INBOX INSIGHTS: The Job AI Can’t Do, Enterprise AI Part 1 (2026-05-20)

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INBOX INSIGHTS: The Job AI Can't Do, Enterprise AI Part 1 (2026-05-20)

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The Job AI Can’t Do

Block let almost 4,000 people go in February. That’s not a typo. They went from roughly 10,000 employees to under 6,000 in a single cut, and Jack Dorsey said the reason was AI.

Atlassian cut 10% of its global workforce a few weeks later, citing the “AI era.” Baker McKenzie, the law firm, is letting go of up to 1,000 people for the same reason. In the first quarter of 2026 alone, the tech industry laid off close to 79,000 workers, and nearly half of those cuts were officially blamed on AI replacing the need for humans.

So yeah, the fear is real. I get it.

Here’s the thing, though. There’s a study from HBR earlier this year that I can’t stop thinking about. They looked at why companies are actually doing these layoffs, and the finding was that most of the cuts aren’t happening because AI is doing the work better. They’re happening because executives think it will eventually. The headcount is getting cut based on AI’s potential, not its performance.

Let that sink in for a moment.

Companies are firing people for a future that hasn’t actually arrived yet.

A lot of people are losing their jobs to a thing that hasn’t replaced them, which is its own kind of horrifying. But it’s also useful information because it tells you something important about what the people doing the cutting actually believe AI can and can’t do.

The Pivot Point (for me)

Three days ago, will.i.am gave the undergraduate commencement address at WPI. WPI is my graduate alma mater, so I was paying attention.

He talked about AI taking jobs, and I expected the usual: “Don’t worry, you’ll be fine, AI is a tool, blah blah.” That’s not what he said.

What he said was this:

“Pick a problem, solve it. That solution is an industry.”

And then a few minutes later:

“This is the time for imagination. AI is not imagining. It’s regurgitation of the human imagination. Turn that off.”

I had to sit with that one.

Not because it’s some grand philosophical moment. Because it’s the cleanest articulation I’ve heard of what AI actually is and isn’t. And what the humans in the room are supposed to do about it.

AI is fantastic at solutions. It is genuinely bad at finding problems worth solving. It doesn’t wake up at 3 a.m. wondering why nobody has fixed something. It doesn’t notice that the person in front of it is struggling. It doesn’t have a stake. It will happily generate 47 variations of an answer to the wrong question and never tell you the question was wrong.

That’s the gap.

And that gap is where the work is for the time being, at least.

Reverse-Engineering the 5P Framework by Trust Insights™

If you know me, you know I have a framework for that. The 5P Framework by Trust Insights™ (Purpose, People, Process, Platform, Performance) is how I plan everything. The order is deliberate. You start with Purpose, because if you don’t know what you are trying to do, the rest doesn’t matter.

But will.i.am’s speech got me thinking about a different way to use it.

The 5P assumes you already know what problem you’re solving. You bring the Purpose. The framework helps you execute against it. That’s great when you’re inside an existing job, an existing business, an existing engagement.

What if you don’t have a Purpose yet? What if the actual problem in front of you is, “I don’t even know what I should be doing with the next ten years of my career”?

You can run the 5P in reverse, and the reverse version is a tool for finding your problem.

Here’s how:

Start with Performance. Not the project’s performance. Yours. What does a meaningful outcome look like for you? Not the answer your LinkedIn bio would give. The actual one. More autonomy? More income? Work that doesn’t make you hate Sundays? Be specific. “I want to make $200K working four days a week from home” is a Performance target. “I want to be successful” is not.

Then Platform. What do you have access to? And I don’t just mean AI tools, although obviously, yes. What’s your network? What’s your training? What’s the software you already know how to use? What’s the certification that opens doors? Inventory it honestly. Most people radically underestimate what they have here.

Then Process. What are you actually good at doing, repeatedly, without burning out? Not what you’re impressive at. What you can do on a Tuesday afternoon when you’re tired and still produce something useful. That’s your real Process capacity. Everything else is theater.

Then People. Who are the humans you understand? Whose problems do you see clearly because you’ve lived something similar? Whose language do you already speak? You will solve problems for people you understand, badly for people you don’t, and never for people you can’t be bothered to listen to.

And the answer that falls out the bottom is your Purpose. The intersection of “outcome I want” + “tools I have” + “work I can do” + “people I get.” That’s a problem that you are uniquely positioned to solve.

That’s the move. Not “what job should I apply for?” That’s the question AI is currently answering by removing your seat.

The real question is this: What problem that you can see, and most people can’t, are you going to solve?

The Reality Check

I don’t want to pretend the fear isn’t valid. It is. The layoffs are real, the disruption is real, and a lot of people are going to have to rebuild careers they didn’t choose to rebuild.

But the version of the future where everyone fights AI for the same shrinking set of jobs is the worst version. The better version, and I think will.i.am is right about this, is the one where more people stop applying to roles defined by others and start defining work around problems they’re uniquely qualified to see.

That is the thing AI cannot do. It cannot care. It cannot notice. It cannot decide what matters.

That is your job. It has always been your job. The technology just made it more obvious.

So, what’s the problem only you can see?

Run the 5P backward and find out.

*And if you want to learn how the 5P Framework by Trust Insights™ works in the forward direction (planning AI projects, change initiatives, marketing strategy), *start here*.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss why the latest AI buzzwords are just familiar project management concepts in disguise. You will learn how to look past the hype to identify the foundational principles you already possess. You will discover why the 5P framework provides the structure necessary to guide agentic AI toward success. You will see how creating clear definitions of “done” stops your AI projects from failing. You will master the process of building robust work plans that keep your autonomous tools on the rails.

Watch/listen to this episode of In-Ear Insights here »

Last time on So What? The Marketing Analytics and Insights Livestream, we reviewed the state of local AI models. Catch the episode replay here!

This week on So What? we’ll be getting to know knowledge graphs. Are you following our YouTube channel? If not, click/tap here to follow us!

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In this week’s Data Diaries, we kick off a seven-part series on enterprise AI with the question every CFO eventually asks: how do you actually know what this stuff is worth?

Start with TRIPS — Trust Insights’ five-factor screen for whether a task even belongs near an AI system. Take any task on your desk and score it for Time, Repetitiveness, Importance, Pain, and Sufficient data. High scores across all five mark a candidate task for AI; low scores mark a task you keep with a human.

The screen replaces the vendor pitch with a workbench test you can run before you spend a dollar. Now lock the financial vocabulary. ROI sits as a very strict financial measure: earned minus spent, divided by spent. Most AI savings claims do not clear that bar — they show up as time returned to people, not dollars on the income statement.

Adoption runs near-universal across the Fortune 1000 today; outcomes do not. The discipline gap, not the spend gap, separates the leaders from everyone else.

So what does that gap actually look like in practice? If your team measures the hours a given task consumes and you know what you pay that person, you can infer a savings figure. That is an inference, not a calculation. Suppose Chris spends one hour on a task that used to take twenty — the math says you saved nineteen hours of payroll.

If Chris then spends the other nineteen hours playing video games at work, you really haven’t saved any money. The savings exist only when you redeploy the hours into work that moves a number leadership already tracks. All of this requires good existing measurement. If you do not have good existing measurement, you cannot infer what AI is worth.

That measurement gap lands as the hardest part for most enterprises, and it sets up everything else in this series. Departments with line of sight to sales objectives prove value easily. HR, Legal, and Finance prove value only when they connect the freed hours to something the board already counts.

What do you do with that discipline? Map every initiative to one of three horizons, and locate each horizon on the TRIPS curve.

Horizon 1, Defensive Efficiency, automates the highly templated work that scores hot on every TRIPS dimension and returns hours inside existing workflows.

Horizon 2, Core Augmentation, drives margin gains inside your core value chain by pairing humans with AI on tasks that score high on Pain and Importance but lower on Repetitiveness.

Horizon 3, Net-New Disruption, builds entirely new products around problems your TRIPS screen flagged as too important to leave templated. A company living only in Horizon 1 watches a Horizon 3 competitor eat its lunch by next fiscal year.

Run that map through the 5P Framework by Trust Insights™ — Purpose, People, Process, Platform, Performance — before you scale anything. At the enterprise level, leaders express corporate strategy through AI, stand up governance ahead of scale, and pick a hub-and-spoke operating model with central oversight.

Mid-market companies sit outside the EU AI Act’s primary crosshairs, but enterprise customers push the requirements down through procurement RFPs, so the playbook still applies.

At the SMB end, you compete by staying fast, focused, and credible. Agencies productize what enterprises now need — audit trails, evaluation rubrics, ISO/IEC 42001-aligned documentation — and carry a service line competitors lack.

The leaders do not pull away because they stockpiled more AI. They pull away because they apply more discipline around AI.

Next week we walk the governance layer — the AI Council, the EU AI Act enforcement clock, the U.S. state laws, the Bartz precedent, and what every company needs in writing before the next regulator letter lands. Stay tuned!

Almost every AI course is the same, conceptually. They show you how to prompt, how to set things up – the cooking equivalents of how to use a blender or how to cook a dish. These are foundation skills, and while they’re good and important, you know what’s missing from all of them? How to run a restaurant successfully. That’s the big miss. We’re so focused on the how that we completely lose sight of the why and the what.

This is why our capstone course, the AI-Ready Strategist, is different. It’s not a collection of prompting techniques or a set of recipes; it’s about why we do things with AI. AI strategy has nothing to do with prompting or the shiny object of the day — it has everything to do with extracting value from AI and avoiding preventable disasters. This course is for everyone in a decision-making capacity because it answers the questions almost every AI hype artist ignores: Why are you even considering AI in the first place? What will you do with it? If your AI strategy is the equivalent of obsessing over blenders while your steakhouse goes out of business, this is the course to get you back on course.

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