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If you’ve been following FullStack HR for a while, you know I’ve been writing about how AI will impact HR since January 2023.

I’ve been both positive and negative. And while it’s probably fair to put me more on the “AI hype” end of the spectrum, this was one of my conclusions in that first article about ChatGPT:

Since then, I’ve relentlessly tried to put the pieces together: where is this going, and what does it mean for HR and the work we do?

I’ve done so by talking to people, both at the labs and in other organizations. I listen to so many podcasts and YouTube videos a week that I sometimes wonder if it’s healthy. On my bike, running, commuting, cooking. (Always at 2x speed, just to get more information in.) I read the news every morning, every night, and usually in between.

And then, perhaps most importantly, I test the models deeply and thoroughly. Ask my wife, and she’ll tell you about the late nights.

Along the way, I’ve encouraged you to play around with the technology yourself. To use it, understand it, and form your own opinion.

Having worked with GPT-6 Astra since launch, mine has become considerably firmer.

I’m now convinced that, from a technical perspective, AI can replace a substantial share of the work we do in front of a computer. In my own work, I’m seeing it complete tasks at an equal or better quality than I would expect from HRBPs on my team.

I thought it would take us a bit longer to get here.

My bad.

The computer use capabilities are mind-blowing. I remember being blown away when this launched. Fast-forward to today, and Astra can do (almost) whatever I do on a computer, fast.

That’s my experience with the work I’ve given it so far. It’s also why I’ve become so convinced that we’ve crossed the bridge where we, on one side, hypothesize about what AI could do and, on the other, conclude what I can do. We are now on the can-do-side.

As an example, we’ve been talking about MCP and connectors here, and sure, they’ll continue to make your life easier. But if your HR system lacks one, the AI may now be able to use the same interface you do and act on your behalf.

(Yes, simplification, I know. Access, permissions, and getting the result right still matter. But technically, once again… the capability is there.)

So if we are on the can-do side, what should we expect? Just because we are on that side doesn't mean we'll be replaced from one day to the next. Other factors affect how an AI operates. Governance, data structure, laws. Things we’ve put in place to safeguard our organizations, and things we still need to take seriously.

But when an AI gets stuck, we need to understand why. It might lack the capability. It might lack the information. Or it might be perfectly capable of taking the next step but lack permission to do so.

Those are different problems.

My guess is that more of the limitations we’ll encounter will come from everything around the model. That doesn’t make them small problems, but it does change what we need to work on.

Astra isn’t perfect. Neither are humans. And as said, technical capability alone won’t mean mass layoffs overnight. Organizations still need to figure out how to use it, how to check the work, and who takes responsibility for the result.

But if I’ve been a bit on the fence about when and how this will impact us in HR, I’m more convinced than ever that it will not only impact our work; it will change how we work, or whether we work at all.

We’ve been marching towards this for a while. I’ve spent years writing about it and trying to understand it. Even so, working with Astra has made me reconsider the timing. What I wrote about in January 2023 is materializing faster than I expected.

The pace is not slowing down, and I’ll keep trying to figure out what that means for HR and how we work until there is nothing left to figure out.

We’ll see when that happens.

And to the skeptics out there: if GPT-6 on Ultra has failed you, please let me know in the comments. I would love to hear where it’s falling short, because I’ve struggled to make it fail at the work I’ve given it.

Tell me what you tried, where it got stuck, and what you had to fix.

Welcome to FullStack HR, and an extra welcome to the 12 people who have signed up since last week.

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Happy Wednesday,

I feel fully back from summer now, slowly but steadily moving back into the daily routines of working with organizations and leaders almost daily.

It’s great to be back meeting teams and leaders; it’s where I form and hone most of my thinking, and I’m going to go against the jante law and say that I’m getting pretty good at working with organizations on AI and leadership.

The convergence of having spent my life in HR with great mentors and colleagues, paired with working in the tech industry, and being insanely passionate about HR Tech and how it will impact work since 2010, has led to this, and I feel happy and fortunate to be here.

If you need help rethinking your leadership programs around AI, reach out! And the offer from summer still stands! If you need a second opinion on HR Tech, leadership programs, or anything else regarding AI, I’d be happy to help for free. (3 people have done so so far!)

That said. We need to talk about skills again, so let’s get to it.

And yes, I know we have talked about skills before, but we need to do it again since skills are evolving and are in almost all AI tools today.

ChatGPT has them.

Claude has them.

Copilot Studio (and Copilot Cowork) has them.

Skills are how organizations extract value from AI tools right now.
(At least if you want to extract value beyond the individual use cases.)

But if you look under the hood, what is a skill then? At it’s core, a skill is a set of text instructions that tells a model how to do a certain thing. That’s the essence of it! A text file.

How you create one differs a bit between the different tools, but the substance is identical everywhere. Written instructions, saved somewhere the model can find them, reused every time that task comes around again.

That’s it. (No secret witchcraft.)

Two ways to create one

So how do you create one? I see two ways. (There are probably more ways, but this is how I do it.)

The first way is to let the model analyze what you already do.

If you have a process document of some sort, where you’ve clearly defined the steps that process takes, you already have a skill sitting there in draft form. Feed it in and ask for a skill. Most organizations have far more of these documents than they think, quietly rotting in a SharePoint folder somebody created in 2019.

The second way is to just do the work you want done.

You do the work once, together with the model, and let it capture the workflow while you go. You correct as you move. You say what went wrong, you explain what you want different, you get picky about the format. Then you ask it to package the whole thing into a skill.

I prefer this method, but we are all different, so use whichever one you can start with today.

A concrete example: performance reviews.

Let’s take an example, because this sometimes gets abstract fast.

Say you’re about to run performance reviews in your organization. The obvious move is to build a skill for the review itself, and sure, you can. But the more useful move sits one step earlier.

Build a feedback-gathering skill.

A skill that asks the manager a set of very specific questions about each of the people reporting to them. Not “how has it gone”, which produces nothing. The questions you’d ask if you were sitting in the room with them. What did this person deliver over the past six months? Where did they surprise you? Where did you have to step in? What have you been avoiding telling them? What would you write if you knew they’d read it tomorrow?

The skill asks. The manager answers. The skill structures the result.

Then you send that along with your performance review guidelines.
”Dear manager, you’re about to head into performance reviews. Use this skill to extract what you already know about your people before you write a single word.”

Two things already exist in your organization: your questions and your guidelines, and this skill makes them run and be repeatable. Sure, you could build an agent that did this; sure, you could buy a tool that did this, but you can also just create a skill, and it will take you what, 30 minutes?

If you want to try this skill, you can download it here and test it out.
(You could also adapt it to your own process; just ask Claude or ChatGPT to adapt it to your process.)

How to upload skills

Here’s how.

Claude

ChatGPT

And this is the hard way. You can also just tell Claude / ChatGPT to learn the skill and point it to the file.

And you can do this for whatever you want! (Create a skill that is.)
This isn’t a special category of task reserved for clever people. Create a skill for whatever recurring thing you have or where you want to be specific about something.

And yes, you could potentially connect this to your performance review system or HRIS.

Because for a skill to work at its most effective, it needs access to tools and systems.

A skill that writes a manager briefing from nothing is a writing exercise. A skill that writes a manager briefing from the real engagement data, the actual team composition, and the actual notes from last quarter is a different animal entirely.

That’s what connectors and MCP are for, and I’ve written about that separately, so I won’t repeat it here. Connectors and MCP, explained for people who have never touched either.

The chicken and the egg

Which brings us to the question: What do you do first? Do you create skills, or do you connect stuff to your AI system?

Ideally, you connect first. A skill with access is worth considerably more than a skill without it.

But a connection on its own doesn’t do anything either. A connection also needs instructions. Nobody’s HR function got better because someone plugged in a system. It gets better when something knows what to do with what’s now reachable.

So the answer is that you’ll do both, and you’ll start with whichever one you can get your hands on this week. If your systems are locked down and IT hasn’t come back to you yet, start with skills. Find workarounds to the information you need. If you already have connectors sitting there unused, start writing instructions for them. Neither order is wrong, but standing still and doing nothing is.

And as said, it’s very easy to create skills. (Almost too easy.)
You describe the work, you correct it a few times, you ask for a skill, and you have one.

The perhaps hardest part is knowing what to make a skill out of. But I suggest you start by picking the thing you’ve explained to an AI more than three times in the last couple of days.

That’s the whole selection criteria. If you’ve written “remember to...” or “always structure it like this...” or “you missed the same thing again, my friend” to a model more than a couple of times, you’ve found your first skill.

Do the work once. Let it capture how you did it. Save it.

Then go connect something to it.

And oh, with clients and friends, I’ve created 16 others skills for you.

Free to use, no sign-up required.
BUT if you do like them and feel that this has been useful, as “payment” I would love if you shared this post on LinkedIn.

Share

Download the skills here.

(For how to install them, scroll up!)

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Andy Doyle has a title that I’ve never seen elsewhere: Chief People and Agent Officer. Today he joins me on the podcast to talk about the title and the numbers behind the title. Kantar has around 12,000 employees and around 15,000 agents (!), so the agents already outnumber the people, and that gap widens every week. And who built them? Well, most of them were built by colleagues who wanted their own workday to be easier, not by the tech or IT department.

In January 2025, Kantar went from 300 Copilot licenses in pilot to 2,000, and three months later everybody had one. They made that bet before they knew how the productivity would pay for the licenses. (Spoiler from the episode: I really like this approach.)

But I’m getting ahead of myself; here is some of what we get into.

• Why they run AI training every Tuesday

• Change influencers, experimentation, and permission to fail

• Kantar’s Agent Factory and the path from no-code to pro-code

• The roughly 70 employees outside IT who have been trained to build agents

• Examples from HR data, HR service delivery and recruitment

• What happens when an agent starts being shared and needs stronger security, controls, and ownership

Andy also talks about the 30 (!) agents he has working for him personally. He also has one sentence for everyone who hasn't started yet, which I will let him deliver himself…

Listen to the episode here

🎧 Spotify →

🎧 Apple Podcasts →

Hit follow while you are there!

Connect with Andy on LinkedIn → https://www.linkedin.com/in/ajdoyle/

And are YOU doing interesting stuff with AI and want to be on the podcast? Reach out! johanness@sundlo.com

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Happy Tuesday,

This is the written version of today’s podcast episode. If you’d rather listen, it’s on Spotify and Apple Podcasts.

Next week... I'm closing out the Startup People Summit hosted by none other than Matt McFarlane.

The program is 🤯, and I'll wake up a bit (too) early so I don't miss anything.

My talk will center around that you can just...do things now. Build things.
And how you do them. And why you need to do them.
And why you shouldn't do pilots.

See you there? —>

And on Friday this week, I’ll do an update on the latest in the world of AI from a leadership perspective. What do you need to know? What’s the latest updates?
100% free and you only need to be subscribed to this very newsletter.
(Which I hope you are!)

In May, I wrote that AI is a work tool, not a perk. The argument was simple. If you buy Copilot, give everyone the full license, because the alternative is building an A-team and a B-team inside your organization.

Three months and a lot of client work later, I want to come back to it. Not because I’ve changed my mind, but because I expected (hoped?) to see the gap start to close by now. I don’t see that. What I see is organizations fuelling it.

Nobody is doing this out of malice

I don’t think this is done out of “evil”; there is no bad intent anywhere in the chain. Nobody sat in a meeting and decided who should be allowed to pull ahead. (I sincerely hope this at least) Instead, it’s a budget line, a pilot that never ended, a license count that made sense in October 2025. Good people, sensible decisions, and an A-team and a B-team at the end of it.

And I get it. We’ve always had fast adopters. People who are extra curious, extra invested, who want to learn new things, whether that’s to have an easier day at work or to stay ahead. That part isn’t new and isn’t a problem.

The problem is what the organization does with that difference, and right now, most of them are widening it rather than closing it.

Enter almost any organization, and you can sometimes visibly see or at least feel both groups from the same spot. One group is far ahead; they’re running fast, they have superpowers, and they either pay for their own Claude and ChatGPT accounts or they’ve nagged their employer into giving them a proper license.

The other group has barely started, or they’ve tried it once or twice but haven't found lasting results for various reasons.

Same company, same floor, sometimes the same team, yet so different results or thoughts.

So how do we end up here?

Someone knocks on the door and asks for a business license. They get it, because they asked and because they clearly know what they want it for. The person who didn’t knock doesn’t get one. Nobody made a decision about who pulls ahead. The distribution follows who was comfortable asking.

If you call yourself an equal opportunity employer, that has to include the tools. Not only for the ones who are loud and vocal about wanting a paid license. You roll it out universally, or you’re letting confidence decide who gets to be productive.

When I look at the organizations where this has gone sideways, the common denominator isn’t budget. It’s that nobody defined why this matters.

Why do we care about AI? Why is this an investment we need to make? Because it is an investment, a conscious one, and it has two halves. The tooling is the easy half. The other half is helping every person answer the question “what does this mean for me and my job on Monday morning?”

Without that second half, you’ve bought licenses and called it a transformation. (yey!)

You can’t be strategic if you can’t be practical

And before we talk about this, please keep my sample in mind. My sample is organizations that want to work with AI in this shape and form. The organizations I work with lean forward by definition, so don’t draw too many conclusions from what I see.

But what I will say is that the ones doing this well share one trait. Leadership has a working understanding of what AI can and cannot do. Not a vendor deck; hands-on with the tools. (If you’ve been reading this newsletter since the dawn of time, this is hardly a surprise, but I will keep repeating this until we have all lead teams onboard. Current estimate is May 2057.)

But yes, that’s my firm belief after many (many) lead team sessions this year. If you can’t be practical, you can’t be strategic. Without a basic, rudimentary grasp of how the thing works, it’s close to impossible to get any of this moving.

And the pace right now is punishing. It’s been fast since ChatGPT launched, but the last six months are something else. Every month you leave the B-team where it is, the distance grows.

The fix hasn’t changed since May. Give everyone the full tool, train everyone, and tell people why. What has changed is how much catching up your B-team has to do once you finally decide to.

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Happy Thursday,

On Tuesday, I promised you real cases from people who are experimenting and driving AI forward in their companies. Not “AI this and AI that”. People who build, measure, and kill what doesn’t work.

Today you get the first one:

Joe Shahmoradian is Director of Talent Acquisition for EMEA at Morningstar. 15 years in TA, a journalism degree he never used for journalism, and one of the most hands-on AI builders I’ve met in this field.
Everything we talk about in this episode is stuff his team runs today.

Some highlights to give you a taste!

His team cut post-call admin by 75% with AI transcription on screening calls. The first tool he tried got killed because it treated small talk and candidate answers with the same diligence. How he found the right one, and why he’s now pointing the same model at interview quality and fairness, is in the episode.

He built Amira, an AI teammate that works like a journalist and scans the market around the clock. Market intelligence that used to take five days now goes out as short video briefings in about 90 minutes, before stakeholders even ask.

Joe built an AI clone of himself before paternity leave, with access to his emails, chats, and every document he’d ever made. Lying awake one night, he started wondering what would happen if someone maliciously prompted it to send an email in his name. So he got up and tested it…

We also get into how Morningstar moved AI adoption from enthusiastic volunteers to structure, with an AI Academy, a champions community that grew from five people to nearly 200, and an idea hub with ownership.

Plus Joe’s answer to what he’d do in his first 90 days at a company starting from zero. Spoiler, it starts with governance and it skips the demo-agent theater.

Listen to the episode here

🎧 Spotify →

🎧 Apple Podcasts →

Hit follow while you’re there. The next guests are already recorded, and the bar is the same for all of them. People who do stuff.

Connect with Joe on LinkedIn, he is always happy to talk about this.

And are YOU doing interesting stuff with AI and want to be on the podcast?
Reach out!

Welcome to FullStack HR, and an extra welcome to the 72 people who have signed up since last week.

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Happy Tuesday,

Before we dive into the episode, I’ve been looking at your survey results on what you want FullStack HR to be this fall - thanks to all who filled out the form!

First, it’s clear that most of you want practical, tangible stuff. AI tools and prompts that you can use directly- I will do my utmost to provide that, keeping the same cadence as before summer and sending those practical updates out on Saturdays.

Secondly, you also want news and trends. To accommodate that, what I will trial is a monthly update here on FullStack HR, live. I will do three and then evaluate, turn out, etc.
The first one will be next Friday at 3 PM CET / 9 AM EST. You will need to be a subscriber to watch it, and I will send out the invite shortly.

Thirdly, real cases from other companies. This is in the making, and I’ve recorded two podcast episodes with, in my view, insanely interesting podcast guests who do stuff. Not talking about “AI this and I that”; these are people who are experimenting and driving AI forward in their respective companies.
I will post the first episodes in the newsletter, but make sure you hit the follow button either on Spotify or Apple Podcasts - I can guarantee that you don’t want to miss the episodes!

Are YOU doing interesting stuff with AI and want to be on the podcast? Reach out!

Ambitions are high for the fall, and I hope you’ll tag along!

All that said, let’s get to it.

I’ve been slow for a couple of weeks. I wrote all summer and needed two weeks off to gather my thoughts. Instead, I discussed…this topic. A lot of conversations, most of them circling the same thing. Human writing versus AI slop.

The debate got new fuel on August 14, when Anthropic started watermarking everything Claude writes. It’s an invisible signature built on Google DeepMind’s SynthID approach, where the model’s word choices get nudged into a pattern a detector can read later. Anthropic rolled it out globally to meet the EU AI Act. Some people say they’ve already reverse-engineered it and can strip it out.
I’m not so sure. It also isn’t the point.

Substack shipped its version of the same idea in July. Any reader can hit the three dots on a post and see what percentage Pangram thinks is AI-assisted. I have my objections to Pangram, but they seem to do a decent job. (With emphasis on decent.)

So we have watermarks on one side, detectors on the other, and a long argument about transparency in between. My question is simpler. Why do we care?

And I’ve landed on the idea that I don’t care whether a human or a machine produced the slop. I care that it’s slop.

I use AI constantly. To gather my thoughts, to get new ones, to summarize what I already think, to challenge it. That’s where the value sits for me.

I have also let AI write entire pieces on its own. Those tanked. You could tell immediately. Not thought through. But I’ve written pieces with no AI anywhere near them that got exactly the same response. Rushed. Boring. Is this even true? Same verdict, no machine involved.

The problem was never the tool. It was me not thinking hard enough.

Which brings me to how I made this piece. I dictate in the car, then I let Claude or ChatGPT turn the dictation into an article. I’ve worked this way for years. The thoughts are mine, the detours are mine, the chain of thought you can hear on the podcast version is mine. I have no idea what Pangram makes of this particular text, and I don’t care. Sure, it’s sometimes a bit off, and I’m guessing that it will flag this as partly AI-generated, which it is. I mean, AI wrote this, but the thoughts and the rambles are mine. (As you can hear in the podcast version.)

It is what it is, but the part of the Substack feature I like is the box next to the score, “How I make this,” where you write your own explanation of how you use AI. That turns a verdict into something you can learn from, and there’s plenty to learn from how other people work.

What puzzles me is the off switch. You can disable scanning on your posts, and readers get “AI detection unavailable” instead of a number. I see creators I’m fairly sure use AI turning it off. For me, that reads as shame or uncertainty.
Say it instead! I use AI here; here is where, here is how.
That’s a better post than the one you’re protecting.

So the summary for me is, don’t be afraid to use AI in your writing.
I’d prefer you did, if it makes the piece better. Get good at prompting. Get good at making your point land. Own the result, and be open about how you got there.

If what comes out is sloppy, that’s not the AI.
That’s you not having thought it through.

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Curious to learn more about Claude Cowork and ChatGPT Work?
I run the third cohort of the popular Cowork for HR starting on the 27th of August.
Three sessions, and the first 10 to sign up get a 30-minute private coaching session with me.

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For the last couple of years, as a kind of sanity check, my co-worker Sara and I have been reviewing HR ads.
First, we did it manually. Row by row. (For Sweden only, but still.) Looking for whether anyone happened to drop in at least one mention of AI. No one did in the beginning, and that was maybe not surprising at all- new technology, uncertainty about whether this was hype or something more long-term sustainable.

But last year when we did the Swedish count again, there were still…almost none.

During the summer, I did the Swedish count again. (When I say I, in this case I mean Claude + the excellent open API provided by the national Swedish Public Employment Service)
There were still… almost none.

But what if we widen the lens? Looking beyond Sweden, what happens then?
In how many job ads will AI pop up? I started to count (but again, when I say I, I mean Claude + Monid), and all in all, we’ve analyzed 3,350 job ads in HR.

Before we dive into the data, it’s perhaps good to answer the question of why this matters. Is it at all interesting to look at? Unsurprisingly, my answer to this is yes, for several reasons.

First, I believe this tells us something about the true mindset of organizations right now. My assumption is that if AI is important and/or adopted in an organization, that will be reflected in how they hire. I know this is a somewhat flawed assumption; it could be that AI is important even if it’s not in a job ad or that AI skills are just seen as a commodity in the same way as we expect people to work with email these days.

Yet, I don’t think we’re there.

I think this is one of the best indications to see where we are with the use of AI in HR.

(And yes, I know that putting it in a job ad is easy and that it could be merely words, but I still think it shows some kind of ambition.)

Secondly, research on “learning-by-hiring” shows that recruiting people with relevant expertise can be an important way to introduce and spread knowledge1 within an organization, particularly when new hires collaborate closely with existing employees. It is not the only route to upskilling, but it can be a key component. So, are organizations making enough use of it?

But the data, then?

As mentioned, I’ve analyzed 3,350 HR job ads across the US and 20 European countries. It’s a seven-day snapshot from late July and early August, so think of it as a signal rather than the complete picture. But what does that signal tell us?

5.4% of them require AI as a hard requirement.

And an additional 4.1% ask for it as a skill that is desired, bringing the total to where an AI skill is asked for to 9.6%.

If we look at the total mention of AI, we see this in 18.5% of all job ads. That could be mentions such as “we work with AI” or that the organization is selling an AI-enabled product.

I’ve also analyzed the different roles within HR.
L&D is the leader here. The difference is not vast, but it’s there.

If we widen it to mentions, we see that 18.5% of all job ads mention AI, but if we dig a bit deeper, we see that a lot of these have little to do with the job and are describing the employer. Not ideal but not super bad either; I’ll take that over no mention at least.

Ok, but those who mention that AI skills are a good thing to have - what kind of skills are they looking for?

You’ll see the results below, but I do want to point out a couple of things.

1. It’s interesting that HR Leadership is so low in analytics and insights with AI. Why? Isn’t this a core feature for HR Leadership, or are we putting the HRBP:s to the test here?

2. Same goes for responsible and lawful AI; my expectation would be that this would be super-high for HR Leadership. (Maybe that was more of a hope?)

3. HR Generalist? Congrats, you're building agents now!

4. It’s very clear that we’re looking for people who can build stuff inside the HR core workflow and not machine learning experts.
   

Tooling then? When AI is asked for, what kind of tools do they prefer you use when working? Claude!
Before I get too excited about seeing Claude on top here, it’s worth noting that this dataset is super small. But still, Claude is on top.

Also a big shout-out to one of my very first sponsors, Metaview, for making it into three job ads!

If you want to see the full dataset, head over here. You’ll see whether Europe or the US is in the lead, and what that looks like, country by country.
You can also download the data, upload it into whatever LLM you prefer, and see the methodology and caveats behind this.

What to make out of all of this?

It’s very tempting to scream that this is disastrous and that we need to do better, but that would not be a great analysis. There could and probably is more to this than first meets the eye, and I’ve laid out a couple of these ifs and buts above.

So what is it then? It’s this.

Not hiring people with AI skills won’t be the end of your organization. After all, it’s still a new technology, a new skill. But it’s a missed opportunity.

I understand that vetting such skills is hard if you don’t have them yourself. So maybe don’t make them a requirement, but at least signal in the ad that these are skills you value. That you and your team are curious about.
(After writing the intial article, I got nudge by Martyn Redstone about Sova Assessments AI Readiness - worth checking out to solve this problem!)

Of course, you need to match that job ad with reality. If you want curious AI people on your team, you have to let them be curious AI people once they start.

It could also be that we already treat these skills as something internalized—something we assume people have without needing to vet or make explicit. Think of the email example above. No one asks candidates today whether they can send an email or use the internet. We assume they have those skills.

I would love for that to be the case, but I don’t think that’s what we see here.

My hypothesis is that we still don’t see this as a core competency in HR, and I worry about that. (I’ve written about this several times this summer.)

As a candidate, I would be worried if AI wasn’t mentioned anywhere in the recruitment process. (And preferably not as “we don’t allow it here”.)

The job ad is a great way to signal to candidates and the rest of the organization that this matters to you.

It also helps raise the bar for your current team, new hires, and yourself.
Use it.

If you haven’t yet subscribed, join the 11600+ smart, curious HR folks by subscribing here:

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Curious to learn more about Claude Cowork and ChatGPT Work?
I run the third cohort of the popular Cowork for HR starting on the 27th of August.
Three sessions, and the first 10 to sign up get a 30-minute private coaching session with me.

Join now

Happy Tuesday,

I know it’s the middle of summer…but autumn is getting close, and what better than a few of my predictions for what’s to come in the upcoming months?

1. The gap widens.

The organizations that started early with AI, that have been on this question for a while, that built things, shipped things, and developed capability will come back this autumn with rocket fuel.

This summer’s releases (Fable 5 and ChatGPT 5.6) amplify everything they already have in place. The ones who positioned themselves well didn’t do it by talking about AI. They did it by doing things.

And the distribution right now is wildly uneven. On one end you have all of Silicon Valley going “this is great, let’s go.” The capability in some places is insane. I’ve had glimpses into environments that are fully native in this. Where people just run entire departments and even companies with AI. It’s something else entirely.

On the other end, a lot of organizations are still standing on the platform, stamping their feet or torn between what models to choose, if any, and what should be allowed or not.

This autumn, I think the difference starts showing for real. The ones who did the work will get leverage from it.

We’re about to find out what that leverage looks like.

2. Every leadership consultant has discovered AI.

You can feel this almost everywhere. In PR outlets, on LinkedIn, and in requests that I get for collaborations. Everyone in the traditional leadership space is engaged in this now. The problem is that many of them aren’t practitioners.

Leadership still matters. As we saw a couple of weeks ago, a good manager sits in a decisive position for whether this goes well or not. So, on one hand, it’s great that these leadership consultants are now waking up to the fact that AI is an important and natural part of workplaces. But you need to get practical. You need to explore what’s possible and know the technology, hands-on.

Agencies are reaching out wanting to build capability. Great, of course. But this technology has been around for almost four years. Now you start?

If you are considering buying AI + leadership courses during this fall, be very stringent around their AI capabilities. What have they done? What have they built? Do they understand what’s possible and what’s not possible? Always happy to help with second opinions here; just reach out, and I’ll help you for free. This is important.

(How can I write this, for free? Won’t I get spammed with requests? No, I won’t because most of you won’t reach out even if you should…)

3. Cost drives open source.

Token cost stays in focus. The trend is open source, driven by the hunt for the lowest possible cost. Locking yourself into a single model is not a great idea. Spread your bets.

My most advanced clients have built their own harness around this and could now swap to Kimi K3 the day it drops. Big upfront investment in hardware. But as open source catches up as it has this summer, running it gets cheap.

One more thing. If you’re stuck in Microsoft with Copilot at work, you’re limited. I like Microsoft, they do a lot of good work. But you can’t practice on what’s happening outside that wall.

I’ve said it before, and I’ll say it again: Buy your own computer. Get your own license on ChatGPT or Claude. It’s probably the best career investment you’ll make this half year. Not doing it is professional negligence, and that’s not hyping it; it’s just what it is.

Because how are you supposed to lead an organization if you don’t understand the capability that exists in the world right now?

Those are the three big ones, what did I miss?

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In Sweden, we have a saying. Don’t cross the bridge to get water.
And that is very applicable when it comes to working with AI, in many different ways, but we’re going to talk about one very specific way today- meta-prompting.

When you meta-prompt, you let the AI do the work for you, and how I usually do that depends a bit on the task I have in front of me, but I’m going to show you both ways.

Let’s get to it!

Help me with this.

This is a fairly easy yet powerful one; I run this whenever I’m in a slight time hurry but still want good results. What I’m doing here is letting the AI do the heavy lifting and creating the prompt for me.

The more you describe what you want the AI to do, the better. If it’s about anything related to a document you have, be sure to upload the document as well.

So, for example, if you want to create a dashboard that visualizes your employee engagement data, make sure you upload the data set.

Let’s see how a meta prompt would look in that instance.

Help me write a prompt for creating an employee engagement data dashboard that I can show to my executive team. 

The data you should visualize is attached. 

Ask me any clarifying questions before creating the prompt.

In ChatGPT, this would look like this (and it would look very similar in whatever tool you are using as well).

And sure enough, it’s following instructions!

Instead of using the proposed answers, you can also give your feedback and guidance on what you want.

After you’ve answered all the questions, the AI will give you the prompt.
In this case… the prompt is long (which it usually is when meta-prompting like this)

Create a polished, interactive employee engagement dashboard as a working web app using the attached Excel workbook.

## Purpose

The dashboard will be presented to an executive leadership team. It should help them quickly understand:

1. The overall state of employee engagement.
2. Where the organisation is performing well.
3. Where intervention is most needed.
4. Which workplace drivers are associated with engagement, advocacy and retention.
5. What concrete actions leadership should consider.

The tone must be executive, concise and evidence-based. Avoid HR jargon, generic advice and unnecessary detail.

## Data source

Use the attached Excel workbook.

Use:

* `Raw Data` as the primary analytical source.
* `Survey Items` for question definitions, constructs and scales.
* `README` for methodology and responsible-use notes.
* The existing `Dashboard` sheet only as a calculation cross-check. Recalculate all results directly from `Raw Data`.

The file contains 252 synthetic respondents from Nordholm Systems AB.

Do not invent historical data, benchmarks, response rates or external comparisons. There is only one survey wave, so do not create trend charts.

## Build requirements

Create a fully functioning, responsive single-page web application, not a static image or wireframe.

The main view should be suitable for presentation on a 16:9 executive meeting screen. Allow users to explore further without making the initial view crowded.

Include an option to upload a replacement Excel file using the same data structure so the dashboard can be reused.

All filters, calculations, charts and narrative insights must update dynamically.

## Executive overview

At the top, show six compact KPI cards:

* Number of respondents
* Engagement index, scale 0 to 6
* Driver index, scale 1 to 5
* eNPS
* Average intent to stay, scale 1 to 5
* Percentage flagged as at risk

For each KPI:

* Show the value clearly.
* Show the scale where relevant.
* Include a brief plain-language interpretation.
* Do not label a score as good or bad using an invented benchmark.
* When filters are active, clearly indicate the filtered sample size.

Below the KPI cards, generate a concise executive summary containing no more than five sentences:

* Overall situation
* Most important concern
* Strongest organisational asset
* Most important segment difference
* Recommended leadership focus

The summary must update when filters change.

## Main dashboard sections

### 1. Engagement overview

Show:

* Distribution across Highly engaged, Engaged, Neutral and Disengaged
* Average Vigor, Dedication and Absorption scores
* The number and percentage in each engagement band

Use a horizontal stacked bar or similarly compact visual. Avoid pie charts.

Explain that the engagement score uses a 0 to 6 scale.

### 2. Organisational comparison

Create a sortable comparison table or horizontal bar chart by department.

Include:

* Respondent count
* Engagement index
* Driver index
* eNPS
* Intent to stay
* Percentage at risk
* Difference from the company average

Allow the user to switch the grouping between:

* Department
* Team
* Location
* Work arrangement
* Job level
* Tenure band

Clearly display sample size beside every group.

Do not automatically interpret small differences as meaningful.

### 3. Engagement drivers

Display all 12 driver scores ranked from lowest to highest:

* Manager support
* Recognition
* Role clarity
* Influence
* Development
* Team support
* Psychological safety
* Workload manageable
* Work-life balance
* Change clarity
* Meaning at work
* Tools and resources

For each driver, show:

* Average score on the 1 to 5 scale
* Difference from the overall driver average
* Correlation with Engagement Index
* Correlation with Intent to Stay
* Correlation with Recommend 0 to 10

Use Pearson correlation and label it clearly as an association, not evidence of causation.

Add a driver-priority matrix:

* Horizontal axis: average driver score
* Vertical axis: correlation with Engagement Index
* Label every driver
* Distinguish between potential priorities, strengths to protect and lower-priority issues
* Explain the logic in one short sentence

Do not use a single unexplained composite score. Leadership should be able to see whether something is a priority because it scores poorly, is strongly associated with an outcome, affects many people or appears repeatedly in comments.

### 4. What requires attention

Create a section titled **Leadership priorities**.

Identify the three most important issues using a balanced assessment of:

* Low driver scores
* Associations with engagement, eNPS and retention
* Percentage of employees affected
* At-risk concentration
* Comment-theme frequency
* Organisational segments where the issue is most pronounced

For each priority, provide:

* The issue
* The evidence
* The groups most affected
* A concrete proposed leadership action
* A suggested executive owner or function
* The first practical next step
* One leading indicator to monitor
* One employee-outcome measure to monitor

Recommendations must be specific to the data. Do not provide generic recommendations such as “improve communication” without describing what leaders should actually change.

Separate organisation-wide actions from local department or team actions.

### 5. What should be protected

Create a section titled **Strengths to protect and scale**.

Identify the three strongest aspects of the employee experience based on:

* High scores
* Positive relationships with engagement or retention
* Consistency across departments
* Positive comment themes
* Strong results in particular organisational groups

For each strength, explain:

* Why it matters
* Where it is strongest
* What leadership should continue doing
* How the organisation could learn from strong groups without assuming that their practices caused the result
* A warning sign that would indicate the strength is weakening

### 6. Retention and advocacy

Show:

* Promoter, Passive and Detractor distribution
* eNPS by organisational group
* Intent-to-stay distribution
* At-risk percentage by organisational group
* Groups combining lower engagement, negative eNPS and higher risk

Do not expose individual respondents or Manager IDs.

### 7. Employee comments

Analyse `Comment_Theme` and `Open_Comment`.

Show:

* Number of non-empty comments
* Theme frequency
* Positive and negative themes
* How themes differ across departments
* Whether qualitative themes support or contradict the quantitative findings

Do not show a long comment feed on the executive homepage.

Provide a separate expandable comments section with a small number of representative anonymised comments. Do not show comments from groups below the confidentiality threshold.

Do not claim sentiment accuracy beyond what can reasonably be inferred from the text.

## Filters

Include compact filters for:

* Department
* Location
* Work arrangement
* Employment type
* Job level
* Tenure band
* Age band
* Gender

Include a prominent **Reset filters** button.

Filters should update the complete dashboard, including KPI cards, charts, priorities and narrative summaries.

Show active filters clearly.

## Confidentiality and responsible reporting

Use a default minimum reporting threshold of seven respondents.

When a selected group contains fewer than seven respondents:

* Suppress scores and comments.
* Display “Insufficient responses”.
* Do not expose individual-level records.
* Do not allow combinations of filters that reveal a very small group.

Make the threshold easy to change in the code.

Treat demographic comparisons as descriptive only. Do not make assumptions about individuals or protected groups.

Include a small methodology note stating:

* The data is synthetic.
* The survey structure is illustrative and not a validated version of UWES, COPSOQ or another published instrument.
* Correlations do not establish causality.
* Results should be followed by employee dialogue before decisions are made.

## Visual design

Use an executive, modern and restrained design.

Use:

* Warm off-white background
* Charcoal text
* Muted coral or orange as the main accent
* Soft teal for positive indicators
* Muted red only for genuine risk indicators
* Generous whitespace
* Clear hierarchy
* Minimal chart decoration
* Accessible contrast
* Direct labels where possible

Avoid:

* Excessive gradients
* Decorative illustrations
* Large blocks of text
* Traffic-light colouring of every metric
* Gauge charts
* 3D charts
* Multiple pie charts
* Overly playful design
* Automatically colouring every below-average score red

Use colour together with labels and icons so meaning does not depend on colour alone.

## Interaction and presentation features

Include:

* A presentation mode that prioritises the executive overview
* Tooltips explaining each metric and scale
* Download or print-friendly executive summary
* Export of the currently filtered summary as PDF or a print-ready page
* A visible timestamp or survey-period label
* A short “How to read this dashboard” explanation

## Analytical rules

Use unrounded values for calculations and round only for presentation.

Presentation rounding:

* Index scores: two decimals
* Percentages: one decimal
* eNPS: whole number where possible
* Correlations: two decimals

Do not infer statistical significance merely from ranking differences.

Do not create external normative benchmarks. Company averages may be used as internal reference lines.

When identifying strong or weak departments, consider sample size and multiple measures rather than relying on one metric.

## Calculation validation

Before finalising the dashboard, verify that the unfiltered results are approximately:

* Respondents: 252
* Engagement Index: 3.77 out of 6
* Driver Index: 3.48 out of 5
* eNPS: +5
* Intent to Stay: 3.93 out of 5
* At risk: 32.5%
* Highly engaged: 16.7%
* Engaged: 45.6%
* Neutral: 34.1%
* Disengaged: 3.6%

The lowest company-level driver scores should be approximately:

* Workload manageable: 3.03
* Change clarity: 3.20
* Recognition: 3.31

The highest company-level driver scores should be approximately:

* Meaning at work: 3.84
* Team support: 3.82
* Tools and resources: 3.76

The drivers most strongly associated with engagement should include psychological safety, manager support and recognition. Verify this from the raw data instead of hardcoding the conclusion.

Potential organisational hotspots to investigate include:

* Sales, with lower engagement, negative eNPS and a relatively high at-risk percentage
* Finance, with negative eNPS and lower manager support
* Engineering, where advocacy and intent to stay may require attention
* Product, which appears comparatively strong on engagement and eNPS

Treat these only as validation clues. All displayed findings must be generated from the workbook calculations.

## Final output

Build the working app first.

After building it, provide a concise explanation covering:

1. The dashboard structure.
2. The main calculation logic.
3. The confidentiality rules.
4. The three most important findings in the unfiltered data.
5. Any technical or methodological limitations.

Do not ask additional questions. Make reasonable implementation decisions while following these requirements.

But why do this? As you see above, the prompt is usually way, way longer than if you were just to type “build me an employee engagement dashboard,” and my experience is that this yields a way better, more competent result.

Sure, it takes about 5-10 extra minutes upfront, but you have to spend less time adjusting after you get the initial results.

By the way, this is the result from the prompt. A nice-looking dashboard with all the info, as described. Dummy data, of course but I would say it’s rather nice-looking.

The ramble

That was the first of the two that I mentioned, typing and adding data. I do that from time to time, but this one I do almost daily.

What I do is that I start Wispr Flow and then talk naturally about what I want to do, the things I would want the AI to do, and in the end I add something like this.

Turn this in to a strong, cohesive prompt to solve the task that I just described. 

Together with a ramble, it looks something like this.

And the output is this prompt:

Write a compelling promotional message announcing the third cohort of Cloud Co-work, beginning on 27 August and running for a few weeks.

The message should speak directly to HR professionals and people working with HR, people operations, learning and development, or organizational transformation. Help them understand why practical knowledge of AI is becoming increasingly valuable in their work.

Explain that this cohort has evolved to include hands-on use of ChatGPT for work, along with an accessible introduction to how Codex can support more advanced tasks and automation. Emphasize practical applications: reducing repetitive administration, improving research and communication, accelerating documentation, creating more efficient workflows, and giving HR professionals more time for thoughtful, human-centered work.

Position Cloud Co-work as a supportive, collaborative learning experience—not a technical course. Participants should feel that they can join without being AI experts and learn by applying the technology to real workplace challenges alongside others.

Use a confident, warm, and credible voice. Avoid exaggerated promises, fear-based language, and dense technical jargon. Create a sense of timely opportunity without making the message feel aggressively promotional.

Include:

- A strong opening that captures why HR should understand these technologies now
- A clear announcement that this is the third cohort
- The start date: 27 August
- A concise description of ChatGPT for work and Codex
- Concrete examples of what HR professionals could automate or improve
- A clear explanation of who the cohort is for
- A reassuring statement that no advanced technical knowledge is required
- A compelling call to action inviting people to express interest, request details, or reserve a place

Write the primary version as a polished LinkedIn post of approximately 150–250 words. End with a short, memorable line that reinforces the central idea: AI should help HR spend less time on repetitive work and more time on people.

And there we have it; that’s the prompt I could use to create that promotional message for the course that starts on the 27th. Wink wink, the link is here.

I prefer the latter because I find these models are great at understanding my ramble, taking that into account, and then creating one beautiful prompt out of it. It also gives me the opportunity to look things through after I’ve rambled.
Has it interpreted me in the way that I wanted? Anything that I would like to add or change?

Do I use this all the time? No, I don’t, but I do use it, especially the latter one here, on a daily basis. It’s time-effective and gives me better results, and it’s a nifty little prompt trick to have in your AI arsenal going forward.

Let me know if there’s anything I can clarify or make easier here, should you need it. If not, then meta-prompt away!

One of the perks of writing a semi-large newsletter is everything that lands in your inbox. PR pitches, media monitoring, reports. Most of it is forgettable.

But one thing has struck me lately, there is a lot of “HR must” going around.

HR must lead the AI transformation. HR must own the change. HR must secure a seat at the table.

I have pushed that thesis myself. In this newsletter, on stages, in workshops. I have said it so many times it has become a reflex.

But what if it is wrong?

What if AI should not be treated as a people question at all? What if it should just be pushed out as technology? Install, activate, measure. More tech, less human. No change journey, no workshops, no HR strategy. Just rollout.

That sounds cold. But be honest. A lot of the technology we use today arrived exactly like that. Nobody invited HR when email showed up. Excel never got a change program. It just arrived, and we adapted.

And as I mentioned last week, what if the hype is true? What if agents can handle onboarding, salary reviews, screening, and engagement surveys better than we can? Then the question is no longer how HR should lead AI. The question is what is left.

Does HR need to exist? What is our self-justification in all of this? What is our role when the work that built this function can be automated?

I get a bit despondent at times. Not about the technology, about us. Because we defend our position so relentlessly instead of challenging it.

Every report, every panel, every LinkedIn post lands on the same conclusion.

“HR is more important than ever!”
Is that true, or is it just what we need to hear?

There is also an entire industry that makes its living telling HR how indispensable HR is. (I am part of it.)

Few things in life are static. Why would HR be?

The people function is roughly a hundred years old. Humans organized work for thousands of years without us. In the grand scheme of things, we are a small anomaly, not a law of nature. Yet we act as if our existence were a given.
As if we will always be here.

My point is not that we should shut it all down. My point is simpler. A function that cannot answer why it exists will get the answer from someone else. From a CFO with a savings target. From a CTO with an agent platform. From a CEO who notices the rollout went just fine without us.

So stop defending. Start challenging. Ask the question internally before someone else asks it for you. What do we do that creates value, and what do we do simply because we have always done it?

If we don’t reinvent ourselves, someone else will.

What if all of it is true?

Not as a prediction. I’m not saying it is. This is a thought experiment, nothing more. But stay with me for a moment.

What if what’s happening with AI right now isn’t a bubble. What if it isn’t another wave of technology that washes over us and leaves everything roughly as it was. What if it’s real.

What if agents become a natural part of the workforce. Not tools we open when we need them, but colleagues that take on tasks, own processes and deliver while we sleep.

What if our organizations employ fewer people a few years from now. Not because the business is shrinking, but because it’s growing with a workforce that’s largely not human.

What if AI turns out to be a better boss than a human. Fairer feedback, no favoritism, always available, never tired, never in a bad mood. What does that do to our idea of leadership?

What if this is one of those shifts that history later divides into before and after.

What if work as we know it changes at its core. Not in twenty years. Now, while we’re busy debating whether it’s hype.

Because that’s where I get stuck. We spend enormous energy debating whether this is a bubble. Overrated or underrated. Hype or substance. It’s a comfortable debate, because as long as the question stays open, none of us has to change anything.

But playing with the thought costs nothing. So play along.

If you knew it was true. That agents become colleagues, that organizations get rebuilt, that work gets redefined. Would you act the way you do today?

Would you prioritize the same things at work? Would your own development look the way it does? Would your organization keep moving in the same direction, at the same pace?

Or would you choose to do something else?

The honest answer to that question is the interesting part. Because if the answer is that you’d do something else, that says something about today. It means you’re waiting for a certainty that will never arrive. No one sends a message the day the hype officially becomes reality.

I’m not saying it’s true. I’m only saying, what if.

These are the thoughts I’ve been carrying this summer.

What if it’s true?

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So they finally arrived, the new models from OpenAI and Anthropic.

In passing I mentioned Fable 5 and how good it was. And it was. For three days we got to test it before it was pulled away from us, but then a week or so ago we got it back. With a promise (threat?) that it was only included until July 7. Included as in you can use it with your regular Claude subscription.

I wasn’t the only one staying up a little too late testing everything possible with Fable 5 before July 7... only to watch Anthropic extend the deadline to July 12.

What happens after that is, at the time of writing, uncertain. Maybe it stops being included in our regular plans and you’ll have to pay per use (I happened to leave a small automation running for a day on a schedule I forgot to change and it cost $40...) or maybe they extend it again.

What speaks for an extension is that OpenAI on Thursday released ChatGPT 5.6 Sol / Terra / Luna.

What’s the difference between them? I think this Reddit post explains it in an extremely pedagogical way.

To add even more complexity, each model also has multiple settings that control the intelligence. Terra and Sol also have an Ultra mode. So three models with a total of 14 (!) different modes.
OpenAI and naming, huh...

On top of that, they're doing what they previously announced and merging the ChatGPT and Codex apps on desktop. It's pretty obvious they've been glancing hard at Claude Cowork, and in a countermove (?) Anthropic redesigned the Claude app this week and removed (!) the Cowork tab, moving it to the chat page.

Hard to keep up? It’s not easy for me either, especially not ChatGPT Work compared to what used to be “just” Codex.

And I wasn’t alone in being confused. Even Ethan Mollick was confused but got an answer from Dominik Kundel at OpenAI.

What he’s saying in plain language is that the biggest update is about how things look. Which in my view is a good thing. Codex was (is?) incredibly good.

I’ve been running Claude Cowork and ChatGPT Codex in parallel since February, and my experience of the new ChatGPT Work so far is that I haven’t fallen off my chair yet, but I understand the logic in making this more accessible. Codex has been a bit complex, and I think Claude Cowork has gained ground largely because it’s focused on “work”.

The takeaway from all of this?

I’ve said it before and I’ll say it again. Everything points to the coding capability from Claude Code and Codex moving from engineers to everyone who sits in front of a screen all day. We saw it with Cowork (in both the Claude and Copilot versions) and we’re seeing it with Work.

They’re both extremely capable tools, and the organizations I know are working with them see a real upside. Building an agent-like, automated flow in either tool doesn’t take many minutes. What does tend to take time is giving the system the right permissions and having an IT department competent enough to make the right trade-offs.

That’s where the battle this fall will be fought. Who has a forward-leaning IT department that can ensure data integrity while still taking advantage of the tools we’ve now been given. If I generalize, the US is further ahead than Europe here, and I have more Americans who have taken my Cowork for HR course than Europeans.

The models then? I’ve touched on them both. They’re obviously brilliant. Both Fable 5 and ChatGPT 5.6 Sol Ultra are something quite special. I personally prefer Fable 5 as a colleague over 5.6 Sol Ultra when it comes to bouncing things back and forth, but 5.6 Sol Ultra’s ability to take information, compile it and present it... it’s crazy in a good way.

But models aside. They’re good enough for the vast majority of organizations now. It’s not about that anymore. It’s about other things, like what I talk about above regarding competence in the IT department, for example. And competence in HR too, for that matter.

So what should you do now?

Test, obviously! I’ve given this advice to quite a few people this spring. But I’d argue it’s professional negligence not to have either ChatGPT Work or Claude Cowork downloaded. If you’re not allowed to have it on your work computer, buy your own machine, a cheap used one is enough, and run one of them privately there.

Not testing and not understanding what these tools can and can’t do (which is fewer and fewer things) is professional negligence. There are few things we in HR need to understand more than what these tools can do now. Because if you’re good at expressing yourself, think through what your goal is, and give a clear picture of what you want to achieve, these models will give it to you.

The direction from OpenAI, Anthropic and Microsoft (and maybe Google? Hello Google?) is clear. They’re on their way to creating tools that can do pretty much anything that happens in front of a computer. Whether that’s good or bad is up for debate, but to be able to debate it you need to understand what they can and can’t do.

If you need a push to get started, I’m running another round of Cowork for HR. ChatGPT Work will of course be included in it too. The first 10 to sign up also get 30 minutes of individual coaching on setting up ChatGPT Work or Claude Cowork.

Sign up here

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I'm always writing these intros after I've written or dictated the piece (today is writing because we have summer vacation in Sweden and I'm putting my kids to sleep at the same time), and as I wrote this, I felt more and more frustrated.
We knew this, we knew. We know this, we do.
Yet. Here we are, and it's such a failure.
(But more on that later… agitated again!)

But first, thank you to all 100+ people who have given input to the survey I emailed about yesterday. Helps me a lot to shape what to write about here!
Still open for answers, of course.

Also! Next week I'll do a webinar with the CEO, Jeet, from Kinfolk. It's free and given the discussions Jeet and I have been having leading up to this, it's going to be a great one…

I'm a big fan of and his newsletter. (To such an extent that we gave everyone who spoke at AI Day last year a year's worth of subscription to his newsletter as the speaking gift.

Last year, Lenny and ran an interesting study about tech people's relationship with AI, and they are duplicating that again. It's well worth a read, and I don't mind you jumping over to the Substack to read it before you jump back here. Over 5300 people participated, so the data is very solid.

Why this is important? Because I think this is our canary in the coal mine.

The people working in tech are at the forefront of what's to come for the rest of us, and the survey paints quite a grim picture.
Burnout numbers are up. Job optimism is down.

55.7% rate themselves as “moderately”, “very,” or “completely” burned out.

Is it in smaller companies that apply a 996 system, one might wonder then? Maybe, but not solely. Burnout climbs by company size. And who usually has the biggest HR orgs? Larger organizations.

This is such a failure on our end.
By us, I mean HR.

And as mentioned above, these are the canaries in the coal mine. This is what's likely in store for us all. Sure, I've chosen the darkest figures from the survey. There are good things in there as well; productivity is up, people are shipping stuff and have discovered new amazing capabilities.

But at what cost?

Shouldn't we, HR, be the protectors of this? Shouldn't we make sure that we have long-term sustainable organizations? Shouldn't we safeguard that people feel safe and supported?

And here we are.

The most prevalent fear in the survey is not being replaced by AI; it's being overworked.

This is what happens when you fall asleep at the wheel, when you let AI be this ambiguous project that is run without a clear mandate, without clear objectives, without thinking things through from a human, organizational perspective.

From the get-go, I've been saying that we, HR, matter in this. That we need to rise to the occasion because this is impactful technology. And I'm a bit frustrated that we don't take this more seriously.

Because what's the one thing that combats all of the above?

No one should be surprised by that. So why haven't we doubled down on this?
We should be on top of this, but we clearly aren't (even if you shouldn't draw too big conclusions from one survey alone).

I think we can shift the trend here, and we can also let them be the canaries in the coal mine, meaning that for occupations beyond tech, we might be able to not let it reach as high levels as tech.

We just need to make the effort; we need to understand that our jobs are no longer purely centered around people. We need to understand tech; we need to understand how it impacts people, and we need to do it fast.

Read all tips on Lenny’s page for what to do if you are an employer and do them.
And when you double down on leadership, make sure you choose a vendor that knows about AI. Not only “oh we’ve tried Copilot”.

Your leadership vendor must have deep, thorough knowledge of AI and what you can and cannot do with it, because this is the tech impacting your people.

I can not stress this enough.
Choose your vendor carefully, and I’ll be happy to give second opinions for free if you need any. Just shoot me an email.

Puh. Got a bit agitated here, but I’m mainly just saddened by this; we should and can do better. We need to.

Today I’m flipping it. I need something from you.

I’m planning the autumn for FullStack HR, and I’d rather build it around what you need than what I guess you need. So I put together a short survey about what you want more of, what you want less of, and what would make this newsletter genuinely useful for you this fall.

It takes three minutes (or two if you are quick!).

👉 Take the survey

That’s it. No pitch at the end, no funnel. I read every answer myself, and they will directly shape what you get from me after the summer!

Thank you!

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Happy Sunday!

It’s now possible to publish articles in different languages here on Substack (yey!)
Also possible for you to translate any article into your preferred language!

I will try to write articles in both English and Swedish, but if time is short, English will be the main language.

During the last six months, one of the things I’ve been doing is building agents within organizations. And while it’s somewhat easy to build them, the most overlooked question is still: What should we build in the first place?

That question is the most important one to ask before starting to build any agent. So this week, let’s talk about that and try to figure out what to build.

Also, if this resonates, I’ve made a skill out of it that you can upload to Claude/ChatGPT. It’s in the end.

Most organizations I meet these days have done one or more of these three things. But it’s usually the case that they’ve bought licenses, written a policy, and, at best, run a training or two.

And then placed the entire responsibility on the individual.
Get good at prompting. Use the tool every day.

Nothing wrong with that. It’s where you start. But individual value doesn’t scale into organizational value on its own. That’s why so many companies talk about AI while so few can point at measurable impact. Someone gets faster at writing emails, and the org chart shrugs.

But that’s also the whole allure around agents: they become a bridge. A well-chosen agent takes something one person figured out for themselves and makes it available to a team, or a whole organization. Someone builds a collective agreement coach for their own use. Shared with the entire management layer, it becomes something else entirely.

But only if it was the right thing to build.
Building these days is quite easy (at least if you have access to Codex/Claude Code), but even if you use Microsoft, it’s somewhat easy. I was part of an event where an organization built 2000 agents in a single push. Workshops, energy, everyone building.
It was truly great!

But. A few months later, we looked at usage. Three of them were alive.

Not because the technology was bad. Because nobody had asked why each agent existed, who it was for, or who would own it in six months. The building was easy, and that was the problem. When building is easy, the thinking becomes the bottleneck. And most organizations haven’t noticed that the bottleneck moved; we still perceive it to be in the “building phase”.

Hunt for friction

So how do you find the right thing to build? You map.

When we learn AI tools as individuals, the question is “what hurts for me?”. When we think about agents, the question moves up a level. What hurts for my team? What do the people around me do over and over that is repetitive, time-consuming, or plain frustrating?

List three to five of those. Not solutions, not agent ideas but friction.

If you get completely stuck at team level, fine, start with yourself. But aim upward. The value lives in the shared friction, not the personal one.

Describe the friction, not the problem

When doing this, people write down problems. “We have high employee turnover.” True, painful, and useless as a starting point.

That’s a problem. It’s not friction.

The friction is that you spend hours every month onboarding people who quit after three months, hours you don’t have, while your own work piles up. Now we’re somewhere. Now you can see when the frustration arises, who it hits, and what the real cost is.

So for every item on your list, push one level deeper. When does it hurt? Who does it hurt most? What does it feel like on the receiving end? Where can I get data to better understand and support my friction?

The models are good enough these days to build the right thing if you describe the friction honestly. What they can’t do is rescue you from a fuzzy problem statement. The most common mistake I see when organizations are doing this is not technical. It’s starting with the attitude of “let’s see what it comes up with”.

That makes the whole thing impossible to evaluate. Is it doing well? Is it failing? Nobody knows, because nobody defined what good means.

Flip it. Before anything gets built, you should be able to state the end goal in one sentence. Give clear answers about what applies in our collective agreement. Turn incoming meeting notes into a first draft of a follow-up email. One narrow, concrete goal.

If you can’t say it in one sentence, the problem is too broad. Narrow it until you can. A narrow goal is easy to test and easy to judge. A vague one just produces a demo that impresses everyone once and helps no one twice.

Maybe it shouldn’t be an agent at all

One question from the session deserves its own heading. Someone asked when to use classic automation, when to use RPA, and when to use an agent.

The rule of thumb is simple. If the flow is static and rule-based, if A then B then C every single time, classic automation is fantastic. The chain never breaks. An agent earns its place only when there’s a step in the middle that requires judgment, where information has to be weighed and a path chosen.

If nothing needs judging, you don’t need an agent. A fair share of today’s “agent initiatives” are automation projects wearing a trendier name. That’s not a technicality. It’s part of deciding what to build.

Last one, and it’s short. Before anything gets built, name an owner. A person, not a team, not “we’ll figure it out”.

An agent without an owner is an agent that’s dead in three months. I’ve watched it happen enough times that I now treat it as a law of nature.

The skill that separates organizations getting value from agents isn’t technical. It’s diagnostic. The people who get good at this aren’t the ones who know the most platforms. They’re the ones who can look at their team’s week and say “there, that’s the friction, and this is exactly what solving it looks like”.

That skill is trainable. It costs nothing. And you can start this week, without opening a single tool.

What’s the friction? Who does it hurt? What does solved look like, in one sentence? Should it even be an agent? Who owns it?

Answer those five, and you’ve done the hardest part.

The rest, honestly, is just buttons.

Need it as a skill? Download it here.

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Understanding Claude’s potential and limits in HR.

On 15 July I’m joining Kinfolk for a conversation about the current stage of AI Adoption and where teams are building internally vs evaluating external solutions at the moment.

Most HR and People Operations leaders I work with are deep into the pilot stage now. The enthusiasm is real but many organisations are still working on what a sustainable AI setup looks like for an HR team, outside of individual productivity.

Building with Claude for individual work makes sense. Drafting policies, summarising documents, writing job descriptions. But when teams try to extend that into their operational layer, and start working on the employee lifecycle requests, the data flows, the governance, the accountability, the gap between “this works for me” and “this works for my team at scale” becomes clear very fast.

We’ll be working through the build vs. buy decision with the infrastructure context it deserves and understanding what are good use cases for internal builds with tools like Claude (or Copilot) and where it’s worth exploring a third-party solution.

Register: luma.com/halvjnly

Wednesday 15 July · 5pm BST / 9am PT / 12pm ET

Happy Tuesday,

Listen to this one on Spotify.

Last week I was in Almedalen.

For those of you outside Sweden, Almedalen is our big political week. Once a year, the whole country more or less relocates to Visby, a small medieval town on Gotland, and for a week, politicians, business leaders, lobbyists, journalists, and everyone with an agenda gather in the same few streets to talk. It is where politics and business meet. It is loud, it is intense, and this year there was a lot of talk about AI. (Surprise, surprise.)

And somewhere in the middle of one of these sessions, it hit me. All these politicians and business leaders, the people shaping how the country thinks about this technology, their version of AI is mainly…Copilot. (Or at best the free version of ChatGPT.)

But that’s their reference point. That is the picture in their heads when they stand on a stage and talk about what AI can and cannot do for society. And I could not stop thinking about it for the rest of the week.

I think this is because almost nobody picks their own model. People ride along with whatever their organization landed on, and eight times out of ten that means Copilot.

And the way it got chosen is usually no choice at all. It got chosen on the logic of “well, we have Microsoft, so this is the model we have.” Congrats, good luck, and you, dear colleague, get to adapt to it. Nobody ran a needs analysis and nobody asked the one question that matters: Does this solve the right problem for the work we do?

Don’t get me wrong. Copilot has gone from a so-so pick to a fairly okay one. It is not a bad choice. You can defend it. But is it the best and only model for an organization? There my answer is, without hesitation, no.

So let me explain what I mean, because I don’t want this to read as brand snobbery.

We already accept that different jobs need different tools. We pick different computers depending on the work. We pick different software depending on the craft. For some people, Photoshop is mission-critical. For others, it is completely irrelevant. Nobody blinks at that.

So why do we stop thinking the second we get to AI?

Because when we stop thinking and just default to Copilot, two things break. And both are quietly expensive.

You build the wrong picture of what is possible

The first thing that breaks is your sense of what AI can even do.

Organizations build their entire expectation of what is possible on the one model they have touched. To be precise, it is the people inside the organization forming that impression. But the effect is organizational. The shared sense of “this is what AI is” gets anchored to a tool that is not at the front of the field.

Think of it like a screwdriver. Before you had a proper screwdriver, getting a screw out was hard, so of course the screwdriver felt like a leap. It works. It makes the job more efficient. And if a screwdriver is all you have ever held, you genuinely believe you have arrived at the utopia of screwing in a screw. What you don’t know is that there is a power drill sitting on the shelf, basically frictionless, that does the whole job while you press one button. That is roughly where we are with these models. What you were handed is the screwdriver. Claude and ChatGPT are far closer to the power drill.

And when the screwdriver is all you have, you set the wrong expectation for the entire organization. No wonder people feel lost on AI when their whole worldview is built around that one tool and nothing past it. Which, to bring it back to Almedalen, is exactly the level the conversation was stuck at.

There is a timing piece here too. Technically, raw problem-solving stopped being the bottleneck for most organizations at the start of this year. From the arrival of Opus 4.5, if you picked the right model and gave it the right conditions, the problem-solving most organizations need has been well within reach. But if everything you have ever seen is the default tool, you missed that shift completely. And then I understand why you are still hiring for problems that are already solved.

Your best people are already working somewhere else

The second thing that breaks is shadow IT, and it is still everywhere.

I have lost count of how many people I have met who got hard, explicit instructions. You may only use Copilot.

And then every single one of them has ChatGPT open on the side. The people who want to be further ahead, the ones pushing this forward, the internal enthusiasts you most want to capture and put to work, those are exactly the people who quietly switched to something else.

That is not good from a lot of angles. Not good for security. Not good for you as an employer, because the data is going somewhere you have zero visibility into. There is a whole pile of problems, but security and knowledge sharing are almost the first to go. When people work in the shadows they tell you less about what they did, and the openness to share evaporates. You lose the exact thing you wanted, which is people building out in the open, where you can see it and learn from it.

And soon this becomes an employer branding question

The third is that over time I believe this becomes an employer branding issue.

Think back to how it went when the phone arrived, or when social media arrived. (For those of us who were around for it.) Not being allowed to use LinkedIn at work used to be a real thing. People argued about it. Today it is a non-issue at almost every workplace. In a lot of places you are expected to share what you do on LinkedIn. The same noise happened every time one of these things first showed up, and then it faded.

The same arc is coming here. You will have someone arriving fresh from university, or from another company, used to working with their own agents, used to bringing their own reality and their own setup with them. And then they walk into a locked-down corporate environment and meet nothing but flatness compared to what they had in Codex or Claude Cowork. I know this happens, because I have met plenty of people living that exact gap, people who keep actively working in something else on the side. We can argue about the scale. But I know it is still going on. So it becomes a question candidates start asking. Which model do I get to use? What does my token budget look like? How am I allowed to use all of these?

So what do you do about it

You secure all these models and offer employees the choice. Choose what you prefer working with, but in our secure environment.

I know that is a cost, but so is a data breach, and so is the cost of people working on their own, without sharing the benefits of what they do.

So in most cases, my question is simply this. Why not? What is the reason, other than cost, for not letting people choose between Claude, ChatGPT, Copilot, and Gemini? You can offer all four.

So what is the reason you are not doing it?

If you tell me it is security, compliance, governance, then what you have is a lazy IT department that cannot be bothered to do the work. That is it. There is nothing else. Make no mistake, all of this can be made safer, but it takes effort.

This is going to become an employer branding question and an organizational question, and the organizations that solve it fastest are the ones that win.

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Understanding Claude’s potential and limits in HR.

On 15 July I’m joining Kinfolk for a conversation about the current stage of AI Adoption and where teams are building internally vs evaluating external solutions at the moment.

Most HR and People Operations leaders I work with are deep into the pilot stage now. The enthusiasm is real but many organisations are still working on what a sustainable AI setup looks like for an HR team, outside of individual productivity.

Building with Claude for individual work makes sense. Drafting policies, summarising documents, writing job descriptions. But when teams try to extend that into their operational layer, and start working on the employee lifecycle requests, the data flows, the governance, the accountability, the gap between “this works for me” and “this works for my team at scale” becomes clear very fast.

We’ll be working through the build vs. buy decision with the infrastructure context it deserves and understanding what are good use cases for internal builds with tools like Claude (or Copilot) and where it’s worth exploring a third-party solution.

Register: luma.com/halvjnly

Wednesday 15 July · 5pm BST / 9am PT / 12pm ET

Happy Saturday,

You open up ChatGPT, Copilot or Claude, the cursor blinks, and you freeze. Not because you cannot type. Because youdo not know what to type. So they write something thin like “write feedback to an employee who misses deadlines,” get back something generic and a bit corporate, and quietly conclude that AI is overrated.

It is not overrated. The starting habit is just wrong. And after a few hundred workshops, this is the single fastest fix I know.

Let’s get to it.

Most prompt advice is too complicated

If you search for how to prompt, you drown in frameworks. Personas, delimiters, role assignments, “act as a world-class expert in,” temperature settings, twelve-part mega-prompts. (I know, I know, I’m guilty of contributing to it all…)

HR people and managers do not need to be mega advanced in prompt engineering. (Never liked the term.) You have a job to do and four minutes between meetings. You need a better starting move, not a certification.

The good news is that the highest-leverage upgrade is also the simplest, and you can teach it to an entire team in about ninety seconds.

The problem is (usually) missing context

When you write to the model as per above, it’s usually vague or way too general. But the model is not vague because it is dumb. It is vague because you were vague.

When you ask AI to write feedback about missed deadlines, it has no idea whether this is a first slip or a six-month pattern, whether the person is overloaded or careless, whether you want warm or firm, or what the deadline miss cost. So it does the only thing it can, it writes a safe, average, slightly soulless draft that fits every situation and therefore fits yours badly.

You did not give it enough to work with, so it guessed. The fix is not a cleverer prompt, the fix is to stop asking AI to answer before it has the context, and instead make it pull the context out of you first.

The 3-question prompt

This is the whole trick. Paste this on top of almost anything.

Before you answer, ask me three clarifying questions that would help you
give a better, more practical answer.

That is it. You flip the model from answering to interviewing. It asks you three sharp questions, you spend thirty seconds answering them, and the output is on a completely different level, because now it is built on your real situation instead of a generic one. If you want to be even more proficient, I can highly recommend using a tool such as Wispr, which lets you talk to your model(s) super-easily.

Save it as a text snippet, a skill, a keyboard shortcut, a pinned note. Whatever means you will reuse it. The power is not in the words, it is in the habit.

Five examples from the HR and manager day

Let me show it on five things you probably do in the upcomoing weeks.

1. Manager feedback. You have someone slipping on deadlines, and you want to say something useful, not just annoyed.

I need to write feedback to an employee who is missing deadlines.
Before you draft it, ask me three clarifying questions.

The model might come back with, is this a repeated pattern or a recent thing, what has it cost the team or the customer, and do you want the tone supportive, direct or formal. You answer in a sentence each, and now the draft sounds like a real manager who knows the situation rather than a policy handbook.

2. Job ad. You have a tired old ad and want it to actually attract the right people.

Help me improve this job ad. Before you rewrite it, ask me three
questions about the role, the audience, and what we want to signal.

Now it asks who you are really trying to reach and what would make a strong candidate choose you, instead of just polishing the same corporate filler.

3. Onboarding. A new hire starts Monday and you want their first week to not be chaos.

Help me plan a first week for a new hire. Before you build the plan,
ask me three questions about the role, the team, and what a great
first week would look like for us.

The questions force you to actually decide what good looks like, which is most of the work anyway.

4. Policy communication. You have to explain a new policy to managers without it landing as legal mush.

Help me explain this policy to managers in plain English. Before you
draft the explanation, ask me three questions about the audience, the
sensitivity, and the behavior we want to see change.

It stops guessing the reading level and the politics, and you get something managers will actually read and act on.

5. Meeting prep. You have a tense topic and thirty minutes to handle it well.

Help me plan a 30-minute team discussion about workload. Before you
suggest an agenda, ask me three questions.

Now it asks what outcome you need and what tension is already in the room, and the agenda is built for your team rather than a textbook team.

Five examples, one habit. Notice you did not learn five prompts. You learned one move and pointed it at five tasks.

When not to use it

Two honest cautions, because practical does not mean reckless.

First, the model asking you questions does not change what you are allowed to feed it. Do not break any IT rules here (but question them if they restrict you).
If you need to, strip it down, anonymize it, talk about the situation in general terms, or use the enterprise tool your IT and HR have cleared. The three-question habit works just as well on an anonymized version.

Second, this helps you think and draft. It does not make the decision. A termination, a discrimination complaint, a legal call, a performance rating that affects someone’s pay, those stay with a human who owns the outcome and signs their name to it. Use AI to prepare the thinking, not to outsource the responsibility.

If you teach your team one thing about AI this quarter, make it this. Before you ask AI for an answer, ask it what it needs to know.

That is the entire lesson. It needs no training budget, no new license, and no framework. A manager can demonstrate it live in a standup in two minutes, and the people who freeze at the blinking cursor suddenly have a way in.

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Happy Monday

Fable 5 was pulled back from us EU users late Friday / early Saturday, and I do miss it already. Strangely, how quickly you can get accustomed to using something. Wasn’t even a week in of using it, but the feel was different with Fable 5. Let’s see how this all plays out.

This was intended to be published on Saturday but then life happened and things got in the way. But better late than never. Here you’ll get a 30-45 min exercise to run with your team, and this works with almost any team. I’ve run it with HR, finance, IT, sales, legal, marketing, customer service and what not.

If you want me to run it with your team + even more practical exercises and tips and tricks, reach out.

I’m fully booked in September → mid-October, but I do have spots left in August + November, and I’m always happy to run sessions like these!

Most AI sessions go the same way. Someone runs an impressive demo, everyone nods, and on Monday nobody changes a thing. The problem is not the tool; the problem is that the value was shown on a made-up example nobody in the room recognizes.

This exercise flips that. Instead of you showing what AI can do, people discover it on their own problems. It takes 30 to 45 minutes, needs no prep beyond wifi, and works in any room. There are two reasons it works.

1. People are far more likely to start when they see AI solve a task they own.

2. On work they have done before, they already know what good looks like, so they can judge the result instead of trusting it blindly.

I’ve run it on god knows how many people now; it’s well above 7,000 at this point, and I know this is an effective way to get people to use (gen)AI.

If you need help running this, reach out.

There’s a PowerPoint you can download here to accompany this workshop; feel free to adopt it as your template and as you see fit. This is uploaded here under CC-BY, meaning it’s totally fine to use it as long as you mention me and, ideally, point people to subscribe here. :)

Step 1. List your problems. Five minutes, alone and quiet.

Ask everyone to write down tasks that are recurring, time-critical, stressful or just boring. Aim for three to five. The only real requirement is that it ties to their own role and stays concrete. “Summarise long email threads” can be tested. “Communication” cannot. Tell them this is private working material and they will choose just one or two to share later, so they can keep the rest to themselves. That makes the list honest, which is important.

The question to put on screen. What in your week do you wish you didn’t have to do yourself?

Step 3. Warm-up.

Start with a warm-up so everyone is logged in and past the first hurdle. “I work as X. How can I best use AI in my role?” This is to get people settled in on using the tool and also to make sure that everyone is logged in. Great time to help people with initial hurdles before diving it to what matters.

Step 3. Test AI live. Fifteen to twenty minutes, hands on the keyboard.

Then each person picks one real problem and describes it the way they would talk to a colleague, getting as much context out as possible rather than hunting for a clever prompt. A move that works well is to ask the model to ask you three questions back before it answers. Then push the result. Ask for improvement, say what was missing, and note roughly how long this would have taken by hand.

This is the heart of the session, so be active in the room. In practice it is usually two or three people who need help, often the same ones you can spot in advance, so hover near them. The rest sort it out at the table, and one good hit spreads fast. The usual problem is not running out of things to do, it is that people want to keep going! Let them.

Step 4. Share at the table. Ten minutes, out loud and open.

Go around with three questions. Which problem did you test and what happened? Did the result surprise you, either way? What will you try this week? Sharing makes the value visible to everyone and builds the habit of talking openly about AI, which is one of the biggest reasons adoption sticks or fades. The third question matters most.

The pitfalls

The most common one is taking over the screen and demoing. Then it is a lecture again. Sit on your hands! Other ones are abstract problems that cannot be tested, where people start hunting for the perfect prompt instead of a good dialogue, and, last but not least, end without a concrete next step.

How to avoid the last one?

Ask everyone to name one thing they will try next week. Not something they should learn, but something they have already tried in the room and now take with them. Have them put it in the calendar, because the most common failure is going back and doing exactly what you always did. People rarely commit to a self-reminder, but they happily remind a colleague, so pair them up. A spoken promise in front of the group is what makes something happen.

And there you have it!
Hope this can serve as a useful base!

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Happy Wednesday!

I know, I know. Claude Fable 5 dropped yesterday, so you probably expected me to write about that.

And I will. Tonight I’m recording a podcast episode about it, once I’ve had proper time to test it. Because my first reaction was a simple “oh my god.” I get the hype, at least to some extent. Especially for non-coding work.

But I’m getting ahead of myself. That’s what the podcast is for. Drop a follow and I’ll give you the full breakdown of the latest release from Anthropic there.

Today, I want to talk about something else entirely.

Shame.

Let’s get to it.¨

Today I want to talk about AI shame.

What do I mean by that? When I’m out in organizations, I keep meeting people who carry a bit of AI shame, and they try to cover it up. Let me explain.

Over this past winter and spring I’ve done roughly 110 workshops. I’m not saying that to brag. (I’m not even sure it’s a good thing.) If you do the math on how many workshops that is per day, it’s a lot. It’s a bit too much. I’ve been spread too thin, and I’m spending the autumn figuring out how to do less. I’ll personally do fewer workshops going forward, and we’ll hire people so we can still help just as many organizations.

But here’s the huge upside of all that volume. Data. Every organization I meet gives me data points, and I think that’s marvelous. I’ve written about this in the newsletter before. I see it as a real privilege to sit down with people and ask, “Hey, what’s happening in your world of AI?” I’m good at capturing meeting notes and transcribing conversations, so I’ve built up quite a ledger on what AI adoption looks like right now. And it’s from almost everywhere; there’s barely a part of the world I haven’t worked with, so I feel reasonably well informed on a global level.

Which brings me back to AI shame, because it comes straight out of that data. This isn’t about singling anyone out. It’s a summary of a lot of conversations with a lot of organizations.

The pattern I keep seeing

When I walk into a workshop, I always ask the same thing. “How would you rate your own AI proficiency? Where do you think you’re at?” It’s a hard question. People interpret it differently, and you can argue endlessly about what AI knowledge even is.

But here’s what happens more and more, and it’s happened far more often in 2026 than in 2025. The buyer tells me up front, “We’re quite AI proficient here. You can skip the basics, go straight to processes, team benefits, organizational impact.”

The first few times I heard that, I was thrilled. Finally I get to talk about the stuff I’m most passionate about!

Then I started pushing a little. “Okay, show me. How are you using AI today? What’s your proficiency really at? Tell me more about what you’re doing.” And you realize people are using AI, sure, but they’re using a sliver of what’s possible.

Why it happens, and why I get it

I understand why we ended up here, just look back at the support people have been given. Very little education, few real processes and policies that scare people rather than support them. Across this whole ChatGPT era, the help has been minimal. So how would anyone be proficient when they’ve had almost nothing to lean on?

The problem shows up the moment you try to move forward. You start talking about building agentic processes and adopting an agentic mindset, and you get blank stares back. The conversation gets harder because you’re trying to skip a step. You’re trying to leap past the grunt work.

And there’s no way around that grunt work. Using ChatGPT, Copilot, or Claude for your own work takes effort. You have to understand what the model can do and how it can help you. Skip that, and every conversation after it gets harder.

This is more common at the top

I’m a little hesitant to say this, but it’s slightly more prevalent in leadership teams. Conceptually, they know they should have done the work. They just haven’t, usually because there’s no time, and sometimes because they quietly feel a bit too senior to get back down in the weeds and play around.

It’s a combination, and I’m not generalizing about everyone. But it matters, because in a leadership setting it’s hard to lead an organization through this if you don’t know the true nature of the models yourself. You can’t suddenly set sensible expectations about AI in the workplace if you don’t understand what these tools are.

So this is really one observation. Don’t skip the step.

I know you’d love to be further ahead. That ambition is great but you still have to do the work to get there.

What to do then?

I’ve been saying the same thing for three years now. Educate your people! Give them help and support. Make time for them to change how they work, to play with the tools, to genuinely evaluate what the models can do. Otherwise AI adoption becomes a token gesture, something nice you say you’re doing but not really doing.

This is not rocket science. We need to support our people, and right now, that is getting a basic understanding of what the tools can do.

I’ve run programs for truck drivers who don’t even have a computer in front of them, for obvious reasons (usually a bad thing when you drive a truck). We still trained them on what AI can do, because they play a role in the organization and need to understand what’s happening across it. They also use AI in their spare time. They ask it for advice on salary, on how to handle a manager or a coworker. They’re being informed by AI off the clock, so their employer decided it was important they understand it. Include everyone.

The cure for AI shame is education, and it starts at the top.

One caveat about my data

All this said, my data set is skewed, and I know it. When people bring me in, they want someone practical, because that’s still my biggest selling point. I love being practical. That doesn’t mean I can’t be strategic; I’ve done plenty of that too, but I lean practical because so few people do. Most consultants in the AI space want to focus only on strategy.

So the people I meet are the ones who struggle with the practical side, or who’ve at least admitted they want to get practical. It’s easy to get pulled into “we need to think strategically about this” or “we need to stay high level.” I’d rather get practical and run practical sessions, because right now that’s exactly where you need to be.