All true. But there’s a more specific problem underneath those explanations.

HR teams are standing up automations and calling them AI initiatives. I don’t believe those are the same thing, but only because people tend to conflate three things.

Automation, Chat AI, & Agentic AI.

Those are not the same. And calling them the same thing could be costing you.

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Above the line, below the line.

Automation is a doer and has been around for a long time. It takes work you shouldn’t have to do and removes it. A rule fires, a task completes, a form routes itself. This is real value. It frees time, reduces error, and returns capacity.

It’s below the line support, clearing what bogs you down without changing what you’re capable of thinking or deciding.

Chat and agentic AI are something else. Used well, they can be a reasoning partner. They process context, surface patterns, and push back on assumptions. They don’t just execute what you tell them to do, but instead can help you question whether what you’re doing makes sense. That’s above the line support. They change what’s possible, not just what’s faster.

HR teams have a pattern that predates AI by decades. When a process is broken, the instinct is to build around it. A form that takes too long gets a shortcut. A reporting structure that creates confusion gets a workaround meeting. The underlying problem stays. The workaround becomes permanent.

Automation is the best tool ever built for scaling that instinct from what I’ve seen. It makes workarounds fast, reliable, and invisible. The broken process is still there. Nobody has to think about it anymore.

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But that’s the real danger of calling automation either chat or agentic AI. Not a labeling error. The wrong intervention at the wrong level. You needed something that would ask whether the process should exist. You got something that executes it without complaint, forever, at scale.

Gartner found only one in fifty AI investments delivers transformational value. (2) That number makes more sense when you understand what most organizations are actually investing in.

This isn’t an argument against automation. Automation is amazing when it’s done correctly and maintained. The work it removes is real, and the time it returns is real. The problem is when it substitutes for the harder question.

The harder question is what AI as a reasoning partner forces you to sit with. Does this process deserve to be faster, or does it deserve to be rethought? Those are different questions. One is an efficiency question. The other is a design question.

The next time your team announces an AI initiative, ask what it’s actually doing. If it’s routing, triggering, completing, or removing, that’s automation. Probably good automation. But not chat or agentic AI, and not a substitute for the question you were supposed to ask first.

1. https://www.forbes.com/sites/cindyrodriguezconstable/2026/02/27/most-ai-investments-are-failing-the--problem-isnt-the-technology/

2. https://www.gartner.com/en/newsroom/press-releases/2025-10-28-gartner-survey-shows-88-percent-of-hr-leaders-say-their-organizations-have-not-realized-significant-business-value-from-ai-tools

That is not a follow-through failure. It is how scores are designed to work.

Survey requests to employees jumped 71% since 2020. Response rates crashed in many organizations over the same period. The common read is survey fatigue. The real problem is the format.

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The number is the permission slip.

A 3.4 is designed to compress complexity. That compression does not just simplify the data. It creates the political conditions for inaction. Scores can be trended, benchmarked, and moved past in a meeting. McKinsey’s analysis of more than 20 academic studies found the primary reason employees stop participating in pulse surveys isn’t volume. It’s that nothing happens after. The score goes in, the number comes out, and leadership moves on.

What rarely gets examined is why moving on is so easy. The score is an average, and averaging is how you flatten the fact that a 3.4 on belonging can mean completely different things depending on who you ask. A team of engineers buried in a remote structure and a group of coordinators who don’t know where they stand with their manager can both average to the same number. The score papers over that divergence. Leadership never has to sit with the specific human situation underneath it.

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This is where voice AI makes a real argument.

Not as a better survey tool. But as a format that produces data that is structurally harder to deprioritize. When someone says “I don’t think my manager knows I exist,” that is not a score. It is a sentence. It resists being averaged. It cannot be benchmarked against Q3. It has to be answered.

We have an opportunity to get this context at scale by using voice AI to run surveys. I’d much rather speak about my points of view and allow my train of thought to run, rather than typing it out or selecting an answer to a question.

There are real concerns worth naming before anyone deploys this. AI transcription is less accurate for non-native English speakers, with error rates running 10 to 22 percentage points higher than for native speakers. Voice data raises genuine questions about privacy, especially when employees don’t know how recordings will be used or whether candor carries professional risk. These are implementation problems, not arguments against the format. But they are not small, and any rollout that skips them will corrode the trust it was trying to measure.

The case for voice AI in pulse surveys is not just that it collects richer data. It is that it changes the political economy of what leadership can do with the data. A score can be tabled. A trend line can be monitored. An anonymized voice transcript saying, in specific words, what is wrong at work is harder to explain away in the next all-hands.

What does your organization do when a score drops? Would the answer change if leadership had heard the voice behind it?

I’ve built a few of these myself. Lately I’ve started asking a question that kills the mood a little.

Built toward what?

That’s the reversal. We used to start with the goal and work backward to the small moves that got us there. Now most of HR starts with the small move and hopes a goal shows up later.

It won’t. Not on its own.

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Where it actually breaks

The individual wins are real. A better screening tool screens better. A scheduling bot fixes scheduling. Nobody is disputing that the parts work.

The problem shows up in the seams. Right now, in a typical HR org:

• Many disconnected systems are running side by side, each solving its own slice of the employee lifecycle.

• More than two-thirds of companies say those systems still don’t talk to each other.

• The employee becomes the integration layer, logging into four places to do what should have taken one.

That’s not a legacy IT problem left over from 2015. That’s what happens when every team solves its own process in isolation, one AI tool at a time, with no shared picture of the experience underneath it. Each tool becomes an island. Somebody still has to build the bridge. Right now, that somebody is the employee.

The counter I’d make if I were you

Vision-first AI programs don’t have a clean track record either. Analysts expect more than 4 in 10 current agentic AI projects to get shelved within two years, buried under cost, complexity, and promises nobody could keep.

Fair. Point taken.

But that’s not the same failure. Those projects didn’t collapse because someone had a destination in mind. They collapsed because they tried to map every stop on the route before the train left the station.

Starting with the goal doesn’t require that. It requires one sentence, what experience are you building toward, before you pick the next tool. Not a five-year architecture diagram. A destination. Everything after that is sequencing, not prophecy.

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Before your next AI tool, answer this

Whether it’s a chatbot, a screening model, or an assistant that drafts your offer letters, someone on your team should be able to answer this without stalling:

If the honest answer is a shrug, or “it saves time,” that’s not a goal. That’s activity dressed up as strategy.

You don’t need the whole architecture mapped before you start. You need to know where you’re walking before you decide which shoes to buy.

So the real question isn’t whether you’re using AI in HR. Almost everyone is now. It’s whether you could draw a straight line from the last tool you adopted to an actual destination, or whether you just liked the momentum.

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It’s not a tooling failure. It’s an attention failure, arriving as AI adoption climbs and focus efficiency hits a three year low. Attention is becoming the metric that decides whether AI actually pays off. HR is positioned to design for it, not by policing screen time, but by deciding what the work itself protects.

The instinct is to treat this as an adoption problem. Roll out more training, mandate more AI use, move faster. Microsoft’s Work Trend Index research says the opposite is happening. Focus efficiency has dropped to a three year low even as AI adoption climbed sharply, and employees now spend more time communicating than creating inside Microsoft 365 apps. Microsoft’s own researchers call this the productivity conversation outpacing the organizational design conversation.

Companies are tuning the tool and ignoring the system it landed in.

Workslop is what that gap looks like up close. Ten minutes generating a report that would have taken two hours to actually think through, then someone downstream decides whether to rewrite it, send it back, or quietly absorb the cleanup.

Output went up. Thinking didn’t.

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A related study from the same research group, covering twelve thousand workers, found the effect isn’t universal. Workers who engage AI as a thinking partner are markedly more productive than those who use it to cut corners. The technology isn’t the variable, the system around it is, whether a manager rewards a fast answer over a right one, whether anyone protected the time it takes to think before generating. That’s a work design question, not an IT rollout question.

If attention is the real constraint on AI’s return, HR’s job changes shape. Less mandating tools, more auditing meeting load, review cycles, and what gets rewarded when work moves fast versus when it moves right. Start there. Pull one team’s calendar and count hours spent reviewing AI output versus producing it. Some organizations are already merging HR and IT into a single people and technology function to deal with exactly this. That’s not a threat to HR’s relevance, it’s a preview of what the job becomes if HR doesn’t claim it first. So ask what your AI rollout actually optimized for this year, faster output or better thinking. If you’re not sure, you already have your answer.

That gap closing is the actual story behind skills-based talent strategy, and most organizations talking about it haven’t caught up to what it means.

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Skills-based hiring and development has been framed mostly as a sourcing fix. Stop requiring degrees, look at what people can demonstrate instead of what’s on their resume, widen the funnel. That’s real, and it matters. But it treats the skill itself as a fixed thing someone either has or doesn’t, acquired the old way, through years of formal education or a long runway of lived experience.

What’s actually changing is the acquisition timeline itself. The thing that used to take a person five years and a credential can now take someone with the right curiosity and a working relationship with AI a fraction of that, in a live work context, on a real problem.

When this goes unaddressed, organizations keep designing roles and career paths around the old timeline. A person on a team shows real aptitude for something adjacent to their role, but the org has no mechanism to recognize or use that until they’ve spent years “earning” it through traditional channels. Meanwhile that person is already building the capability on their own, on the side, with tools that didn’t exist three years ago. The organization either misses what’s sitting right in front of it, or the person takes that capability somewhere that will use it.

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This stays invisible because most performance and career conversations are still built around role definitions, not human capability. Managers are trained to evaluate people against a job description, not against what someone is becoming. And admitting that someone on your team could do something genuinely different from their current role raises uncomfortable questions about whether the current role, or the current org chart, still makes sense.

What might help isn’t a new framework. It’s a different question in regular one-on-ones: not “how are you doing in your role,” but “what have you been building or learning that nobody asked you to.” That question surfaces capability before it becomes a resignation letter. It also requires managers to actually sit with the answer, rather than redirect it back toward the job description.

If someone on your team is already using AI to move into territory outside their role, would you know? And if you did know, would your org have anywhere to put that?

Think about your day as a jar. Big rocks first, the strategic work that defines your role. Medium rocks next, important but not foundational. Then pebbles and sand, the small administrative tasks that fill whatever space is left.

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Most AI adoption in HR right now is happening entirely at the sand level. Drafting emails. Summarizing meeting notes. Cleaning up a job description. Useful, sure. But sand was never the problem. Sand fits itself into whatever space remains, that’s the whole point of sand. You could automate every grain of it and your week would look basically the same, just with more open calendar blocks you’ll fill with more sand.

The rocks are where AI could actually change something, and almost nobody is using it there. I built a conversational AI agent that conducts talent assessments for managers, something that used to require a facilitator, a calendar, and ninety minutes per person. It now runs without me, at scale, for over 250 managers. That’s not a sand-level task. That’s a structural shift in how an entire function operates.

The assessment process didn’t get faster. It got rebuilt. The difference between using AI to write faster and using AI to rethink how the work gets done in the first place is the difference between efficiency and transformation, and most HR leaders are still stuck on efficiency.

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The rock-level work is harder to hand to AI because it requires us to actually examine what we do and why, not just how fast we do it. Sand-level prompting doesn’t ask you to confront anything about your function’s relevance. You can prompt your way through a hundred emails and never once ask whether the process generating those emails should exist.

Rock-level thinking does ask that. It forces the question of whether the thing you’ve always done is still the right thing to do, and that’s a much harder conversation than “can you make this sound more professional.”

This is also why so many AI rollouts stall at the sand layer and never move. It’s not a skills problem. It’s that rock-level redesign requires authority, time, and a willingness to be wrong about how your function has always worked, three things that are in short supply for most HR practitioners.

If AI can already do your sand, what does that free your rocks to become, and have you actually let it change anything yet?

What do you do with that?

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This is not a thought experiment for 2035. AI agents are already embedded in workflows at organizations. They are completing tasks, sending communications, making decisions within defined parameters. That said, it may not feel like it now, but situations like this could come up at any moment.

AI agents are not employees in the legal sense. They are not headcount in the traditional sense. But they are becoming participants in the work experience of your employees, and that means HR has a category problem it hasn’t acknowledged yet.

HR is built for humans. The frameworks, the policies, the emotional intelligence competencies, the coaching conversations, and all of it is calibrated for human behavior, human motivation, human psychology. When an AI agent does something that affects an employee’s experience at work, none of those tools were designed for this. Not the investigation process. Not the corrective action framework. Not the well-being conversation you’d have with the person on the receiving end.

The “inappropriate AI” scenario is actually the easy version of the problem. Easy because at least there’s an incident to respond to. Harder is the ambient version, like what happens to an employee’s relationship to their work when a meaningful portion of their collaboration happens with a non-human entity? The AI doesn’t have a bad day. It doesn’t notice when you’re struggling. It doesn’t build context about who you are across time the way a colleague does. It completes the task. And over time, if enough of the daily texture of work shifts to the agent rather than to people, something changes.

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We don’t have a clean name for that change yet. But HRBPs are going to need one. Because employees won’t be able to articulate it either. They’ll know something feels different about their work. They may not be able to say it’s the AI. They may not even connect it. But disengagement, isolation, and loss of meaning don’t require a conscious diagnosis. They accumulate quietly.

The long-term shift here isn’t that AI replaces humans. It’s subtler and harder to manage. It’s that the human texture of work thins out, not all at once, but gradually, in ways that are easy to miss until they’re not. And HR is currently not set up to catch this. Not because the people in those roles aren’t capable, but because the field has not started building the muscle.

If an AI agent is interacting with your employees every day, do you have any idea what that experience is like for them? Not the productivity metrics. Not the adoption data. The actual human experience of working alongside something that isn’t human. If you don’t know, what are you waiting for to find out?

This is one of the quieter, less-talked-about challenges in HR. What happens when a leadership change resets a relationship you spent years building.

The first thing you actually do when a new leader joins isn’t orient them to your programs or hand them an org chart. It’s figure out where you stand. Most HR people have had both versions. The leader who walks in knowing what HR is actually for, who pulls you in before you ask and treats you like a business partner from day one. And the leader who views HR as the function that runs compliance training and handles leaves. When you had the first version, and the replacement is the second, you feel the gap fast. The work hasn’t changed. The access has.

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So you do what you have to do. You find who the new leader has already connected with. You work the relational network until you find an opening. It’s not always clean, and it’s not the kind of thing that shows up in HR certifications. But it’s the real work.

What makes this genuinely hard isn’t the navigation. It’s the carrying cost. You have context, history, institutional memory about what works and doesn’t in this organization. That knowledge sits idle while you wait for the invitation to use it. Decisions get made without you. Patterns you could have flagged get set. And if the access gap lasts long enough, the new leader builds an operating model around your absence. By the time they realize what they’re missing, the issues are already in the building and getting worse.

The structural reason this keeps happening is that trust isn’t portable. The relationship you had with the last leader was built from specific moments, specific conversations, specific situations where you showed up in a way that mattered. None of that transfers to the next person. They don’t inherit your track record. They inherit a title and a reporting line. And HR’s value is notoriously invisible until something goes wrong, or until you get far enough into a relationship that the behind-the-scenes work becomes visible. New leaders aren’t usually thinking about how to onboard their HR partner. They’re focused on building their own credibility.

There’s a mode you eventually hit in these situations that’s harder than it sounds. You have to hold two things at once. First, your POV on how things should work, and second, a real support for the direction a new leader is taking. That tension shows up in small moments. A comment about how something used to be handled. A reflex to say “yes, but here’s the history on that.” The release valve is realizing those two things don’t always have to be in conflict. You can offer what you know without fighting for it. You can let new directions unfold without abandoning your judgment.

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There’s real freedom in that, once you find it. You stop protecting what you built before. You stop needing the new leader to arrive at the same conclusions as the last one. You become useful to where things are going, not where they were.

Think about the last leadership change that affected your role. Did you spend more energy protecting the access you had, or getting curious about what the new leader was actually trying to build? Those are different instincts. Only one of them gets you into the room.

Not in a transactional sense. Not “they helped me hit a deadline” or “they gave me a good review.” Actually changed how you think, how you work, or how you see yourself at work. If you sat down and made the list, I suspect it would be longer than you expect. And I suspect most of those people have no idea they’re on it.

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My leader left recently. Senior, experienced, the kind of person who had real influence over how the org operated and how people inside it felt about their work.

After they left, I started having conversations. And I kept running into the same thing. People I didn’t expect to know them well. People who had a story. A real one. Not “we were on a project together.” Something more personal. Something that stuck. The reach of their impact was wider than the org chart suggested, and it was moving in directions I hadn’t anticipated.

That’s the thing about presence. It doesn’t follow reporting lines.

We tend to measure workplace impact vertically. Scope, headcount, results. These aren’t bad measures. But they mostly capture what flows through authority. They largely miss what flows through how someone actually shows up. The way they remembered details about your life. The way they pushed back without making you defensive. The way they made the work feel like something shared rather than something transacted.

That kind of impact is hard to quantify. It doesn’t show up cleanly in a performance review. But it absolutely shows up in a culture, stretched across hundreds of small moments, shaping what people believe about the place they work and the people they work alongside.

When someone like that leaves, you feel it before you can name it. Something is quieter. Something is slightly less warm. The cost is real. It just doesn’t appear on a balance sheet.

Why does this stay invisible for so long? Because organizations are wired to notice outputs, not inputs. The deliverable. The result. We are far less practiced at noticing the texture of someone’s presence. These things resist measurement, so they resist recognition, until the absence forces the accounting.

There’s also the pace. When you’re heads-down and moving fast, you don’t often stop to say: this person is making my work life meaningfully better. You feel it. You don’t name it until they’re gone.

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I found myself turning the lens inward. Where am I showing up that way? Where am I not? I know there are people I connect with deeply. Real mutual impact. And I know there are others where it simply doesn’t click. I can’t manufacture warmth. But I can be more intentional about where I’m bringing it, and where I’m letting the pace of the day make me less present than I want to be.

For those of us in HR who support leaders, there’s an additional layer. We don’t just need to think about how we show up. We also need to help the leaders we support see this in themselves. A leader’s presence scales. Their patterns, the texture of their attention, travel through the organization in ways that are hard to trace and impossible to miss.

This is not soft stuff. The quality of presence inside a team shapes what people are willing to try, willing to say, and willing to stay for.

Go back to that opening question. Make the list. Then ask yourself: do the people on it know they’re on it?

And while you’re at it. Do you know who has you on theirs?

Most organizations approach AI adoption as a rollout problem. You introduce the tool, you run training, you track usage, you measure productivity gains. The assumption underneath all of it is that the gap is informational. People don’t know how to use AI, so you teach them. Then they will use it.

That framing is wrong. Or at least, it’s incomplete.

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What actually keeps people from building with AI isn’t a knowledge gap. It’s a permission gap. A sense that experimentation is allowed, but building is reserved for people with different job titles. The people in corporate functions, HR included, have been trained for years to be good consumers of technology and to leave the building to engineers and product teams. That muscle memory doesn’t get overwritten by a lunch-and-learn.

What does overwrite it is seeing someone in your own orbit, not a vendor, not a consultant, not a keynote speaker, build something real. When I shared my screen and worked through problems live, I wasn’t just demonstrating a tool. I was demonstrating that it was possible. For them. In their context. With their problems.

The requests for access to new tools came within days. The ideas that followed weren’t “here’s what we could ask IT to build someday.” They were “here’s what I want to try next week.” That’s a different kind of sentence. It signals ownership.

HR leaders talk a lot about building AI capability across their organizations. Most of the interventions that follow look like training programs. Those programs have their place. But they are building informed consumers, not builders. And informed consumers are one budget cycle away from disengagement when the tool changes or the ROI doesn’t materialize fast enough.

Builders stick. Builders iterate. Builders pull others in.

If you are the only person on your team who builds AI tools, you are not ahead of the curve. You are a bottleneck with a good story.

Someone on your team shows up to a meeting with something you didn’t ask for. It’s good. Maybe better than an idea you’re currently building. And you have about 15 seconds to decide what happens next. Not just for this idea. For this person’s willingness to bring you the next one.

Most managers blow it. Not because they’re bad at their jobs. Because they’re trying to do two things at once.

The moment someone presents an unsolicited idea, two separate calculations happen in a manager’s head simultaneously: is this idea good, and can we actually use it? Those are different questions. But they almost always get answered at the same time, out loud, in the same breath.

This happened to me.

A team member shows up with a redesigned UX that is genuinely better than what’s already been built. I can clearly see it, know it, but I immediately start explaining why it wouldn’t work due to all the red tape. The idea gets constrained before it gets acknowledged. The person walks away not quite sure whether their work was good or just inconvenient.

The problem is not the redirect. The redirect was probably right. The problem is sequencing.

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When ideas get constrained before they get acknowledged, people learn something. They learn that bringing unsolicited thinking is risky. Not because they’ll be punished, but because they’ll be ignored in the specific way that hurts most: their work will be evaluated for its utility before it’s recognized for its quality.

That’s how you get a team that stops pressure-testing things. Stops reaching. Stops showing up to meetings with something you didn’t ask for. The creativity doesn’t disappear. It gets redirected somewhere safer, which usually means somewhere less useful to you.

This keeps happening because the redirect feels like the urgent task. The timeline is real, the investment in the current version is real, and a manager’s job is to keep things moving. Acknowledging the idea first feels like slowing down, or worse, like raising someone’s hopes before letting them down. So managers skip straight to the decision. It feels efficient. It usually isn’t.

The fix is not complicated, but it requires discipline in the moment. Name the win before you state the reality. Not as a softener. As a true statement that happens to come first.

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“This is really strong work. The UX you’re showing is genuinely better in some important ways. And given where we are, with the investment already made and the timeline we’re on, we’re going forward with the current version.”

Both things are true. Say them in that order.

Then do one more thing: create a visible parking spot for the idea. “Let’s capture what we’d steal from this if we were starting from scratch.” That sentence turns disappointment into contribution. The idea doesn’t disappear into the void. It becomes input. Close by naming the behavior you want more of. “Keep building and pressure-testing like this. Even when we can’t act on it immediately, it makes the work better.” That tells them: this wasn’t a mistake. This was leadership behavior.

Think about the last time someone on your team brought you something you couldn’t use. Did they know their idea was good before you told them why it wouldn’t work? If you’re not sure, you probably went in the wrong order.

That gap is what I keep running into.

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HR practitioners are having more ideas about AI than almost any other function. We are attending the sessions, listening to the podcasts, collecting the use cases. We are genuinely curious and genuinely engaged. And most of us are still not shipping anything.

I have been sitting with why that is, because “I’m too busy” is not an honest answer. “I’m not technical” stopped being a real excuse about eighteen months ago.

Zachary Parris named something useful on the Modern People Leader podcast. He said it comes down to mindset, not tools, not capability. The people who are building things have made a specific internal shift. The people who are not building have not made it yet, including me, until recently.

The shift is not about knowing more. It is about tolerating the gap between the idea and the messy, imperfect first version of the thing. Parris framed it directly: you do not pitch the workflow. You build the workflow and show it running.

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We are trained to get things right before we put them in front of people. Every piece of guidance, every framework, every communication has to be approved, reviewed, considered from multiple angles. That instinct is useful in a lot of HR work. It is death to building. The version you will actually learn from is the one you made in two hours and put in front of a real person to watch them use.

There is a cost to staying in ideas mode, and it is not just missed opportunity in the abstract. It is credibility. HR has been talking about being strategic, about demonstrating value, about having a seat at the table for twenty years. Building is the clearest way to make that case right now. When you ship something, even something small, you are not describing what HR could become. You are showing it.

What Parris also said, and what I keep returning to, is that the problem statement has to come first. Before you touch a tool or a prompt or a prototype, you have to genuinely understand what you are solving for. That discipline HR actually has. We know how to diagnose. We know how to ask what the real problem is underneath the stated one. That is a real edge. Most of us are just not using it to build anything.

So here is the honest question. You have had at least one idea in the last ninety days that could have become a real tool, a working agent, a prototype someone could actually try. What is still sitting in a note somewhere? And what would it take to give it a deadline?

Most AI enablement at work is a lunch-and-learn with a lot of “coming soon,” or a vendor demo where everything works perfectly and nothing looks like your actual problems. People leave those sessions having watched something happen to someone else. That’s a different experience than being in the room when a thing gets made.

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What happened in our session was different, and I didn’t plan it that way. I was building live. When something broke, they watched me debug it. When I wasn’t sure which direction to go, I asked the group. Their questions weren’t softballs. They pushed on things I hadn’t thought through. One person asked something that completely reframed what the agent should actually do. We changed course mid-build because of it. That’s what made it work.

When AI stays only in demos and decks, the people closest to the actual work never develop a feel for what it can do. They wait to be told what to do with the thing. In HR, that has real consequences. We have data problems, process problems, capacity problems. Priorities sitting in a slide deck that nobody has time to fully address. If the people on the team who understand those problems intimately never get close enough to AI to imagine a solution, we leave the gap open.

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Building in front of people feels vulnerable. Something might not work. The demo might fail. Most leaders default to presenting things that are already polished because polished things protect credibility. That instinct costs something. When you only show people finished products, you accidentally communicate that building is beyond them. You create an expert-audience dynamic instead of a collaborative one. And you miss the chance to find out that someone on your team will ask the question that makes the whole thing better.

Share the screen. Build something you actually need. Pick one real problem your team is sitting on, something tied to actual work and actual priorities, then build toward it out loud, with them in the room. Let them steer. Let them see you figure it out in real time.

When the session ends, you’ll have something real. And a team that was part of making it. That changes how they relate to the tool.

I ran it. What came back hit harder than I expected. Not because it was cruel. Because it was close.

The things it got right, it got uncomfortably right. Patterns I’d named out loud but hadn’t fully reckoned with. Tensions I’d described as external circumstances rather than choices. The gap between who I say I’m building toward and what I actually spend my days doing. It wasn’t reading my mind. It was reading my own words back to me with no incentive to soften them. That’s a different experience than journaling. That’s a different experience than a performance review. It’s closer to what a really honest friend would say if they weren’t worried about the relationship.

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But here’s the part I didn’t expect. Some of what surfaced weren’t things I already knew and had been avoiding. They were things I had been struggling to name at all. Vague discomforts I’d been carrying around without being able to articulate them clearly. Seeing them written out plainly, without hedging or diplomatic softening, gave me language for something that had just been static. That clarity felt like a release more than an indictment. Sometimes the most useful thing isn’t a new insight. It’s finally being able to name the thing that’s been sitting just out of reach.

What made it land even harder was that some of what came back had also come up in therapy. Recently. I’d already done the work of naming certain patterns with someone trained to help me see them. And here was the same thread showing up again, surfaced from nothing but my own words in a work context. That kind of convergence is hard to dismiss. When the same truth finds you from two completely different directions, it’s probably not a coincidence.

Where it missed, it missed because it’s only seeing part of the picture. It doesn’t have access to my full life, just the slice I’ve shared in a particular context. So some of what it offered felt incomplete rather than wrong. A diagnosis without the full chart. Still worth something, but worth holding loosely.

What makes this hard isn’t the feedback itself. It’s that once you’ve read it, you can’t unknow it. The comfortable move is to treat it like an interesting exercise, take a screenshot, share it as content, and move on without sitting in what it actually said. Most of us will do exactly that.

So what do you actually do with it? I don’t think there’s a clean answer. But I’ve been sitting with the idea that the value isn’t in the list. It’s in picking one thing, just one, and asking whether the way I’m spending my time right now is consistent with wanting to change it. Not a plan. Not a framework. Just that one honest question applied to one pattern that showed up on the list.

The prompt is worth running. But running it is the easy part. The harder question is what you do the day after, when the sting has faded and your calendar looks exactly the same as it did before.

FULL PROMPT:

“Based on everything you know about me from our conversations, tell me 10 brutal truths about myself that I probably don't want to hear. Be completely honest, don't sugarcoat anything, and tell me exactly what's holding me back.”

https://www.linkedin.com/in/debhaasai/

That invoice isn’t a flex. It’s a blueprint. AI spend functioning as headcount is no longer a thought experiment. It’s a live operating model, and it’s running right now inside companies that didn’t ask your workforce planning team for permission. A CPO put it plainly in the comments: the question she’s fielding isn’t “how many people do we need?” It’s three questions at once. What work requires a human? What gets augmented? What gets fully replaced by an agent? And critically, what does each of those cost? That reframe matters because it moves the conversation out of replacement anxiety and into something more honest: capacity modeling. We’ve always planned for human capacity. We just haven’t built the muscle to plan for agent capacity alongside it.

Workforce planning built on headcount alone is going to produce bad answers. Not eventually. Now. If a four-person team can operate with the output capacity of a much larger organization by routing $113K through an API, then every headcount model that doesn’t account for agent capacity is already working with incomplete inputs. The decisions that flow from that, hiring plans, org design, succession, skills investment, will be structurally miscalibrated. And the gap between what leaders think they need and what the work actually requires will keep widening until someone names it. By then, the cost isn’t just strategic. It’s credibility.

Because workforce planning has always been a headcount conversation. The tools are built for it. The mental models are built for it. Adding agent capacity as a real variable, with its own cost structure, output metrics, and reliability envelope, requires rethinking infrastructure that most HR functions haven’t had to question before. That’s not laziness. It’s gravity.

Start with the CPO’s ratio: human capacity plus agent capacity versus the work that needs to get done. Not AI spend versus revenue. That’s a finance metric. The people metric is about coverage, capability, and judgment. A practical place to begin is mapping where agents are already operating inside your organization, even informally, before anyone built a policy around it. Most teams are further along than their workforce plans reflect. From there, the question becomes what you actually track. Token spend by function is a start, but it doesn’t tell you much on its own. The more useful layer is mapping that spend against what the agent is reliably producing, and where a human is still catching errors, making judgment calls, or providing the context the agent can’t hold. That margin is where your real workforce planning data lives.

If an agent is functioning like headcount in your organization, is it in your workforce plan? If not, what are you actually planning?

I haven’t been able to stop thinking about what that means for people like me. People who aren’t writing books or giving keynote talks. People who just have strong perspectives on their own experience in the working world.

Thought leadership, as we’ve known it, has always been aspirational and polished. The big ideas. The frameworks. The insights delivered from a stage or published in a carefully edited article. It’s impressive. It’s also increasingly useless.

Because what most people actually need isn’t another framework. It’s someone showing them how they tackled a messy problem and got it over the finish line. Not the polished version where everything worked perfectly. The real version where half of it didn’t work and they had to figure it out anyway.

In a world where anyone can use AI to generate polished-sounding insights, what becomes valuable is the stuff AI can’t create. The specific experience of actually doing something. The decisions you made when the path wasn’t clear. The thing you tried that completely failed and what you learned from it.

That’s thought doership. And I think we need a lot more of it.

People consume endless content about best practices and big ideas, but they still don’t know how to actually implement anything. Because the distance between “here’s a compelling framework” and “here’s how I made this work in my specific messy context” is massive.

We keep elevating people who sound authoritative over people who are actually doing the work. The person who writes beautifully about organizational change gets more attention than the person who just led a successful restructure and could tell you exactly what went wrong and how they recovered.

I’m frankly tired of reading highly polished insights that don’t tell me anything about the reality of getting things done at work. I want the tips and tricks. The workarounds. The conversations that unlocked progress. The real mechanics of how someone moved something forward.

Thought leadership became valuable when access to expertise was scarce. AI has changed that equation. Anyone can now generate something that sounds like thought leadership. Well-structured arguments. Compelling frameworks. Professional polish.

What is scarce is specificity. The details that can only come from actually doing something. AI can’t generate that because it didn’t live through it.

But there’s also a cultural barrier. We’re conditioned to think sharing knowledge means having all the answers. That admitting what didn’t work makes you look incompetent instead of honest.

We need to value thought doership over thought leadership. That means sharing how you had an idea and ran with it, even if it didn’t turn out perfectly. Being specific about the decisions you made and why, including the ones that turned out to be wrong.

It means showing the real side. Not the version where you had it all figured out from the beginning. The version where you were uncertain, made mistakes, adjusted, and eventually got to something that worked.

I want to see more people bringing their version of thought doership. The HR person who can explain how they built a process that actually got used. The designer who can show the five iterations that didn’t work before landing on the one that did.

You’ve done things at work that other people could learn from. Not because you’re an expert. Because you’ve navigated situations and learned from what happened.

What’s something you’ve done recently that didn’t go perfectly but taught you something worth sharing? And what would it look like to share the real version instead of the polished one?

They’re calling it efficiency. What they’re actually doing is destroying the only place where future leaders learn how to lead.

Middle management is where people learn to lead before the stakes are existential. It’s where you figure out how to give difficult feedback when it’s one team member, not an entire division. Where you learn to navigate organizational politics when the consequences are manageable. Where you make mistakes that teach you how to lead without those mistakes taking down the business.

Eliminating this layer doesn’t just reduce headcount. It removes the training ground for everyone who’s supposed to run the company in five years.

I’m watching this play out across industries. Companies cut middle management to reduce costs, then three years later they’re scrambling because they have no one ready to step into senior leadership roles. The gap between individual contributor and executive is so wide that people can’t make the jump. They’ve never managed people. Never navigated cross-functional conflict. Never had to make decisions with incomplete information.

The executives who talk about removing layers to “empower people” aren’t acknowledging what happens when you skip crucial developmental experiences. You don’t empower people by giving them more responsibility without the scaffolding to learn how to handle it. You set them up to fail.

This shows up in succession planning conversations that go nowhere. Leadership looks at the organization and realizes there’s no one ready to take over when executives retire. Not because people aren’t talented, but because they haven’t had the experiences that build executive capability. And you can’t manufacture ten years of leadership development in six months when someone suddenly needs to step up.

Middle managers aren’t just executing strategy from above and managing work from below. They’re learning how to translate vision into action. How to build teams. How to develop people. How to handle the complexity of competing priorities. These are the capabilities that make someone ready for senior leadership, and you don’t learn them by going straight from individual contributor to executive.

Organizations that eliminated middle management in the name of efficiency are going to face a leadership crisis. Not today. But soon. When their current executives retire or leave and there’s no one prepared to replace them.

You can hire senior leaders from outside. But external hires don’t have institutional knowledge. They don’t understand your culture. They don’t have relationships built over years. You’re trading long-term leadership development for short-term cost savings, and that trade-off will eventually break you.

What companies need to understand is that middle management isn’t just a cost center. It’s an investment in future leadership. Cutting it saves money now and creates a catastrophic gap later.

If you’ve already eliminated these roles, you need a different plan for how people develop leadership capabilities. Stretch assignments. Acting roles. Formal development programs. Something that replaces what middle management used to provide naturally.

If you haven’t cut these roles yet, think carefully about what you’re actually eliminating. It’s not just organizational layers. It’s the mechanism that builds the leaders you’re going to need.

Does your organization have a leadership pipeline? Or did you eliminate the layer where leaders were supposed to develop?

People can now engage with AI at any moment to generate ideas, solve problems, and produce output at speeds that weren’t possible six months ago. We’re treating this as a productivity breakthrough. In reality, we’re creating the conditions for a new kind of cognitive burnout that traditional wellness programs aren’t designed to address.

I’ve noticed something in my own work that contradicts what we keep hearing about AI and efficiency. Short sessions with AI tools drain me faster than hours of traditional work ever did. I can accomplish more in 30 minutes than I used to in three hours, but I hit a wall quicker than I ever have before.

This isn’t about capacity or discipline. It’s about cognitive load.

When you work with AI, you’re not just completing tasks. You’re evaluating exponentially more information, weighing more options, and making rapid decisions about what’s good enough versus what needs refinement. The intensity of processing is fundamentally different from traditional work, even when the output looks similar.

Traditional work had natural constraints that created built-in recovery periods. You could only research so much in an afternoon. You could only draft so many versions before running out of time. The limits weren’t ideal, but they forced cognitive breaks.

AI removes those constraints entirely. You can iterate endlessly, research infinitely, and generate option after option without the tool ever signaling that it’s time to stop. Your brain doesn’t get natural pause points because the technology never gets tired.

This dynamic is showing up across roles and industries. The content creator producing three times the volume but feeling completely depleted by noon. The developer coding faster than ever but unable to think clearly by mid-afternoon. The strategist generating insights at unprecedented speed but lacking the clarity to evaluate which ones actually matter.

Productivity metrics are up. Cognitive sustainability is collapsing. And most organizations are celebrating the former without measuring the latter.

Companies are about to face a form of burnout that doesn’t fit existing frameworks. This isn’t about working too many hours or lacking work-life balance. It’s about brains processing volumes of possibility that exceed what we’re built to handle.

If organizations don’t build wellbeing strategies specifically for AI-augmented work, they’ll watch their people crash in ways that meditation apps and mental health days can’t address. The problem isn’t stress management. It’s cognitive overload from working with infinitely generative tools that never signal when enough is enough.

What companies actually need are policies around AI usage that go beyond data security and productivity optimization. We need frameworks for when to engage with AI and when to step away from it. We need work structures that build in cognitive recovery, not just task completion. We need protected space for human connection that allows brains to slow down and be present instead of constantly processing outputs and evaluating options.

Organizations need to foster moments where people aren’t optimizing, iterating, or generating. Where they’re simply thinking. Or deliberately not thinking at all.

This shift requires intention and foresight. The companies that will succeed in five years won’t just be the ones that adopted AI fastest. They’ll be the ones that figured out how to use it sustainably, without burning through their people’s cognitive capacity in the process.

Does your organization have a wellbeing strategy designed for how people actually work with AI? Or are you measuring productivity gains while ignoring the cognitive costs that will eventually show up in your retention and performance data?

Leadership is thrilled about the productivity gains. But my friend is watching something else happen. People are starting to ask questions they didn’t ask before. “If AI can do this part of my job, what am I actually here for?”

The efficiency gains everyone celebrates are creating an identity crisis nobody planned for.

Share From the People Desk

Companies seeing explosive AI adoption are facing a challenge most haven’t named yet. Their people are going to demand a new value proposition when the fundamental nature of their work is changing this rapidly.

This isn’t about job security. It’s about purpose. If significant portions of someone’s role can now be handled by AI, the parts that remain need to matter more.

At the same time, employees are being asked to show up differently. Not just to use AI tools, but to bring ideas and new ways of thinking. The implicit contract is shifting from “execute your responsibilities reliably” to “think creatively about how we should be working.”

That’s a fundamentally different value proposition than most organizations were built on.

Your best people leave because they can’t articulate their value anymore. They’re productive. They’re using AI effectively. But they feel like they’re operating tools instead of contributing something that matters.

Engagement drops even as productivity rises. People are accomplishing more but feeling less connected to the work. The metrics look great. The culture feels hollow.

You create a divide between people who adapt and people who resist. The organization fractures because there’s no shared understanding of what work means now.

Culture changes slowly. AI adoption is happening fast. The gap between how quickly the tools are changing work and how quickly organizations can evolve their culture is massive.

Most organizations don’t know what they actually value in human contribution yet. We’re still figuring out what humans should do when AI can handle tasks we used to think required human intelligence.

Leadership is often disconnected from how AI is actually changing day-to-day work. They see productivity gains in dashboards. They don’t see the designer questioning whether their creative judgment matters when AI can generate a dozen options instantly.

Companies need to redefine what they’re asking from employees in the AI era. If AI handles execution, what does the organization need from humans? Judgment? Creativity? Strategic thinking? Whatever it is, it needs to be explicit and valued in how people are measured and rewarded.

This means having honest conversations about how roles are changing. Not in the abstract. Specifically. “Here’s what your role used to require. Here’s what it requires now. Here’s what we value from you that AI can’t provide.”

Culture has to shift from valuing productivity to valuing contribution. When AI can generate infinite output, volume stops being meaningful. What someone adds that AI can’t becomes the measure that matters.

Your organization is probably already in the middle of this shift whether you’ve named it or not. AI is changing how work gets done. People are questioning what their role means.

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The question isn’t whether your culture needs to change. It’s whether you’re going to shape that change intentionally or let it happen to you.

What does your employee value proposition actually look like when AI can handle much of what people used to do? And are you having that conversation, or hoping it resolves itself?

Everyone congratulated her. Leadership was excited. She seemed excited. Big title. More money. Recognition for years of excellent work.

Three months later, she’s on the phone with me. Miserable.

“I spend all day in meetings. Client calls. Team check-ins. Budget reviews. I haven’t actually designed anything in weeks. This isn’t what I wanted.”

I ask the obvious question. “Did you want to be a creative director?”

She looks at me like I’m missing the point. “I wanted to advance my career. This was the only way to do that.”

And that’s the problem. In most agencies, the only path up is into management. If you want more money, more influence, more recognition, you have to stop doing the work you’re good at and start managing people.

For some roles, that works. Account people often want to manage. They like the coordination, the client relationships, the strategic oversight.

But creatives? Strategists? Producers? Most of them got into this work because they love the craft. And management takes them away from it.

Your best creative becomes a creative director. Your top strategist becomes a strategy director. Your senior producer becomes a production lead.

Now they spend more time in meetings than doing the work they love. They’re managing teams, reviewing work, handling client politics, sitting in status updates.

And they’re miserable. Because the skills that made them great at their craft, curiosity, attention to detail, love of the work itself, don’t automatically translate to being good at management. And even if they could be good managers, many of them don’t want to be.

But agencies keep pushing people into management because that’s how career progression works. Individual contributor roles top out. If you want to keep growing, keep earning more, keep having influence, you move into leadership.

From my perspective, this creates multiple problems at once.

You lose your best practitioners. The designer who elevated every project they touched is now directing others instead of designing. The strategist who could see insights nobody else saw is now in budget meetings. Their craft suffers because they’re not doing it anymore.

You gain mediocre managers. Just because someone’s great at design doesn’t mean they’re great at managing designers. The skills are different. And when you promote people into management because they’re good at the work, not because they’re good at leading people, you get managers who struggle.

The person who got promoted is unhappy. They thought advancement meant doing more of what they love at a higher level. Instead, it means doing less of what they love and more of what drains them.

Their team suffers. A reluctant manager who doesn’t want to be managing can’t effectively develop people, give good feedback, or create the conditions for great work. The team feels it. They don’t get the leadership they need because their manager would rather be doing the work themselves.

I’ve watched this play out across every discipline. Creatives who became creative directors and stopped making. Strategists who became strategy leads and stopped doing deep thinking work. Producers who became production managers and spent all their time in administrative tasks they hated.

And in almost every case, there was a moment where they wished they could go back. But they couldn’t, because going backward felt like failure. And the agency needed them in that leadership role, even if it wasn’t right for them.

Here’s what happens when you force people into management they don’t want.

You lose talent. Your best people leave because the only way to advance is into a role they don’t want. They go to places that let them stay in the craft while still growing. Or they go freelance so they can do the work without the management burden.

I watched an agency lose a brilliant strategist who’d been there for seven years. She was being groomed for a director role. Managing a team. Overseeing multiple accounts. She didn’t want it. She wanted to do strategy. But that wasn’t an option at her level. So she left for a consultancy that had principal-level IC roles. The agency lost her expertise because they couldn’t create a path that fit what she actually wanted.

Quality declines because your best practitioners aren’t practicing anymore. They’re managing. The work gets done by less experienced people without the same level of oversight or craft because the person who used to do it brilliantly is now in meetings all day.

You create a management layer that’s ineffective. People who don’t want to manage don’t do it well. They avoid difficult conversations. They don’t develop their teams. They micromanage because they’d rather just do the work themselves. And the people reporting to them suffer for it.

Culture gets weird when people are promoted into roles they’re not suited for. Everyone can tell when someone’s a reluctant manager. It creates resentment. The person in the role resents being there. Their team resents not getting real leadership. And everyone else wonders why the agency keeps promoting people who clearly don’t want the job.

During one particularly bad stretch, an agency promoted three people into leadership roles within a few months. A designer to creative director. A strategist to strategy lead. A producer to production manager. Within a year, all three were struggling. The teams weren’t performing. The newly promoted people were burned out. And morale was terrible because everyone could see the promotions weren’t working.

So why do agencies keep doing this?

The traditional career ladder only goes one direction. Up means management. That’s how it’s always worked in most industries. And agencies haven’t figured out a different model.

There’s also an assumption that your best practitioners should lead. If you’re great at the work, you should teach others to do it. You should set the standard. You should guide the next generation. That sounds logical until you realize teaching and managing are different skills than doing.

Financially, it’s easier to justify paying someone more if they’re managing a team. Leadership roles come with bigger budgets. IC roles, even senior ones, have caps. If you want to pay someone what they’re worth, putting them in a management role is the clearest path.

And honestly, agencies need managers. Someone has to lead the teams. Someone has to interface with clients at a strategic level. Someone has to make decisions about staffing and priorities. If your most experienced people won’t do it, who will?

But just because you need managers doesn’t mean everyone should become one.

Here’s what I think needs to change. Agencies need to build real IC career paths that don’t top out at mid-level.

Create senior IC roles with real authority and compensation. Principal designer. Lead strategist. Lead senior producer. These aren’t just fancy titles for people who don’t want to manage. They’re roles with influence, autonomy, and pay that competes with management track roles.

These people should be the craft leaders. They set the standard for quality. They mentor junior people, not through formal management but through their work and guidance. They’re on the most important projects. They have a voice in how the discipline evolves.

But they’re not managing teams. They’re not in operational meetings about utilization and budgets. They’re doing the work at the highest level and making everyone around them better through their expertise.

I’ve seen a few agencies do this well. They created parallel tracks. One for people who want to manage. One for people who want to stay in the craft. Both tracks go high. Both have compensation that reflects seniority. Both have influence. They’re just different paths.

The management track leads teams, handles operations, interfaces with clients on strategic relationships. The IC track does the highest-level work, mentors through craft, and sets the quality bar.

Make the choice explicit early. Don’t wait until someone’s been promoted into management to ask if they actually wanted it. Have conversations earlier in people’s careers about what they want. Some people want to manage. Some people want to stay in the craft. Neither is wrong. But knowing which path someone wants helps you develop them appropriately.

One agency I worked with started doing “career pathing” conversations with everyone at the senior level. Not performance reviews. Just conversations about where they saw themselves in three years. Did they want to lead a team? Did they want to be the go-to expert in their craft? Did they want to move into strategy or client relationships?

The conversations were simple. But they changed how the agency promoted people. They stopped automatically pushing everyone toward management and started creating paths that actually fit what people wanted.

Recognize that managing is a skill that needs development. If someone does want to move into management, don’t just promote them and hope they figure it out. Train them. Coach them. Give them support as they learn a completely different set of skills.

Too many agencies promote someone to creative director or strategy lead and then act surprised when they struggle. Managing people is hard. It requires skills most people don’t have naturally. If you’re going to put someone in that role, invest in helping them succeed.

Let people try management without it being permanent. Create project lead roles or interim leadership opportunities where someone can test whether they like managing before fully committing. If they discover it’s not for them, they can go back to IC work without it being a demotion.

Picture this. Your senior designer has been with you for five years. She’s incredibly talented. You want to promote her. Instead of automatically making her a creative director, you ask what she wants. She’s not sure. She likes the idea of more influence but doesn’t know if she wants to manage.

You create a six-month trial. She leads a small team on one major project. Gets coaching on giving feedback and developing people. Tries the management side while still doing some hands-on work.

At the end of six months, she knows. She doesn’t want to manage full-time. So you create a principal designer role for her. She’s the go-to for the agency’s most important design work. She mentors junior designers. She has significant influence on creative direction. But she’s not managing a team day-to-day.

She’s happy. The agency gets to keep her expertise. And the junior designers have someone setting a high bar without a reluctant manager forcing them into a structure they don’t want.

You know who on your team doesn’t want to manage but is being pushed that direction because it’s the only path up.

The question is whether you’re going to keep forcing people into roles they don’t want, or whether you’re going to build paths that actually match how people want to grow.

Not everyone should manage. And that’s okay. The best designer doesn’t automatically become the best creative director. The best strategist doesn’t automatically become the best strategy lead.

What do your IC career paths actually look like? And are they real paths with compensation and influence, or are they just consolation prizes for people who don’t want to manage?