generating content about it.

Three components make it practical:

1. A multilingual product ontology

This is not a glossary. A glossary tells the model what to call things. An ontology tells the model what things are — their properties, their relationships to other concepts, and crucially, what can and cannot be said about them in each market.

The difference matters because AI systems don’t operate on surface-form substitution. They reason about concepts. A model given a glossary entry knows a word. A model given an ontology entry knows a thing — including that it’s unavailable in the EU, that it belongs to the enterprise tier only, and that it requires a specific legal disclaimer in three markets.

2. Market availability as structured data

What exists where is one of the most consistently neglected pieces of knowledge in AI pipelines. It needs to be encoded explicitly — not as a note in a document somewhere, but as a structured property attached to each product concept, available to the AI at the moment it generates content.

When a model has this information in context, it cannot describe an unavailable feature. When it doesn’t, it will, every time, because it has no reason not to.

3. A Market DNA definition

This is where cultural knowledge becomes operational. A Market DNA definition is structured data — not a style guide written for humans — that encodes the register conventions, tone values, communication norms, and explicit constraints for a specific market.

A high Uncertainty Avoidance score for Germany becomes a concrete instruction: precision over brevity, acknowledge limitations explicitly, avoid optimistic vagueness. A collectivist orientation for Japan becomes: emphasise organisational benefits over individual achievement. These travel with every generation request, applied consistently, verifiably.

The Questions to Ask Your Team This Week

Pull up one piece of AI-generated content from a non-English market. Ask:

• Does this describe any feature that may not be available in this market?

• Does this use our approved terminology, or something close to it?

• Does the tone match how our customers in this market expect to be addressed?

If you can’t answer those questions confidently, not because the content looks wrong, but because you don’t have the infrastructure to check, that’s the gap semantic governance closes.

The Takeaway for Leaders

The fluency of modern AI creates a specific organisational risk: it makes problems invisible until they’re expensive. Semantic governance is the structural answer. Not a prompt, not a review checklist, but an architecture that keeps AI systems anchored to what your product actually is, in every market where it exists.

This is infrastructure work. It doesn’t produce flashy demos. What it produces is the difference between a multilingual AI system that generates content that looks right and one that is right. In markets where brand trust takes years to build and can be damaged in a single content release, that’s the only difference that matters.

I’ve spent the better part of a decade building this infrastructure inside large organisations and wishing a better version of it existed. That frustration became Black Ice. The thinking behind it lives at The AI-Ready Localizer — practical infrastructure for anyone governing multilingual AI seriously.

What to Remember

Thank you, ! Here she is articulating something I’ve been trying to say to executive coaching clients for a year and not finding the language for: fluent isn’t the same as accurate, and the markets you can’t audit are the ones paying the highest price for the difference.

If you’re running AI in markets you can’t read, the question isn’t whether you have a problem. It’s whether you have the infrastructure to find it. This is the same conversation I have with leaders building out the AI Leadership Triad. Adaptability and decision-making both fall apart fast when the system you’re leading produces output you can’t verify.

Subscribe to Julia at The AI-Ready Localizer for the structural side of this work. She’s the kind of practitioner I trust precisely because she sounds like an engineer, not a hype merchant.

Questions Leaders Are Asking

What is semantic governance in AI? Semantic governance is the practice of making a product’s meaning explicit and keeping that meaning connected to every AI system generating content about it. It has three structural pieces: a multilingual product ontology, structured market-availability data, and a Market DNA definition per region. Without it, multilingual AI sounds fluent and quietly produces wrong feature claims, off-brand terminology, and tone that misses the market.

Why does multilingual AI fail in non-English markets? Multilingual LLMs reason conceptually in English even when generating text in other languages, a pattern researchers call language spilling. Even with retrieval systems, accuracy drops of up to 29 percent in non-English queries are documented. The output reads fluent to non-native reviewers and can quietly contradict regulatory rules, market availability, or cultural register without anyone catching it for months.

What’s the difference between an AI glossary and an AI ontology? A glossary tells the model what to call things. An ontology tells the model what things are: their properties, their relationships to other concepts, and what can and can’t be said about them in each market. That difference is structural. AI systems don’t operate on surface-form substitution. They reason about concepts. A glossary patches the surface. An ontology fixes the reasoning.

Can prompt engineering solve multilingual AI accuracy? No, not on its own. Telling a model to “write for a Japanese audience” produces surface adjustments, not consistent, verifiable cultural alignment. (For more on why context beats prompts, see my piece on context engineering with Gemini.) Prompts vary across users and sessions. Structural infrastructure (ontology, market data, Market DNA) travels with every generation request and applies consistently.

How do I audit AI content in a language no one on my team reads? Start by encoding the things you can verify without language fluency: feature availability per market, approved terminology, and explicit “do not say” lists. These become structural checks the AI can be measured against, regardless of language. Once those are in place, sample translations for cultural register through a native reviewer or a structured Market DNA definition rather than informal review.

Is this only for enterprise companies? No. Any company shipping AI-generated content into more than one market faces this risk. Smaller teams feel it harder because they lack the in-house language coverage to spot quiet errors. Whether you build the ontology yourself, use a platform like Black Ice, or start with a simple structured market-availability table, the principle is the same: the AI needs to be told what your product actually is, per market.

Written by a human, for humans.

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Outline

(00:13) – Welcome

(01:05) – From newspapers to MediaFeed

(05:26) – The content funnel, top to bottom

(07:23) – Why you resist the urge to sell

(10:08) – Teach them to fish, they still pay you

(13:43) – What trust looks like in 2026

(14:17) – Lazy AI versus strategic AI

(18:33) – The tidal wave of slop

(21:23) – Canonical tags and being known for something

(30:06) – “This is shit. You’re a good writer.”

(43:52) – Denial of Death

My Takeaways

Resist the sell, or lose the trust. Michael’s whole model runs on real editorial content, and the hardest discipline in it is refusing to sell inside the piece, even when the piece only exists because you have something to sell. His reasoning landed for me because it cuts both ways. “People are smart enough to figure that out and will undermine your brand ultimately. And it also probably won’t help your sales.” So you lose the trust and you don’t even get the conversion. That’s the worst trade in content, and most people make it without noticing.

Teach people to fish. Here’s the counterintuitive one. Michael talked about creating content that shows people how to do, on their own, the exact thing his client’s service provides. Sounds insane until you hear him out. “You can teach people how to fish. They’re still going to want you to go fishing for them sometimes.” A small slice does it themselves, a much larger slice reads it and goes, that sounds like a lot of work, I’ll just pay you. You become the encyclopedia Britannica of whatever it is you do, and the trust comes with it.

“This is shit. You’re a good writer.” This is the moment I’ll be telling people about. Michael tried handing his podcast write-ups to AI, fed it samples of his own writing, the whole thing. His co-host read it and said, pardon the French, “this is shit, you’re a good writer, what the hell are you doing?” So he stopped and went back to writing them himself. I felt that one, because it’s exactly why I still write my own Monday and Thursday pieces by hand and only bring AI in afterward to check whether I’m rambling.

The tool gets you efficiency, but the voice is still yours to bring.

About Michael Schreiber

is the CEO of MediaFeed, a content syndication company that helps brands, nonprofits, and journalists develop and distribute real editorial work. A longtime journalist whose path ran through newspapers, documentaries, and financial outlets including TheStreet and Credit.com, he also hosts a local podcast covering his New Jersey town. Find his work at mediafeed.co, on Instagram, and 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.

A quick note before we start. This is part 2 of a paired conversation with , who writes AI & No-Code Exits for solo founders building with AI and no-code. His half, “5 Things Solo Founders Do With AI That Every Leader Should Steal,” ran on his platform first.

Joshua and I traded notes to write these two together. He sees AI from the solo-founder side, where there’s no leadership layer between him and the tools.

I see it from the org side, watching real AI rollouts succeed or stall inside the companies running them.

The pattern is the same on both sides. The advantage compounds when the human builds the habits, because the tools all eventually look the same and the habits don’t. Here are the five I see in leaders who actually compound.

Here’s the thing, the internet went mainstream years ago, then smartphones did, and now in 2026 AI has too. If you’ve got a phone and a connection, you have access to almost the same AI a billion-dollar company is using, on a free plan. That’s never been true before for any tool, in any era.

Which means the question every no-code founder is asking right now is the right one. What do I do to give myself a chance to succeed in this?

Most founders try to answer it by picking the right tool. Open ten tabs, watch ten tutorials, sign up for the trial of whatever the loudest creator on X is hyping this week. That’s not a strategy. That’s tool tourism, and tool tourism doesn’t compound.

The advantage that actually compounds isn’t the tools. The tools are the easy part. The advantage is the habits the tools enable. Founders who build the same five repeatable habits I’ve been building for the last few years end up with a different relationship to AI than the founders who keep tool-shopping. They use AI to think, not just to ship.

I have four businesses. I write a newsletter that publishes twice a week. I build apps for clients. I coach leaders who are figuring out how AI fits into their work. None of that scales without a small set of habits I run on autopilot at this point. Here they are…

Read them as the operating system. The framework I’ve been building over the last year, the AI Leadership Triad (Adaptability, Innovation, Creativity), is the lens. These five habits are the practice. Habits 1 and 4 build adaptability. Habit 2 builds innovation. Habit 5 builds creativity. Habit 3 is the throughline that keeps the other four sharp.

Quick sidenote… if you write a newsletter or you're thinking about starting one, I built Newsletter Compass to help creators grow and optimize without the busywork. Click to learn more.

The leader failure mode (read this first)

Most leaders I work with think AI’s job is to make their team faster. More articles, more decks, more code shipped, more output per dollar. That’s part of it. But that framing caps the ceiling. A leader using AI for productivity is competing against every other leader using AI for productivity, which means everyone is shipping more of the same average output, faster.

Leaders who actually build sustainable advantage in 2026 do something different. They use AI to think, decide, and rebuild faster than the leaders who are still using it to crank out one more newsletter draft. The five habits below are how that gets operationalized.

1. Stay hungry & test things on real problems.

You don't need to understand AI's internals. You don't need to know what RAG (retrieval augmented generation) means or what the keyboard shortcut is in Claude Code. You do need to understand what AI actually solves and what it doesn't, and the only way to figure that out is by testing it on the real problems you're already trying to solve.

Most people use 10% of AI’s capability, and the gap is almost never about intelligence. It’s about reps. The 10% group reads. The 90% group runs the prompt, watches what comes back, tweaks, runs again, tweaks again, and ten reps later they’ve internalized something the readers will never get from any tutorial.

Read about a new model? Try it on something real this week. See a new prompt floating around? Adapt it to your business and run it once. See someone claim a new technique saves them 40 hours a month? Test the claim on your actual workflow. I built five Claude Skills that genuinely save me about 40 hours a month, but only because I tested every one of them on real things I was already doing. The hours-saved number is downstream of the testing reps. There’s no shortcut.

The cost of testing is zero. The cost of not testing is six months of watching someone less experienced than you ship faster, because they spent those six months in the sandbox while you were reading about it.

2. When you hit a wall, ask if AI can remove it.

This one is a reflex, not a habit. Every time you hit a hurdle in the work, your default question should be, “Is there a way I can use AI to make this easier?” Not “is there a SaaS product I can buy for this,” but “can I build the small thing that solves this problem the next ten times I’ll hit it?”

A coaching client of mine was running into a real wall. Their marketing team was burning hours every single week repurposing one written article into platform-specific posts for LinkedIn, Twitter, Substack, Instagram, and the rest. The team was excellent at the writing part. They were exhausted by the formatting, the character-count math, and the platform-specific best practices that change every six months. Different post, same chore, every Monday.

So I built them a content repurposer. It runs on Next.js, hooks into a Supabase database, scrapes the source article, validates it, and uses a Claude prompt that knows the client’s tone, values, and business context. They paste the source article in, they pick the platforms they want, they get clean platform-ready output in minutes. Character limits respected. Organic discovery best practices for each platform baked in. Hours back every single week, redirected from admin into the creative side of the business that only humans can do.

The whole thing took a few days. The wall was real, the fix didn’t have to be, and AI made it possible in a few hours of focused work.

This is the bar in 2026. My seven-year-old built a working app in twelve minutes once she figured out how to talk to the tool. The “I can’t build this” excuse is gone. The only question left is whether your default response to a wall is “look for a tool that already exists” or “build the small thing that handles this for me, forever.”

3. Update your context. You evolved, but AI didn’t notice.

Most leaders who use AI seriously eventually build context. A system prompt. A long doc. A personal CLAUDE.md file. They tell the model who they are, what they're building, what their preferences are, what their tone sounds like. That afternoon of setting up your AI context is the single highest-impact move you can make. It changes the quality of every interaction that comes after.

Then they leave it for two years.

That’s like promoting someone in your company and never giving them a new job description, just asking them to keep using the old one and expecting new results. They can't, because nobody told them what the new job actually is, and neither can the model.

I forgot this at first. My context kept telling AI about a version of me from a year and a half ago. The me who was figuring out the thing I’d already figured out. So I’d ask the model for help on something real, something I was actively wrestling with now, and I’d get answers that were calibrated for someone two steps behind me. Useful for a junior version of me. Useless for the current one.

The fix is simple. Every quarter, I rewrite my context across my stack. What I’m working on right now. What I’ve already moved past. What I’m currently obsessed with. What’s no longer in scope. What my voice sounds like this quarter, not last year. What my four businesses are actually trying to accomplish in the next ninety days, not the version I wrote at the start of last year.

The model gets sharper because the picture is current. It stops hedging. It starts giving me answers calibrated for the version of me that’s actually in the room.

If your context is older than your last business pivot, your AI is working from outdated specs. That’s not the model’s fault.

4. Go beyond the first answer.

If you take whatever AI gives you on the first try and run with it, you don’t need AI. You can use Google. You’ll miss the whole point.

The first answer is a draft. It’s the model’s average response to a prompt, given its average understanding of the situation. The good answer is the third or fourth, after you’ve pushed back, narrowed the question, given more context, asked it to argue the other side, asked it what it’s missing, asked it what assumption it just made that you didn’t tell it to. That’s where AI stops being a search engine and starts being a thinking partner.

I’ve been pushing past the first answer for years now, and I genuinely don’t remember what it’s like not to. If your AI never disagrees with you, you’re using it wrong. The model is trained to be agreeable, and confirmation bias plays out in AI exchanges the same way it plays out between two humans who already agree with each other. You ask, it confirms, you ship. Six months later, you’re competing against thirty other founders who shipped the same exact thing.

Push the model. Tell it to disagree. Tell it to argue the strongest case against your position. Tell it what you tried that didn’t work and ask what you missed. Watch the answer change. The first one was the consensus. The third one is the insight.

If you’re not gonna go beyond the first answer, just stick to Google search. You’re missing the whole benefit of AI.

5. Every six months, ask AI to interview you.

This is the one most people haven’t tried. It’s also the one that surprised me the most when I started running it on myself.

Twice a year, I open a conversation and ask the model to interview me about my business, my voice, my writing style, my humor, what I’m building, what I believe, and what I’m currently wrestling with. Not a quick five-question survey. A real interview. Twenty, thirty questions, follow-ups, the works. Then I save the output and put a calendar reminder six months out to do it again.

I’ve been blown away by some of the questions the model asks. Not because they’re clever, but because they’re the questions I should have been asking myself and wasn’t. Some of them are sharper than the questions I’d come up with on my own. The reason is the same reason you can read your own draft ten times and still miss the typo that’s been sitting on line three the whole time. We get blind to ourselves. Having an external, patient, structured set of questions about who you are and what you’re doing puts the spelling errors in your own thinking on the page where you can finally see them.

The comparison across cycles is the real payoff. You see how your business focus shifted. How your tone evolved. What you used to care about that you don’t care about anymore. What you’re newly obsessed with. That’s information no journal entry would surface, because you wouldn’t have asked yourself the right questions.

This habit is also why the trust premium is going to matter more in 2026 than it did in 2025. When production is cheap, the thing readers and customers actually pay for is trust. Trust comes from a clear, consistent voice that’s recognizably yours, sustained over time. The self-interview is how you build voice clarity at scale, on a cadence, without faking it.

If you only pick one of these five habits, pick this one. Try it tomorrow. Here’s a prompt to use if you don’t want to write your own:

Act as a thoughtful interviewer. Your job is to interview me about my business, my voice, my writing style, my humor, my values, and what I’m currently building. Ask me 25 to 30 questions, one at a time. Wait for my answer before asking the next. Use my answers to drive your follow-up questions. After the interview, summarize back to me what I sound like, what I care about, what I’m building, and where my thinking has changed compared to a typical founder in my space. Save this output so I can compare it to another interview six months from now.

Run it. Save the answer. Put a calendar reminder six months out. Run it again. You’ll see your evolution on the page in a way no journal will give you.

These five habits aren’t really a checklist, they’re a way of working that compounds over time into something sustainable. Habit 1 builds adaptability through reps. Habit 2 builds innovation by turning every wall into a build. Habit 4 builds adaptability again, harder, by refusing the consensus answer. Habit 5 builds creativity through voice clarity. Habit 3 keeps all four sharp by making sure the AI you’re working with is calibrated for the current version of you.

The leverage isn’t in doing all five. The leverage is in turning even one of them into a reflex. Pick the one you’re weakest on. Run it for thirty days. The compounding shows up around month three.

Pick one of these and run it tomorrow. Tell me in the comments which one you tried, and what surprised you when you ran it.

I write about exactly this kind of thing every week at Leadership in Change, where I unpack how leaders, founders, and operators are actually using AI to build, lead, and grow. If your work is in newsletters specifically, I also built Newsletter Compass for the creators in this audience who are trying to grow theirs.

Written by humans, for humans.

Subscribe on Apple Podcasts for Free

The frame that I’m still going over is that communication is music. It has melody, rhythm, and silence, and once you hear it that way you start sounding like a person instead of a content machine. We also got into why authenticity is the thing that survives the age of AI slop, and the dead-simple framework he uses to never ramble again. Watch the full conversation above.

Outline

(00:00) – Meet Christopher Chin

(02:13) – The composer who became a coder

(04:25) – Head down vs. speak up

(06:17) – Communication is music

(08:22) – Mindset over technique

(13:02) – Why silence does the work

(17:37) – Standing out in the age of AI slop

(20:06) – Where AI actually belongs

(28:33) – Bring out your hidden speaker

(32:16) – Three steps for when you freeze

A Few Things That Stuck With Me

Communication is music. The line I keep coming back to is when Chris stopped describing communication as information and started describing it as something you compose. He grew up wanting to write music for films, and when he finally pulled that side of himself back into how he talks, his whole delivery changed.

“Music has melody and rhythm and tonality, and the voice does the same thing. By not viewing communication as just information, but as an experience you create, that’s how you engage.”

I felt that one personally. I grew up speaking Spanish, my mom’s from Argentina, and there’s so much emotion baked right into the words, and I’d never really clocked that the lilt and the pacing were doing half the work.

Mindset over technique. Here’s the thing most communication advice gets wrong. Chris said you can Google “three steps to executive presence” all day, and if you just copy the hand gestures and the posture, none of it lands.

“The real way to achieve authentic, effortless communication is to work on your mindset and your understanding of why these things work. If I teach you the core of executive presence is coming in with a calm, steady, grounded presence, knowing that no matter what’s thrown your way you can handle it, that changes how you communicate.”

It reframes the whole thing, because you stop trying to perform confidence and start building the kind of steadiness that makes the performance unnecessary.

The fast-food test for AI. We got into AI slop, the wave of generic content flooding every feed, and Chris had the cleanest way of explaining why it bounces right off you. He compared it to McDonald’s fries: the same everywhere, engineered to be fine, forgotten the second you finish.

“It could be saying the most brilliant things in the world, but I want to hear that from an actual person. If I sense there’s no human behind that screen, I don’t want to hear it. I want lived experience.”

Honestly, this is the whole reason I run these as live conversations instead of just publishing a guest article. You’d never get Chris, the actual person, from a block of text.

Nerves are the flip side of excitement. For anyone who freezes the second they have to speak, this was the reframe of the episode. Chris pointed out that the butterflies before a talk and the butterflies before a trip you’ve been dying to take are the same physical thing.

“The physical sensation you experience when you’re nervous, the butterflies in the stomach, is the same exact sensation when you’re excited. The thing that differentiates them is your perception.”

So you don’t actually have to calm down, you just have to decide the feeling means you’re about to do something that matters.

(Quick aside: a lot of my 1:1 coaching work is exactly this, helping leaders close the gap between how good their thinking is and how clearly it lands in the room. If that’s you, here’s where to start.)

One Question to Sit With

Where are you underselling yourself at work, not because your thinking isn’t strong, but because you’ve never put in the reps on saying it out loud where it counts?

Watch the full conversation above, and then go subscribe to The Hidden Speaker.

About Christopher Chen

Christopher Chen is a TEDx and international keynote speaker who coaches leaders and teams on communication, and he writes The Hidden Speaker on Substack. He started out shy, introverted, and behind a screen as a coder before building his practice around helping people speak with confidence. He’s posted around 180 videos on his YouTube channel and releases weekly previews from his upcoming book on communicating in the age of AI. Subscribe to The Hidden Speaker.

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.

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Written by a human, for humans.

Here’s the thing about most partnership proposals: we often sit on them. Not because we don’t care, but because reading a dense RFP, mapping it against strategy, building a board deck, and drafting an external response is two weeks of work, and nobody has two weeks. So the proposal stays on the desk. And every day it does, somebody else is already building a relationship with the partner you were about to evaluate.

That’s the freeze I keep seeing in coaching conversations, and it has a real cost. Which is why I asked and at Cash & Cache to write this one up. They wrote the original guide on building digital prototypes without a technical team for us last October, and this is the natural sequel.

Cash & Cache

Where strategy meets execution — exploring how AI is transforming businesses, markets, and value.

By Ashwin Francis

The piece below uses a federal health IT consortium scenario and walks through the full 2-hour workflow inside Google Workspace. Read it like a playbook.

From Partnership Proposal to Board Decision: An AI Workflow for Leaders

A partnership proposal lands in your inbox. Dense document. Complex evaluation criteria. Board meets in two weeks. You need to research the organization, assess strategic fit, prepare internal briefs, build a board presentation, and draft a response.

That sequence took me under two hours using tools already available inside Google Workspace, plus NotebookLM for research.

I ran this workflow on a real scenario.

The RFP is an actual document from a federal health IT consortium. The responding company, Vibrant Tech Consulting, is a mock firm I created with realistic capabilities, past engagements, and strategic priorities to keep the exercise close to production without exposing private company information.

Everything that follows is replicable on your next deal.

One thing worth noting upfront: I built this entire workflow within the Google ecosystem. If your company runs on Google Workspace, you don’t need to switch tools or get IT approval for anything new. NotebookLM, Gemini, and Canva (available as a Gemini tool) handle research, analysis, deliverables, and presentations without leaving the environment your organization already pays for.

However, if you have no compliance hurdles, I suggest you use Gamma AI for slides presentation for its quality and seamless UI/UX.

Phase 1: Research (NotebookLM)

I uploaded all documents (on the external RFP, and my company strategic files) into one NotebookLM notebook: the consortium’s RFP, Vibrant’s FY2026 strategic priorities, and a portfolio of three past healthcare engagements with financials and client references.

Note: I have data sharing compliance with AI tools that I use in company systems, such as these. Always check with IT regarding compliance before you start working with AI.

Then I queried across all three:

Based on all uploaded sources, give me:

1. A 3-sentence summary of what this consortium does and their market position

2. Key financial indicators (revenue, funding, profitability if available)

3. Their leadership team and relevant backgrounds

4. Past partnerships or deals they’ve announced (and how those went)

5. Any red flags: lawsuits, leadership changes, negative press, failed partnerships

6. Where their proposal aligns with our strategic priorities

7. Where their proposal conflicts or creates risk

NotebookLM cross-referenced all three documents and returned a structured analysis.

It identified alignment with my company’s expansion goals but flagged three conflicts: a hardware procurement gap, missing regional regulatory expertise, and advanced reporting requirements outside current capabilities.

I’ve written before about using NotebookLM to turn large research bases into executive-ready outputs. That workflow handles pure research synthesis. This one goes further by feeding the research into a structured assessment and producing board-ready deliverables.

One finding needed more investigation. I asked NotebookLM to dig into the hardware procurement gap with the latest data points. It connected a gap in the strategic priorities document to a mitigation strategy from a past engagement (subcontracted hardware specialists on a 12-site project) and a FY2026 growth target to formalize that relationship. That kind of cross-document connection, linking a gap in one file to evidence in another, is where a persistent research notebook pays for itself.

Phase 2: Assess the Partnership (Gemini)

I carried the research into Gemini and ran a 5-prompt assessment.

Prompt 1 (Context): Loaded my role (sales consultant, life-sciences vertical), the research findings, strategic priorities, and intent.

“I’m a sales consultant at [Company], [vertical]. 
We received a partnership proposal from [Organization]. 
Context: [Paste key research findings from NotebookLM] 
Our strategic priorities: [Reference uploaded documents] 
Intent: Assess whether this partnership is worth pursuing, 
and under what conditions. Honest assessment, not optimistic. 
Don’t generate anything yet. Confirm you understand.”

Gemini confirmed and flagged four things it had noted: the hardware gap, the vertical mismatch between our pharma focus and the consortium’s clinical provider focus, legacy documentation risk, and our 70%+ capability coverage requirement.

Prompt 2 (Scope):

“Before analyzing, ask me 3-5 clarifying questions about what my leadership team 
needs to decide on this partnership.”

It asked five questions I hadn’t fully worked through: 
Is leadership bridging pharma and clinical data, or expanding into provider consulting? 
Are they expecting direct revenue or reference value? 
Should this partnership source the hardware subcontractor? 
Is Midwest expansion the primary motivation? 

Each answer shaped every deliverable that followed.

Prompt 3 (Structure): I prompted it to generate a Partnership Assessment Brief.

“Produce a Partnership Assessment Brief:

1. PARTNERSHIP SNAPSHOT (who, what, what’s at stake)                                                   2. STRATEGIC FIT (strong alignment / moderate / misalignment)                                          3. FINANCIAL ANALYSIS (costs, returns, break-even)                                                           4. RISK ASSESSMENT (top 3 if we pursue, top 3 if we decline)                                              5. RECOMMENDATION (pursue / pursue with conditions / decline)

Two pages max. Direct language. Leadership has 15 minutes with this.

Recommendation: Pursue with conditions. Use the partnership strictly 
for business development and networking.”

Prompt 4 (Refine): The risk assessment was too generic. I asked Gemini to make risks specific to the actual partnership terms, industry, and company. The refined version referenced specific RFP sections, and flagged exact compliance liabilities.

Prompt 5 (Check): What assumptions about the consortium should I verify directly with them? What assumptions about our capabilities should I confirm with the team? Where is the assessment too optimistic?

Gemini surfaced facts and caveatsI hadn’t considered. The check step caught it before I put it in a board memo.

This is the kind of implementation-first workflow we publish weekly at Cash & Cache. Free subscribers get the frameworks. Paid subscribers get the full prompt libraries, templates, and workflow blueprints.

Unlock the Library → Cash & Cache Library

Phase 3: Internal Deliverables (Gemini + Canva)

With the assessment complete, I packaged it for three audiences.

Executive Summary (Email)

“Turn this partnership assessment into a 5-bullet executive summary. Lead

with the recommendation. Include the top risk and key condition. End with

what we need to decide by [deadline].”

Five bullets. Recommendation first. Top risk second. Key condition third. Decision deadline at the end. Under 200 words.

Executive Infographic

I asked Gemini to generate a minimalist infographic summarizing the assessment. White background, dark text, 1200x630px landscape.

Board Presentation (Gemini + Canva Integration)

I could have exported slide content to Google Slides or used an external tool like Gamma AI, which produces polished presentations quickly if you have access to it. But I wanted to test whether the entire workflow could stay inside Google Workspace.

I enabled the Canva feature in Gemini’s tools. Then I prompted:

“I am a consultant preparing a professional presentation for C-suite

internal strategy and decision making. Using the partnership assessment

from my research, create a 5-slide presentation. Refer to the assessment

documents for all slide content, statistics, and chart data.

Slide 1: Partnership opportunity (who, what, why it matters)

Slide 2: Strategic fit (alignment vs misalignment)

Slide 3: Financial analysis (costs, returns, break-even)

Slide 4: Risks and conditions

Slide 5: Recommendation and next steps

Keep text minimal. One key point per slide. Include key statistics 
and visual guidelines for each.”

Gemini produced the content with key statistics for each slide: “Target: 40% Health IT Revenue Share” for the opportunity, “75% Internal Capability Coverage” for strategic fit, “2x Break-Even Milestone” for financials, “Gap Fulfillment: 1 Strategic Hardware Partner” for risks, “Decision Deadline: April 30th” for the recommendation.

I then asked Gemini to extend this into five separate infographics using the Canva integration. Created them iteratively, refining the design: white background, dark professional typography, data-dense charts (donut charts for market composition, radar charts for capability gaps, bar charts with labeled values for financial projections, bubble charts for risk mapping).

Gemini produced five infographic images. I also asked for an HTML version of all five pages for browser-based presentation and a downloadable PDF of all infographic slides for email distribution.

The PDF version can be seen here:

Nlc Strategic Assessment Report

251KB ∙ PDF file

Download

Download

No external presentation tool. Five infographic slides created, refined, and downloaded within Gemini using Canva. Ready for a board meeting, email attachment, or shared Drive folder.

For teams constrained to Google Workspace, this removes the “I need an external presentation tool” barrier entirely.

Board Decision Memo

Turn this into a 2-page decision memo for the board. Structure: Executive

Summary, Background, Analysis, Recommendation, Required Decision, Timeline.

Formal tone.

Gemini produced a formal memo with financial projections, risk analysis, specific timelines (April 15-25 for subcontractor vetting, April 30 decision deadline, May 1 formal initiation), and board authorization language.

Phase 4: Draft External Communications (Gemini)

With internal alignment secured, I can generate any of the three response drafts covering all outcomes by prompting:

“If pursuing (with conditions): Referenced the consortium’s regional network 
as specifically compelling. Stated three conditions: vertical alignment, 
technical modernization, hardware fulfillment model. 
Proposed a 20-minute introductory call.

If declining: Acknowledged the specific proposal. 
Stated the vertical mismatch honestly. 
Left the door open for future conversations.

If counter-proposing: Proposed three alternative terms: 
using the partnership as a testing ground for modern tools instead of 
adopting legacy frameworks, focusing on pharma-clinical bridge projects, 
and integrating Vibrant’s subcontractor network for hardware. 
Framed as evolution, not rejection.”

The Complete Workflow

NotebookLM: Research hub. Upload the proposal, your strategic priorities, your past deal history. Query and cross-reference.

Gemini: Assessment and deliverable engine. 5-prompt pattern for analysis. Executive summary, infographic, slide content, board memo, external response drafts.

Canva (via Gemini): Presentation layer. Five infographic slides created, refined, and downloaded as images. HTML version for browser presentation. PDF for distribution.

Time: About 2 hours total. 30 minutes researching. 40 minutes assessing. 30 minutes on deliverables and presentation. 15-20 minutes on external communications.

The tools can vary. If you have access to Gamma AI, it produces polished slide decks faster than the Canva-in-Gemini approach. If you’re on Claude or ChatGPT instead of Gemini, the 5-prompt pattern works identically.

But if your company runs on Google Workspace and that’s what you have, this workflow runs end-to-end without leaving the ecosystem. Research in NotebookLM. Analysis and deliverables in Gemini. Presentations through Canva integration. All stored in Drive. No tool-switching or raising IT tickets. I wrote about why tool-agnostic thinking matters more than any single platform. The same 5-prompt pattern in this article produced 70-75% consistent output across three different AI tools.

What Stayed Mine

AI handled the synthesis, formatting, and first drafts. I handled the judgment.

Every recommendation Gemini made, I evaluated. Every assumption it surfaced, I verified against what I knew about the team. The board memo still needed my read on organizational dynamics. The external response still needed my sense of the relationship.

Thank you, !

Here’s what I keep coming back to: most leaders aren’t behind on AI because they don’t have the right tools. They’re behind because they haven’t built the muscle of running their existing tools as a complete workflow, and that gap is paid in time they don’t get back.

Time is the one thing you can’t buy back. The point of AI, for any leader, is to stop spending it where it doesn’t belong.

If it would help to talk this through, my calendar is here. The first conversation is free.

Subscribe to Raghav and Ashwin at Cash & Cache for more workflows like this one.

Questions Leaders Are Asking

Do I need to pay for any of these tools, or does this work in the free tier? NotebookLM and Gemini are both available on Google’s free tiers, with usage limits. Canva is free for the basic image generation Gemini calls. The whole workflow runs without a paid subscription if your usage stays modest. Heavier usage justifies upgrading to Gemini Advanced or NotebookLM Plus, but you don’t need them to start.

How much of the 2-hour estimate is realistic for someone doing this for the first time? First time, expect 3 to 4 hours. The prompt patterns take a few runs to internalize, and the assessment step is where most people slow down because they haven’t decided what their leadership team needs to decide yet. By the third or fourth time, 2 hours is realistic. By the tenth, faster.

I’m not a designer. Can I really build a board-ready slide deck this way without it looking unprofessional? Yes, with limits. Canva inside Gemini handles infographic-style slides cleanly because it’s working from text and data, not freeform design. For genuinely polished decks, Gamma AI is faster and produces tighter results. Use the Gemini-Canva path when you need to stay inside Workspace for compliance reasons. Use Gamma when you don’t.

What’s the most common reason this workflow fails when leaders try it? They skip Phase 2 prompt 2: asking Gemini what it needs clarified before generating. Without that, the assessment comes back polished but shallow because Gemini is filling gaps with assumptions. The clarifying questions are what make the output usable in a board memo. Don’t skip them.

Does this only work for partnership proposals, or can I use this same workflow for other dense documents? It works for any dense decision document: RFPs, vendor evaluations, M&A teasers, board-ask packages, contract reviews, candidate slates. Anything where you have multiple source files, need a structured answer, and have a deadline. The 5-prompt pattern in Phase 2 is the part you reuse. The notebook structure and deliverables flex around the use case.

If I’m not on Google Workspace, what’s the closest equivalent stack? Claude Projects plus Claude artifacts handles most of it. Upload sources to a Project, run the same 5-prompt assessment in the chat, and ask Claude to generate the memo and quote cards. For slides, Gamma AI works regardless of your suite. ChatGPT with Custom GPTs covers the same ground if you’re on a Microsoft 365 stack.

About the Authors

Raghav Mehra and Ashwin Francis are the creators of Cash & Cache, a Substack on AI workflows for finance and product leaders. They previously contributed The Ultimate Guide to Building Digital Solutions Without Technical Teams for Leadership in Change in October 2025.

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

Written by humans, for humans.

A few weeks ago, I had a last-minute session to run with my team on using AI for content repurposing. I didn’t have time to build a presentation, at all, but one was needed. So I did something that would have been impossible a year ago: I dictated all my thoughts into Claude using Wispr Flow, activated the brand skill I’d built for our nonprofit (which already had our colors, fonts, and tone baked in), and asked it to generate a slide deck. Claude gave me a few options for layout and text density, I picked one, and the presentation was ready. I didn’t make a single edit. All I had to do was get in the room and present.

Without Claude, I simply would not have had a presentation that day.

That moment crystallized something I’d been feeling for months. I run four brands: LeadershipinChange.com (this newsletter), Cozora.org (an AI live learning community), NewsletterCompass.com (an AI toolkit for newsletter creators), and my AI coaching business (jsalinas.org). Different colors, different fonts, different energy. For years, that meant opening PowerPoint (if you are under 30, “slide deck” ;) ), hunting for the right template, adjusting layouts, fixing alignment issues, swapping icons, and Googling “how to make slides pop” like everybody else. In fact, this is one of the most common patterns I see in coaching conversations with leaders working with AI: hours lost to manual tasks instead of substance and strategy.

I haven’t opened PowerPoint in months. I create every slide deck in Claude now, and the whole process takes about 10 minutes. Here’s how.

In this post, you’ll learn:

• How to generate a complete, downloadable slide deck inside Claude in under 10 minutes

• How to set up a brand skill so every deck matches your brand automatically

• A copy-paste prompt you can use right now with your next presentation

Quick Win (< 60 seconds) Open Claude right now, paste any document or set of notes, and type: “Create a 5-slide presentation from this content with minimal text per slide.” You’ll have a downloadable slide deck in under 2 minutes. Try it before you keep reading.

The Formatting Tax Is Real

Here’s a number that should bother you.

According to a GfK study, professionals spend approximately 40% of their PowerPoint time on formatting alone, which averages out to about eight hours per month per employee (GfK/empower, “The Big PowerPoint Study”). Not thinking about what to say, not refining the message. Formatting…

And this isn’t some rare once-a-year task. According to Decktopus’s 2026 State of Presentations report, 83% of professionals create or deliver presentations at least a few times per month, and 44% do it weekly or more (Decktopus, 2026).

I call this the Formatting Tax: the invisible hours you lose making slides look right instead of making the message land right.

I was paying it across four brands, which meant four sets of colors, four sets of fonts, and four completely different visual languages. The tax was enormous.

Most people’s solution is Gamma, or Canva, or some other AI slide tool. Those work well enough until you realize you’re still fixing layouts, still adjusting brand colors, still clicking through a design UI that puts polish ahead of message. The tool changed, but the tax didn’t go away.

What actually eliminated the Formatting Tax for me was discovering that Claude generates complete, downloadable slide decks directly inside the chat. No external tools. No templates to maintain. No PowerPoint at all.

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

Which Sounds Like You?

“I need systems, not just ideas” -> Join Premium (Starting at $49/yr, $1,345+ value): Tested prompts, frameworks, direct coaching access. Start here

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

The Workflow: Content In, Slide Deck Out

My actual process has three steps, and none of them involve opening PowerPoint.

Step 1: Paste your content.

Whatever you’re presenting (an article, meeting notes, a project update, a strategy brief), paste it directly into Claude. The AI doesn’t need a polished template. It needs your thinking.

Step 2: Tell Claude exactly what you want.

This is where specificity matters. You need to tell it how many slides, how much text per slide, and what the presentation is for. I wrote about this principle in Get 90% Better AI Responses With One Afternoon Setup: the more context you give Claude up front, the less you fix afterward.

Here’s the exact prompt I use, feel free to steal it… I created a presentation for this post.

Copy-Paste Prompt:

Create a professional slide deck presentation from the following content:

[PASTE YOUR CONTENT HERE]

Requirements:

1. [NUMBER] slides total

2. Minimal text per slide: headlines and short bullet points only, no full paragraphs

3. Clean, modern design with strong visual hierarchy and consistent color scheme

4. One key idea per slide

5. Include speaker notes for each slide with talking points

6. Smooth transitions between slides

7. The audience is [DESCRIBE YOUR AUDIENCE]

8. The goal of this presentation is [YOUR GOAL]

9. Use my [NAME BRAND] brand

Note: 9 requires a Claude Skill having been created with the brand guide you wish to use, more on that below.

Stop reading for a second. Think about your next presentation. The one you’ve been putting off, or the one coming up next week. Paste your content into Claude with this prompt and see what comes back.

Step 3: Download and present.

Claude generates the entire deck as an artifact you can download with one click, open it in any browser, go full screen, and present. Done.

Check out what my example resulted in…

I can now focus on the people I’m presenting to, the message I’m presenting, NOT formatting.

The Brand Skill Multiplier

Here’s where it becomes genuinely powerful.

I built a Claude skill for each of my four business brands. A skill is a set of instructions that Claude follows every time you activate it. Each brand skill contains the exact hex colors, fonts, tone, logo placement, and design language for that specific business.

For the example above, the Leadership in Change Presentation I requested, here was Claude’s process. See how clearly and accurately it pulled my brand…

So when I need a Cozora presentation, I activate the Cozora brand skill and paste my content. A Leadership in Change deck? I switch to the LiC skill. A World Relief internal presentation? Same thing, different skill. Every output matches the correct brand without me touching a single color picker or font dropdown.

If you want to set up your own brand skill, I wrote a complete walkthrough in 5 Claude Skills That Save Me 40 Hours Monthly.

The short version: open Claude, go to your profile settings, create a new skill, and paste in your brand guidelines with hex codes for colors, font names, logo rules, and the overall tone of your visual identity. Once it’s saved, every slide deck you generate follows those rules automatically.

Here… take a peek behind the curtain at my Leadership in Change Brand Skill on Claude (spoiler, it is not 10 lines).

The beauty of this approach is that it scales with zero additional effort. Whether you’re running one brand or four, the brand skill handles the formatting work once so you never touch it again. If you want help building this into your leadership practice, book a free discovery call.

For the Newsletter Writers Reading This

The Formatting Tax doesn’t just live in PowerPoint. It lives in your Substack editor too: the blank screen, the headline you’ve rewritten six times, the Sunday-night stat hunt. That friction is exactly why and I built Newsletter Compass, an AI toolkit that sets up once around your newsletter, your voice, and your audience so you can spend your time on the writing only you can do. Take a look (50% off for life with code WELCOME).

What This Approach Handles (and What It Doesn’t)

I want to be straight about the edges. These slide decks are clean and professional, but they won’t win a graphic design award. If you need pixel-perfect custom animations, embedded videos, or the kind of polish that a dedicated design team produces, you’ll still want that human touch for the final 20%.

But for the other 80%? The weekly team updates, the quarterly reviews, the training decks, the client presentations that need to be solid, on-brand, and done by tomorrow? This workflow handles all of it. And if you’re wondering how to share it with your team, I just download the file, drop it into Google Drive, and present from there. It works exactly like you’d expect.

In my case, that’s strategizing, planning my speaking approach, thinking about which personal story or metaphor will actually land with the people in the room. My time is not well spent designing PowerPoint slides, and it never was. This workflow gives that time back to the work that only I can do, and that’s the real win.

AI is allowing me to be my best self as a presenter, not because it makes my slides prettier, but because it gets the formatting out of the way so I can focus on what actually matters: the message and the people hearing it.

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If You Only Remember This

• The Formatting Tax is real: professionals spend 40% of their PowerPoint time on formatting instead of message quality, and Claude eliminates that entirely by generating complete, downloadable slide decks from your content.

• A brand skill turns a one-time setup into permanent brand consistency across every future presentation, which means zero time spent on colors, fonts, or templates ever again.

• You’re doing AI well when it gives you time to do what only you can do: strategize, plan your approach, choose the right story. That’s the real win, not prettier slides.

Try creating a presentation by Monday using this workflow. If any of it isn’t working, shoot me a direct message and I’ll be glad to help you personally.

Questions Leaders Are Asking

How do I create a slide deck using Claude AI? Paste your content directly into Claude and ask it to create a slide deck presentation as a downloadable HTML file. Specify the number of slides, text density (minimal bullet points work best), and your audience. Claude generates the complete deck as an artifact you can download and open in any browser, no PowerPoint or external tools needed.

Can Claude match my company’s brand guidelines in presentations? Yes, by setting up a brand skill in Claude with your exact hex colors, fonts, logo placement rules, and design tone. Once saved, the skill applies your brand automatically to every deck you generate. Joel Salinas uses separate brand skills for four different businesses, and each output matches the correct brand without any manual formatting.

Is an HTML slide deck as professional as a PowerPoint presentation? For most professional use cases, team updates, quarterly reviews, training decks, and client presentations, HTML slide decks from Claude are clean, well-structured, and fully presentable in any browser. They lack the advanced animations and highly customized layouts of dedicated design software, so keynote-level visual presentations may still benefit from a designer for the final polish.

How long does it take to create a branded presentation with Claude? Most decks take under 10 minutes from pasting content to downloading the finished presentation. Setting up a brand skill takes about 15 to 20 minutes the first time, but that investment pays off on every single deck you create afterward.

What makes this different from Gamma or other AI slide tools? Claude generates complete slide decks natively as downloadable files with no external tools, no templates to maintain, and no manual design adjustments. Tools like Gamma still require you to fix layouts, adjust brand elements, and work within their design UI. Claude with a brand skill eliminates the formatting layer entirely, so you spend your time on the message instead.

Sources Referenced

1. GfK/empower Suite, “The Big PowerPoint Study” (B2B study on PowerPoint productivity) — https://www.empowersuite.com/hubfs/Marketing/Downloads/The-Big-PowerPoint-Study.pdf

2. Decktopus, “Top Presentation Statistics for 2026: The State of Presentations” — https://www.decktopus.com/blog/top-presentation-statistics

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Here’s where this conversation started. I keep hearing the same thing from the leaders I coach, and what they’re asking about on the surface is strategy or tools or timing, but the thing underneath it, almost every time, is fear. Fear of not adapting to what’s coming, fear of getting left behind, fear of letting their team down when they’re not sure how to prepare them.

I brought on because she’s spent the last fourteen years walking straight into fear on purpose, one a year. She’s a two-time TEDx speaker, author of Kicking the People-Pleasing Habit, and host of the One Fear Per Year podcast. What I didn’t expect was how cleanly her work maps onto what AI leaders are actually wrestling with right now.

Outline

(00:00) – Why this conversation is different

(01:55) – Janice’s origin and the prison of fear

(03:00) – Year one was a marathon

(07:00) – What 14 years inside fear taught her

(08:30) – Fear of not being good enough

(12:30) – Writing Kicking the People-Pleasing Habit

(14:50) – The through-line for AI leaders

(19:25) – Caring vs. people-pleasing

(21:30) – Awareness, choice, community

(32:00) – Dropping to her knees on stage

(34:00) – One fear per year, your turn

A Few Things That Stuck With Me

The through-line nobody names. About fifteen minutes into the conversation, I realized the leaders I coach almost never use the word “afraid.” They talk about strategy, vendor selection, team readiness, timing. But the avoidance pattern is the same one Janice has been studying for fourteen years. Here’s how she said it:

“I would say an underlying thing is just the fear of not being good enough. And then there’s the fear of change. Or the fear of the unknown. None of us know what’s going to happen.”

That’s why a CEO keeps deferring the AI conversation with their team, and why a director quietly pushes the rollout one more quarter. The tool is the easy thing to talk about. The harder thing is sitting in the room and saying out loud that the team isn’t ready, or that the leader isn’t sure they’re ready either.

Fear lives in the future. Janice made a point that stopped me in the middle of the live. Fear is always anticipatory, which means you can only fight it from a place you’re not actually standing in. Her words:

“Fear is always this anticipated thing in the future. What’s going to happen? Worst case scenario. To lay in bed last night worrying about this conversation, that’s the fear. Coming back to the present moment is taking your power back.”

Read it again with AI in mind. Most of the AI anxiety I see in coaching is about an imagined version of next year, which is also why it’s almost impossible to act on. You can’t fix a future you haven’t reached yet.

Year one was a marathon. I want to come back to this part. Janice went straight at the one thing she was certain she couldn’t do, ran 26.2 miles, and learned the lesson that turned the whole project into a fourteen-year practice:

“Accountability is where it’s at. Community is where it’s at. We can do so many things and go so many places with the right people around us.”

That’s a leadership lesson dressed up as a running story. You don’t walk through fear alone, and you don’t push your team through AI adoption alone either.

The on-stage moment. A couple of years ago, in front of a room of CEOs and professional speakers, Janice had a physical fear reaction mid-talk and dropped to her knees on stage to keep from running off. Afterward, she described it as a spiritual experience, a moment of surrender where the facade dropped and the audience saw the actual Janice for the first time. Her line right after has been in my head since the live ended:

“There’s nothing worse than being held back by your fear. Not even this.”

I told her on the live it was the strongest line of the conversation. I still think so.

(If you’re a leader sitting with one of these fears right now and the AI piece is the surface story, this is exactly the conversation I have in 1:1 executive coaching. Sometimes the strategy work starts with naming what you’ve been avoiding.)

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One Question to Sit With

What’s the one fear you’d walk through this year, on purpose, if you knew you had three hundred and sixty-five days to figure it out?

Watch the full conversation above. Then go subscribe to Janice at janiceburt.com and grab her book Kicking the People-Pleasing Habit.

If you want help thinking through your own AI strategy or online presence, start here.

Written by a human, for humans.

Here’s the thing, I was talking to a leader not long ago, and they brought up the same frustration I hear from almost every executive I talk to: trying to keep up with every AI tool. Chase every release, and the best you get is a shallow understanding of all of them. That’s where leaders miss the moves that actually matter.

I don’t try to keep up with every release. I co-founded Cozora partly to solve that problem: hearing weekly from experts who go deep on each tool category, so I don’t have to drown in announcement cycles. Most weeks, there’s nothing worth writing about.

This week is one of the exceptions. Google shipped Gemini 3.5 Flash and Gemini Omni, and both deserve a leader’s attention. The tech press is going to get the takeaway wrong, though, so let’s walk through what’s actually there.

In this post, you’ll learn:

• The chess test I use to decide which AI releases earn my time

• What Gemini 3.5 Flash actually delivers, behind the speed headline

• What Gemini Omni changes for marketing and creative leaders

• The one move worth making this week, and the one most leaders will get wrong

The Chess Test

A serious chess player doesn’t study every game ever played. They study the games that changed how the game is played. AI releases work the same way.

Here’s the three-question filter I run every release through:

1. Does it change a workflow I actually run? A workflow I’m running this week where failure would cost something real, not a hypothetical I might pick up someday.

2. Does the improvement compound? A 10% speed bump is interesting. A 30-point hallucination drop changes what I can hand off to AI in the first place.

3. Does it shift what’s possible, or just what’s faster? Faster is a feature update I can ignore until next quarter. A release that shifts what’s possible is a board change I have to plan around now.

If a release doesn’t clear two of three, I let it pass. Most leaders I coach don’t have a filter like this. They have a feed instead. Every release feels equally urgent, which means none of them gets real attention. That’s the trap, and the discipline of knowing what to ignore is what adaptability in the AI Leadership Triad actually looks like in practice.

Why Gemini 3.5 Flash Passes

Gemini 3.5 Flash is Google’s high-throughput agentic model. It runs at roughly 277 tokens per second at $1.50 per million input tokens. Those are the numbers you’ll see in every headline.

Here’s what actually matters: the hallucination rate dropped 31 points compared to Gemini 3 Flash, from 92% down to 61%, per Artificial Analysis data published in May 2026. That’s the win. Speed lets you scale a workflow. Lower hallucinations let you trust a workflow with something that matters. Macquarie Bank is already using Flash to reason over 100-plus page tax and onboarding documents, the kind of work that used to take their team weeks. That’s the agentic era arriving in a regulated industry, not a demo.

Compared to Claude Sonnet 4.6 at $3 per million input tokens and GPT-5.5 at $5, Flash is the cheapest agentic model on the headline numbers, running roughly 4x faster than either. Whether that holds in practice depends on the next two paragraphs.

Now the catch. Two of them.

First, the headline price isn’t the real price. On complex benchmarks, Flash runs roughly 5.5x more expensive than its predecessor because it emits about twice as many tokens to reach the same answer. Compare effective cost rather than sticker price before you migrate.

Second, long-context retrieval regressed. At 128K tokens, recall dropped to 77%, down from 85% on Gemini 3.1 Pro. If you’re feeding Flash a 200-page board packet and expecting it to reliably pull the right paragraph back, you’re going to see hallucinations of a different flavor. Test before you trust.

Don’t pick a model based on one number. Pick based on the workflow you’re putting it in.

Here’s Google’s quick Gemini 3.5 Flash demo… (credit, Google)

Why Gemini Omni Passes

Gemini Omni is Google’s Veo 4 native multimodal model. It produces 15 to 30-second clips at 4K resolution, with multi-angle scene control from a single prompt and conversational editing. Tell it to swap a coffee cup for a tea cup, and it does, without destroying the rest of the scene. That’s new.

For marketing and creative leaders, this passes the chess test for one reason: it collapses production cycles you currently outsource. The first draft of a product demo, a recruitment video, a customer testimonial sequence: work that took a week now takes an afternoon. That has real implications for how creative teams should balance AI and human craft.

The gate is cost. Omni eats tokens at a rate that makes it the wrong default for everyday content. It’s a specialized tool for high-value storytelling, not a commodity utility for filling a content calendar. Use it where the final asset justifies the spend. Skip it where a Loom recording would do.

Here’s a glimpse of what it can do…

The One Move This Week

Pick one workflow where hallucinations have actually hurt you in the past 90 days. Pilot Gemini 3.5 Flash on that workflow for 30 days. Don’t migrate everything. Don’t announce a strategy. Measure cost-per-completed-task, not cost-per-token. Then decide.

Now… what’s the last AI release you tried to adopt that didn’t stick, and what would have made you pass on it earlier?

For further reading, check out ‘s latest also on this release:

Product with Attitude

Gemini Omni Flash: Cute Videos, Serious Product Strategy, and the Future of Editable Reality

Read more

a month ago · 53 likes · 21 comments · Karo (Product with Attitude)

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Questions Leaders Are Asking

What is Gemini 3.5 Flash?

Gemini 3.5 Flash is Google’s high-throughput agentic AI model, released in May 2026. It runs at roughly 277 tokens per second at $1.50 per million input tokens and $9 per million output tokens. The most consequential improvement over Gemini 3 Flash is a 31-point reduction in hallucinations, dropping from 92% to 61%.

Is Gemini 3.5 Flash actually cheaper than Claude or GPT-5.5?

Not on a real-world basis. Headline pricing is $1.50 per million input tokens, less than Claude Sonnet 4.6 ($3) or GPT-5.5 ($5). But Gemini 3.5 Flash emits roughly twice as many tokens to reach the same answer, making it about 5.5x more expensive than its predecessor on complex benchmarks. Look at effective cost rather than the sticker price.

What is Gemini Omni used for?

Gemini Omni is Google’s Veo 4 multimodal model, designed for production-grade video. It generates 15 to 30-second clips at 4K resolution with multi-angle scene control and conversational editing. It’s a specialized tool for high-value storytelling like marketing assets, recruitment videos, and product demos, not for everyday content workflows.

Should I switch from Claude or ChatGPT to Gemini 3.5 Flash?

Not without testing. Pilot Gemini 3.5 Flash on one bounded workflow where hallucinations have hurt you, for 30 days. Measure cost-per-completed-task, not cost-per-token. The pricing advantage gets eaten by Flash’s token chattiness on complex tasks, and long-context recall regressed to 77% at 128K. Test before you migrate.

How do I decide which AI releases to actually pay attention to?

Run every release through three questions: Does it change a workflow you actually run this week? Does the improvement compound (a 30-point hallucination drop, not a 10% speed bump)? Does it shift what’s possible or just what’s faster? If a release doesn’t clear two of three, let it pass. Most don’t.

What is the agentic era in AI?

The agentic era describes AI models capable of autonomous, multi-step reasoning over real workflows, not just one-shot text generation. Gemini 3.5 Flash is a benchmark example: it runs production agentic loops at high speed with significantly lower hallucinations. Enterprises like Macquarie Bank now use it for 100-plus page document reasoning in regulated industries.

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.

Written by a human, for humans.

Here’s the thing about AI mandates… They almost never arrive as requests, they arrive as pressure.

Someone senior says “we should do something with AI,” and suddenly your sprint has a commitment nobody scoped, nobody owned, and nobody agreed to. Three weeks later, you are cleaning up a pilot that quietly became permanent scope.

Every leader I coach through AI adoption sees this same pattern. A VP asks for a “quick AI summary tool” in a Tuesday meeting, by Friday it is on the roadmap, by month-end it is an integration commitment, and nobody can remember who said yes. I have watched this play out with the startup CEOs, marketing VPs, and mission-driven leaders, which is one of the reasons most AI implementations fail before they even get a fair test.

has much to share in this area. He writes Empathy Engine on leadership and team decision quality, and this piece names the three structural failures sitting underneath almost every AI pilot that goes sideways: mandate pressure, decision collapse, and intake collapse.

This is one of the most common patterns I see inside fractional CAIO engagements with mid-market teams. If your team is living it, you are not alone.

I will let Mark take it from here.

Empathy Engine

Leadership, teamwork, and collaboration strategies for a smarter, stronger workplace. Be part of the movement refining teams. Lock in your PreSale now and help make my book Collaborate Better the next great bestseller📘 CollaborateBetter.us

By Mark S. Carroll

AI Didn’t Break Leadership. It Exposed the Decision Gap

You can feel it in every meeting now. More ideas, more urgency, more requests to “do something with AI” and somehow worse decisions. The room sounds productive. Risks get mentioned. Possibilities multiply. Then the meeting ends, and the actual work enters the same overloaded, under-owned system that was already vulnerable to the loudest voice in the room.

If you own a roadmap, lead delivery, or sponsor AI initiatives, this is about why your quarter keeps turning into cleanup and why the standard fixes keep making it worse.

Most organizations still misdiagnose what is happening. They think the problem is communication. Too many voices. Not enough alignment. Too much change at once. So they respond with the move that sounds responsible: more meetings, more stakeholder review, more sessions designed to get everyone on the same page. That can improve sequencing. It can surface disagreement. But it only solves the problem at the level of coordination.

A team that cannot answer who decides, based on what, and under which constraints does not need another alignment session. It needs a different operating condition.

The Misdiagnosis and the AI Mandate Trap

AI did not create organizational chaos from scratch. It accelerated existing weaknesses. It multiplies options faster than trust can scale. It compresses timelines faster than governance can adapt. It raises perceived urgency without increasing decision clarity. That is why so many teams feel like they are drowning. They are not facing more work. They are facing faster consequences for old weaknesses.

This is the AI mandate trap. The mandate arrives carrying symbolic urgency before it carries a business case. A normal request must prove itself. An AI request often arrives pre-blessed. Teams start from social permission instead of disciplined intake. The social permission is not neutral: it signals that pushback reads as resistance, that asking for a business case sounds like obstruction, that the fastest path to safety is visible enthusiasm rather than honest triage. The result is familiar: more input, more escalation, less ownership.

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The popular story is that the technology is not ready. The deeper story is upstream of that. The organization has no reliable way to convert excitement into accountable decisions.

The financial pattern reinforces the structural one. Organizations that cut headcount to fund AI adoption routinely discover the savings were a myth. Technical debt compounds. Rehiring costs triple. Token overruns spike past the salaries these were supposed to replace. The cost-cycle reversal is predictable, and it starts the moment intake discipline gets skipped.

The Decision Moment

Every team hits the same moment. A decision must be made. The room is full. The stakes feel high. And something subtle breaks. Priority shifts without explanation. Risks vanish until after delivery. Execution reality gets replaced by optimism. The meeting ends, but the rationale does not survive the room.

The tempting move is to trust that enough senior people talking long enough will produce a sound answer. Under normal conditions, sometimes they do. Under pressure, that faith gets expensive.

That is not a personality problem. It is a decision survivability problem. Most teams trust the social chemistry of the room more than the structure of the decision. They assume that if enough smart people contribute, the answer will somehow become sound. Under time pressure, that faith is costly. The evidence is clear: under stress and social evaluation, decisions improve when cognitive load is lowered, the first step is obvious, the prompts are concrete, and the structure supports team communication rather than solo memory. Structure matters more when the room gets hot, not less.

That is where the Fab Four matters (my Beatles inspired approach to assure there’s team within your team to manage ongoing decisions effectively). Each lens names what the room tends to leave out:

• Priority (Paul): what matters most right now, not what arrived most recently.

  • Product Manager/Owner

• Reality (George): what the team can deliver well given the people and systems available today.

  • Lead Engineer, Architect

• Risk (John): what assumptions are being treated as certainties and should not be.

  • Quality Assurance, Testing

• Impact (Ringo): who lives with the consequences after delivery, not just during approval.

  • Business Analyst

The point is not richer discussion for its own sake. The point is constrained lenses that make the decision strong enough to survive contact with reality. If those four things/perspectives are not explicit before the meeting ends, the decision is running on social chemistry, not structure.

Then comes the rule most teams avoid. Someone still must take the bet. The team lead decides not in isolation and not by consensus, but after running the circuit. Without that rule, the circuit becomes theater.

Without that structure, most organizations default to the same pattern: status meetings become forensic investigations, informal agreements dissolve under pressure because ownership was never documented, and reactive fire drills consume a fifth of engineering bandwidth before anyone names the drain. That is the decision ghost. It haunts velocity long after the meeting that created it ends.

The alternative is a structured decision circuit where each lens has a name and each decision has a single accountable owner. The Fab Four provides that circuit. Here is how the four lenses map in practice.

The Intake Collapse

Even teams that fix decision quality often fail one step later. Decisions do not arrive one at a time anymore. AI does not slow down after the first pilot. It accelerates. New requests keep coming. New experiments surface. New urgency keeps arriving.

Most teams believe they have a prioritization problem. What they actually have is intake collapse. That distinction matters. Prioritization assumes the work has entered a system that can see it clearly. Intake collapse means the front door is already broken. Every request arrives wearing the same urgent badge. Everything gets discussed. Nothing gets filtered.

This is exactly why triage and intake discipline is one of the strongest demand clusters among product and delivery leads right now. They are not asking for more AI inspiration. They are asking for a front door.

The five-step intake funnel provides that front door. Each step does a specific job:

• Capture: keeps requests from bypassing visibility before they gain social momentum.

• Clarify: forces someone to define what “done” means before work begins to sprawl.

• Qualify: introduces value, effort, and risk before politics harden into commitment.

• Approve: makes the tradeoff visible to the right people at the right moment.

• Commit: introduces limits which is where most organizations lose their nerve.

Organizations are willing to discuss tradeoffs. They are much less willing to enforce them. The intake funnel exists to close this gap.

Consider what this looks like in practice. A VP asks for an AI feature to summarize customer calls. In the old system, it enters as a strategic priority because it came from above. In the new system, the team runs the map, discovers there is no named data-risk owner and no success threshold, and routes it into experiment rather than commitment. The meeting ends with a bounded pilot instead of an invisible obligation. That is the difference between a system that protects delivery and one that rewards escalation.

The evidence favors short, phase-specific, printable tools that fit inside existing rituals and can be run under stress in one to three minutes. A dense one-pager gets ignored. A shallow, concrete, role-anchored intake path has a fighting chance.

WHAT YOU NOW KNOW

The diagnosis is now complete. AI did not create these problems. It accelerated three existing weaknesses that were already costing teams their quarters: mandates that arrive pre-blessed instead of earned, decisions that do not survive the room because the room trusts chemistry over structure, and intake systems that treat every request as equally urgent until the sprint collapses.

None of those failures are communication problems but structure problems. And knowing that changes what you look for in the next meeting.

What this article did not cover is what happens once work gets through the front door. The cost of treating all work the same once it enters the system. What governance actually looks like when accountability travels with the work instead of sitting above it in a committee. How teams draw the line between a safe experiment and an invisible commitment before the pilot becomes permanent scope.

Thank you, .

Mark’s framing lands because it names what most leaders feel but cannot articulate: the front door is the governance moment. Not the committee meeting, not the slide review. The intake itself.

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If your team is the one cleaning up after these mandates, you are not alone… in fact, most AI investments that fail do so for exactly this reason.

For more of Mark’s work, subscribe to Empathy Engine.

FAQ — “QUESTIONS LEADERS ARE ASKING”

1. What is the AI mandate trap? The AI mandate trap is the pattern where AI requests arrive pre-blessed with social permission, bypassing normal intake discipline. Teams start from enthusiasm instead of scrutiny, pushback reads as obstruction, and pilots quietly become permanent commitments nobody formally agreed to. The fix is structural intake, not more alignment meetings.

2. Why do AI pilots keep becoming permanent scope? Because most teams lack a structured intake funnel that forces every request to prove readiness before work starts. Without a commitment step that defines the experiment boundary, a pilot keeps absorbing new asks until it becomes an obligation. Bounded experiments need explicit success thresholds and named owners before the first sprint.

3. How should product or delivery leads triage AI requests? Run every AI request through a five-step funnel: capture so it stays visible, clarify what “done” looks like, qualify value and effort, approve the tradeoff explicitly, and commit with limits. Requests that cannot name a success threshold or data-risk owner route to experiment, not commitment.

4. What is the difference between an AI experiment and an AI commitment? An experiment has a named owner, a bounded timeline, a single success metric, and a kill criterion. A commitment has none of these and runs on whoever shouted loudest at approval. Most AI work enters orgs as an implied commitment. The fix is routing it back to experiment until the bounds are explicit.

5. How do I push back on an AI mandate without sounding like I am resisting AI? Run the mandate through the intake funnel publicly. Ask for the success threshold, the data-risk owner, and the experiment boundary. Frame the questions as “help me deliver this well,” not “should we do this.” Structure makes the pushback impersonal and makes the mandate answer for itself.

6. What does good AI governance actually look like for a small team? Short, printable tools that run inside existing rituals in one to three minutes. Dense policy documents get ignored. A one-page intake form, a four-lens decision circuit (priority, reality, risk, impact), and a single kill criterion per pilot carry most of the governance weight for teams under 200 people.

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

Written by a human, for humans.

Ray Dalio built Bridgewater into one of the world's largest hedge funds on a set of written principles. At its peak, the firm managed $150 billion.

I‘ve read Ray Dalio’s Principles: Life and Work more than once. Re-listened to it last week, actually, and I keep coming back to it because it works. (If building that kind of operating clarity is something you’re working on, that’s exactly what I help leaders do.)

Dalio asks every leader to do something most never do: write down how you think, document the logic behind your decisions, and build a system that holds you to your own standards when you’re tired, distracted, or under pressure. That philosophy has shaped how I lead and how I coach, because great decisions come from consistency, not from being the smartest person in the room.

What I didn’t expect was to watch Dalio take that same philosophy and encode it directly into AI. What he built is harder to ignore than anything he’s said about leadership in years, and the part that applies to you doesn’t require Bridgewater’s budget or engineering team.

In this post, you’ll learn:

• How Ray Dalio encoded his decision-making principles into AI at Bridgewater

• The difference between reactive AI and proactive, principle-based AI

• What the Principles Stack is and how to build one in Claude or ChatGPT today

• How to download a free Claude skill built on Dalio’s 12 core principles (premium members)

What Dalio Actually Built

Most coverage of Bridgewater and AI focuses on investment returns. That’s the wrong lens for this conversation.

In early 2026, Bridgewater (Dalio's hedge fund, one of the largest in the world) launched an AI-driven strategy called AIA Labs that surpassed $5 billion in assets under management. The number matters less than the method. Traditional quant models follow pre-set statistical rules. AIA Labs uses machine learning that adapts, discovers patterns no human analyst would find, and gets better over time. Dalio moved from rule-following to principle-learning. That shift is the point.

Digital Ray is the part that stopped me. Dalio trained an AI on decades of his personal writings, values, and perspectives.

Not a search engine or a chatbot, but a thinking partner built in Dalio’s image.

Then there’s PriOS, short for Principles Operating System, the internal software Bridgewater uses to run management decisions. Four tools that automate management itself, but the two that matter most for leaders outside Bridgewater are The Coach and the Dispute Resolver. The Coach lets any employee input a question and receive guidance rooted in Dalio’s codified principles, not generic AI advice, but his specific decision-making logic available to everyone on the team. The Dispute Resolver provides a structured path of questions that guides disagreements to a principled resolution based on the framework, not on whoever has more authority.

The other two tools, a real-time peer rating system and an evidence-based performance profile for every employee, round out the suite.

The goal across all four: three-quarters of all management decisions at Bridgewater made and communicated by PriOS.

What strikes me about this is how directly it maps to something I see in coaching constantly. Leaders have good instincts, but they have no system for making those instincts consistent across their team. Dalio built that system. The question is whether the rest of us will.

Quick Win (< 60 seconds) Open Claude settings instructions and paste this: “Before giving me any advice, apply Ray Dalio’s 5-Step Process: ask me to clarify my actual goal, identify the specific problem standing in the way, diagnose the root cause rather than the symptom, suggest a solution that fixes that root, and define who does what by when. Now ask me what I’m working on.”

Use this today on any business or team decision you’re wrestling with. It changes the quality of Claude’s response immediately.

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This article gives you the framework. If you’re looking at your own leadership situation and wondering how to set this up, here’s the best next step.

Join Premium ($49/yr): Get the full Dalio Principles Advisor skill as a free download, plus tested prompt libraries, frameworks, and exclusive resources every month. Start here →

Also, I want to thank my newest partner, Jotform.

Did you know that the premier form creator now integrates with Claude? Well, they do! And as a Leadership in Change Subscriber, you get 50% off. Try it today!

The Shift Nobody’s Talking About

Here’s what most AI articles miss about what Dalio built.

The standard way most leaders use AI, including most of the leaders I work with, is reactive. You have a problem, you ask the AI, it gives you an answer. It's useful, but it's limited, because you're still the bottleneck. The AI is waiting for your questions, and its answers are filtered through the internet’s statistical average, not your principles.

Dalio’s approach is different. His AI works in parallel with him. It doesn’t wait for questions. It processes the same information he does and reaches conclusions based on his specific values and reasoning style. He calls this “parallel decision-making,” and it’s the actual breakthrough buried inside the Bridgewater story.

For AI to function as a genuine thinking partner, it needs to understand three things about you:

1. Your preferences — the choices you consistently favor and why

2. Your decision-making style — the methodology you actually use to reach conclusions

3. Your principles — the specific, named rules you hold yourself to

Stop and look at this table. Which column describes how you currently use AI?

Most leaders are in the left column. Dalio built the right column. The Principles Stack is how you get there.

The question isn’t whether you use it. It’s whether it knows how you think.

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The Principles Stack

Here’s the framework: The Principles Stack.

The Principles Stack is the practice of encoding a proven decision-maker’s mental model into your AI, so every problem you bring to Claude gets filtered through that lens before it reaches you, rather than filtered through the internet’s average opinion on what you should do.

Dalio did it with his own principles. You can do it with his. Three steps:

Step 1: Choose a framework with named, specific rules.

Dalio’s Principles works because it isn’t vague. “Embrace reality and deal with it” is a rule you can act on. “Pain plus reflection equals progress” is a rule. Compare that to vague values like “be transparent” or “be curious” — those give AI nothing to work with. Named, specific principles do. This is also why I’ve written about the importance of building AI that actually pushes back on you — a principles filter creates the disagreement that makes AI useful.

Step 2: Load it as operating logic, not just context.

Most people give Claude information about their situation. The Principles Stack gives Claude a filter — a set of rules it applies to every response before it answers you. That’s a fundamentally different setup. I explored the foundation of this kind of system in How to Build Your AI Decision Partner. The Dalio skill takes that further by encoding an entire proven framework.

Step 3: Bring it your real decisions.

Stop asking “what should I do?” Start asking: “Here’s the situation. Run it through the framework. Give me an honest diagnosis, including where I might be fooling myself.”

That last part, the blind spot check, is the piece most AI interactions skip entirely. Dalio built it into PriOS by design. I built it into the skill.

If you want help applying this to your specific leadership context, book a free discovery call and we’ll map it out together. ;)

What My Own Version Looks Like

I’m not Ray Dalio. I don’t run a hedge fund. But I lead four businesses, coach executives, and write a newsletter, and I’ve encoded three of my own principles into a Claude project I use every day:

1. Always ask “who is the human on the other side of this?” before drafting.

2. If the recommendation could be written by any AI leadership blog, kill it.

3. Push back on me when the easy answer is the wrong one.

Three sentences. That’s the entire stack. And it changes every single response I get back. The point isn’t to copy mine. The point is yours probably already exists, written down somewhere or living in your head, and you’ve never given AI access to it.

Now, I have built a Claude skill based on Dalio’s framework. It’s called the Dalio Decision Advisor.

When you invoke it, Claude asks what decision you’re working through, then delivers a brief analysis: a Reality Check that separates fact from feeling, one clear Recommendation with a concrete first step, and a Blind Spot Check that names what you might be avoiding. If you need more, ask it to go deeper. It’ll run the full 5-Step root cause diagnosis and map the 2-4 Dalio principles most relevant to your situation.

It’s designed for Cowork. Paste the instructions, and it’s ready to run. If you want to see how I use Claude skills across my full workflow, 5 Claude Skills That Save Me 40 Hours Monthly is a good place to start.

This skill is built on Dalio's publicly documented framework from Principles, not an official Bridgewater product. It's my interpretation of his decision-making logic, encoded into a Claude workflow any leader can use.

Premium members download it free here…

Not a premium member? The Quick Win prompt above gives you the same logic manually. Try it today. Don’t wait on the perfect setup.

And if you want this kind of principled AI system built for your team or your specific role, I can help.

If You Only Remember This

• Ray Dalio didn’t use AI to do more. He used it to think better, consistently, by his own principles.

• The Principles Stack works because named, specific principles give AI a real filter. Vague values don’t. The more precise your framework, the more precise what comes back.

• AI that doesn't know your principles will only ever give you the internet's average answer. Dalio refused to settle for that, so should you.

What about you? Is there another leader’s decision-making framework you’d want to encode into your own AI, maybe someone whose thinking you’ve studied for years? Drop it in the comments. I’m genuinely curious who else’s principles belong in a Claude skill.

Questions Leaders Are Asking

What is Ray Dalio’s Principles book about? Principles: Life and Work (2017) documents the decision-making framework Dalio built over five decades at Bridgewater Associates. The book covers radical transparency, believability-weighted decision making, and the 5-Step Process for turning goals into outcomes. It has sold over 4 million copies and is widely used in leadership development programs across industries.

How is Ray Dalio using AI at Bridgewater in 2026? Bridgewater’s AI strategy includes three components: AIA Labs (a machine learning investment strategy surpassing $5 billion AUM as of early 2026), Digital Ray (an AI system trained on Dalio’s personal values, achieving 95% effectiveness on life and work advice), and PriOS (a Principles Operating System that automates management decisions through tools like The Coach, Dot Collector, Baseball Cards, and Dispute Resolver).

What is the Principles Stack? The Principles Stack is the practice of encoding a specific, named decision-making framework into an AI tool so it filters every response through that lens. Rather than asking AI for generic advice, you give it a structured set of principles that shape how it diagnoses problems and forms recommendations. Dalio’s framework is one example — any named, principle-based system works the same way.

What is the difference between standard AI and a curated “My AI”? Standard AI is reactive — it answers questions using general internet data. A curated “My AI” (Dalio’s term) is proactive — it works in parallel with the user, making decisions based on that person’s specific values, preferences, and decision-making style. The goal is decision partnership, not information retrieval.

How do I use Ray Dalio’s Principles with Claude? The fastest approach is to paste the Quick Win prompt from this article into any Claude session. For a full structured experience, download the Dalio Principles Advisor Claude skill (available to premium members above) and install it in Claude Code or as Project Instructions in Claude.ai. The skill runs every challenge through 12 Dalio principles and ends every session with a blind spot check.

Sources

• HedgeCo Insights, “From Principles to Algorithmic Intelligence: The Evolution of Decision-Making at Bridgewater and Beyond,” March 2026

• Ray Dalio, Principles: Life and Work, Simon & Schuster, September 2017

• Digital Ray efficacy data: 95% life/work advice, 80% market/economic analysis — March 2026 research brief

• PriOS management target (three-quarters of decisions): March 2026 research brief

Joel Salinas is an Executive AI Coach who helps leaders at small and mid-sized businesses and nonprofits implement AI without the hype. He writes the Leadership in Change newsletter and runs jsalinas.org.

Written by a human, for humans.

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Outline

(00:00) – Welcome David Allen and the one-paragraph version of GTD

(02:00) – Mind like water and the strategic case for a clear head

(05:00) – The accidental career and how the five steps came together

(09:00) – Why AI didn’t change the principles, just the volume

(12:20) – Channel creep and the new pressure on knowledge workers

(15:00) – Decision support, not decision making

(19:00) – The Tesla farmer-strike story and AI already inside your life

(22:00) – Critical thinking as a muscle AI quietly atrophies

(26:00) – Pen and paper still wins, because your phone is a black hole

(33:00) – Addiction to ambient anxiety and why GTD doesn’t stick

(38:00) – Journaling as creative capture

(40:00) – One integrated system, no home/work split

(44:00) – Moral Ambition by Rutger Bregman

A Few Things That Stuck With Me

Decision support, not decision making. Honestly, this is the cleanest mental model I’ve heard yet for what AI actually is in a leader’s workflow. David put it this way: “I use ChatGPT three or four times a day. Where’s the local place to buy the best old cheese here in Amsterdam? It’ll give me a lot of good data.” That’s decision support. Then he turned the screw: “Trusting it to be able to make the decision about what to buy for mom’s birthday might be the inappropriate thing to do.” That line between the two is where most leaders quietly get into trouble, because they start treating decision support like decision making and forget that they still have to be the one who chooses. (If your AI never disagrees with you, you’re using it wrong.)

Channel creep. Look, this is the one I’m still chewing on, and David coined the term live. The volume of work hasn’t really changed that much in the last 25 years, but the channels have multiplied so fast that most leaders are now checking five or six different places just to make sure they’re not missing something that matters. Slack. Outlook. Asana. Google Meets. Two phone notification streams. He asked me on camera how many things I actually have to check to feel like I’m seeing the right stuff, and I rattled them off without thinking. That’s the diagnosis. (Start 2026 with an AI tool detox.)

Your phone is a black hole. Here’s the thing. David has been doing this for 40 years and he still uses pen and paper for capture, and his reasoning lands the second you hear it: “For most people, their phones are black holes. They throw stuff in there and they don’t process it. They don’t deal with it.” I caught myself agreeing with him out loud. My own setup is a double-tap on the back of my phone that fires dictation into my email, because the second I open the screen for any other reason, I’m gone, and so is the thought I was trying to keep. The phone is genuinely the worst place to put the thing you most need to think about later. (The complete Second Brain blueprint.)

Addiction to ambient anxiety. Honestly, this was the answer to a question I didn’t know I was carrying into the conversation, which was why so many leaders read Getting Things Done, get clear for a weekend, and then quietly fall off. David’s line: “Be aware of your addiction to ambient anxiety. Your comfort zone is a lot more comfortable than being out of your comfort zone, which is having absolutely nothing on your mind.” Read that twice. What looks like a discipline problem from the outside is almost always your nervous system getting bored of feeling calm and going hunting for something to worry about, which is a much harder thing to coach somebody through than a missing checklist. (Don’t outsource your thinking, even to AI.)

If you’re a leader running on adrenaline and you can feel it showing up in your decisions, that’s the work I do one-on-one. Start the conversation here.

When you check your phone right now, is it because something actually needs you, or because you’ve gotten too comfortable being needed?

Watch the full conversation above. If anything from David landed for you, go subscribe to David Allen on Substack and read Getting Things Done. 49 minutes with David is the cheapest leadership coaching you’ll find this year, and the book is the next one on top of it.

Written by a human, for humans.

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