Every meeting you have ever called was a way of getting context into a room. Software is now making calls that used to pass through that room, and in HR it is making them with almost none of the context a person would have carried in. Closing that gap is the next thing HR owns, and no function in your company has further to go.
Think about the last real decision your leadership team made in a meeting. Not a status update, an actual decision. Ask yourself why it happened in a room full of people instead of in one person's inbox.
We tell ourselves it is about buy-in. It mostly is not. We put people in the room because each of them carries something no document holds, and somewhere in the conversation one of them leans in and says "wait, that will not work, because," and catches a decision that was about to be made on too little information.
That is what a meeting is for (at its best). It is a context buffer. For as long as we have had organizations, a person in the room has been the thing standing between us and a decision being made on thin evidence.
That human context buffer is now disappearing from workforce decisions. HR should be more concerned than most functions, because its most consequential decisions depend on context that was never written down. That is the warning worth acting on this quarter.
The room knew something the data did not
Here is the shape of the problem, using a case most HR leaders have lived through some version of:
A team gets flagged as high risk: engagement down, output flat, attrition climbing. A dashboard suggests intervention. But the HR business partner knows the team just absorbed a failed reorg, its leadership is temporarily unsettled, and the attrition spike came from two departures in a small team. The right move is patience, not a blunt instrument.
Same numbers on the table, opposite call. What flipped it was context that no dashboard held and no software had recorded.
Context: the quantitative and qualitative knowledge required to interpret a decision correctly: the numbers, the definitions behind them, the history that produced them, the exceptions, and the judgment people apply in practice. Some of it sits in databases. Much of it sits in people and has never been captured.
An experienced HRBP may sense that something is wrong and seek more information. An agent will not do that unless the workflow is explicitly designed to recognize ambiguity, retrieve more context, or escalate to a person. Without those controls, it can turn incomplete information into action faster than a human ever could.
Whether it helps you or embarrasses you depends on more than the quality of the software. It depends on whether the workflow has access to the right organizational context and knows when that context is insufficient.
Why AI under-delivers in HR, but not in the rest of your company
The models underneath every tool you have been shown were trained on the public record, and on a staggering amount of it. Broad knowledge of HR concepts, employment law, management research, and common operating practices. These tools arrive at your company genuinely well read about what has been said outside about your company and about HR.
But they have also never seen your benefits policy. They have not seen your compensation plan (and I would be worried about you if they had). They have not seen the memo explaining why your pay bands are shaped the way they are, or the honest account of why the last reorg stalled in month three, or which of your approval chains are load-bearing and which are ceremonial. None of that is on the internet. For better or worse, most of it is not written down at all, in any system you own, in any form a machine could read.
That gap, between the public record these tools are trained on (a lot of Reddit, by the way) and the private reality of the company you actually run, is the whole of the problem. Every competitor you have can buy the same software you can. None of them can buy your unique organizational context. Of everything in the AI stack you are about to buy, your context is the only piece that is genuinely yours, and in HR it happens to be the piece no one ever captured.
The piece of AI history worth carrying into your next vendor meeting
There is one reference worth having in your pocket, and if you have not encountered it, that is not a gap in your reading. It has lived mostly inside the engineering world and only recently entered mainstream business conversation.
In 2017, eight researchers, most of them at Google, published a paper with an unusually confident title: “Attention Is All You Need.” It introduced the transformer, the architecture underneath most modern language models and generative AI tools.
The technical details are not the useful part for an HR leader. What happened next is.
Over the following years, AI companies made enormous gains in model capability. They competed on architecture, scale, training data, and training methods, and they still do. But as those models became capable enough to summarize, draft, analyze, reason, and act, a different problem moved to the foreground: capable with what knowledge?
A model can arrive knowing the general principles of compensation, workforce planning, and organizational design. It still does not know why your pay bands are shaped the way they are, which approval chains are load-bearing, or why the last reorganization stalled in month three. General capability does not supply private organizational reality.
That is the problem behind context windows, retrieval, memory, and connectors. These are different technologies, but they address versions of the same operational question: how do we put the right information in front of the model at the moment it does the work? Evaluation answers the corresponding question: how do we know whether that information was sufficient and the resulting action was sound?
Model quality still matters. But inside an enterprise, the distance between an impressive demonstration and a dependable workflow often comes down to the context surrounding the model. That is why AI platforms are racing to connect themselves to company documents, systems, history, and workflows. The model supplies general capability. The organization must supply its particular reality.
Every competitor can buy access to models comparable to the ones you can buy. None of them can buy your definitions, institutional history, exceptions, or accumulated judgment. Your context is the part of the AI stack that is genuinely yours.
That is the lesson HR should carry forward. Attention helped make general-purpose AI possible. Context makes that capability useful—and safe—inside a particular organization. It is not all you need, but it is the part only you can provide.
And the burden of providing it is not distributed evenly across functions.
Your peers in marketing and finance do not have the same problem
Here is where I think HR's situation is genuinely different, and it is the part worth taking to your CEO.
Take marketing. A model trained on the internet arrives already fluent in that domain, because most of marketing's raw material is public by design. Competitor campaigns are public. Pricing pages are public. The entire published literature of advertising, positioning, and brand is public. Customer behavior sits in systems that were purpose-built to record it, and where marketing needs outside data it can go buy it from a dozen vendors. Your CMO has private context too, but the software shows up already knowing the shape of the work.
Now take finance. Finance had a century of regulators forcing it to write everything down. There is a chart of accounts. There is GAAP, and IFRS, and an audit function whose entire job is to verify that the documented version matches the real one. Every public company files a detailed, structured, standardized account of itself every quarter, which means these models have read millions of examples of exactly the artifact your CFO produces. They know what deferred revenue is, and more usefully, they know what your books are supposed to look like, because a regulator required your books to be legible.
Then there is HR. No regulator ever requires you to document why a job is designed the way it is. There is no agreement on a standards body, let alone legal requirement, to define "manager" in a way two of your business units would agree on. There is rarely an audit that checks whether your documented process matches your real one, and if you commissioned that audit tomorrow you already know roughly what it would find. The most consequential parts of this function are tacit and human to human, either by deliberate design or by simple default, and they were never encoded anywhere.
A great deal of HR context is unwritten for defensible reasons. Employee relations conversations, the real story behind a leader's move, the accommodation quietly granted, the exception approved once and never formalized. Privacy, legal exposure, and basic decency all argue for some of it staying off the record, and I am not proposing you write down everything anyone ever told you in confidence. But there is an enormous middle ground between the genuinely confidential and the merely undocumented. Much of HR’s operating logic lives there.
So the distance between what these tools know and what they need to know is wider in HR than in any other function you fund. And it is widest exactly where being wrong is most expensive: pay, promotion, exit, leave, accommodation, and investigation. A confidently wrong action in HR does not land on a campaign metric. It lands on a person.
Context is much bigger than the numbers and you have already paid for half of it
I defined context above as everything your organization knows about itself, the quantitative and the qualitative together. What matters here is the split inside that definition.
The quantitative half you likely know well, because you likely funded it over the past decade in the form of analytics and data warehousing. Headcount, requisitions, spans, attrition, survey scores, the warehouse, the dashboards.
The qualitative half is everything else, and most of it is not sitting in a database: the reasoning behind a decision, the knowledge in one person's head, the recorded conversation, the working document, the exception nobody logged.
Both halves are part of the whole of context. Businesses just went after the half that computers could help with first and then called the whole thing "analytics."
I include myself in that. I spent 15+ years in this work, first as an HR practitioner and then building People Analytics functions at Meta, Uber, and Nike, and continued that work later at One Model, where I spoke with a few hundred People Analytics teams a year and got an unusually wide view of how it actually gets done within HR teams. So let me say plainly what my field spent the past 20 years doing, because it is the most useful thing a CHRO can understand about the next five years.
Almost none of the value of analytics is created by the visible final step of data science. The final report you got in your hand was important, helpful, and may have driven a decision forward, but the reason that report was valuable happened much earlier.
That value was created upstream, in the unglamorous work of making a number mean something, and that work had a specific shape. Before anyone could trust a workforce number, five questions had to be answered:
Where did it come from?
What happened to it on the way here?
Which definitions and organizational structures shaped it?
Is it authoritative, or do multiple versions disagree?
Is it clear enough for someone to act on?
Your People Analytics and HR technology teams have been climbing that ladder for two decades, and for those HR teams that invested, it has worked. The best HR teams run on numbers now in a way that simply was not an option in 2010.
Now HR must apply that discipline to qualitative context: structured interviews instead of data feeds, transcription instead of extraction, and sourced claims instead of anonymous assertions. The material also needs permissions, retention rules, and a way to distinguish established facts from individual perspectives.
Same discipline, new material. That is the single most important sentence for you in this piece, because it tells you what kind of project you are approving. Not a science experiment, not a new department, and not a knowledge management initiative with a taxonomy committee. It is the second half of a build your organization already knows how to do, and the reason it has not happened is that nobody has been told it is the job.
Your systems capture what people do. Your surveys capture what people say they do. What almost no company captures systematically is what people say, in their own words, about how the work really happens. Much of the function’s operating logic lives in that third channel.
Trust is the condition, not the compliance step
If your first reaction to "go capture what people actually say" is a concern about surveillance, that is the correct reaction and it is why I do not think this work can be handed to IT.
Five guardrails have to be present from the first conversation.
Transparency. Tell people what is being captured, why it is needed, who can access it, and how long it will be retained. Do not bury that information in a policy update.
Purpose limits. Use the context to improve decisions, not to monitor individual performance. The moment this becomes workforce surveillance, you lose the candor that made it valuable.
Data minimization and access. Capture only what the defined use case requires. Exclude sensitive personal details unless they are strictly necessary and legally appropriate, and restrict access by role.
Correction and expiration. Give people a way to challenge inaccurate context. Require time-sensitive claims to be revalidated rather than allowing them to become permanent organizational truth.
Representation. Capture perspectives across levels, locations, tenure, and relevant employee groups. If you interview only the loudest or most senior people, you encode their version of the company into every downstream decision.
Build this the way you would want it built if it were your own words being recorded. Trust is not a footnote on the work. It is the condition that makes the resulting context usable at all, and judging that condition is an HR competency before it is anyone else's.
What the CHRO actually owns
I know how the ownership argument sounds from where you are sitting. Everything lands on the CHRO right now, usually while the budget moves in the other direction, and one more mandate is not a gift.
So let me be precise about what is actually yours here, because most of the work is not.
HR should own the meaning and governance of workforce context, not every technical component. The CHRO sponsors the work. People Analytics and HR technology curate the content. IT provides architecture and access controls. Legal and privacy leaders define permitted uses, and business leaders validate how the work actually happens.
What cannot come from them is the decision that curating organizational context is the job rather than a side project that happens after the dashboards ship. That is a scope decision and a funding decision, and it only gets made at the top of the house. It is also the only part currently missing in most large companies, which is why the work has stalled in so many of them while everyone agrees it matters.
And if HR does not claim the work, the vacuum will get filled by whoever needs the answer first. Finance will need a real model of your workforce the moment it plans capacity or models a reduction, and headcount alone will mislead it. IT will need one the moment it tries to automate a process nobody has honestly described. Both will build a thin version out of system data, because system data is what is sitting there available, and every decision downstream of it will get the human element wrong in the same predictable ways.
There is a version of the next five years where HR ends up more central to the company than it has ever been, because context becomes the thing every other function's decisions run on and HR is the function that owns it. That position does not get granted at a table. It goes to whoever does the work.
What to say when your CEO asks
Most CHROs I talk to have already been asked, in front of a board or close to it, what HR's AI plan is. The trap in that moment is to answer with a list of pilots, because pilots invite a follow-up question about results, and the results in HR are going to lag the rest of the company for the reasons discussed above, and that is not HR's fault.
The more defensible answer runs something like this. HR’s immediate AI constraint is not access to models. It is that the way our workforce decisions actually work, including their definitions, exceptions, and decision rights, has never been captured in a form software can use safely. We are addressing one domain first, with clear governance and human escalation, and we will not automate a process we cannot describe or evaluate.
That answer does two things a pilot list cannot. It explains the gap before someone else explains it less generously, and it puts HR in the position of naming a company-wide condition rather than defending a function-level shortfall.
The tools coming into your function arrive with broad knowledge of what the world has written down. They do not arrive knowing your organization—and much of your organization has never been written down. Nothing about the way HR has always worked will produce that record on its own. Someone has to decide to go get it.
For a hundred years we got away with a thin picture of ourselves, because there was always a person in the room to supply the rest. Build the fuller picture while those people are still in the room to help you get it right.
Frequently asked questions
Q: What is organizational context, and how is it different from HR data?
Organizational context is everything your company knows about itself, the quantitative and the qualitative together. HR data is the quantitative half: headcount, requisitions, spans, attrition, survey scores. The other half is the reasoning behind a decision, the workaround that keeps a process moving, and the judgment a long-tenured HR business partner applies without being asked, and almost none of it sits anywhere a machine can read.
Q: Why do AI tools underperform in HR compared with marketing and finance?
Because the models arrived already fluent in those domains and knowing almost nothing about yours. Marketing's raw material is public by design, and a century of regulation forced finance to write itself down in standard formats, so the models have read millions of examples of both. No regulator ever required HR to document why a job is designed the way it is, so the operating logic of the function was never written anywhere a model could learn it.
Q: Is this knowledge management with a new name?
No. Knowledge management projects usually open with a taxonomy committee and close with a repository nobody opens. This is the discipline your People Analytics team already applied to workforce numbers, run again on different material: structured interviews instead of data feeds, transcription instead of extraction, and claims tagged with their sources instead of rows tied to a system of record.
Q: How long does a first version take, and what do you get?
Pick one domain, usually workforce planning or talent acquisition, and plan on 10 to 20 structured conversations across two to four weeks. Three artifacts come out of it: a definition set for your core terms that records how each one is known to break, a map of your highest-volume processes as they actually run, and a log of your recurring workforce decisions and who makes them. Then you decide whether to do a second domain with something real in hand.
Q: Should HR or IT own organizational context?
HR. Judging consent, purpose limits, and whose voice gets captured is an HR competency before it is anyone else's, and getting it wrong costs you the candor that made the work worth doing. Your People Analytics and HR technology teams are the right builders; what only a CHRO can supply is the decision that curating context is the job rather than a side project that happens after the dashboards ship.
Related reading
The End of People Analytics As We Knew It, this argument written for People Analytics leaders.
Organizational Literacy: The Real HR Transformation Gap, on why data-literate HR functions still cannot read themselves.
Why HR Needs a Context Layer to Win the Next Wave of AI, the architecture underneath this and what retrieval needs in order to work.
The Hidden Data Deficit That Will Sink Your HR AI Strategy, Ian O'Keefe on the same gap from the CEO's chair.
You Cannot Transform HR That You Cannot See, on six HR mandates that all need the same missing input.
Written by
Richard Rosenow
Richard Rosenow is Co-Founder and Chief Product Officer of Ikona Analytics, where he owns the ISD (Ikona Systems Diagnostic) methodology, client delivery, and product. He started as an HR business partner at Citi and spent 15 years in People Analytics at GE, Meta, Uber, Nike, and Argo AI, then as VP of People Analytics Strategy at One Model. He created the Workforce Systems Leader and People Data Supply Chain frameworks, and is an HR Tech 100 honoree.
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