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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.

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Happy Saturday,

I finally had the time to record a video! And this one is about MCPs.
We talked about that a couple of weeks ago, but this is the practical version of it - this is how you do it!

Let me know in the comments if you have any questions!

The walkthrough (video transcription)

Can you connect your HR system, your employee engagement system, your ATS to Claude or to ChatGPT? Yes, potentially you can, and you do that through something called MCP. And the one who is going to explain all of this to you is me. I am Johannes Sundlo, and I help organizations, and especially leaders in HR, to understand what is going on in this world of AI, with a focus on how you actually do the stuff as well.

I am going to show you MCP connectors today. If you have no clue what an MCP connector is, I have a link below where I explain what an MCP connector is and how you utilize it. But today I am going to show you how you connect it to Claude and to ChatGPT. So let us jump right over.

Claude

This is Claude, the web version of Claude. I am going to show you how to add an MCP connector into Claude, and I am going to do that by showcasing a demo. So it is a demo MCP. You should be able to play around with it, because it is non-sensitive information. It is read only. It is from my blog and from Ethan Mollick’s Substack as well, so it is quite non-sensitive information. But this is how you do it.

You click customize, and then you are taken to this screen. On this screen, you click Connectors. Once here, you click on the little plus sign. You can either browse connectors, which are pre-made connectors that Claude has already provided you with and that you can easily connect to, for example Figma or Notion. If I wanted to connect to Figma or Notion, I could easily just click the plus sign, and then I am taken to a login site where I have to connect the two.

You can also search. So let us search, for example, for Workday. It is not available here. So if you want to connect something like Workday or something else, and you have an MCP server at your disposal, and you need an MCP server at your disposal to connect the two, then this is how you do it.

We close this one, we click on the plus sign, we add a custom connector, we write the name, “morning brief” for this one, and then I drop in the URL to my MCP server. In this case, I am good to go, because I do not have any authentication here, so you can just access this MCP server.

But if you are trying to connect Workday or your ATS or anything else, most likely they will ask for authentication, and you get that from your vendor or from your IT department. It is probably a good idea to include your IT department in this. If you are thinking about connecting an MCP server into your environment, it is always good to check with them, because this poses a security risk to some extent. If you let Claude, for example, write stuff back through your system, that could be a risk. In this case, we are only going to allow read. So we are only going to read stuff from the MCP server. That is less risky, so I am fine with this.

I am now going to add this MCP server, and then we are good to go. We can try it here by creating a new chat and saying, “Check my morning brief.” I think that is enough. We will see. But I think it will understand that, okay, I am going to see now if I have anything connected to morning brief. And yes, it spins up the MCP server immediately. Now it checks the latest text from Ethan Mollick in this instance, and it is most likely going to pull my stuff as well. We will see. Yes, my stuff is coming below Ethan, which is the right order, in all honesty.

So that was Claude. That is how you connect an MCP server into Claude.

ChatGPT

Let us jump over to ChatGPT and see the same thing here. What we do in ChatGPT is that we click Apps. We have the same experience here, where you have pre-installed apps that you can easily connect to. But that is not what we are going to do here either.

We are going to click on the settings bar in the top right corner. We scroll down to Advanced settings. We click Developer mode, and we enable that. They say this is an elevated risk, so once again, be a bit cautious and involve your IT department if that is something you want to do. Some IT departments have disabled this as well, so it might be that you do not see this. But if you are able to click on Developer mode, then what you see is that you have the option to create an app.

That is what we are going to do. We call it the morning brief again. Let us see, because I saw there was lingering stuff from my old demos here. But “create a morning brief,” we will say. Authentication, no authentication on this one, and I understand the risks. Then we create it.

Now ChatGPT has evaluated the MCP server, and it is good to go. But we also need to connect it. So we basically said, “Look at this MCP server and make sure everything is good and dandy.” Once that is done, we need to click Connect. And there we go, now we are connected.

Wrapping up

That is how you add MCP servers both to Claude and to ChatGPT. The benefit of this is, of course, that you can now start talking to your data. In this instance it is a bit silly, in all honesty. It is just the newsletters. But you understand the basics of it, and this is how you add an MCP server. So it is fairly simple, I would say. But always, of course, be cautious and involve your IT department when you add stuff like this.

That is it for today, and I will catch you in the next one.

New to MCP? Read the primer first: Connectors and MCP, explained for people who have never touched either.

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Happy Tuesday,

Talking about it out in the void still, but the podcast is getting more and more listeners! Two episodes on there that haven’t been published here.
One about why your AI consultants need to be practical and another one about whether we are in an AI bubble or not. My aim is to keep this up during the summer as well. Very much liking the format so far!

Listen on Spotify here.
Listen on Apple Podcaster here.

But let’s get on with today’s article that is a bit of a thought experiment. This is not a finished model, and I don’t have the answers. It’s more of an idea and a bit of a call to action that we need to rethink this.

Let’s get to it.

I just listened to a podcast with Nate, whose Substack I highly recommend.
(Thanks Tue for the recommendation!)

Nate didn’t talk about performance reviews directly, he talked about what you as an employee need to showcase in this AI era to prove your value and your worth. And that connects right back into the performance part of things for me.

We’ve been measuring tasks for 70 years

Performance reviews have been around for what, 70 years? I wasn’t part of the initial phases here because I’m not that old, but for as long as I’ve been in the workforce, they’ve looked pretty much the same.

Sure, there have been various attempts at doing it slightly differently, but the basis of it has stayed the same. I’ve been fortunate enough to work with forward-thinking companies, so the review was maybe named something else, but if you look at what we did it was a standard process where you got evaluated once a year against a goal or some value the company had. And at its core it was fairly task-based.

Whenever I’ve been part of setting up different performance review structures, the team and I did our best to make them as objective and as reliable as humanly possible. But we also know that humans are poor raters in general. We have recency bias, we have tons of other biases, which all also play a part in this. But no matter how we twisted and turned this, most managers still landed in the tasks.

”How many tasks did you achieve in the past year, did you reach the projects you set out to reach, did you deliver what you said you’d deliver.”

That’s my experience working with I don’t know how many organizations and managers over the years, and I think it’s roughly the same right now.

What happens when the task runs without you

Now what’s happening is that AI can supercharge that process, so you can take on more projects, get through more tasks and be more productive. And when I talk about it like that it sounds great, we get more stuff done. But then there’s the Nate conversation about human judgment, and how do you as an employee show that you’ve used your judgment in the process.

Because if the AI can do the task, let’s assume it can, then what are we rewarding? Take my bookkeeping. It’s more or less fully automated right now, I have an agent gathering all the receipts and another uploading them. The task runs without me, but it still requires me. I’m the one who has to make sense of it all. Does my manager know I’m still needed? Do we as an employer even understand what’s going on in that space anymore?

I think we need to steer away from that mindset of tasks being the output we value people on.

I know a lot of companies aspire to evaluate against their values instead, but as said I do think we still fall back on tasks to a large extent. So how do we move away from it, and where does the human factor come into play? How do we know there’s a human in the loop at all? And do we even need to attribute the right things to the human and the right things to the AI, or should we just reward the human for using the AI in the first place?

Using AI vs using it well

Because there’s a difference between rewarding someone for using AI and rewarding them for using it well. It’s fairly easy today to open ChatGPT or Claude or Gemini or Copilot and get a task done, and I’d guess most of you reading this already have that skill. But I’ve been in plenty of discussions with managers where someone used AI and created more work further down the line. The task got delivered, the output got a thumbs up, it did the thing it was supposed to do, and then it created a ripple through the organization afterward that someone else had to deal with.

I think this requires more from managers, not less.

It’s easy to think AI will make your life easier as a manager, that you can just let it do stuff for you, which you of course can, but leading people who work with AI takes more conscious effort, not less. Because you’re the one who has to make sure the people you lead are not just using the tools, but using them in the right way to get valid outputs, and that they validate what the AI gives them.

The CHRO who saw it coming

One of the first proper AI conversations I had was with a CHRO, well over three years ago now. She’d trained her whole HR team early on, which is how we met. A year later she called me and said she had two people who used to be standard performers before AI, and now they were superstars, using it in everything and getting so much done. And she had two people who used to be high performers, not using AI and falling behind. It was performance review time, so what was she supposed to do, how was she supposed to attribute her pool of money across these people.

Back then I didn’t push as hard on the value question as I would today. We talked about output and whether they were providing value, but it wasn’t as focused on value as I’d be now. We landed on her wanting to reward the behavior of using AI, that was the answer at the time. Looking back, that was probably an early signal of what’s coming even more now.

So what is the review even for?

I think it’s worth reopening the whole thing and asking what the performance review is even for. Usually the answer is that it’s the one structured moment where an employee gets to talk to their manager somewhere during the year and gets some help to grow. But is that the best vehicle for it even now? Should we let AI support that growth together with the manager, should it be a combination of the two, or should we scrap the annual review altogether and build something else? Or is it even enough for now to say that as long as you’re part of the development process and using AI, that’s good enough?

I don’t have the answers here. I think different organizations will land in very different places on this, and that’s fine. But is it worth having the conversation? 100%. What does good performance look like in the age of AI?

I don’t have a clean answer for you, I just think we need to stop assuming the old one still fits.

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Happy Saturday,

On Tuesday, I argued that we in HR need to get more technical. Not to write code. To understand what’s happening, to be a sharp counterpart to IT and to your vendors, and to know what’s possible.

A few of you wrote back and said, essentially, “agreed, but I still don’t know what half these words mean.”

Fair.

So this is the practical follow-up. A dictionary of the AI words you keep hearing in meetings, in vendor demos, and in your own team’s chat. The ones you nod along to and then quietly look up afterwards.

Two rules for myself here. Every word is explained the way I’d explain it to an HR colleague over coffee, and tied to why it matters for your work, not a developer’s. And every one links to a source from the people who build this stuff. (Opus 4.8 have assisted with all the research here - thanks!)
So you can go a level deeper when you want to, and trust that it’s the real thing.

I haven’t put them in alphabetical order; instead, I’ve grouped them by the situation you’re in when you might hear them.

Save it and send this to your team. That’s the whole point. The fastest way to raise the technical floor of an HR function is for everyone to use the same terminology!

Let’s go.

The basics

Prompt. The instruction you give the model. “Write me a job ad for a payroll specialist” is a prompt. Output quality is mostly decided by input quality, so vague prompt, vague answer. The highest-leverage skill in your team right now is writing sharper prompts, and it costs nothing to learn.

Token. The unit the model reads and writes in. Not a word, not a letter, something in between. A common word like “the” is one token. “Tokenization” gets split into “token” and “ization.” Rough rule, one token is about three-quarters of a word. OpenAI explains it plainly in their help article on tokens. Tokens are how usage and cost get measured, so when a vendor quotes a price “per token,” they mean per chunk of text going in and coming out.

Context window. How much the model can hold in its working memory at once, counted in tokens. Your message, the document you pasted, the whole conversation, and the model’s own answer all count toward the same limit. Paste in a 90-page agreement and ask a question and the whole thing has to fit, or the start gets pushed out of view. This is why the AI sometimes “loses the plot” deep into a long chat. It isn’t broken and it isn’t forgetting. The relevant text scrolled past the edge of the window. IBM has a good resource here for more deeply explaining what a context window is.

Hallucination. When the model confidently states something false. A fake court case, a statistic that doesn’t exist, a policy clause that was never written. It isn’t lying, because it has no concept of truth. It’s predicting the next most plausible chunk of text, and plausible is not the same as true. Anthropic traced what happens inside the model when this occurs. There’s a feature that fires for “known entity” and one for “can’t answer,” and a hallucination is essentially the wrong one winning. This is the entire reason “human in the loop” exists. A reference letter, a contract clause, a salary benchmark drafted by AI all need a human to check them.

If you want to see how deep this goes, their write-up Tracing the thoughts of a language model is the clearest thing out there.

Data layers (and similar)

RAG (Retrieval-Augmented Generation). A way to let the model answer from your documents without retraining anything. It retrieves the relevant passage from your handbook, your policies, your FAQs, then writes an answer grounded in that. The technique came out of Meta’s research team in 2020, and their own explanation has a neat detail. Swap the documents and you change what the model “knows” without touching the model itself. This is what’s under the hood of nearly every “AI HR assistant” that answers employee questions from company policy. When a vendor says it’s “grounded in your data,” they almost always mean RAG.

Fine-tuning. Adjusting the model itself by training it further on your data. Heavier, more expensive, and rarely what you need. Worth knowing mostly so you can tell it apart from RAG. If a vendor says they’re “fine-tuning a model on your HR data,” ask hard questions about cost, about data privacy, and about whether plain RAG would have done the job for a fraction of the price.

Grounding. The broader idea of making the model answer from real, retrievable sources instead of its own memory. A grounded answer can point to where it came from. An ungrounded one is the model talking from memory, and that’s exactly where hallucinations live. “Can it cite the source?” is one of the best questions you can ask any HR AI vendor.

System prompt. The standing set of instructions that sits above every conversation and tells the model how to behave, every time, before you type a word. Your one-off prompt is what you ask today. The system prompt is the personality and the rules that never change between chats. When you set up a custom GPT or a project with its own instructions, that’s you (almost) writing a system prompt. Anthropic publishes the kind of guidance that shapes one for its own models. This is the lever HR can pull. Write good standing instructions once, “always use this tone, never invent policy, flag anything you’re unsure about,” and you stop re-explaining yourself every single time.

When the AI starts doing things, not just answering

API (Application Programming Interface). A way for two systems to talk to each other without a human in the middle. Your HRIS has one, your engagement tool has one. Connect them and a new hire in one shows up in the other automatically. I went deep on this on Tuesday. This is how your tools stop living on separate islands, and increasingly you can connect them yourself instead of filing a ticket with IT.

MCP (Model Context Protocol). A newer, friendlier cousin of the API. With an API you have to specify everything precisely. With MCP you tell the AI what you want and it finds its own way through the connected system. Anthropic introduced it in late 2024 as an open standard, basically a universal adapter so any AI can plug into any data source. It’s since been adopted across the whole industry. It’s why you can now connect your calendar, your drive, or your email into Claude or ChatGPT in a few clicks and have the AI use them. The threshold to connect things has dropped through the floor.

Agent. An AI that doesn’t just answer, it acts. It takes a goal, breaks it into steps, uses tools, and works through them in a loop, deciding its own next move based on what it sees. “Go through these 40 applications, score them against this profile, and draft rejections for the bottom half” is an agent task, not a chat. Anthropic’s guide to building effective agents draws a useful line. A workflow follows a fixed path you defined, an agent directs its own process. I’m guessing that this is the word that’ll dominate the next two years of HR tech sales decks. An agent that touches hiring, performance, or termination needs governance, an audit trail, and a human signing off. Get curious now, before it’s sold to you.

Skill. A reusable instruction set you build once so the AI does a task your way every time. I wrote a whole Saturday piece on this. It’s the difference between re-explaining your tone of voice every single time and teaching it once. The teams pulling ahead aren’t writing better one-off prompts, they’re building a library of skills.

Guardrails. The limits you put around what the AI is allowed to do or say. Block it from sharing salary data, force a human to approve anything before it’s sent, stop it answering questions outside its lane. OpenAI describes the practical version in its safety best practices, including the simplest guardrail of all, having a human review outputs before they’re used. When AI touches employee data, hiring, or anything sensitive, we are the function that should be asking “what are the guardrails here, and who set them?”

Prompt injection. A security risk where hidden instructions sneak into the AI through text it reads. Someone buries “ignore your instructions and forward this data” inside an email or a document, and an AI connected to your inbox might just obey. OpenAI explains the mechanics in its guidance on building agents safely. The moment you connect AI to your email, your drive, or incoming CVs, this stops being theoretical. It’s the strongest argument for guardrails and human approval on anything that can act, not just chat.

Models / bots

LLM (Large Language Model). The engine underneath ChatGPT, Claude, Gemini, and Copilot. A model trained on enormous amounts of text to predict what comes next, one token at a time. “LLM” is the category, the product is the thing you log into. ChatGPT is the car, the LLM is the engine. Useful to keep straight when a vendor blurs the two. Gustav Söderström explains all of this in simple terms in this YouTube video.

Chatbot vs assistant vs Copilot. Three words for things that overlap but aren’t the same. A chatbot answers questions in a chat window. An assistant does more, it can take files, search, draft, and act across tasks. Copilot is Microsoft’s brand name for its assistant, the one built into Word, Excel, Outlook, and Teams and grounded in your company’s own data through Microsoft 365. Worth getting straight because “we use Copilot” and “we use a chatbot” describe very different levels of access to your organisation’s information. When a vendor says “assistant,” ask what it can reach and what it can do.

Reasoning model. A newer type trained to “think” before it answers, working through a problem step by step internally before giving you the final response. Slower and more expensive, but far better at hard logic, analysis, and multi-step problems. OpenAI introduced the idea with its o1 models, describing them as designed to spend more time thinking, much like a person would. For a quick email, you don’t need it. For untangling a messy comp structure or pressure-testing a reorg, switch to one. Most tools now let you pick, so knowing when to reach for it saves time and gives better answers.

Multimodal. A model that handles more than text. Images, audio, documents, sometimes video. You can hand it a screenshot of a spreadsheet or a photo of a whiteboard and it reads it. Google built Gemini as multimodal from the ground up, rather than bolting it on afterward. In practice it means “paste the thing” instead of retyping it. Photograph the flip chart from your workshop and ask the AI to turn it into clean notes. That’s multimodal, and it’s a genuine time-saver.

Training data and knowledge cutoff. The text a model learned from, and the date that text stops. A model trained up to a certain point simply doesn’t know what happened after it, which is why it can’t tell you last week’s news unless it searches the web. Anthropic states the cutoff plainly in its model documentation. Two things matter for HR here. The model can’t know your latest policy change unless you give it. And because it learned from human text, it can absorb human bias, which is exactly why a person has to check anything that affects hiring or people decisions.

Inference. The technical word for the moment the model is running and producing an answer, as opposed to when it was being trained. You’ll mostly meet it in vendor pricing and capacity talk, “inference costs,” “inference speed.” Now you know it just means the model doing its job in real time, not learning anything new while it does.

How to use this

Don’t try to memorize all of these! That’s not the point.

The point is recognition. Next time one of these lands in a meeting or a vendor demo, you’ll know what’s being said, and more importantly, you’ll know what to ask back. “Is that grounded, or could it be hallucinating?” “Is this an API or an MCP?” “What are the guardrails, and who set them?”

That’s what getting more technical looks like. Not necessarily writing code but speaking the “language” well enough to be a sharp counterpart to IT, to your vendors, and to your own team. And clicking through to the source when you want to understand something one level deeper, instead of taking a sales deck’s word for it.

Here’s my one ask if you found this useful! Forward this to someone on your team who’s been quietly nodding along. You’ll look good, they’ll get smarter, and the whole function levels up a notch - hopefully!

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Hey,

I’ve bought new technical gear.
And it’s allowing one thing. For me to record myself more easily while in the car.
Which means I will increase the velocity of my podcast.

So! The plan now is this for the FullStack HR-ecosystem (and you’ve probably seen some of it already)

Tuesdays = longer content
Saturdays = practical tips & tricks

I’m not quite there yet to record videos about tips & tricks, but that is the plan for the fall.

But the podcast then? It will be shorter, faster episodes in between, about news, thoughts, and general updates. All about the intersection between AI, leadership, and HR.

I will push the podcast out every now and then, but head over to Spotify or Apple Podcasts (or search FullStackHR in your favorite podcast player!).

That said, let’s get to it!

PS. If you see FullStackHR AI & HR Daily in your podcast player… that is my AI bot, Winston, creating a podcast for me every now and then about news he thinks I should listen to…

I got a question the other week. Do we in HR need to be more technical?

The short answer is yes.

But that wouldn’t make for a very good article if I just said yes and left it at that. So let me explain myself, because this yes comes with a fair amount of nuance.

I’ve said it before. You don’t need to become a programmer. And I stand by that. But I believe you need to be at least somewhat technically interested right now to understand what’s going on. And for some of us, that means a pretty significant upgrade of our skills.

So what do I mean by that?

I don’t mean you should write code. That would be counterproductive. Because what’s happening with tools like Claude Code and Codex isn’t that more people need to become programmers. It’s that more people need to be able to validate code. You don’t need to write it. You need to be able to judge what comes out.

What I mean is that you need a sense for where the technology is heading, and a basic understanding of how it works. What makes a product good, and what sits under the hood? How does an API work? What’s a database, and what different versions are there? How does AI connect to databases, to APIs, to MCP servers?

Why?

Because this affects us. With Codex, Claude Code, Antigravity, and all the other development tools available now, we need to understand that we have agency. We can do things ourselves with these tools.

I’m not saying you should build your own ATS or your own HRIS. (Even if you can do that.) But we need to know that the possibility exists. And it’s worth starting to play around with. Try Lovable, try Replit, or why not ChatGPT Codex or Google’s Antigravity.

It matters that you start poking at them, because it informs you about what’s possible and what isn’t. And what a lot of organizations are now discovering is that it also makes you a better buyer when you procure HR services that someone else is building.

Because we can reasonably expect our vendors' productivity to have gone up now. That their pace of building new things should have increased if they’re using the same tools. Hopefully, they have skilled people who are not only good at creating things but also at validating what comes out.

This felt scary to me too

And yes, it can feel scary in the beginning. Completely understandable. I can relate. I’ve been lucky enough to work in tech companies for most of my career, and it was intimidating at first, even though I’m genuinely interested in everything to do with tech.

When I started at Spotify, I had zero to very little knowledge about how to build backend services. I had to learn. And I think it’s important that you go through some form of upskilling or reskilling in your role. Not to become a programmer. But to try these tools out.

Because when you do, you inevitably learn things about code along the way.

API. MCP. What’s the deal?

So let’s jump into some of the acronyms here. API? What does it even stand for? It stands for application programming interface. It’s a way to connect different systems. Most people have probably heard the term; it’s not new. But at its core it’s a way for computers to talk to each other without having a middleman in the form of a human.

Let’s say you need to connect your HR system to your employee engagement system. Usually they have APIs, so you can connect the two. Update the HR system and the engagement system updates automatically. The person who gets hired is added, the person who leaves is removed, based on the information in your HRIS. No back-and-forth with spreadsheets or going row by row to delete stuff.

If this happened, we needed to connect the systems, which we normally did in HR a few years back, by going to IT and asking if they could connect the systems. They put it in a backlog. They came back and asked for the API documentation. There was a meeting. And that was that.

What we’re seeing more and more now is that you can do it yourself. You can connect systems fairly easily, either through a no-code tool or directly in the system. Or you use MCP. Another acronym? Yes.

MCP stands for Model Context Protocol. A bit of a glorified API, you could say, that sends information between systems but without the hassle. With an API you need to be very specific. This is the information I want to retrieve, this is what you should return, this is what you should publish. With MCP you can instead tell an AI to do something, and the MCP finds its way through the system on its own.

And it’s fairly easy today to connect MCP servers from different vendors into Claude or ChatGPT.

The threshold has dropped. Now it takes curiosity.

What used to be hard and required a lot of IT development doesn’t require the same thing anymore, if we’re curious. And that’s where the curiosity comes back in to play! We need to understand this. We need to play with it.

Obviously, I wouldn’t start by connecting my real HRIS to my real engagement system in production. I’d do it in a sandbox where I can test, see what works and what doesn’t. Maybe a small personal project where I get to feel out what’s what.

But we need to do it. I think it’s our responsibility as HR people to take this leap. It’s not good enough to say you’re not interested, or that you chose HR because you’re not technically inclined. We need to do this, because this is where the world is heading. We’re not going to have less tech in our roles. So we need to start educating ourselves toward that future.

And yes, it’s the most worn-out analogy there is, I’m as tired of it as you are. But it’s still true. Wayne Gretzky and his “skate to where the puck is going.” (Feel free to roll your eyes.) But if you want to future-proof your career and your organization, I think this is a capability we need to start building.

It only takes one

I’ve worked with several organizations where you can see the difference made by the people who did this. It doesn’t have to be the CHRO. Not even the HRBP. It can be an HR assistant fresh out of university who takes it on themselves to explore what we can and can’t do.

And the upside for the organizations actively trying to figure this out is enormous. Someone who can be a good counterpart to IT, because we’ll still need their help to some degree. Someone who can be a good counterpart to the vendors where you buy external systems. And someone who can be a teacher and change agent internally.

It’s an important role to fill. And I’d strongly advise you to upskill the whole team, not just one person. To have the whole team start thinking, pondering and testing.

It doesn’t have to be complicated, and it doesn’t have to be expensive. You can learn a lot from tutorials on YouTube. You can take cheap courses for a couple of hundred bucks and be up and running. This is not an expensive technology to learn.

No excuses

It’s easy to say “yeah yeah, I know I should, but I don’t have the time, and it’s so hard, and I don’t know where to start.” And every other excuse you can come up with.

But don’t make any excuses for this. Because this is where the world is heading. I’m a firm believer that tech will become even more important. You can ignore it, but then you’ll have to play catch-up instead.

And sure, the technology will get easier and easier. I genuinely believe that too. But the understanding will help you regardless. I’m completely certain of that. Even if this becomes super simple in the future, this foundational understanding will help you.

It’s time well spent.

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Happy Saturday,

Quick thing before we start.

I am running beta cohort 2 of Cowork for HR, kicking off on the 4th of June.

Three live sessions where we build real HR workflows together, the kind you actually use on a Monday morning rather than the kind that look nice in a demo. There are a few spots left. If you want in, jump on the interest list. If you have read my last two pieces and thought “fine, but how do I make this thing talk to my actual tools,” that cohort is the answer in long form. This newsletter is the short version.

Join Cohort 2

Because here is where we are in the arc!

A few weeks ago I wrote about Cowork, the desktop agent that works on your computer the way you do. Then I wrote about Skills, the reusable work recipes that make the AI do the task your way every time instead of forgetting on day two. Both of those pieces had a quiet assumption baked in. They assumed the AI could reach your stuff. Your calendar, your inbox, tour files in Drive or SharePoint.

That assumption is what today is about. The plumbing and how the AI reaches your tools.

The two words you need are connectors and MCP. They sound technical. They are less technical than they sound, which is true of almost everything in this field once someone explains it without trying to impress you.

Let me try to be that someone. (Let me know in the comments how I did…)

The problem first, because the problem makes the solution obvious

Imagine you hire a brilliant new HR assistant. Sharp, fast, knows employment law cold. On day one you ask them to pull last quarter’s exit interviews and summarize the patterns.

They cannot. Not because they are not capable. Because they do not have a login to anything. No email access. No Drive. No HRIS. They are sitting at a desk with a brilliant brain and no keys to the building.

That is an AI without connectors. All the capability, none of the access.

A connector is the key card. It is how the AI reaches one specific tool. One connector for Gmail. One for your calendar. One for Slack. One for Notion. Each one is a separate login that you approve once, and then the AI can read from that tool, and in some cases act in it, on your behalf.

That is the whole concept. A connector connects the AI to one of your tools. The name does its job for once.

Now the part everyone trips over, MCP

If a connector is a key card, MCP is the reason every building started using the same kind of card reader.

MCP stands for Model Context Protocol. But ignore the wording, it is what its.

In the ancient times of 2023-2024, every tool spoke its own language to the AI. If Notion wanted to work with ChatGPT, someone built a special Notion-to-ChatGPT bridge. If Notion also wanted to work with Claude, someone built a second, different bridge. And again for Copilot. Three tools, three AIs, nine separate bridges, each one custom, each one expensive, each one breaking on a different Tuesday.

It was a mess. The kind of mess that quietly eats budgets.

MCP is the agreement that fixed it. It is a shared standard, invented by Anthropic and given away for free, that says “here is one way for any tool to talk to any AI.” A tool builds an MCP server once, and after that it works in Claude, ChatGPT, Copilot, and a few hundred other places. Build once, plug in everywhere.

The comparison people use is usually USB-C. You remember the drawer? The Lightning cable, the micro-USB, the weird proprietary one for the camera you no longer own. USB-C ended that. One connector, every device. MCP is USB-C for AI.

This standard went from a blog post in late 2024 to being supported by Anthropic, OpenAI, Google, and Microsoft inside twelve months. That basically does not happen. When the four companies that agree on almost nothing all agree on one thing inside a year, the thing is not a fad, it is infrastructure. MCP is now governed by the Linux Foundation, which is the boring sentence that tells you it is here to stay. (Sorry, Linux is great.)

So when an HR software vendor tells you “we have an MCP server,” they are saying “you can plug us into your AI with a few clicks instead of a six-month integration project.” That is a sentence worth getting excited about in a procurement meeting!

Connector versus API, since IT will say “API” at you

You will end up in a room with IT. When you do, someone will ask why you cannot use the existing API.

Here is the difference in one breath.

An API is narrow and literal. Each request asks one exact question. “Give me the start date for employee 12345.” It answers that, nothing more. Useful, rigid, like a vending machine where you must know the button.

An MCP gives the AI a structured menu of what a tool can do and lets it choose the right move for the task. More flexible, more capable in skilled hands. Closer to handing someone the keys to the supply room and trusting them to find what they need.

That extra flexibility is exactly why IT will have questions, and exactly why those questions are fair. More on that below. For now, the line that works in the meeting is simple. An API answers one fixed question. An MCP gives the agent a toolkit and lets it work. The power and the caution both come from the same place.

The three big AIs, and what the connector experience is like in each

You have three realistic choices of where to do this work. They all run on MCP underneath, so the foundation is shared. What differs is the feel, the price floor, and how much your IT department is already involved whether you like it or not.

Here is the comparison, but please note that this is the May 2026 version, and it’s for the “chat interfaces” - if you use the coding apps on these, it’s different.

The plain-language version of that table?

If your whole company already runs on Microsoft 365, Copilot is the smooth choice, because it reaches Outlook, SharePoint, and OneDrive through the same login and the same security rules you already have. Most of the time your IT admin switches it on and you do not configure anything yourself. The tradeoff is that it is the most complex model under the hood, with the most gatekeeping. That gatekeeping is a feature when you are a 5,000-person regulated business and a mild annoyance when you just want to try something on a Tuesday.

If your company has settled on ChatGPT, its Apps give you the widest spread of third-party tools. Slack, HubSpot, Canva, Stripe, Atlassian, the lot. The Plus plan covers most of it. It is cloud only, so it never touches files sitting on your own laptop.

If you want the lowest barrier to entry, Claude wins, and I say that as someone who uses it daily so take the bias as disclosed. The free plan already reaches the full catalog and lets you add one custom connection. It is also the only one of the three that runs tools locally on your own computer, which matters for sensitive HR data you would rather not send anywhere. That local option is the quiet reason a lot of HR people land here.

None of these is wrong. The real question is rarely “which integration do I get,” because they mostly share the same ones through MCP. The real question is “which app do I want to live inside all day.” Pick the room you want to work in.

How you connect something, in Claude, step by step

Enough theory. Let me walk one all the way through so it stops being abstract. I will use Claude because it has the fewest moving parts, and the shape is nearly identical in the other two.

Say you want to connect Notion.

1. Click your account icon, bottom left corner.

2. Choose Settings.

3. In the left sidebar, click Connectors.

4. Click Browse connectors to open the catalog.

5. Find Notion and click Connect.

6. A login window opens. Sign in to Notion the way you normally would.

7. Read the permissions list. Read it properly, this is the part people skip. Then approve.

8. You land back in Claude. Notion shows a green status. Done.
   

That is it. Maybe ninety seconds. You can disconnect it from the same page whenever you want, and you should disconnect things you stop using, the same way you would deactivate a key card for someone who left the team.

The flow in ChatGPT is the same idea. Profile icon, Settings, Connectors or Apps, find the tool, Connect, approve. In Copilot it is usually your admin who flips it on, and you find the result in the Agents panel on the left.

Using it once it is connected

Once a tool is connected, you mostly just talk.

You type “search my Drive for the Q4 onboarding deck” or “create a task in Asana for the offer letter review” and the AI picks the right tool on its own. You do not memorize commands. You describe the outcome.

If you want to be explicit, all three let you summon a specific tool. In ChatGPT you type @ and pick it, like @Gmail find messages from Lisa last week. In Claude you hit the plus button or type a slash to choose connectors. Small conveniences, not requirements.

And before the AI does anything that changes your data, sending an email, deleting a file, updating a record, it stops and shows you exactly what it is about to do and waits for your yes. That pause is your friend. We will come back to why you should resist the urge to click “always allow” on everything.

A grounded HR example, end to end

Let me make this concrete with something you might do this month.

You connect three things. Your calendar, your file storage where policies and templates live, and your meeting transcription tool if you use one like Granola or Fireflies.

Now you can ask “what did I promise the manager group in our June sync, and have I sent it yet.” The AI reads the transcript, checks your sent mail, cross-references the calendar, and tells you. No tab-switching and no digging through your own inbox like an archaeologist.

Pair that with a Skill, the reusable recipe from the last newsletter, and it compounds. You build an exit-interview-summary skill once. Then you say “pull last quarter’s exit interviews from Drive and run the summary skill.” The connector fetches the files. The skill shapes the analysis your way. You read a finished draft instead of starting from a blank page and a sigh.

Connectors give the AI reach. Skills give it judgment. Together they turn it from a clever stranger into a colleague who knows how your function works.

The cautious part, because HR of all functions should read this

I would be doing you a disservice if I made this sound risk-free. It is not, and the risks are the kind we need to understand.

When the AI acts in your tools, you are accountable for what it does, the same as if you handed the task to an assistant. If it sends the email, that email is yours. The vendors are explicit about this, and so am I.

Read the permissions before you approve a connection, and keep them as narrow as the tool allows. A connector that only needs to read should not be granted the right to delete.

Resist “always allow.” There is a mode in these tools that lets the AI act without pausing to ask. It is convenient, and it meaningfully raises your exposure to a class of attack called prompt injection, where hidden instructions in a document or web page try to hijack the agent. Use the pauses, they exist for you.

Only connect tools and custom servers you trust. If a vendor offers an MCP server, “do I trust this vendor” is the entire security question in plain clothes.

And the big one for those of us in the EU. Be careful with GDPR and with sensitive HR processes. Approval to summarize a recruiting note is not approval to run a termination workflow. Treat each new use case as its own conversation with your data protection officer and your legal team. Bring them in early. The difference between a smooth rollout and a mess is almost always whether IT, legal, and your DPO were in the room at the start or found out afterward.

None of this is a reason to avoid the technology. It is a reason to walk in with the answers ready instead of being slowed down by questions you did not anticipate. Security here is a question that can be answered. The answer just requires the right people in the room.

Wrapping up

Strip away the acronyms and this is simple.

A connector is a key card that lets the AI reach one of your tools. MCP is the shared standard that made every tool and every AI agree to use the same kind of card, so you plug in with clicks instead of projects. The three big AIs all run on it, and the choice between them is mostly about which app you want to work in and how deep you already sit inside Microsoft.

Start small. Connect one tool you touch every day, your calendar or your Drive. Approve the permissions consciously. Ask it to do one real thing. Watch it reach into the building it could not enter yesterday.

Then add a second tool. Then pair it with a skill. That is the whole game, and it is less hard than it sounds, which, again, is roughly true of most things in this field.

If you want to build this properly with a group, the Cowork for HR cohort on the 4th of June is exactly that. A few spots left.

Questions? Email me at johannes@sundlo.com. I am not always fast, but I respond.

Good luck.

This guide is free and meant to be shared, copied, adapted, and pulled apart. If you build something useful on top of it, send it back!

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Cowork cohort Beta 2 is starting soon!
The early bird price is gone, but there is still room for a couple more people.
It is very practical, and even though we talk about Claude Cowork, you can use the same principles for Copilot Cowork or OpenAI’s Codex.

Sign up here.

Happy Tuesday,

101, that’s how many lectures / workshops / sessions or call it whatever this year.
I know it’s too much, but as said before, the true upside of this is information back to me. You see what’s working in organizations and what’s not.

Every client's work is a data point if treated as such, which builds a clearer and clearer picture of how to do this whole AI transformation. Most of my articles these days stem from that collective knowledge. This one certainly does.

So let’s get to it.

There have always been fast adopters and slower adopters in workplaces. The volunteers and the volun-tolds.

Most of the time, that gap closes on its own. Someone shows the slower group a shortcut, a system gets rolled out company-wide, training fills in the rest.

Generative AI is different, and what many employers are doing right now might risk making the gap permanent.

A group of employees, often a small one, has figured out generative AI on their own. They started with the free version of ChatGPT at home. They got curious and now use it daily.

Another group, often a larger one, has not done this. They have not been pushed by curiosity, they have not had time, and they have not seen anyone they trust show them why it matters.

People adopt new things at different speeds, so far, nothing new there.

But, and this is the important thing here, what a lot of employers are doing right now is to look at the situation and say, sensibly enough, “let us give the people who are already using AI the proper tools.” Copilot for Microsoft 365 Business, ChatGPT Enterprise or Claude Enterprise. The full model with the safety guardrails, the data protection, and the integrations.

Then the employer looks at everyone else and says, “and you get Copilot Chat. Or nothing. Because it costs too much to give the full thing to everyone.”

So we are giving the most powerful productivity tool of our generation to the people who are already pulling ahead. And to the people who were already behind, we are saying, “here, take this stripped-down version. Welcome to your paper and pencil.”

And in one stroke, we’ve widened the gap even further between the two.

This is a work tool.

Generative AI is not a perk for the AI-curious. It is not a power-user feature for the technically inclined. It is a work tool. The same way a laptop is a work tool, the same way email is a work tool.

You do not give email only to the employees who showed early interest in email. You give it to everyone because the work requires it. We are at the same point with AI. The work requires it, so everyone needs it.

The fact that some employees got there first on their own does not change the answer. It only tells you who needs less training, it does not tell you who deserves access.

I have been saying this for years. I am sorry, IT, for repeating myself. But we have to start working together on this one. In most organizations the AI tooling decision lands in IT. Which makes sense on the surface. IT handles licenses, security, and deployment.

But when IT owns the rollout without HR and leadership in the room, the framing shifts. The tool becomes something for technical people and early adopters who knock on the door. The conversation turns into “who has shown they can use this responsibly,” “who has the use case to justify a license,” “who can articulate the ROI.”

That sounds reasonable, but it is also exactly how you end up with two-tier deployment.

IT is not the enemy in this story. IT is doing what IT has always done, which is roll out technology safely, with use cases and license discipline. That is the right behavior for ninety percent of what IT deploys.

This is the ten percent where it is the wrong behavior. Because this is not really a technical rollout. (Once again, repeating myself here but still…) It is a change in how people work, and how people work belongs to HR and to operational leadership, not to a procurement spreadsheet.

The organizations that get this right have leaders who walked into the room and said something different. This is not a technical tool. It is a work tool. Everyone gets it. We train everyone, we support everyone, we expect everyone to figure out their own use cases.

The best leaders I have worked with do three things at once.

1. They redraw the map. AI access is decoupled from technical fluency. It sits where laptops and email sit. Default on for everyone.

2. They invest in showing people what it is for. Not a generic intro session at lunch. Concrete, role-specific examples from peers. The communicator sees what another communicator did with it. The HR partner sees what another HR partner did. The procurement specialist sees what another procurement specialist did.

3. They protect the time it takes to learn. They make it explicit that the first few weeks of regular use will feel slower, not faster, and that this is fine. They build in space for the awkward middle phase that most adoption efforts skip.

Here’s your fax machine.

Imagine it is 1995 and your company is rolling out personal computers.

You give the desktop PC to the employees who already use a computer at home in the evenings. Same applies to anyone who has shown technical curiosity.

Everyone else gets paper and pencil. “Sorry, we noticed you have not shown enough interest in computing. Maybe next year, if you can demonstrate some initiative.”

Nobody would do this. The absurdity is obvious.

Yet that is what we are doing with generative AI in 2026. The only difference is that the gap is fresh enough that the consequences have not yet hit the labor law system or the union negotiation table.

They will.

I am not a lawyer. But systematic differentiation in access to fundamental work tools, based on something as vague as “engagement” or “self-driven curiosity,” is going to be challenged. When AI access becomes a measurable factor in performance, productivity, and promotion, and it already is, then unequal access becomes an HR risk.

Imagine it’s 2028. A union representative looks at your AI tooling policy and asks, “why did you give the Enterprise license to person A and the basic version to person B, when they are in the same role?”

“Because person A used it more on their own time” is not going to be a defensible answer.

It will look like one of those decisions everyone wishes they had pushed back on at the time.

What good looks like

Either your organization is going to work with generative AI as a core capability, in which case everyone gets it. Same access, same model, same support. You differentiate by training intensity and use case, not by license tier.

Or your organization is not yet ready to deploy at scale, in which case nobody gets it yet. You run controlled pilots with clearly defined participants and timelines. You learn, you decide. You roll out to everyone, or you do not roll out at all.

What you do not do is the middle path. The “let us give it to the keen ones and see what happens” path. It looks pragmatic in the short term and looks like discriminatory tooling in the long term.

I am pushing it hard here. The middle path is where most organizations sit right now, often by accident more than by design. That is the pattern that leads nowhere.

What to do.

If you are an HR leader or a CIO reading this and recognizing your own organization, here is the move.

1. Pull the data on who currently has AI licenses. Look at the distribution by role, level, location, gender, age, and tenure. Be honest about the patterns you see. Most of the time, they are not pretty.

2. Decide. Are you in or are you out? Write it down. Get the executive team to sign it.

3. If you are in, build a deployment plan that gets the full tool into every role within twelve months, with training that meets people where they are.

4. If you are not in yet, stop the partial deployment. Pause the licenses. Run a real pilot with clear boundaries and a real evaluation.

5. Tell your employees what you decided and why. The worst thing you can do is leave the current quiet two-tier system in place and hope nobody notices.

We talk a lot about AI replacing jobs. (I did it last week.) The more pressing question, for the next few years, is which humans get to use AI and which humans have to compete against the ones who do.

That decision is being made right now in procurement meetings and IT budget reviews. Often without HR in the room, without a labor lawyer in the room and without any reflection on what kind of workplace this creates.

Then in two years we will wonder why engagement scores diverged so sharply between departments. Why the same role has wildly different output. Why people in their fifties are quietly being managed out. Why the union is suddenly very interested in tooling policies.

It will not be a mystery, we will have done it to ourselves.

The fix is not complicated.

Treat AI as a work tool. Give it to everyone, train everyone, and support everyone. Redraw the map so AI access is not coupled to who happened to show technical curiosity first. And do it with IT, not around IT.

Anything less and you are building an A-team and a B-team inside your own walls.

Once that gap is locked in, it is much harder to close than to prevent.

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Last week, I talked about Cowork, and I mentioned “Skills” in between. Skills, however, require a bit more attention! So here we are, a longer article around skills.

Because most people who start using AI notice the same thing pretty quickly. The AI can do a lot, but it doesn’t do the work your way.

It can write a text, it can analyze a document, it can summarize a policy, and it can build an interview guide. Impressive stuff, until you realize you’ve now explained “we always start with the purpose, not the agenda” for the fourteenth time this week, and the AI is looking at you with the same blank enthusiasm as on day one.

That’s where skills come in!

A skill is, at its core, a reusable instruction for the AI. It describes how the AI should handle a specific type of task. Think of it as a small work recipe.

Not a recipe for food. A recipe for work. Though, if it helps, picture a tired but reliable cookbook that doesn’t forget the salt.

For example:

When I ask you to analyze an employee survey, always start by identifying the three biggest patterns, show differences between groups, be clear about uncertainty, and end with concrete questions for leadership.

That’s a typical skill.

The skill doesn’t make the AI magical. It makes the AI consistent. Which, frankly, is a much rarer quality.

A simple analogy. A new colleague.

Imagine you hire a new HR Business Partner. The person is sharp, quick, and experienced. But they don’t know how you work yet.

The first time you ask them to prepare materials for a manager workshop, you have to explain a lot.

We usually start with the purpose. Then we want three discussion questions. Avoid too much theory. The managers want practical examples. And don’t use long-winded text. They will stop reading.

Second time, you say it again.

Third time, you smile politely while dying a little inside.

Eventually, you write a simple template and slide it across the desk with a hopeful look.

When you build manager workshops with us, follow this structure.

That’s roughly what a skill is for AI.

It gives the AI a reusable way of working, so you don’t start from zero every time. Or, more honestly, so you don’t start from “oh no, not again.”

Another analogy. A checklist.

A skill is also like a checklist.

Pilots use checklists not because they lack competence, but because forgetting something at 30,000 feet has consequences that emails generally don’t. In the same way, a skill helps the AI remember what matters in a specific task. The stakes are lower. The principle is the same.

For HR, when the AI helps write a job ad, it might always need to check:

• Is the language inclusive?

• Are the requirements realistic, or did we secretly write a job ad for a unicorn?

• Are “must-haves” and “nice-to-haves” mixed up?

• Is the role clearly described?

• Is the text clear to an outside candidate who has never heard of your internal team names?

This can become a skill. Call it job-ad-review.

Next time you ask the AI to review a job ad, it uses the same checklist. Every time. Without sighing.

What does a skill look like in practice?

In practice, a skill is usually a small folder with a file called SKILL.md. Not very glamorous. The good things rarely are.

That file describes:

• what the skill does

• when it should be used

• what steps the AI should follow

• what format the answer should have

• what risks the AI should watch for

• what examples, templates, or references belong to it

That’s it. No magic. No witchcraft. Just a markdown file that knows its job.

Which tools support SKILL.md today?

This space moves fast, so this might get outdated quite quickly, but as of May 2026, SKILL.md is already relevant in several major tools.

ChatGPT has skills in beta for Business, Enterprise, Edu, Teachers, and Healthcare. Skills are supported in Codex and the API, and OpenAI skills follow the Open Agent Skills standard.

Claude supports Agent Skills across Claude.ai, Claude Code, Claude Agent SDK, and Claude Developer Platform.

The Microsoft Agent Framework describes Agent Skills as portable packages of instructions, scripts, and resources that give agents specialized capabilities and follow an open specification. It sounds important when read out loud.

It has also become available in Copilot Cowork, which I mentioned last week.

When should you build a skill?

Don’t build a skill for everything. That way lies a folder full of skills you forgot existed and a creeping sense of regret.

Build a skill when all three of these conditions are met.

1. The task comes up often.

2. You want the AI to handle it in a specific way.

3. There are steps, rules, or quality criteria the AI tends to miss otherwise.

HR examples where skills work well:

• reviewing job ads

• creating interview questions

• analyzing employee surveys

• summarizing exit interviews

• building onboarding plans

• writing manager briefings

• doing first-pass analysis of sick leave patterns

• building competency matrices

• reviewing HR policies

• writing change communication

Examples where you probably don’t need a skill:

• “Rewrite this sentence”

• “Summarize this briefly”

• “What does this term mean?”

• “Give me five ideas”

Too simple, too varied. Regular prompting handles it just fine, and your skills folder will thank you.

Simply put, if you find yourself often writing:

Remember to… Always do it like this… Use this structure… You missed the same thing again, my friend…

You’ve probably found something that should become a skill.

How do you build a skill in practice?

The easiest way to build a skill is not to start with a blank file. Blank files are where good intentions go to die.

The easiest way is to do the work once.

Pick a real piece of work. For example:

• you analyze an employee survey

• you review a job ad

• you build an onboarding plan

• you draft interview questions

• you summarize exit interviews

• you prepare a manager briefing for a difficult change

Do the task together with Claude or ChatGPT. Write instructions. Correct. Say what went wrong. Explain what you want different. Add examples and be a bit picky. This is the one situation where picky pays off!

Once you’ve worked through the task, and the result is good, say:

Turn this into a skill. Capture the workflow, my corrections, the format, the risks, and the things you can’t miss. Create a SKILL.md I can reuse next time I do this kind of work.

This is the best way to build a skill, in my view. Instead of trying to write the perfect instruction up front like a wizard, you extract a working method from something you’ve already done. The skills documentation from both ChatGPT and Claude says the same thing. Good skills come from real expertise, real work, corrections, formats, and recurring mistakes. Not from generic “best practice” articles written by people who have never actually used the tool in anger.

Both OpenAI and Claude have a built-in skill-creator to help you do this. It’s nice when the tool helps you build the thing that helps you use the tool.

What makes a good skill good?

How do you know if it’s good once Claude or ChatGPT has built it? You test it. Obviously. But you also review it. If it doesn’t work the way you wanted, these are good things to check. Did your skill cover them?

1. It has a narrow purpose

A bad skill tries to do everything.

HR assistant that helps with HR questions.

That’s not a skill. That’s a job description.

A better skill is narrower.

Reviews job ads for clarity, inclusive language, realistic requirements, and candidate perspective.

Concrete. Clear use case. Easy for the AI to recognize. Easy for you to remember six months from now.

2. It says when to use it

A skill needs to describe both what it does and when it should be used. The description works as a trigger. If it’s vague, the AI might never use the skill, or use it at the wrong time, which is somehow worse.

Bad:

description: Analyzes HR data.

Better:

description: Analyzes employee survey results. Use when the user asks for analysis of survey data, eNPS, engagement scores, leadership index, or free-text comments from employee surveys.

3. It contains concrete steps

The AI doesn’t need a lecture. It needs a way of working.

Write something like this.

1. Identify the biggest patterns.

2. Compare groups where data allows.

3. Separate fact, possible interpretation, and recommendation.

4. Flag risks with small samples.

5. End with three questions leadership should discuss.

Much better than:

Do a professional and insightful analysis.

Saying “be professional” to an AI is like saying “be funny” to a stranger at a wedding. Technically a directive. Practically useless.

4. It has a clear output format

If you want a specific format, show it. The AI cannot read your mind. This is a feature, not a bug.

Example:

## Summary
[Short summary in 3 to 5 sentences]

## Biggest patterns
1. [Pattern]
2. [Pattern]
3. [Pattern]

## Risks in interpretation
- [Risk or uncertainty]

## Recommended next steps
1. [Concrete action]
2. [Concrete action]
3. [Concrete action]

AI models follow concrete templates much better than vague instructions. Show, don’t tell.

5. It includes what the AI would otherwise miss

A skill shouldn’t explain the obvious. It should include what’s specific to the task, the organization, or the domain.

For HR, that could be:

• be careful with personal data

• don’t draw conclusions from groups that are too small

• separate legal risk from leadership risk

• don’t write as if correlation equals causation

• don’t make medical interpretations from sick leave data

• don’t recommend termination without flagging the need for legal review

• anonymize free text where people can be identified

This is often the real value in HR skills. It’s the institutional wisdom you’ve spent years building, finally written down somewhere other than your own head.

A concrete HR example. From task to skill.

Say you often help managers interpret results from employee surveys.

The first time, you do it manually with the AI.

Here are the results from a pulse survey. Help me find the biggest patterns. Be careful with small groups. Separate data, interpretation, and recommendation. End with five questions the manager should discuss with the team.

The AI responds. It’s fine. It’s not great.

You correct.

This got too generic. I want you to be clearer about the difference between what the data actually shows and what is just a possible interpretation. Also add risks around response rate and small groups. And please don’t sound like we’re diagnosing the team. We’re not therapists.

When the answer is finally good, you say:

Build a skill from this workflow. Call it employee-survey-analysis. It should be used when I’m analyzing engagement data, eNPS, pulse surveys, or free-text comments from employee surveys. Include the workflow, output format, HR risks, and quality control.

The tool can then create something like this:

---
name: employee-survey-analysis
description: Analyzes employee survey results, pulse checks, eNPS data and free-text comments. Use when the user asks for HR analysis of engagement data, employee survey results, leadership scores or team feedback.
---

# Employee survey analysis

## Goal
Turn employee survey data into clear patterns, careful interpretation and practical leadership questions.

## Instructions
1. Start by identifying the three most important patterns.
2. Separate data, interpretation and recommendation.
3. Compare groups only when the sample size appears reasonable.
4. Flag uncertainty when response rate, group size or context is missing.
5. Look specifically for patterns related to workload, leadership, clarity, trust and psychological safety.
6. Do not make medical, legal or individual-level conclusions.
7. End with practical questions for the manager or leadership team.

## Output format
Use these sections:

1. Short summary
2. Three most important patterns
3. What the data shows
4. Possible interpretations
5. Risks and uncertainty
6. Recommended next steps
7. Questions for the manager

## HR guardrails
- Do not identify individuals from free-text comments.
- Do not overinterpret small groups.
- Do not present correlation as causation.
- Do not recommend disciplinary or legal action without saying that human HR/legal review is needed.

That’s a useful first version.

But it’s not finished, first versions rarely are. (Or at least mine certainly aren’t.)

The next step is to test it on three different real cases.

1. A clear result with high response rate. The easy mode.

2. A messy result with lots of free text. The realistic mode.

3. A sensitive result with small groups. The “please don’t mess this up” mode.

After each test, ask:

What did the skill miss? What got too generic? What risks should it have caught? How should SKILL.md be adjusted?

That’s how you build a good HR skill. Not as a perfect prompt or description, but as a way of working that gets a little smarter every time it’s used.

Wrapping up

Skills aren’t a new piece of complicated AI jargon. They’re just a way of packaging recurring ways of working. They sound fancier than they are, which is roughly true of most things in tech.

For HR, this is especially useful because so many tasks require judgment, structure, and care. It’s not enough for the AI to “write well.” It needs to understand how the task should be done responsibly. Writing well without judgment is how you end up with an eloquent disaster.

A good HR skill does three things.

1. It makes the work more consistent.

2. It reduces the risk of missing important steps.

3. It helps the AI work more like an experienced HR person, and less like a very confident intern.

Start simple. Pick a recurring task. Do it once together with the AI. Correct until the result is good. Then ask Claude or ChatGPT to build a skill from the workflow. Test it on real cases. Adjust when it misses something.

It’s not harder than that.

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Happy Wednesday,

Let’s get straight into it.

I have lost count of how many times I’ve gotten the question: Is AI taking our jobs or not? Last time I got it was yesterday, speaking to a group of executives.

I always answer politely, but I’m also a bit tired of the debate about AI and jobs. Not of AI. Of how we talk about it. It has been stuck in the same loop for three years now, and both sides have started making things up.

One side says no jobs are being lost to AI. That it’s overblown, that Block was just correcting pandemic over-hiring, that the aggregates aren’t moving. Calm down.

The other side says all jobs are being lost to AI. That 300 million will disappear, that Gen Z is doomed, that anyone who doesn’t learn to prompt is out in eighteen months. Calm down.

Both have half the truth and pretend to have all of it. We need to stop picking sides and start holding two thoughts in our heads at the same time (which is surprisingly hard for people).

Jobs are being lost to AI.

Earlier this month, the CEO of Coinbase, Brian Armstrong, laid off 14 percent of Coinbase. He talked about rebuilding the company as an intelligence, with humans at the edges aiming it.

In his view, this is an entirely new view of what a company is. AI in the middle, people at the edges. Not engineers, designers and product managers running the work, but player-coaches and one-person teams aiming agents. If even half the tech CEOs are starting to think like this about their companies, it’s the biggest reshaping of knowledge work since the office was invented.

And Coinbase isn’t alone. Block laid off 4,000 people in March and Jack Dorsey said straight out it was AI. As the reason. Block had built an internal AI tool called Goose that had been running for over a year, and they reorganised around AI before the layoffs. Pinterest, CrowdStrike, Chegg, Snap, Shopify have all done the same.

But the layoffs aren’t the most important evidence; it’s what the technology can do.

MIT’s Crashing Waves vs. Rising Tides study from April, based on over 17,000 evaluations of AI models across more than 3,000 work tasks from the US Department of Labor, concluded that AI isn’t arriving as sudden waves over narrow areas but as a rising tide across nearly all text-based tasks at the same time. Models went from 50 percent success on hour-long tasks in 2024 to 65 percent in 2025, and the projection is 80-95 percent on most text-based tasks by 2029.

METR measures the same thing from another angle. The length of tasks an AI can complete autonomously has been doubling every seven months for six years.

And OpenAI’s own GDPval benchmark shows that frontier models are approaching industry experts with 14 years of experience in quality of delivery, measured across 44 occupations.

Anyone claiming nothing is happening hasn’t looked properly. To some extent, AI is taking jobs, whether we like it or not.

Jobs are not being lost to AI.

At the same time, one can argue that job losses are highly overstated.

Oracle is cutting 18 percent while the stock is down 25 percent on the year. IBM paused hiring with AI as the stated reason and then quietly rehired when it didn’t pan out. Sam Altman has warned that some companies AI-wash layoffs they would have made anyway.

And one interesting detail is that none of the major companies that announced AI-driven layoffs, not Block, Coinbase, Pinterest or Shopify, presented any concrete AI productivity metrics on their earnings calls before the cuts.

So they are telling the world that AI is doing so much work that they can let thousands of people go. But for their investors, there are no numbers. No reported increase in output per engineer. No measurable productivity gain to justify 4,000 or 700 redundancies. Just the story.

AI has become the most PR-friendly explanation available. Better than we over-hired during the pandemic. Politically safer than interest rates got us. And Wall Street rewards them for it.

So what?

I think both of these can be true at the same time.

It is possible that AI is driving real layoffs in specific roles and age groups, at the same time as many AI-attributed layoffs are financial decisions dressed up in transformation language. Both are true in the same quarterly report.

That’s why the debate runs into the ditch. One side points at Block and Coinbase. The other points at IBM and Oracle. Both are right. And both pretend the other side’s bucket of evidence doesn’t exist.

There’s also the burnout paradox.

Yesterday, Glassdoor released a report on burnout in the US labor market. Mentions of burnout in Glassdoor reviews are up 65 percent year-over-year in the first quarter of 2026, and 2.5 times higher than the pre-pandemic benchmark. The sharpest increase ever recorded came last year.

If AI is already taking over as much work as the big narrative claims, shouldn’t burnout be going down? Less to do, less stress, more breathing room in the schedule?

The opposite is happening. Burnout is rising faster than ever. And there are two readings of why.

1. Either AI isn’t taking over much work yet, and people are working as hard as ever or harder in a nervous economy, or in which case the panic narrative is overblown.

2. AI is taking over some work, but organizations are using the gain to demand more of people instead of easing the pressure. In which case the optimist narrative about AI as liberation is a fairy tale.

Neither reading is that great. And they don’t contradict each other; I believe both are somewhat true, but it isn’t the post-work utopia AI companies write about in their white papers. It isn’t the job apocalypse economists warn about either. It’s something else. A quieter, more exhausting version of both.

But in the long run I lean one way

Last month I sat with a marketing director who was about to break down. Her team had never been able to afford video. They were too few. Then they tried producing with AI and suddenly three people were making twenty videos a month. She thought at first she’d have to let someone go. Two months later she hired one more.

We talk about AI and jobs as if the pie is fixed. As if there’s a set amount of work in the world, and if AI does some of it, there’s less left for people.

That isn’t true historically. And I don’t think it’s true now.

When one person can suddenly do what used to take five, what we always talk about happens. Some roles disappear. Uncomfortable but true. But the other thing happens too. Ambition changes. When an organisation discovers that ten people can deliver what used to take fifty, they don’t stop at ten. They raise the bar. They launch more products. They say yes to customers they used to turn down.

And suddenly they need twenty again. Just that the twenty do ten times more.

This isn’t just historical anecdote, RAND published an analysis of US census data in October. The conclusion was that while some firms have decreased employment through AI replacing tasks, a larger share has reported increases in employment connected to AI adoption.

The gap between what the technology can do and what organisations actually do is where people remain.

And Welburn and Nygaard, the RAND economists, close with exactly what I’m trying to say. Jobs are more than individual tasks. They are a string of tasks assembled in a specific way. Crude calculations of labor market exposure to AI have failed to capture what jobs actually are.

If MIT authors Mertens and Thompson are right about the tide, then the pace of task automation could outpace the pace of ambition expansion. In that case, it isn’t the even redistribution I’m describing, but a systematic displacement where people lose ground faster than organizations scale up their ambition.

But don’t say it will sort itself out

And that’s one of the points to strongly consider for the organizations.
AI is undoubtedly disrupting work, maybe not in terms of destruction but in terms of change. Will we have enough time to reskill or upskill people to do those jobs?
It’s easy here to point to the Industrial Revolution. “We fixed this in the past; there will be more jobs in the long run. Look at the Industrial Revolution”.
Yes, that is true. But it took a while.

I spent all of last Christmas essentially reading up on this. E.P. Thompson, Robert Allen, Joel Mokyr, and Sven Beckert on cotton. Lectures on the Prussian army and German industrialization.

Why does an AI consultant read history books on the Prussian army? Because I didn’t trust the simple story, and I wanted to understand more. My whole work depends on leadership teams making better decisions about what’s happening right now, I needed to understand what happened the last time people said technology would take the jobs. Not what we say happened in hindsight. Those are two different things.

And it wasn’t all sunshine and roses.

It’s true that it evened out into prosperity, eventually. The country boys from the late 1700s first became factory workers in Manchester and Birmingham. Their grandchildren moved into offices in the early 1900s, but not on their own. The school system in its modern form was designed precisely to supply the factory, and later the office, with workers. Disciplined, literate, time-regulated bodies. It took roughly a hundred years to rebuild the education system to industry’s needs. Real wages only began rising in earnest after 1840, seventy years after the first factory opened its doors, according to Robert Allen’s research at Oxford. The middle class emerged, and society as we know it was rebuilt.

But it took 150 years.

And the journey there wasn’t all smooth sailing for people in general. Falling or stagnant real wages for decades. Luddite uprisings for which people were executed. Manchester and Liverpool were the shock cities of the revolution. Child labour in factories that didn’t exist before. Several generations born, living and dying during the transition itself, without ever seeing it pay out.

Thompson wrote an entire book about the handloom weavers crushed by the factory system. A hundred years after they were already gone, he wanted to rescue their memory from what he called the enormous condescension of posterity. What we do, in hindsight, to those who didn’t keep up, when we tell the story as if it was obvious they would lose.

We look back from the balcony of prosperity and tell the journey as if it was calm. It wasn’t. The winners wrote the history, the losers never got to hold the pen.

And once again, the Industrial Revolution took around 150 years to be “fully implemented,” if one might call it that. It’s been less than four years since ChatGPT was publicly released. If this transition runs over 15 years, or 5, do we have time to rebuild education, tax systems, social safety nets, identity, and meaning around work? The Industrial Revolution had the time. We don’t know whether we do.

Invoking the Industrial Revolution as comfort is asking to repeat it. The whole package, not just the prosperity bit. And perhaps without the time that allowed the losers’ grandchildren to eventually benefit.

As Oxford economist Carl Benedikt Frey has put it, most economists will acknowledge that technological progress can cause some adjustment problems in the short run. What is rarely noted is that the short run can be a lifetime.

So what do we do?

Both are true. AI takes jobs. AI creates jobs. And at the same time, very little has happened to the aggregate employment numbers in the three and a half years since ChatGPT launched publicly. Anthropic’s own research from this spring finds no systematic increase in unemployment among highly exposed workers since late 2022, but suggestive evidence that hiring of younger workers has slowed in exposed occupations.

Total collapse hasn’t happened. And at the same time, people are burning out faster than ever.

Companies have started the transition. I wrote in March that some of them are probably doing it by laying people off before AI can fully take over that job. Necessity is the mother of invention. You let people go, then the rest of the organisation is forced to solve it with AI. Economists have started calling it low hire, low fire. Companies freeze new hiring rather than running large-scale layoffs.

It’s never binary when people lose their jobs. Interest rates, markets, pandemics, the wrong product strategy, the wrong timing, AI. It all gets woven together. Ignoring AI as a factor is sticking your head in the sand. Saying it’s only AI is underestimating how labor markets actually work.

That’s why is AI taking the jobs or not is the wrong question. It will never get a clean answer. And the question is passive on top of that, as if we are standing on the sidelines watching the technology roll by.

The right question is what do we want AI to do with working life.

Because that’s a choice. Not an inevitable force of nature.

Do we want more people to work with higher output, so we as a society produce more? Do we want fewer people working, but over longer working lives? Do we want to work fewer days per week, or fewer hours per day? Do we want to use the productivity gains for lower prices, higher wages, or more people in meaningful work? Do we want them to go to shareholders, to workers, or to society?

These aren’t technical questions. They are political and organisational. And right now they are being answered by others, who don’t always have the broader interests in mind.

My point isn’t that I have the answers. My point is that we need to start asking the right type of questions. Holding two thoughts in our heads at the same time. AI takes jobs. AI creates jobs. Both are true. Anyone who can’t hold both will make bad decisions, no matter which side they stand on.

And maybe, if we do the work, our grandchildren will be spared from reading books about us with the next Thompson’s enormous condescension in their eyes.

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Happy Saturday,

I started writing a skills guide, but then I figured, let’s start with a full breakdown of Cowork / Codex instead. So here we go. A complete, free walkthrough of how to set up Cowork (or Codex, or Copilot Cowork). Because AI is moving from “chat with a model” to “build reusable workflows that do real work with you.”

For us in HR, that means policy drafts, job postings, onboarding, employee comms, reporting, and all the repetitive work that eats up your week.

I will also say that I’m running beta cohort 2 of Cowork for HR, starting on the 4th of June.

Three live sessions where we build real HR workflows together.

30% discount if you sign up before the 15th of May.

$553 instead of $790.

Join the interest list here.

That said, let’s get to it.

As most of you know, I’ve spent the past year helping CHROs and their teams get AI to work inside the HR function, not in pilots but in daily operations, in reports and performance cycles, in comp reviews and 1-on-1s. This guide is the consolidated version of what I keep saying in those conversations, written so you, as CHRO, can sit down on a Sunday afternoon and have a working setup by Monday morning.

(Ok, it works in the same way if you are an HRBP, HR Generalist, Recruiter or just a manager…)

The tool in focus is Claude Cowork, Anthropic’s desktop app, but the principles overlap with OpenAI’s Codex and with Copilot Cowork (the version Microsoft just rolled out to its Frontier program).

I say that but it’s also important to note that these three products share a family resemblance, but they are not interchangeable. Claude Cowork is a desktop knowledge-work agent. Codex used to be a coding agent primarily, but is now way more “cowork-like”, but still in my opinion not as user-friendly as Cowork. (It’s getting better day by day, though.)
Copilot Cowork is a Frontier preview that lives inside the Microsoft 365 stack with its own admin and security framing.

The principles in this guide overlap across all three, but the setup steps and the risk model differ. When you read “Cowork” below, mentally substitute the equivalent component in your tool of choice.

Let’s dive in.

What Cowork is

Cowork is a desktop agent rather than a chat or a website, meaning it’s a program you install on your own computer that has access to your files, can drive your browser, read your calendar, and run reusable workflows (called skills) on a schedule. It is not ChatGPT in a new outfit, and the difference becomes obvious once you start using it. Instead of operating in a separate tab where you copy and paste between systems, Cowork operates as you do on your computer, updating an Excel file in Dropbox without you re-uploading it, logging into Workday on your behalf, or following up on a candidate pipeline by reading email, cross-referencing Slack, and updating Teamtailor in one continuous flow.

For better and worse, more on the worse part in a minute.

Cowork is made of three parts

It’s worth understanding the architecture before you set anything up, because most CHROs only install the first piece and then wonder why the leverage feels modest.

1. The Cowork app. This is the desktop app itself, where you chat and run skills. On Mac it’s a three-minute install, while on Windows it can be tricky because it runs inside a virtual machine, so plan to involve IT if you’re on a corporate Windows laptop.

2. Claude for Chrome. This is the browser extension that most CHROs miss, and without it Cowork can’t act on the web on your behalf, which removes about half of the value. The extension runs in Chromium-based browsers, including Google Chrome, Comet, and Atlas; it does not run in Safari, Edge, or Firefox.

   This is the piece that lets Cowork open LinkedIn, scroll your inbox, fill out forms, or log into your HRIS as you. Many browser workflows can be delegated this way, but not all sites or actions work reliably, and Anthropic blocks certain high-risk categories outright. Treat the extension as a powerful generalist that still has limits, not as a universal “anything you can do on the web” button.

3. Skills and connectors. This is where the leverage lives. Skills are reusable workflows that you build once and call thousands of times, while connectors are how Cowork talks to your existing tools (Gmail, Calendar, Slack, your HRIS, your ATS). Without skills and connectors, Cowork is roughly a chat with a slightly better user interface; with them, it starts to feel like a colleague who already knows how your function works.

Skills before prompts

Most CHROs I talk to use AI by opening a chat, throwing in a question, copying the answer, closing the tab, and starting from zero the next day, which is not AI usage so much as an expensive Google search. The shift that creates leverage is moving from one-off prompts to reusable skills, where each skill is a saved instruction set that says when I ask about X, always do Y in format Z, always pull context from file A, and ask me about B if I forgot to mention it.

You build it once, and you call it in three seconds, which means the second run is already cheaper than the first, and by the twentieth run, you’re operating at a different speed than your peers.

How you build a skill is straightforward, and you don’t need to write any code.

Solve the problem manually in Cowork once until you’re satisfied with the output, then say “use skill creator to make a skill out of this” and let Cowork ask you a few clarifying questions about what it should ask next time, whether it should verify output, and where it should pull context from. Once those questions are answered, you have a skill, saved and ready to call.

The difference between writing a fresh prompt every time and calling a skill is roughly the difference between manual math and an Excel macro, and twenty runs into a performance cycle the difference compounds into hours saved every week.

Connectors and MCP

CHROs deploying Cowork end up talking to IT sooner or later, so it helps to have the vocabulary ready before that meeting starts. And the main reason for that is connectors.

A connector is how Cowork talks to an external system, and Cowork / Codex ships with a library of pre-built connectors for the obvious tools (Gmail, Calendar, Slack, Asana, Notion, Drive, OneDrive); you click the plus button, pick a connector, authenticate, and you’re done.

MCP stands for Model Context Protocol, a standard Anthropic created so that LLMs can talk to external systems without bespoke integration work, and when a vendor says “we have an MCP server for X”, what they mean is that Cowork (or ChatGPT, or Copilot Cowork) can connect to X with a few clicks rather than a six-month integration project.

The difference between an MCP and a traditional API is worth understanding before that conversation starts. An API is narrow and prescriptive, where each call asks a very specific question (give me field A for employee 12345). An MCP gives the agent a structured way to discover and use the tools and resources that the server chooses to expose, which makes it more flexible and more powerful in skilled hands. It does not automatically grant total access. The actual surface area depends on the server’s implementation, the scopes you authorize, the auth model, and where the server is hosted. That power is exactly why governance, ownership, and hosting decisions matter more for MCP than for a single API call, and why your IT team will have questions when you start mentioning it.

Many HR tools will have an MCP or get one in 2026, so the practical advice is to ask your vendors directly whether they have an MCP server on their roadmap.

If the tool you need doesn’t have one, you have two paths forward. The first is to use Claude for Chrome and let Cowork act in the browser as you, which works surprisingly well, while the second is to build your own MCP server from the vendor’s API documentation, which is more advanced but doable, and cheap to host on something like Railway or run locally on your machine.

Setup

Before you dive into doing stuff with Cowork, set the foundation properly, because the leverage you get from each workflow is roughly proportional to the amount of context Cowork has about your company and you. Don’t skip this part, it’s worth spending time on this!

The first thing to build is a memory file with company and role context, placed in your workspace and describing the basics of your organization, including company name, employee count, geographies and industry, the org structure at a high level, which HRIS, ATS, comp tools, learning systems, and engagement platforms you use, the timing of your performance cycles, your career levels and naming conventions, the language choices you’ve made internally (”we say colleagues, not employees”), and the labor jurisdiction(s) you operate under.

This single file is the difference between an assistant who sounds like a consulting deck and one who already works at your company.

I’ve created a free CLAUDE-md-template-for-CHROs.md - click the link and you’ll get it for free.
Drop that into Cowork and explain to Cowork that you want it to help you set it up and it will facilitate the whole process!

The second thing to set up is a baseline of three sources connected, starting with your calendar so the system understands meetings and rhythm, then a file storage system such as Drive, SharePoint, or Box where policies, frameworks, and templates live, and finally your meeting transcripts in Krisp, Fireflies, or Otter. With those three connected, Cowork can answer questions like “what did I promise the manager group in June?” without you searching anything manually.
If you don’t use a transcription service, then skip that step (doh).

The third thing is a brand voice skill, which sounds fancy but is straightforward in practice. You upload five finished internal texts, such as a performance template, a CEO communication, a policy doc, a job description, and a values guide, you have the model extract tone rules, and you save the result as a skill that gets called by every future text you produce. (Need a prompt for it? Steal it here.)

That’s it. Plan for one to three hours, and the leverage starts. But do it properly - the above steps, that’s what makes the tool magic!

Scheduling

Up to this point we’ve talked about skills you call manually, and the next step is letting Cowork run them on a schedule, which is where the experience starts to feel less like a tool and more like a colleague. You say “schedule this to run every Monday morning at 7:30 and deliver the result to me in Slack” and Cowork sets up the recurring job, including delivery channel and timing.

My personal setup right now runs through the week. Every morning a summary of new AI and leadership news from the past day lands in my inbox, every Tuesday morning a weekly report on the general labor market arrives, every Friday at 4pm a week recap shows up with what I’ve promised whom and what’s still open, and once a month my bookkeeping tool compiles a P&L for me, run locally with no cloud traffic, which keeps the financial data on my machine

.

There are two practical things to know about scheduling. The computer must be on, because Cowork runs locally, which means your laptop has to be powered on and online when the schedule fires; if you put a schedule at 6am but you’re not a morning person, your Monday job will silently miss its window, so put it at 9am instead. There is a Keep Awake mode for the rare cases when you need it, but it kills battery and should be used consciously rather than as a default.

The second practical thing is that skills save you tokens, because scheduled tasks without an underlying skill consume more tokens than necessary as the model has to think from scratch every time the schedule fires. Build the skill first, then schedule the call to it, and you’ll keep your monthly token budget intact.

Skills governance

When I work with larger organizations on Cowork, one challenge surfaces after a month or two. Everyone is building their own skills, nobody knows which ones exist, half the org is running a suboptimal version of the same skill, and nobody owns maintenance. This is a real problem that isn’t technical so much as a problem of ownership and process, and it gets worse the more useful the tool becomes.

My recommendation, based on how I’ve seen it work in practice is this;

1. Every meaningful skill should be versioned in a central skills repo in GitHub, with submitting an update done as a pull request rather than as a Slack message to whoever feels responsible that week.

2. You need one or two named gatekeepers who approve pull requests, check that the skill works, that it doesn’t conflict with existing ones, and that it follows your naming convention.

3. Every skill must have a named owner responsible for keeping it current, because skills rot when nobody owns them.

4. When a skill is updated, the system should ping Slack or Teams so the team knows to pull the new version; otherwise, people end up running stale variants without realizing it.

Is this overkill for a thirty-person HR function? Yes, and a shared Drive folder with a clear naming convention will do the job there. Is it overkill for a two-thousand-person organization where five managers have already built their own performance review skills with subtly different definitions of “Exceeds”? No, skipping the governance step there saves you nothing in the short run while costing you a six-month mess later.

Security

As said, CHROs deploying this end up in conversations with IT. But most likely you’ll also end up talking to legal and infosec, so it helps to walk in with the answers ready rather than having those conversations slow you down.

Cowork runs on your machine. Your files are stored locally, and any code or commands Claude executes run inside an isolated virtual machine separate from your main operating system. The nuance worth being precise about with IT is that file storage stays local, but the content of files Claude reads is sent to the model’s API for processing, same as any other Claude conversation. The local part is the storage, the execution sandbox, and the connection points to your apps. The inference happens in the cloud.

Cowork asks for explicit permission before deleting files, and computer use asks for permission per application before Claude can interact with it. The place people shoot themselves in the foot is by clicking “Always allow” too aggressively, or by switching on “Act without asking” mode. Anthropic’s own guidance is that this mode “significantly increases the risk of prompt injection attacks,” so it should only be used for well-defined tasks under active supervision.

If you’ve over-granted, permissions can be reviewed and reset in Claude Desktop’s settings (worth verifying the exact path in your version of the app before promising IT a specific click-path).

MCP servers are worth flagging to IT explicitly. A local MCP server runs on your computer with the same permissions as any other program you install. That’s efficient, but it means the trust model collapses to “do you trust the vendor of this MCP.” For sensitive systems like a bookkeeping tool, the recommended pattern is to host the MCP server locally rather than on a public host, so the data path stays inside your environment.

On prompt injection, which is the risk IT will absolutely ask about, Anthropic addresses it on three fronts. Content classifiers scan untrusted material entering Claude’s context, the Chrome extension strips hidden content before the agent sees it, and the model is trained via reinforcement learning to recognize and refuse malicious instructions even when they appear authoritative. The risk is non-zero, not zero, and you should still monitor for unexpected patterns.

When Cowork updates your systems (should you let it), you remain legally accountable for that update, same as if you’d delegated the task to an assistant. Anthropic’s documentation is explicit on this. The user is responsible for all actions taken by Claude on their behalf, including content published, messages sent, transactions, and actions by scheduled tasks.

In the EU? GDPR is where you need to be careful. Anthropic itself currently advises against using Cowork for regulated workloads, because Cowork activity is not captured in audit logs, the Compliance API, or data exports. The framing I use with CHROs is that this is a question that requires a case-by-case assessment together with your data protection officer, not a blanket yes or no, and you have to be honest with them about Anthropic’s own audit-log limitation up front. To put it more directly, do not use Cowork for personal data, employment-law decisions, or sensitive HR processes before your DPO and legal function have approved the specific use case. Approval for a recruiting summary is not approval for a termination workflow. Treat each new use case as its own conversation.

My summary is straightforward. Security around Cowork is a question that can be answered, not a question that can’t, and the difference between the two outcomes is whether your IT, legal, and DPO functions are involved early or whether they discover the rollout after the fact.

But all this said about security. Do not let this stop you from trying this out!
It’s 100% worth spending 20 bucks of your own money on your own computer just trying Cowork out.

As I alluded to in the beginning and in my post earlier this week, this is clearly where all model builders are heading right now.

Questions? Email me at johannes@sundlo.com. I’m not always fast but I respond.

Need help rolling this out in your organization? That’s the kind of work I do more or less every week, where we run workshops for not only CHROs but also lead teams and whole organizations. Not demos but real rollouts where adoption is the success metric. Email if you want to talk!

Otherwise, good luck!

This guide is free and meant to be shared, copied, adapted, and torn apart. If you build on it with your own skills, send them back!