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Most organizations are drowning in employee data and starving for employee insight. They run the annual engagement survey, maybe a pulse or two, collect thousands of data points, generate a deck full of color-coded heat maps, present it to leadership, and then watch almost nothing change. The next year they do it again. The scores move a point or two, nobody can say exactly why, and the whole exercise quietly reinforces the thing employees suspected all along: that nobody was really going to do anything with what they said.

I want to talk about how to break that cycle. Not with a better survey tool. Not with a more sophisticated dashboard. With a fundamentally different way of thinking about what employee listening is actually for.

Here is the uncomfortable truth at the center of this work. The problem was never that organizations could not collect feedback. The problem is that collecting feedback and acting on feedback are two completely different organizational capabilities, and most companies have invested heavily in the first while building almost nothing of the second.

I have spent fifteen years inside this problem. I have built listening systems for organizations with active collective bargaining agreements, where the gap between what employees said and what the organization did had real labor relations consequences. I have run feedback loops inside a global product and engineering organization where the volume of signal was enormous and the challenge was separating the meaningful from the noise. What I have learned is that highly engaged employee listening is not a survey practice. It is an organizational design discipline. And the organizations that figure this out are going to have a structural advantage in a labor market that keeps shifting underneath everyone’s feet.

Why Most Listening Programs Fail Before They Start

Walk into almost any mid-to-large organization and ask about their listening strategy and you will hear about their survey. The annual engagement survey. Maybe a quarterly pulse. They will tell you their participation rate, their score, and how they benchmark against their industry.

What they will almost never be able to tell you is what changed last year as a direct result of what employees said.

This is the failure mode, and it is nearly universal. Organizations treat listening as a measurement activity when it is actually a trust activity. The survey is not the product. It is the opening move in a conversation, and a conversation where only one party ever speaks is not a conversation.

Here is what happens psychologically inside an organization that listens without acting. The first year, participation is high and the feedback is rich because employees believe their input might matter. They tell you real things. Then they watch. And when the answer is that a deck got presented and a few vague commitments got made and nothing in their daily experience actually shifted, they recalibrate. The next year participation drops. The feedback gets shallower, more guarded. That is the trust signal screaming at you. People stop telling you what they actually think because they have learned that telling you costs them effort and changes nothing.

By year three you have trained your workforce to give you exactly the kind of feedback that is least useful: safe, surface-level, and disengaged. Then leadership looks at the declining participation and concludes that employees do not care about engagement, when in fact employees learned precisely the lesson the organization taught them.

The participation rate is not measuring engagement. It is measuring whether employees believe you will do anything with what they say. That is the first thing I work to change when I come into an organization. Not the instrument. The relationship.

Listening Is a System, Not an Event

The single most important shift I help organizations make is moving from listening as an event to listening as a system.

An event has a beginning and an end. The annual survey opens, employees respond, it closes, a presentation happens, and everyone goes back to their actual jobs until next year. A system is continuous. Signal comes in constantly through multiple channels, gets interpreted on an ongoing basis, feeds into decisions as they are being made, and produces visible action employees can connect back to what they said.

That difference is the difference between a program that builds trust and one that erodes it.

A real system requires channel diversity. The annual census is the comprehensive measurement, the thorough physical exam once a year, valuable for its completeness and comparability rather than its score. Pulse surveys are the more frequent vital signs, short and targeted, tracking movement on the variables that matter most. Lifecycle surveys capture the high-stakes transition moments, the first ninety days, a promotion, a manager change, the exit, which are dramatically underused and produce some of the richest signal you will ever get. A new hire at day forty-five sees things with fresh eyes that longtime employees have lost. And the always-on channels, open feedback, manager one-on-ones, skip-levels, employee resource groups, are where listening stops being a survey practice and becomes an organizational capability.

No single channel tells you the truth. The truth emerges from the pattern across channels. When the census, the pulse data, the exit interviews, and the manager conversations all point the same direction, you have found something real. When they conflict, the conflict itself is information.

When I build a system, I do not turn on every channel at once. I sequence them based on what the organization can actually handle, usually starting with a strong census to establish the baseline, then layering in targeted pulses, then lifecycle surveys, then the always-on channels as capability and trust mature. Turning everything on at once generates more signal than a low-trust organization can process and act on, which brings you right back to the listening gap.

The Analytics Discipline: Turning Messy Data Into Real Signal

Let me be direct about something that does not get said enough. Most of what passes for people analytics is decoration, not decision support. It is a dashboard that looks impressive and changes nothing. It is fake precision, numbers to two decimal places to create the impression of rigor while obscuring a methodology that cannot support the conclusions being drawn.

Real people analytics is different, and the difference starts with what you are actually measuring and why. When I analyze listening data, I am not trying to produce a number. I am trying to answer a question, almost always some version of this: where is the system failing people, and what would it take to fix it? The engagement score is not the destination. It is a symptom, and the job is to diagnose the underlying condition.

The first analytical move is disaggregation. The aggregate score is almost useless for action because it averages away the texture that matters. An organization at seventy-two percent might have a uniformly mediocre experience, or a phenomenal one in some teams and a catastrophic one in others that average to seventy-two. Those are completely different organizations requiring completely different interventions. Break the data down by manager, tenure, function, location. When you discover that the variance between your best and worst managers is enormous, you have located the problem. The aggregate told you the organization had room to improve. The disaggregation told you exactly where and exactly who.

The second move is connecting sentiment data to outcome data. Engagement scores in isolation are interesting but not actionable. Connected to attrition, performance, internal mobility, and productivity, they become a business case. When I can show that the teams with the lowest psychological safety are also the teams with the highest regrettable attrition, and that the cost is quantifiable and significant, I have transformed a soft HR metric into a hard operational signal with a dollar figure attached. That is the work that earns HR a seat at the table where decisions actually get made.

The third move, and the most sophisticated, is identifying leading indicators rather than lagging ones. Attrition is a lagging indicator. By the time someone shows up in your turnover data, the decision was made months earlier. The real value is in the early signals that predict the outcome while there is still time to intervene: declining one-on-one frequency, dropping participation in optional activities, a shift in the language people use in open-ended responses, a decline in internal mobility applications from a particular team. These give you the thing that matters most, which is time.

This analytical work does not happen through a survey platform’s built-in reporting, which is designed to make the data look good, not to support hard decisions. The real analysis happens when you take the data out of the platform and interrogate it properly, connecting it to your HRIS and performance and attrition data, running the disaggregation the platform does not offer by default. Increasingly I do parts of this with AI-augmented workflows when they can be leveraged, processing large volumes of qualitative feedback and surfacing thematic patterns across thousands of open-ended responses that would take weeks to find manually. But the AI does not do the thinking. It accelerates the synthesis. The judgment about what the patterns mean remains human work, and it remains the most important part of the entire process.

From Insight to Action: The Translation Almost Nobody Does

This is the part that separates organizations that build trust from organizations that erode it. Most are not bad at collecting data. Many are not even bad at analyzing it. Where they fail, almost universally, is the translation. They generate insight and do not convert it into change. The insight sits in a deck while the employees who provided it watch and learn that their input did not matter. That is the listening gap, the gap between collecting and acting.

Closing it rests on three principles.

First, action must be specific, owned, and time-bound. Vague commitments are worse than none, because they signal that the organization wants credit for responding without being accountable for change. “We will focus on improving communication” is a non-commitment. Real action looks different: we found that new hires lack clarity on what success looks like, so we are implementing a structured thirty-sixty-ninety day expectation framework co-created between every new hire and their manager before day one, owned by this specific leader, in place by this date, measured over the following two quarters. That is something you can hold someone accountable to, and something employees can connect back to what they said.

Second, you cannot act on everything, and trying to is its own failure mode. Rich feedback surfaces dozens of problems. Chase all of them and you solve none of them well. The discipline is prioritization weighed against feasibility, and the courage to focus. One of the most trust-building things an organization can do is come back to employees and say: you told us ten things, here are the three we are tackling first and why, and here is our honest thinking on the timing for the rest. That transparency builds more trust than a scattershot attempt to fix everything.

Third, and this is what makes the whole system work, close the loop visibly. The most important moment in a listening cycle is not the survey or the analysis. It is when the organization comes back and says: here is what you told us, here is what we are doing, and here is how you will know whether it worked. This is the single most neglected step in almost every organization I have worked with. They run the survey, analyze it, make decisions, and never draw the line from employee input to the resulting change, so even when they do act, employees never realize it. I build loop-closing in as a non-negotiable. Every cycle ends with a visible, specific communication: you spoke, we listened, here is what changed. Then the next cycle begins, and employees who saw that their input mattered engage more deeply. That is the virtuous cycle. That is what highly engaged listening actually means.

The Trust Equation, and the Bespoke Approach

Everything above rolls up into one outcome: trust between employees and the leaders who run the organization. Not engagement scores. Not participation. Trust.

Every time an organization asks for feedback, it makes an implicit promise that your input matters and we will do something with it. Keep that promise and you make a deposit in the trust account that pays dividends in every direction: higher quality feedback, greater discretionary effort, lower regrettable attrition, a stronger employer brand. Break it and you do not just fail to build trust, you actively destroy it. Asking and then ignoring is worse than never asking, because it signals either that leadership does not care or cannot act. An organization not prepared to act on what it learns is better off not asking. The survey is not a neutral instrument. It is a promise, and promises have consequences whether you keep them or break them.

This is also why I work the way I do. The dominant model in the market is the platform model: buy a tool, run its templates, read its dashboards, follow its best practices. The platforms are genuinely useful for collecting and storing feedback. But a platform cannot understand your organization. It gives you the same survey, the same benchmarks, the same generic best practices it gives everyone else, and generic listening produces generic results. The hard part was never the data collection the platform handles. The hard part is the interpretation and the action, and those are inherently specific to your culture, your leadership, your history, and the structural realities that determine what action is even possible.

My approach is bespoke. I do not come in with a template. I come in to understand the organization first. What is the history here? Has this organization listened before, and what happened? What is the level of trust right now, and what created it? What are the structural realities, the union dynamics, the functional cultures, that shape both what employees say and what the organization can do about it? Only then do I design the system, tailored to that specific reality. That is more work than running a platform’s templates. It is also dramatically more effective, because it solves the actual problem rather than the one the platform was built for. I think of myself less as a survey administrator and more as an organizational diagnostician. The listening system is the instrument. The value is in the diagnosis and the treatment plan.

What This Looks Like When It Works

When listening works, an employee with a concern, an idea, or a frustration has a real channel to express it, and they use it because they have learned that expression leads somewhere. The signal flows into a system that processes it with rigor, finds the pattern it belongs to, and surfaces it to the people who can act. Those people make a real decision, owned and time-bound, because the data gave them a clear picture of a real problem. The action happens. The employee sees it happen. And the organization comes back and says, explicitly: you told us this, and here is what we did.

That is trust being built, transaction by transaction. And it compounds. An organization that runs this loop well for years has a workforce that trusts leadership, a leadership team that gets honest signal, and an institutional capability to adapt that competitors cannot easily replicate. The engagement scores, where most organizations start and stop, are just the surface indicator of something much deeper.

This is the work. It is not a survey, a dashboard, or a platform. It is the deliberate construction of an organizational capability to sense and respond to the people who do the work, built on trust, powered by analytical rigor, and focused relentlessly on translating insight into achievable action. Because an organization that truly listens, and truly acts on what it hears, is one where people can do their best work. Not despite the systems around them, but because of them.

And in the end, that is what all of this is for.

Drew Soule is an HR executive and people operations strategist who helps organizations build employee listening systems that translate data into action and build trust between employees and leadership. He writes about people operations, organizational design, and the future of HR at drewsoule.net and through The Practitioner Files at thepractitionerfiles.com.

Picture the job. A benefits coordinator opens her queue at eight in the morning and it is already forty cases deep. A leave question that is really three questions. An address change someone submitted twice. A manager asking whether a write up can go in a file, with two people copied who did not need to be. A note from an employee whose insurance card never arrived, written in the tone of someone who has called the carrier four times. She works the list. She finds the right tab, the right policy, the right saved reply, and she closes what she can before the next batch lands. By noon the queue is forty deep again. This is what HR is for most of the people who actually do it.

Most companies still run the function this way, more or less the way they ran it twenty years ago. The titles have grown longer. The tools have multiplied. The work itself has barely moved. It is transactional, it is manual, and it remains the default in a year when the technology to remake every piece of it already sits unused on the same desks.

That is the part worth sitting with. Not that the tools exist. That they exist, and too few of the companies buying them are preparing the people who will have to work alongside them.

Start with what the word actually means, because it gets thrown around until it means nothing. A chatbot answers a question. An agent pursues a goal. Give an agent an objective and a set of tools and it will take a series of steps on its own, deciding what to do next based on what it finds, until the job is finished or it hits something it cannot resolve. The difference is the difference between a calculator and an accountant. One waits to be asked. The other works the problem.

Drop that into the coordinator’s queue and the morning changes shape. The agent takes in each case, reads it, classifies it, pulls the governing policy and the employee record, drafts the response, completes the routine ones from end to end, and surfaces only the handful that carry real ambiguity, with its reasoning attached, for a person to decide. The forty cases become six that need a human. And the six are the ones that always mattered. The leave request where an employee mentions, almost in passing, a condition that quietly triggers a legal obligation the form never asked about. The address change that is really someone fleeing a situation at home. The write up that is the third one this quarter for the same protected complaint. The agent can route those. It should never be the thing that decides them. This is not a forecast. Teams are running versions of it now. The capability is here. The preparation is not.

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So watch what companies actually do with it. Many buy the capability and cut the team in the same breath. Then they stand in front of the organization and call it efficiency.

Sometimes a cut is honest. A company over hired in a boom and the math no longer works. Runway is short and payroll is the largest line on the page. Two teams do identical work after an acquisition. Those situations are real, and pretending they are not is its own kind of dishonesty. A leader who faces one of them and makes the hard call is not the problem.

But too often that is not what happens, and everyone in the building can feel the difference. When the tool arrives and the people disappear together, with no plan for what the survivors will now be able to do that they could not do before, that is not a strategy. It is a confession the company does not hear itself making. It admits it never understood what those people were for. It wanted hands. The instant a machine outran the hands, the people became line items. That is not a story about powerful technology. It is a story about a function that was built to process and never asked to think, run by leaders who never thought to ask.

It helps to understand why the queue exists at all, because the answer is not flattering and it explains the panic. HR began as personnel administration, a back office that filed the paperwork and kept the company out of trouble. Somewhere along the way it split. One track became the business partner who was supposed to sit with leaders and shape the organization. The other became the service center that absorbed the volume so the partners could be freed for the strategic work. The freeing mostly did not happen, because the volume work still needed bodies, and the bodies were cheaper than the systems that might have replaced them. So the queue endured. Most of that manual work survived not because it created value but because automating it well used to be expensive, brittle, and rarely worth the trouble. The company tolerated the cost and called it a service. It was never a strategy anyone chose.

AI removes the excuse, and in removing it, exposes something uncomfortable. It reveals very quickly which companies built a function that thinks and which built a processing center with an HR sign on the door. If the only thing your people function ever optimized was throughput, you are in real trouble now, because throughput just got cheap. If your people were always doing the part the machine still cannot do, you are about to pull away from everyone who was not.

Nearly every argument about AI and work runs on one buried assumption: the machine does the job the person did, one out, one in. Call it substitution. It is the logic under every alarmist headline and every spreadsheet that treats a workforce as a stack of swappable parts. There is a gentler version, augmentation, where the tool sits beside the worker and makes the same job faster. Most of what gets announced as an AI strategy is really substitution wearing augmentation’s clothes. Buy the tool, promise it will empower the team, and quietly plan for the team to be smaller by spring.

Both of those miss the only move that matters, which is elevation. Elevation means the human stops performing the task and starts directing the system that performs it. The work does not get faster. It changes kind. The practitioner who answered fifty questions a day now designs the agent that answers five thousand, sets its guardrails, audits its judgment, intercepts what it gets wrong, and reinvests the recovered hours in the problems that were always too consequential to rush. Same person, larger job. You do not end up with a smaller team doing the same small work. You end up with a leaner core of humans doing far larger and far more valuable work. That is the opening hiding in plain sight, and too few are funding it.

Here is where most pieces like this one start lying, and I would rather not. Elevation is not a happy ending for everyone. A function that runs on agents supervised by skilled people is, in plain fact, a smaller function. The arithmetic that frightens the workforce is not wrong. If one elevated practitioner can do what five used to do, four of those five have to land somewhere, and “go learn to design agentic workflows” is not a path open to every person or wanted by every person. Pretending the lean function employs as many people as the old one is a fairy tale, and people can smell a fairy tale from across the building.

So the honest version comes with obligations, and the obligations are exactly the part leaders skip. If the work is going to change under people, they are owed real reskilling, with real time and real budget, not a single training session and a slogan. They are owed honest conversations early, while there is still room to move, instead of a surprise meeting once the decision is already made. They are owed redeployment where it genuinely exists. And where it does not, they are owed a separation handled with dignity, with runway and real help landing somewhere new, rather than a calendar invite and a walk to the door. The difference between the company that elevates its people and the company that hollows them out is not whether anyone leaves. People leave in both. The difference is whether leadership treated the people as a cost to minimize or a responsibility to honor on the way through a hard transition. One builds a function that talent fights to join. The other builds a reputation that arrives before you do in every future hire.

It is worth being plain about what that does and does not condemn, because the argument is easy to misread as a refusal to ever reduce a team. It is not. Three things look similar from a distance and are not the same at all. The first is the blunt cut, where the tool arrives, the headcount disappears, no one redesigns the work, and the company pockets the savings and calls the subtraction a strategy. The second is deliberate redesign, where the work is mapped, the people are trained, the roles and the pay are rebuilt around the new leverage, and the function is then staffed honestly for what it has become, which may well be smaller. The third is the necessary separation, where some roles genuinely end, but with notice, with transition support, and with the dignity owed to people who trusted the place. The objection is not to the second or the third. It is to the first wearing the costume of the other two. The argument is not that no one should ever reduce a team. The argument is that you do not get to call a reduction a strategy when you skipped the redesign.

And elevation is hard. It is slow. It costs money before it saves any. It asks leaders to learn something genuinely new and then to teach it, which is harder than learning. Plenty of organizations will try it and fail, because they will buy the tools and skip the teaching, or teach without redesigning the work, or declare victory at the press release and wonder a year later why nothing changed. None of that is an argument against the path. It is an argument for taking it seriously instead of sloganizing it.

I said the function gets smaller, and it does. But the story of the people inside it is not only a story of subtraction, and the word we keep reaching for to describe the work at the bottom of it deserves a harder look. Remedial is a condescending name for jobs that are often grinding, relentless, and badly paid. The benefits coordinator clearing her queue. The shared services rep answering the same question for the hundredth time before noon. The onboarding clerk chasing a form someone forgot to sign. These roles get treated as disposable precisely because the work is bounded and the pay is low, and the two facts are taken as proof of each other. Agentic AI is the first thing in a long while that offers a real way to break that loop, not by freezing the jobs in place, but by turning them into something worth far more than they pay today.

Start with how pay actually works, because the romance about hard work obscures it. Wages do not track how difficult a job is or how many hours it eats. They track leverage and scarcity. A role pays little when the work is bounded, quickly learned, and produces a modest amount of value in an hour, which is another way of saying the person is replaceable and what they make in that hour is small. A role pays well when one person’s hour generates a large amount of value and few people can generate it. That is the whole equation. It is why a job can leave you exhausted and still leave you broke. The remedial roles sit at the bottom of it not because the people in them are less able but because the work, as currently designed, hands them no leverage at all. Each hour buys one hour of bounded output. There is a ceiling on what that can ever be worth, and it sits low.

Agentic AI raises that ceiling further than anything since the spreadsheet. The person who learns to direct a system that does the work of ten is no longer trading an hour for an hour of output. She is producing the work of a team, and the value she creates in an hour climbs to match. The ceiling that kept the role cheap is simply gone. What can take its place is a role whose pay tracks the much larger value the person now creates, on one condition, that she holds the skill that makes the leverage possible in the first place. That is the conversion at the center of this. The same person, from the same starting line, now holds a completely different position in the only equation that has ever set the price of work.

None of that is a hopeful guess. It is the pattern technology has followed nearly every time it was paired with investment in people instead of used as a reason to be rid of them. The most cited example is the bank teller, and it gets cited because it embarrassed the forecasts. When the cash machine arrived in the 1970s, the obvious prediction was that it would erase the teller. For decades the opposite happened. Because the machines made each branch cheaper to operate, banks opened far more branches, and the total number of tellers actually rose. The job transformed underneath the people doing it. Counting and dispensing cash, the bounded part, went to the machine. The human part climbed toward what the machine could not touch, knowing the customer, untangling the messy problem, selling the product, holding the relationship that kept the account from leaving. The work moved up the value chain, and the better version of it paid better. The same arc ran through bookkeeping as the ledger went digital and the people who stayed became analysts and advisors rather than clerks, and through drafting as the drawing board gave way to design software and the ones who learned it designed instead of traced.

The analogy is useful, but it can be pushed further than it holds, and the honest version marks the seam before a skeptic does. The teller story turned on a fact specific to banking. Cheaper branches led banks to open more of them, more branches needed more people, and so the total count of tellers grew even as the work changed under them. HR has no equivalent engine. Nothing about clearing a benefits queue more cheaply makes a company want more benefits queues. So the lesson to take from the teller is the narrow one, not the triumphant one. Automation removing the bounded task does not by itself create more seats. It creates a better seat only where an organization deliberately builds higher value work above the part the machine took and keeps a person in it. Where no one builds that work, the task leaves and takes the job with it.

The telephone switchboard operator is the proof of that downside. That job mostly disappeared, and the people who held it were not handed a better one on the way out. Plenty of typing pools and clerical lines went the same way. Technology does not lift a role on its own. It lifts the role when someone invests in moving the people above the automated part, and it eliminates the role when no one bothers. The difference between the teller and the operator was never the sophistication of the machine. It was whether the work sitting above the automated piece was valuable enough to be worth keeping a human for, and whether anyone actually built the path to carry them there. In HR, the work above the automated piece is not marginal. It is the most valuable thing the function does. The path is buildable. The only open question, the same one this essay keeps arriving at, is whether anyone builds it.

Put it in the concrete terms of the function this whole piece is about. A benefits or HR coordinator, depending on the market, tends to earn somewhere in the high five figures, often less than that. The work is high volume and bounded, and the pay reflects the low leverage rather than the difficulty, because the difficulty is real and the pay still does not follow it. Now move the bounded work to agents that person designs and supervises. She is no longer processing cases. She is running the system that processes them, governing its judgment, auditing it for the quiet errors that turn into filings, designing the workflows that keep the queue from refilling. That is people operations systems work, and the roles that look like it, the ones that own the design and the governance instead of the queue, clear six figures and keep rising as the scope grows. The distance between the two is not a bump. It is the difference between a job people escape the moment they can and a career people deliberately build. And the person best positioned to make that jump is not some outside hire with a sharper title. It is the coordinator, because she already carries the one input the entire thing depends on.

That is the promise, and it is fair to be suspicious of it, because the obvious corporate move is to take the operator’s work and keep paying the coordinator’s wage. Hand her the agents, call it growth, and let the title sit where it was while the responsibility triples. Role expansion without pay expansion is the oldest trick in the function, and naming it cheerfully as a development opportunity does not make it anything else. So the pay does not follow the value on its own, and anyone who promises that it will is selling the same fairy tale from the other direction. Pay follows the value through specific machinery, and the machinery has names. The work has to be re leveled, lifted out of the coordinator band and placed in a higher one whose salary range reflects design, governance, and the accountability that rides with them. The job architecture has to recognize the role as a different kind of role and not a busier version of the old one. And none of that happens out of fairness either. It happens when the skill is scarce enough that the person can leave and the company knows it, because the only thing that has ever reliably moved someone into a higher band is the credible possibility that they will take the skill somewhere that pays for it. The protection against role expansion without pay expansion is not the employer’s conscience. It is the employee’s mobility. Which is exactly why the worker who builds the scarce skill early holds the leverage, and the one who waits until everyone has it holds none.

The coordinator is not a special case. The same conversion sits inside most of the bounded roles in the function. The recruiting coordinator who spends her day scheduling interviews and chasing feedback holds a working map of how hiring actually moves and where it stalls, and the person who turns that knowledge into the design and oversight of an agentic hiring operation is doing talent operations, which pays on a different scale entirely. The payroll clerk who knows every edge case that breaks a pay run holds exactly what you need to govern an automated one without quietly shorting someone’s check, and compensation and payroll operations is not a clerk’s wage. In each case the bounded task is the part that leaves, the judgment is the part that stays, and the pay follows the judgment once the person is the one running the system rather than the one buried beneath it.

That is what makes this shift different from the ones before it, and far more promising for exactly the people the old economy left at the bottom. Climbing the value chain used to demand something most remedial workers had no way to get. You could not turn a teller into a financial analyst over a weekend, because the work above demanded years of formal schooling the job below never offered. The skill agentic AI rewards does not work that way. The interface is plain language, not code. What makes a person good at directing these systems is not a credential. It is judgment about the work itself, knowing what a good outcome looks like, knowing the places it tends to break, knowing which case is genuinely routine and which one is a person’s livelihood wearing routine as a disguise. The coordinator who has worked ten thousand cases has precisely that judgment. Her hard won sense of where the work goes wrong is the scarce input, and the system cannot generate it on its own.

It is worth being careful with the word easy, because plain language is not the same thing, and the gap between them is where most reskilling quietly fails. What got lower is the cost of access, not the demand of mastery. The interface no longer requires code, which means no computer science degree stands in the doorway and a coordinator can begin. It does not mean the work is simple. Designing an agentic workflow that survives real cases still asks for process thinking, for the discipline to test a system before trusting it, for documentation and data sense, and for a comfort with ambiguity that not everyone has and not everyone wants. The barrier that fell was the one at the entrance. The barrier of being good at it once you are inside is still standing, and it is real. Mistaking the low entrance for low difficulty is precisely how a company ends up with a training day, a slogan, and a roomful of people who were told they were operators and handed none of the time it takes to become one.

And being closest to the work, for all that it is the scarce advantage, is still not the same as being ready for the seat. Knowing where the queue breaks does not by itself mean a person can design the system that replaces it, audit it for the failures she cannot see from inside her own habits, hold the risk when it touches a protected category, or walk into a room and convince a nervous executive to fund any of it. Those are different muscles, and some of them are learned slowly. The coordinator is the best raw material for the operator, not the finished operator, and the honest version of the promise says exactly that. The skills she lacks are teachable, and teaching them is cheaper and faster than most leaders assume. The lived knowledge she already carries is the part that cannot be bought at any speed, which is why the distance she has to cover to become the operator is far shorter than the distance from teller to analyst ever was, and why she sets out with an advantage no outsider holds. The people who know the bounded work most intimately are frequently the best candidates to run the system that absorbs it, not the worst. A company that cuts them and hires fresh is discarding its cheapest source of the exact talent it will later spend a fortune hunting for.

It helps to be specific about what becoming that operator actually involves, because left vague it sounds like magic, and it is not magic. It is a progression, and it begins with the queue the person already has. First you use the tools on your own work, not to erase yourself but to see, from the inside, what they do well and where they are confidently and dangerously wrong. Then you learn to give a system a clear objective and the boundaries it has to stay inside, which is a real skill and a teachable one. Then you learn to read an output and feel the error in it before it ships, which is mostly your existing domain judgment pointed at a new target. Then you learn the specific failure modes of your specific work, the places these systems reliably break in benefits or hiring or pay, so you know exactly where a human has to sit in the loop. And then you design the workflows other people run, which is the point at which you have stopped being the labor and become the architect. None of those steps requires a degree. Every one of them rewards the person who already knows the work.

Now the hard part, because none of this arrives by gravity, and anyone who has watched the last fifty years has earned every ounce of their doubt. Productivity has climbed for decades while pay for most people sat still. The gains from squeezing more out of each worker did not reach the workers. They went up. So it is fair to ask why the surplus from agentic leverage would land any differently, and the honest answer is that it will not, unless something makes it. The surplus that comes from one person doing the work of ten can be captured three ways. The company can keep all of it by cutting the other nine and pocketing the difference, which is the default and the thing these pages have argued against from the start. The worker can capture a share by holding a skill scarce enough that the company cannot easily replace her, which is the entire mechanism by which any wage has ever risen. Or the two split it. What tips the result toward the worker is scarcity, and right now the orchestration skill is scarce, because so few people have bothered to build it. Scarcity is leverage, and leverage is how a person captures a piece of the value she creates instead of watching it sail past her to a shareholder.

This is also where the two arguments that have run side by side through these pages finally close into one, because until now they have looked as though they might pull apart. One says a company owes its people reskilling, dignity, an honest transition. That is a claim about what is right. The other says wages move only with leverage and scarcity, and markets do not pay anyone for being owed something. The reconciliation is that the moral path and the market mechanism turn out to be the same act seen from two sides. The reskilling a company owes its people is the very thing that makes their skill scarce and their footing strong. Treat them well and you manufacture the leverage that lets them hold a share of what they create. Skip it, and the market does what it always does with the unprotected, which is to pocket the surplus and move on. The moral case does not deliver the raise by itself. It delivers it only when it is built deliberately enough to make the worker hard to replace.

That window is the part to take seriously. The premium will be largest for the people who move while the skill is still rare, and it will compress as the skill spreads, the way every premium eventually does. That is not a reason to wait. It is the reason to move now.

It would be dishonest to pretend this erases the headcount math. One operator running a fleet of agents does the work that several coordinators used to, so even with upskilling there are fewer of these seats than there were of the old ones. But two things sit beside that. The first is that the people who fill the seats are paid like operators rather than clerks, so a function can shrink its headcount without shrinking its payroll, which is a very different thing from gutting it. The second is that clearing the volume work opens room for work that did not exist before. Someone has to govern the AI itself, audit it for bias, own the privacy questions, design the employee experience now that the queue is no longer eating the day, turn the data these systems generate into something a business can actually act on. Those are real jobs, they are skilled jobs, and they did not fit on the org chart of the service center because the service center never had the room. The function does not only redistribute toward fewer and better paid roles. It also expands into work that was always needed and never staffed.

There is one more reason this matters more than any story about efficiency, and it runs straight through the lens I see this entire field through. The remedial roles are held, disproportionately, by the people the traditional ladder served worst. The ones without the degree, without the network, without the early access, and in plenty of cases without a body or a circumstance that fit the path the ladder quietly assumed. A route upward that rewards domain judgment over credentials, that runs on language rather than years of specialized school, and that can be done from anywhere, is a wider door than most of what came before it. That door will not open on its own. Left alone, the gains will pool with the people who already hold the most, and the gap will grow, because that is what gains do when no one designs against them. But the raw material of an actual equalizer is sitting right here, for the first time in a long while, and it would be a particular kind of waste to have it and let it rot. The chance to take work the world treats as throwaway, and the people who do it, and turn both into something leveraged and respected and well paid, does not come around often.

And all of it rests on one load bearing fact. The wage a worker can capture here is exactly the judgment the machine cannot replace, which means the opportunity and the operator are the same person. Strip the human out and you lose both the safeguard and the raise.

There is also a darker possibility that the optimists wave away, and naming it is not a betrayal of the argument. It is the argument. Agentic AI in HR carries risks that are specific and serious. The first is bias at scale. A biased human recruiter makes a handful of bad calls a week. A biased screening agent makes the same bad call ten thousand times before lunch, consistently, defensibly, with a clean audit trail that hides the harm inside its own confidence. Automating a flawed process does not remove the flaw. It industrializes it. The second is surveillance. An agent given access to every message, ticket, and record so it can read sentiment is one configuration change away from a monitoring apparatus no one agreed to. The third is the quiet cruelty of handing people’s worst days to a machine. The cases that reach HR are often the hardest moments of someone’s working life, a harassment complaint, a denied accommodation, a job ending, and routing those to something fluent but not present is its own kind of harm. The fourth is the gap between what vendors sell and what the tools can do, and the legal exposure that opens when an automated decision touches a leave, a complaint, a disability, or a union question, because that is a lawsuit with a paper trail already written.

Every one of those risks points to the same conclusion, which is why the cost cutters have it precisely backwards. The dangers are not a reason to keep AI out of HR. They are the reason you cannot run it without skilled, accountable humans in the seat. The risk is the case for the operator.

Because agents are quick, certain, and wrong in ways that accumulate. They are wrong politely. They are wrong at volume. In most jobs a wrong answer is a typo you fix in the next meeting. In HR a wrong answer has a name attached to it. A leave misclassified so an employee loses a protection the law actually gave them. An accommodation handled as an inconvenience instead of a right, which is at once a moral failure and a filing. A reduction where the selection criteria look neutral on the page and produce a pattern that is anything but. A pay decision that bakes last year’s inequity into next year’s structure because the model learned from the inequity and called it signal. A performance flag a manager trusts because the system sounded sure, attached to a person whose context the system never had.

So the person who can tell when the machine is wrong does not lose value as the machines improve. That person becomes the most valuable one in the building. And here the claim has to be precise, because the loose version of it is easy to knock down. A model can already approximate a great deal of what looks like judgment. It can draft the policy, triage the case, surface the pattern, recommend the call. What it cannot do is own the judgment, stand behind the decision when it lands on a person and be answerable for it. You can automate the drafting of a policy. You cannot automate the instinct that knows how it will land on the person who has to live under it, or the one that catches something quietly off in an output that looks completely clean, and you certainly cannot automate the willingness to be the one accountable when it is wrong. Keep the engine and fire the driver and you have not saved money. You have removed the one thing standing between a fast system and an expensive human mistake. A fleet of agents with no skilled operator is not a high performing team. It is a liability on a short clock.

But a human in the seat is not the same as oversight, and it is worth saying so plainly, because rubber stamping wears the same job title as governance and produces none of the protection. Oversight that means anything is built out of specific habits, and the difference between a function that catches its machine and one that merely signs off on it lives entirely in whether those habits exist. There have to be escalation rules that say, in advance, which cases an agent is forbidden to close on its own and must hand to a person. There has to be sampling, a steady audit of the decisions the system made unwatched, because the errors that matter are the ones no one was looking at when they happened. There has to be bias testing run on the outputs as a matter of routine rather than after a complaint forces it. There have to be privacy boundaries the system cannot cross regardless of how useful crossing them would be. There have to be clear decision rights, a documented line showing which calls belong to the machine, which belong to the operator, and which belong above her. There has to be a path for an employee to contest a decision and reach a human who can actually reverse it. And when something does go wrong, there has to be a name attached to the ownership of it, because accountability that belongs to everyone belongs to no one. None of that is exotic. It is the ordinary machinery of a function that takes its own power seriously. Its absence is how you get a system that is fast, confident, unaccountable, and quietly doing harm with a signature at the bottom of every page.

There is a deeper reason the human in the seat matters, and it cuts against the grain of how these systems are built. A model learns from the record of what has already been written down. But some of the most important knowledge in this work was never written down, because it lives in the gap between what a policy says and how it actually lands on a person. The training data is mostly the policy. It is almost never the lived consequence of the policy, because no one files the consequence.

I do not write that from the outside. I have spent my career moving through organizations whose defaults were never built with someone like me in mind, navigating buildings and processes and unspoken assumptions that quietly took for granted a body and a path that are not mine. That vantage taught me something the org chart never shows: the distance between a policy that complies and a policy that includes, and how completely invisible that distance is to anyone who has never had to cross it. The interactive process that sits at the center of accommodation is the literal opposite of automation. It is individual. It is contextual. It is a conversation that depends entirely on hearing what the form did not think to ask. An agent can route that case and draft that letter. It cannot do the listening, and worse, it cannot know what it is failing to hear, because the thing it is missing is the part no one bothered to record.

This is why a diverse and experienced human core is not a luxury bolted onto an automated function. It is the failsafe. The people who have lived on the receiving end of rigid systems are exactly the people you want auditing those systems, because they can see the failure coming before it has a victim. Build a lean function out of narrow people and what you have built is a fast machine with no one inside it who can feel when it is about to do harm.

The obvious objection is that all of this is temporary. The models keep improving. The gap between what the machine can do and what the human must do is real today, the argument runs, but it is closing, and soon enough the system will be good enough to run with no one in the seat. Wait it out and the operator becomes a cost like any other. I think that argument is wrong in a way that matters, and not for sentimental reasons. Two things do not close as the models get better. The first is accountability. Someone has to own the decision when it lands on a person’s livelihood, and ownership is not a capability you can train into a model. A company cannot put an algorithm on a witness stand. It cannot ask a system to look an employee in the eye and explain why. The law and the basic decency of the thing both require a human who is answerable, and the better the machine gets at acting, the more that answerable human matters, because the actions are faster now and they reach further. The second is that the relational, judgment heavy core of this work is not a gap waiting to be filled. It is the work. Trust is not a task you automate once and tick off. It is something carried by a person the other person actually believes. A machine can write the words of a hard conversation. It cannot be the one trusted to have it. A better model does not shrink that. It raises the value of the people who can do the part no model will ever own.

So why do leaders reach for the cut anyway, even the ones who would fail every part of this argument if you pressed them on it? Because headcount is the lever they know how to pull. A great many of the people making these calls could not describe an agentic workflow on the spot. They cannot tell you where a human belongs in the loop, or why, or what breaks when no one is there. So they feel the ground move beneath them, they reach for the one instrument every pressured executive has always had within reach, and they cut. Then they call it transformation.

But it would be too easy, and not quite honest, to leave it at a failure of imagination. Some leaders genuinely lack the imagination to see another path. Many more can see it perfectly well and are trapped inside incentives that punish the slow, expensive, correct version and reward the fast, visible cut, which is why this is meant as charity and not only as an insult. Boards reward the AI narrative. Public markets price in the efficiency story before a single workflow has been redesigned. There are consultants whose entire deliverable is a smaller org chart with a confident font. A leader who announces a leaner team built around AI gets applause this quarter. A leader who says we are going to spend a year and real money teaching our people to do a harder job gets a room full of questions. The incentives all point at the cut. None of that makes the cut right. It makes the leader who resists it rare, and worth following.

But there is a line, and it stops being subtle the moment you look for it. A leader making a hard, necessary decision owns it, explains the reasoning, and treats the people leaving like people. A leader laundering avoidance hides behind the technology, calls a failure of imagination a strategy, and lets “the AI made us more efficient” do the talking so they never have to say the rest out loud. The first is a difficult call. The second is a leader offloading their own discomfort onto the careers of people who trusted them to know more.

So what does the other path actually look like, the one that does not fit cleanly on a slide? It starts where the cutters never start, which is with the work and not the tool. Before you buy anything, you map what the function actually does and you sort it honestly. Some of it is genuinely judgment light: the address change, the routine verification, the policy lookup, the first pass triage. That work can and should move to agents, and the people doing it today should be the first ones taught to run them, not the first ones shown the door. Some of it is judgment dense and relational and stays firmly with humans: the investigation, the interactive process, the conversation no one wants to have, the design of the program itself. Most of the value in HR always lived in that second pile. The quiet tragedy of the service center model is that it buried your best people in the first.

And the sorting is harder than it sounds, because in HR the category of routine is a trap. Routine is not defined by how simple the request looks on arrival. A leave question can look like a two minute reply and turn out to sit on top of the ADA, an unasked pregnancy accommodation, a state statute, a retaliation risk, or a union provision, none of which the form announced. The only safe definition is the demanding one. A case is routine when the system can confidently rule out every protected, legal, relational, and equity implication, and not one second before. Anything that cannot clear that bar is not routine no matter how trivial it appears, and it belongs with a person. Designing agents that respect that line, that escalate on the faint signal rather than the obvious one, is most of the actual work, and it is exactly the work that requires someone who has been burned by the case that looked simple and was not.

Then you build fluency as a real competency, not as a slogan painted on the wall. Fluency here is not knowing the vocabulary. It is the ability to hand an agent a clear objective, to design the guardrails it runs inside, to read an output and sense when it is confidently wrong, to know the particular failure modes of the particular work, and to hold onto the judgment the system cannot have. That is a skill. It can be taught. It takes time and repetition and a leader willing to spend both. I have spent fifteen years building and rebuilding people functions, across a table from a twelve hundred person bargaining unit in a manufacturing plant, inside a hypergrowth product organization, in the run up to a public offering, and once from nothing in sixty days. The pattern holds across every single one of them. The leverage was never in the headcount. It was in the design, and in the judgment of the people running it. The build that started from zero did not work because it was large. It worked because it was designed well, and the right design pulled attrition down and cost out at the same time, which is the thing the cutters never believe is possible until they watch someone do it.

And you redeploy, on purpose, the hours the agents hand back. That recovered time does not get harvested as savings and walked up to the board. It gets reinvested in the work that was always starved: actually knowing the managers, getting ahead of the problems instead of mopping up after them, designing the programs that stop the queue from forming in the first place. Measured correctly, the lean function does not merely cost less. It produces more of the only things HR was ever supposed to produce, which are trust, fairness, and an organization that people do not quietly plan to leave. I have watched a small team built this way absorb the load of one three times its size, and not by grinding the survivors into dust but by handing the volume to systems and aiming the people at the parts that needed a person. The team got smaller and stronger in the same motion. That version is real. It is simply harder to build than a layoff, and it does not photograph as well.

And this is the part that should keep the cutters awake. The humane path and the smart path are the same path. Elevating your people is not generosity you indulge instead of winning. It is the mechanism of winning. The company that spends the year teaching its people to run AI well will, in three or four years, have a people function that is faster, sharper, less expensive, and more humane than the one that fired its way to efficiency, and the distance between them will not be a rounding error. It will be a moat. You can rehire bodies in a quarter. What you cannot rehire in a quarter is the people who knew where the bodies were buried, what the system gets wrong, and how to keep a fast machine from doing harm. Fire that and you are buying it back later at a premium, if it is for sale at all.

If you are the coordinator, or the generalist, or the partner reading this between cases, none of it is a reason to wait for a leader to save you, because most of them will not. The same shift a short sighted company will use as an excuse to remove you is the one a clear eyed person can use to become impossible to remove. The leverage is available to you directly, and it does not require anyone’s permission. Learn to direct these systems before you are told to. Get your hands on the tools and build something small, a workflow, a draft process, a way to clear part of your own queue, so you understand from the inside where they are strong and where they fail quietly. Make yourself the person who can run the agents and catch them, who can hold the relational, judgment heavy work that does not automate, and who can tell a nervous executive exactly where a human has to stay in the loop and why. That person does not get cut. That person gets handed the function, and over time gets paid like someone who runs it rather than someone who feeds it.

And yet none of that lets the people in charge off the hook, and it would be a quiet betrayal to end as though it did. Individual preparation is real, and it matters, but it is not a substitute for institutional responsibility, and it cannot be allowed to become the alibi’s last move, the one where the burden of a transition the company chose gets transferred onto the workers least able to carry it. A worker can prepare for the future. A company still has to stop using that future as an excuse to abandon the people who built the present. The hard truth of this moment is that the people most exposed are often the ones with the least time to prepare. The opening, for anyone who can find the hours, is that the skill is learnable and the bar is still low, because so few people have bothered to clear it. The ground is moving either way. You can be moved by it, or you can learn to stand on it.

Go back to the coordinator and her forty deep queue. There are two futures in front of her. In one, the tool arrives, the queue empties, and so does her desk, and a leader somewhere puts the savings in a deck and calls it a win. In the other, the tool arrives, the queue empties, and she is the one who learns to run the system that emptied it. She designs its guardrails. She catches what it gets wrong. She spends her recovered mornings on the work that actually needed a human, which was most of it. Same technology. Same arrival. The only variable is whether anyone in charge had the imagination to see her as something more than a pair of hands.

The travesty was never that AI showed up. It is that so many leaders were handed a chance to build something rare out of the people they already had, and used it to make them fewer. The tools are here. The talent is here. What is missing is partly the imagination to put them together, and mostly the nerve to lead people through the change instead of around them, against every incentive that rewards the cut and questions the build.

Lean was supposed to be a discipline. Too many leaders are using it as an alibi. The ones who refuse to will own whatever comes next.

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Article by: Donovan Parish, Contributor

Most organizations read a productive employee relations function as a sign of health. It is the opposite. A high volume of cleanly resolved ER cases is not evidence that HR is working. It is evidence that something upstream is broken, and that the organization has gotten efficient at paying for it.

One caution before the argument, because it is easy to misread. The goal is not fewer people raising concerns. A healthier organization often surfaces more early, because people feel safe naming things while they are still small. The goal is fewer concerns that detonate into formal cases. Raised early is the system working. A grievance, an investigation, a separation: that is the bill for something that was not handled in time.

An ER case is a lagging indicator. By the time a matter reaches that stage, the failure already happened, usually weeks earlier, usually as a conversation a manager chose not to have. Take the manager who lets three months of missed deadlines slide because the conversation feels awkward, then comes to HR once it is a performance problem with a paper trail. The investigation, the documentation, the resolution: all of it is cleanup, and the cost was incurred long before the file was opened.

Follow that and it gets uncomfortable. Organizations with strong managers resolve most friction before it ever becomes ER work. A function buried in formal cases is usually a function cleaning up for managers who are failing upstream at scale, and HR has often become so capable at the cleanup that no one feels pressure to stop creating the work. The function gets excellent at something that should barely need to exist. Then leadership grades it on throughput and resolution time, which is a little like grading a smoke detector on how fast it puts out fires while never asking why the building keeps catching.

This is where most organizations get employee relations wrong, and few will say it out loud. The case is not the product. The pattern is.

Treat each complaint as an isolated event to close, and you optimize for the wrong outcome. The same handful of managers keep generating grievances. The same team keeps losing its strongest people after every reorg. The same policy keeps producing the same dispute at the same point in every cycle. Each case gets resolved, on time, and the mechanism producing them runs untouched. That is not managing risk. It is absorbing it and reporting the absorption as service.

The data inside an ER function is some of the most decision-useful intelligence a company has, and most companies waste it. Resolution rate and time-to-close are vanity metrics; they measure how fast HR cleans up, not what keeps requiring it. The questions that create value are different. Which managers generate disproportionate volume for their headcount. Which structures put people into conflict no mediation will resolve. Which policies read fine on paper and detonate in practice. Where conflict clusters tells you where execution is about to break, which integrations are failing, and which leaders cannot scale. Read correctly, ER caseload is an early-warning instrument. Read incorrectly, it is just a queue.

There is a deeper reason the upstream conversation never happens. Managers rarely avoid it out of laziness. They avoid it because most workplaces quietly reward looking fine over being honest. People present the version of themselves they think the organization wants, problems get managed for appearance rather than named, and a formal case is often just what happens when that performance finally breaks down.

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So what does good look like. Not a faster intake process or a better case-management tool. Good employee relations is wired directly into leadership development and organizational design, and it treats every case as tuition the company has already paid. The spend is sunk. The only return left is whether the organization learns the lesson it just bought and routes it to the manager who needs coaching, the structure that needs redesign, or the policy that needs rewriting.

First, HR is not neutral. It cannot be, and pretending otherwise is the real liability. HR is funded by the organization, accountable to it, and exists to protect it, including from itself. An employee promised a neutral party who then collides with that reality does not just lose a case. They lose their trust, and they are correct to.

But the answer is not the other reflex the profession reaches for, which is consistency. Said the way most people hear it, consistency means the same outcome for the same surface-level act, regardless of who did it. That is not fairness. It is automation, and it is the rulebook a manager hides behind to treat a five-year high performer having one bad quarter exactly like a serial problem, then call the unfairness a principle. Two people can do the apparently same thing and deserve to land in very different places.

What HR actually owes is consistent judgment, not consistent verdicts. The same rigor applied to every case: tenure, track record, context, intent, impact, and pattern all genuinely weighed, every time. What can never vary is the integrity of the analysis. What should almost always vary is the conclusion, because the situations are not the same. That is harder than treating everyone identically, which requires no judgment at all. Looking at a situation in its totality and still landing somewhere you can defend on the merits is the actual work, and it is the work most people avoid because pointing at the policy is easier.

Second, good employee relations frequently ends with no one satisfied. The employee stays unhappy, the manager stays frustrated, and the decision stands anyway. The profession has quietly absorbed the idea that a well-handled matter is one where everyone feels heard, so it measures the work by how people feel at the end of it. That is the wrong scoreboard. The objective was never comfort. It was a decision you can defend as fair, delivered cleanly, knowing the person it lands on will never agree with it.

None of this argues for a smaller HR team or a quieter one. It argues for measuring the right thing. A rising count of formal cases is not proof of effort. It is the organization telling you, in the only language it has, where it is losing capability. The work is to trace the signal back to the manager, the structure, or the policy generating it, and fix the cause.

Which is, finally, why I do this work. I want to build environments where people show up as their whole self, and that phrase gets misread constantly. Whole self does not mean unfiltered, pajamas on, saying whatever you feel. That is just a different costume, performing relaxed instead of performing polished. The distinction that matters is the filter versus the mask. A filter is judgment: choosing how and when to raise something, reading the room, giving hard feedback in a way a person can use. That is EQ, and it stays. A mask is concealment: nodding in the meeting and venting in the parking lot, saying you are fine while the project burns, performing an agreement you do not have. Whole self is the filter without the mask. Real judgment, real accountability, real investment, delivered as yourself, without fear that honesty ends your career. Not lower standards. Higher ones. A workplace full of masks does not stay quiet. It stays quiet right up until it detonates, because the truth had nowhere to go. A workplace where people keep the filter and drop the mask hears the hard thing while it is still just a conversation. Read the symptom. Then go build the place where fewer of them ever start.

There is a moment in every calibration session where the room goes quiet in a particular way.

Seven leaders are looking at the same name on the same slide. The performance is there. The delivery is there. The numbers are fine. But the attrition on that team has been climbing for two quarters in a row, and nobody is saying the obvious thing. So I say it.

“Who is doing the one-on-ones?”

Sometimes the answer is silence. Sometimes it is a version of “I think they are doing them.” Sometimes a peer looks at their shoes. What I have never heard in that moment is a confident, specific, verifiable answer. And that absence tells me everything I need to know about why people are leaving.

Manager effectiveness retention is not a training problem. It is not a survey problem. It is not a culture statement problem. It is an operating system problem. And most HR functions are solving it with the wrong tools.

The Thing We Keep Measuring Instead of Fixing

Here is a number that should make you uncomfortable: Gallup has been publishing research for twenty years showing that managers account for at least 70 percent of the variance in employee engagement scores. Not culture. Not compensation. Not benefits. Managers.

We know this. We have known it for a long time. We put it in presentations. We reference it in talent reviews. And then we go build another listening program, launch another engagement survey, and wait for the results to tell us the same thing they told us eighteen months ago.

The problem is not that we lack data. The problem is that we have been using engagement data as a diagnostic when what we actually need it for is accountability. Those are not the same thing. A diagnostic tells you something is wrong. An accountability structure tells someone they are responsible for fixing it.

Amy Edmondson’s work on psychological safety has been absorbed by enough HR teams that the language shows up everywhere. Safe space. Speak-up culture. Psychological safety training. What almost nobody has done is take that research to its operational conclusion, which is this: psychological safety is not an org-wide variable. It is a team-level variable created or destroyed by a specific manager’s specific daily behaviors. You cannot survey your way to it. You cannot train it into existence in a half-day workshop. You have to design a system in which managers are actually accountable for building it and given real tools to do so.

Most organizations have done neither.

First 90 Day Attrition Is Not an Onboarding Problem

When I diagnosed that 53 percent of exits at a healthcare organization were happening within the first 90 days, the first assumption in the room was onboarding. The portal. The paperwork. The orientation agenda. I understood the instinct. It is concrete. It is fixable. It is also wrong.

Onboarding is infrastructure. Infrastructure matters. But people do not leave bad portals. They leave managers who did not make them real.

What I mean by that: a new hire in the first 90 days is doing two jobs simultaneously. The first job is learning the actual job. The second job is figuring out if they made the right decision. They are reading every signal. The manager who reschedules the one-on-one twice in week two. The team meeting where nobody introduces them. The first project where they are handed a brief and told to figure it out. These signals are not neutral. They are data. And that new hire is running them through a very simple decision algorithm: is this a place where I can do my best work, or did I make a mistake?

First 90 day attrition is a manager effectiveness problem wearing an onboarding costume. The solution is not a better welcome packet. It is a structured manager operating system for the first 90 days that is specific, observable, and accountable. Not a checklist. A system. There is a difference. A checklist is something a manager completes. A system is something that fails visibly when a manager does not show up.

Role clarity, intentional relationship-building, early meaningful work, and explicit feedback loops do not happen by accident in the first 90 days. They happen because someone designed them to happen and built accountability into the design.

What a Manager Operating System Actually Looks Like

I want to be precise here because this concept gets vague fast.

A manager operating system is not a set of competencies. Competency frameworks describe what good looks like. They do not create it. A manager operating system is the set of structured, recurring practices that translate management intent into team experience. One-on-ones are part of it, but only if they happen, only if they have a structure that creates psychological safety rather than status reporting, and only if someone in the organization can tell whether they are actually occurring and whether they are actually working.

This is where most HR functions stop short. We design the practice. We communicate the expectation. We train the skill. And then we walk away and hope the behavior follows. It rarely does, not consistently, not across the full span of a middle management population.

Jeff Pfeffer at Stanford has spent decades documenting the gap between what organizations say they value about people management and what they actually measure and reward. The gap is not small. Organizations say they value people development and then promote the person who hit their number, regardless of how many people left the team in the process. That is not a values problem. It is an incentive structure problem. And incentive structures are designable.

A real manager operating system has three components most organizations skip entirely.

The first is decision rights clarity. Managers cannot operate effectively when they do not know where their authority actually starts and ends. Ambiguity on decision rights is not an empowerment strategy. It is a failure mode. People leave managers who cannot make decisions, and managers cannot make decisions when the org design has not told them which decisions are theirs.

The second is leading indicators instead of lagging ones. Attrition is a lagging indicator. By the time it shows up in your data, the decision is three months old. The leading indicators, one-on-one completion rates, internal mobility application rates, 60-day new hire check-in scores, team-level engagement trend lines, are available earlier and point to the manager specifically. Most organizations are not pulling these by manager and having a conversation about what they mean until someone has already left.

The third is calibration that is actually about behavior, not just performance. The calibration session I described at the beginning of this piece is not unusual. It happens everywhere. And the reason the room goes quiet is that we have built calibration processes that surface outputs but not behaviors. A team can deliver results in the short term under a manager who is slowly hollowing out the culture. The attrition starts to tell the story, but only if someone is reading it that way and only if the calibration conversation is structured to make the connection.

The Contrarian Observation Most HR Teams Are Not Ready For

Here is the thing I want you to sit with.

Universal design, the engineering principle that building for the most constrained use case creates something that works better for everyone, applies directly to this problem. Ramps are not just for wheelchairs. They are for delivery carts, strollers, luggage, and aging knees. Captions are not just for the deaf. They are for noisy rooms, language learners, and anyone watching their phone at midnight without disturbing a partner.

The same logic applies to the manager operating system. When you design a structured first-90-day manager playbook for a new hire who has no context, no relationships, and no idea how to read the organization, you have built something that works better for every new hire, not just the most disoriented one. When you build decision rights clarity for a manager who is overwhelmed and unclear on their authority, you have built something that makes every manager more effective. When you create a leading-indicator dashboard that surfaces team-level risk before it becomes attrition, you have built something that helps the strong manager and catches the one who is struggling before it becomes a crisis.

HR functions that design for the average employee are constantly playing catch-up. HR functions that design for the edges, the person with the most ambiguity, the highest anxiety, the least context, build systems that are genuinely robust. The constraint is the specification. Most of us have not made this connection.

Building from that principle changes the conversation. You stop asking “what does the typical new hire need?” and start asking “what does a new hire need when everything else is stacked against them?” The answer to that second question is the system that actually works.

This Is Not a Middle Management Problem

I want to be precise about one more thing.

This is not an argument that middle managers are failing. It is an argument that we have been setting them up to fail and then measuring the outcomes as if they are personal.

Most managers in most organizations received a promotion based on individual performance, a title change, maybe a training program, and then an expectation that they would figure out the rest. The operating model was never handed to them because the operating model was never built. They are improvising. Some are very good at improvising. A lot of them are just busy.

The manager who is not doing the one-on-ones is usually not a bad person. They are a person who has fifteen direct reports, forty open priorities, a skip-level who only calls when something is on fire, and no system that makes the one-on-one feel urgent until the attrition data shows up six months too late.

That is a shit design problem, not a character problem. And HR’s job is to fix the design.

The question is whether you are going to keep measuring manager effectiveness as an output or start building it as a system. One of those is an annual survey. The other is a retention strategy.

They are not the same thing.

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The grievance had been sitting on my calendar for eleven days.

A maintenance technician at a multi-site manufacturing facility. Thirty-two years with the company. He had requested a modified workstation six months earlier after a documented injury changed what he could do physically. Six months of forms. Six months of back-and-forth between HR generalists and a third-party leave administrator and a supervisor who kept insisting the accommodation was ‘not operationally feasible.’ Six months of a man showing up every day to a job he could still do, in a building that had decided it would rather document its process than solve its problem.

I sat across from him in a conference room that smelled like burnt coffee and old carpet. Drew Soule, HRBP, Southeast Wisconsin, theoretically there to represent the company’s position. And I thought: we have failed this man with extraordinary thoroughness.

I am not telling that story for pity. Not his, and not mine. I am telling it because that grievance hearing is where I understood something that took me another four years to fully articulate: compliance-based disability inclusion has never been about inclusion. It has been about liability management dressed in the language of care. And the distinction matters enormously, because one of those things actually helps people, and one of them mostly helps the company’s legal posture while the person sits in a conference room for six months waiting for a different chair.

I was born with a physical disability. I use a wheelchair. I have navigated every element of a professional career in a world that still designs its physical and organizational infrastructure around the assumption of able-bodied participation. That experience is not incidental to my work as an HR leader. It is foundational to it. I say that not for pity, and not for applause.

## The Compliance Framework Was Designed to Protect the Organization, Not the Employee

Here is the thing most disability inclusion content will not say plainly: the ADA interactive process, as practiced in most organizations, is an adversarial framework with collaborative branding.

It was written and implemented primarily to define the legal boundaries of employer obligation. What counts as reasonable. What constitutes undue hardship. What documentation satisfies the standard. Every step in the process has a legal analog, and HR professionals learn to execute those steps with precisely enough care to defend the process, not necessarily to serve the person.

I am not blaming HR practitioners. I am one of them. I have run that process dozens of times across healthcare, manufacturing, and tech environments. I have seen it handled well and I have seen it handled badly, and even when it is handled well, it is often still fundamentally oriented toward risk containment rather than outcome achievement.

The outcome being: a qualified person who wants to work, continuing to work.

That sounds simple. It is not simple in a framework designed around documentation requirements. It becomes a project management problem instead of a problem-solving conversation, and the person waiting for the answer loses months of professional continuity while HR and legal and the third-party administrator exchange emails.

The contrarian take I am prepared to defend: compliance-based disability inclusion has been a managed failure. Not a failure of effort. Not a failure of intent. A structural failure, because the structure was never built to optimize for the employee’s success. It was built to optimize for the organization’s defensibility.

Design-based inclusion is different in a way that is not semantic. It asks a different first question. Not ‘what are we required to do?’ but ‘what does this person need to do this job well, and how do we build that in from the start?’

## What Design-Based Inclusion Actually Looks Like in Practice

I have been in rooms where this conversation goes right and rooms where it goes wrong, and the difference is almost never resources. It is almost always imagination and the willingness to treat the person as the expert on their own situation.

The technician from that grievance hearing needed a modified workstation height and a different tool storage configuration. Total cost, once someone finally asked the facilities team to look at it: four hundred and thirty dollars and two days of work. The six months of process cost the organization far more than that in administrative overhead, union relations damage, and the very real possibility of losing a thirty-two-year employee who knew things about that line that no one had bothered to document.

Design-based inclusion means organizations stop treating accommodations as exceptions and start treating accessibility as a default design parameter. It means that when you are building a new workflow, a new onboarding process, a new performance review system, someone in that room is asking: who does this exclude, and is that exclusion necessary?

I build HR workflow automations using the Claude API. When I am designing a workflow, I think about the inputs, the context windows, the architectural choices that determine whether the output is useful or just fast. The same discipline applies to org design. If you do not think about who the system fails at the design stage, you will find out who it fails at the grievance stage. That is a worse place to find out.

This is not about lowering standards. It is about questioning assumptions baked into standards that were written without disabled employees at the table. Drew Soule HR consulting work keeps returning to this point across every sector: the organizations doing this well are not doing it because they are more compassionate than other organizations. They are doing it because they have stopped treating inclusion as a compliance exercise and started treating it as an operational design problem with operational solutions.

## The Prediction Most HR Leaders Are Not Ready For

Here is where I am prepared to pick a fight with prevailing HR thinking.

Within the next decade, the organizations that built disability inclusion into their workflow design and workforce planning infrastructure will have a measurable talent advantage over organizations that did not. Not a feel-good advantage. A structural one.

The labor market is tightening in ways that are not going to reverse. The workforce is aging. Chronic illness and disability rates are rising, including among working-age adults. Remote and hybrid work infrastructure, when it is built thoughtfully, removes whole categories of access barriers that previously kept qualified people out of certain roles. AI-assisted workflows, when they are designed with accessibility in mind, can extend the productive range of employees who previously would have been accommodated into narrower roles or managed out entirely.

The organizations that figure this out first are going to access talent pools their competitors are still ignoring. That is not idealism. That is workforce planning.

When I diagnosed that 53% of exits at a healthcare organization were happening within the first 90 days, the pattern was hiding in plain sight across qualitative exit data and manager-level cohort analysis. Redesigning onboarding based on what that data actually said produced a 22% attrition reduction and over $400K in measurable cost avoidance. The insight was not complicated. It required someone willing to ask why instead of just noting that turnover was high.

The same question applies to disability inclusion. Organizations are not asking why their accommodation processes take six months. They are noting the compliance metrics and moving on. Someone needs to ask why. And then someone needs to fix the thing that is actually broken.

I am building this work out of Southeast Wisconsin. The context here is specific: manufacturing floors, distributed workforces, union environments, organizations that have been doing things a certain way since long before disability inclusion had a framework attached to it. That context is not a limitation. It is a laboratory. The problems here are real and the solutions have to work in conditions that are not controlled or coastal or theoretical.

I have been the person in the room who was not expected to be there. Not just in the disability sense, though that is real, but in the career sense, the room sense, the you-do-not-quite-fit-the-model sense. I am not the brightest bulb in the string of lights. But I have kept it burning long enough to know the difference between a system that is trying to include you and a system that has designed you out and is now managing the paperwork.

The difference is everything.

Compliance told that maintenance technician to wait six months. Design would have asked him on day one what he needed, built it in, and moved forward.

We have the tools. We have the data. We have the workforce pressure that makes this a business case and not just an ethics case.

The question is whether HR leaders are willing to stop protecting the process and start redesigning it.

That answer is overdue.

There is a version of AI in HR that lives in keynote decks and LinkedIn carousels. Someone standing on a stage telling you that AI is going to transform everything, that the future of work is here, that you need to get ready.

Then there is the version I work in. Southeast Wisconsin. Real organizations. Real constraints. Real humans whose livelihoods depend on whether the People function gets it right.

I am Drew Soule. I use a wheelchair. I was born with a physical disability. And I have spent 15 years building HR functions in hypergrowth tech, aerospace, financial services, and healthcare, in rooms that most HR practitioners never see. That background matters when I tell you what I am about to tell you about AI.

I am not here to hype it. I am here to show you what an HR automation practitioner actually does with it.

Why the AI in HR Practitioner Gap Is Real and Getting Wider

The gap in this space is not a knowledge gap. Most HR leaders understand what AI is. The gap is a practitioner gap. Very few people in this field are actually building with it, testing it, failing with it, and iterating on it in the context of real HR work.

I started building custom HR workflow automations using the Claude API because I had a specific problem. I was running HRBP work supporting an 800-person global Product and Engineering organization with 10 to 40 concurrent employee relations cases every week. Not 10 to 40 cases per quarter. Per week. That volume is not something you manage with good intentions and a spreadsheet. You need systems. You need structure. You need to eliminate the mechanical load so your judgment is available for the situations that actually require it.

So I built the systems. And I want to talk about what that actually looks like as a generative AI HR use case, because most of what gets published about AI in HR is either so theoretical it has no operational value, or so superficial it tells you nothing about the real work.

What Claude API HR Workflows Look Like in Practice

Let me be specific, because specific beats general every time.

When I am working an ER case, there are layers. There is the documentation layer, the pattern recognition layer, the communication drafting layer, and the judgment layer. For a long time, all of those layers demanded the same thing: my direct attention and time. The result was that genuinely complex situations, the ones that required hard calls and careful thinking, were competing for cognitive space with tasks like drafting an initial acknowledgment letter or structuring a case summary from scattered notes.

The Claude API changes that calculus. I built workflows that handle the mechanical drafting load. Initial case documentation structure. First-draft communication templates calibrated to tone and situation type. Summary scaffolding from raw interview notes. None of this replaces my judgment. All of it clears the path for my judgment to operate where it matters.

This is what HR analytics AI integration looks like when it is grounded in operational reality. Not a dashboard that tells you what already happened. A workflow that changes how the work gets done while it is happening.

I want to say something clearly here because I think it gets lost in most AI in HR conversations. AI is not magic. The Claude API is a tool. A powerful, genuinely useful tool. But the value it creates is entirely dependent on whether the person building with it understands the work first. I understood the work. That is why the tool produced something useful. The tool did not make me better at HR. My HR experience made the tool useful.

The 53% Problem and What Structured Thinking Actually Looks Like

When I joined a healthcare organization and built an entire People Programs function in 60 days, one of the first things I did was diagnose why people were leaving. The answer was not in a model. It was in pattern recognition across qualitative exit interview data, tenure data, and manager-level cohort analysis.

Fifty-three percent of exits were happening in the first 90 days. That number is not a statistic. That is a signal. It tells you exactly where the break is in the employment experience. It tells you that the organization had a hiring story that was not matching the onboarding reality. It tells you that managers were not equipped to close the gap between expectation and experience in the window that mattered most.

We redesigned onboarding. We built manager-level accountability structures. We measured a 22% attrition reduction and over $400,000 in cost avoidance.

I tell that story because it is the right frame for thinking about AI in employee relations and AI in People Operations broadly. The value of AI is not that it finds answers for you. The value is that it accelerates the structured thinking that lets you find answers faster, with more consistency, at higher volume. You still have to know what questions to ask. You still have to know what the patterns mean. The judgment is yours. The load-bearing mechanical work does not have to be.

AI Ethics in People Operations: The Questions I Am Actually Asking

I want to spend some time here because AI ethics in people operations is an area where I see a lot of performative concern and not a lot of rigorous thinking.

I use a wheelchair. I have navigated every element of a professional career in a world that was not designed with me in mind. I have experienced firsthand what it means when systems, processes, and structures carry embedded assumptions that exclude people without ever intending to. That experience is not incidental to how I think about AI in HR. It is central to it.

The risk in AI-assisted HR work is not that the tool will do something dramatic and obviously wrong. The risk is that it will do something subtly wrong in a way that is consistent and scalable. Bias that is invisible in a one-off human decision becomes systemic when it is baked into an automated workflow running at volume.

So when I build, I ask hard questions. What assumptions are embedded in this template? Who does this communication style work for, and who does it leave behind? What does this pattern recognition miss? Who is not represented in the data I am feeding this process?

Those are not abstract ethics questions. They are operational design questions. And if you are an HR automation practitioner building real systems, they are non-negotiable.

## What Labor Relations Taught Me That AI Cannot Replace

Before I was building with the Claude API, I was sitting across the table from union representatives in a 1,200-person multi-site manufacturing and supply chain organization with active collective bargaining agreements. That work has almost nothing in common with the AI-enhanced ER workflows I run now, and it has everything in common with them.

What labor relations teaches you is that the human relationship is the actual product. Not the contract. Not the process. The relationship. And the relationship is built or broken by whether the people across the table believe you are operating in good faith, whether you understand their reality, and whether your accountability matches your stated values.

AI in HR lives or dies by the same standard. The technology is not the point. The trust is the point. When I use AI to draft faster, document more consistently, or identify patterns at scale, the value to the people I serve is that I show up to the actual conversation with more clarity, more preparation, and more genuine attention for them. That is the only version of AI in HR I am interested in building.

HR Tech Practitioner Wisconsin: Why Location Is Not Incidental

I want to say something about being an HR tech practitioner in Wisconsin that I do not see discussed enough.

The organizations I work with here are not Bay Area startups with venture capital runways and a culture of experimentation. They are manufacturers, healthcare systems, financial services firms, and growth-stage tech companies operating in a Midwestern market where trust is built slowly and credibility has to be earned every single time. The tolerance for theoretical frameworks is low. The demand for practical results is high.

That is not a limitation. It is a crucible. It means that everything I have built, every workflow, every framework, every AI-assisted process, has been tested against the hardest standard: does it work for real people in real organizations with real constraints?

That is the standard I hold AI in HR to. Not does it look impressive in a demo. Does it hold up under pressure, at volume, for people whose jobs and livelihoods are the stakes?

The answer, when you build it right, is yes.

Closing: Build It or Watch It

I said at the top that there are two versions of AI in HR. The version that lives in keynotes and slide decks. And the version I work in.

The gap between them is not going to close on its own. It closes when practitioners stop waiting for the technology to be explained to them and start building with it. When HR leaders treat AI as a craft, not a feature. When the standard for success is not whether the tool is impressive but whether the people it serves are better off.

I am an AI in HR practitioner based in Southeast Wisconsin. I use a wheelchair. I have been in the rooms where the hard calls get made. And I am building systems that are worth building.

Not because AI is magic. Because the humans building it understood the work first.