Before any team builds anything with AI, there is a step almost everyone skips: actually reading where the function is right now. Not the aspirational version. Not the version that lives in the strategy deck. The version that exists when you walk through the team's week, hour by hour, and notice what is actually happening.
The cost of skipping the diagnostic is not subtle. It shows up four months later as a half-built workflow nobody adopted, a tool the team works around, a champion who burned out trying to drag the function somewhere it was not ready to go.
Reading first. Building second. Always.
Two axes before six
Before the six-axis diagnostic, there is a faster read worth doing. Score the team on two axes from 0 to 10.
Tooling. What can your team physically do with AI today? At zero, they have heard of ChatGPT. At three, they paste in context and use Custom GPTs. At five, they are building light automations in Zapier or Make. At seven, they can debug API calls and build self-updating dashboards. At nine, they are deploying small internal agents wired to your stack. At ten, they probably should not be in HR any more.
Strategy. What is the AI actually doing inside the function? At zero, nothing. At two, low-stakes time savers like rewording job ads. At five, AI use is encouraged, a champion has built playbooks, KPIs are starting to attach. At seven, AI is the default in some core areas: performance, onboarding, internal comms. At nine, your AI-enabled People workflows are influencing other departments. At ten, AI is not an add-on, it is how the function delivers.
The point is not the number. The point is the gap. Tooling at six and strategy at two means the team can build but is building the wrong things. Strategy at five and tooling at two means leadership has bought the story but the team cannot ship. Both look like progress on a slide and feel like nothing on a Tuesday.
Once you know roughly where you are on the two axes, the six-axis diagnostic tells you what to actually do about it.
The six axes
For a People function, there are six axes that matter. None of them is about the model. All of them are about the team and the work.
1. Data hygiene
Where does the truth about your people live? In one HRIS, or three spreadsheets, or a Notion page maintained by one person who left? The answer determines what you can build.
A function with one clean source of truth can build retrieval-grounded workflows that actually work. A function with three sources cannot. Until the data is unified, AI will produce confidently wrong answers, which is worse than no answer at all. Fix the source before you fix the workflow.
2. Process clarity
For each major workflow, sourcing, screening, interviewing, onboarding, performance, comp review, can the team draw it on a whiteboard in five minutes? If yes, that workflow is buildable. If no, the workflow has to be made legible before it can be augmented. Often the diagnostic conversation alone produces this clarity, and the building gets easier as a side effect.
3. Tool fragmentation
How many tools does the average workflow touch? Three is workable. Seven is not. The ceiling on what you can automate is set by how much glue you can build between systems, and the glue gets brittle fast above five integrations. A function trying to "do AI" while running thirteen disconnected tools should consolidate first.
4. Curiosity distribution
Who in the team has, on their own time, played with AI? Who has not? A function with three or four already-curious people across different sub-teams can stand up the champion model in a quarter. A function with one curious person and twelve sceptics needs a different approach, usually a ground-up enablement programme before any building starts.
This is not about training. It is about who is ready to build now versus who needs to see it work first. Both groups exist in every team. The diagnostic finds them.
5. Sponsor presence
Is there a named senior leader, CPO, Head of People, COO, who will publicly say "this is part of how we work now," and who will defend the time the champions take? Without that, every workflow build will be the first thing dropped when a quarter gets hot. With it, the work survives the first crisis.
The presence of a real sponsor is the single best predictor of whether anything gets built. Better than budget, better than tooling, better than capability.
6. Risk posture
How does the company think about AI risk? Is it a "we won't touch it until we know it's safe" culture, a "move fast and ask forgiveness" culture, or somewhere in between? The right governance model, and the speed at which you can build, is set by this, and pretending otherwise produces friction nobody anticipated.
A regulated company moves differently from a 60-person scaleup. Both can build. The shape of the build is different.
Reading the result
The diagnostic produces a six-line summary. Not a score out of ten. Not a maturity model. A short, honest description of where the function actually stands on each axis, and what that implies about the order of the work.
A typical reading might look like:
Data: one HRIS, mostly clean. Process: TA and onboarding clear, performance murky. Tools: nine in active use, three obsolete. Curiosity: four people leaning in, two TA, one HRBP, one Ops. Sponsor: CPO engaged, Board curious. Risk: cautious culture, EU AI Act exposure.
From that, the build order writes itself. Start with TA workflows where the data is good and the process is clear. Use the four curious people as initial champions. Set up governance early because the risk posture demands it. Leave performance work for phase two, after the process gets cleaned up.
The diagnostic is not the strategy. It is the thing that makes the strategy obvious.
Capability maturity, by function
A useful side effect of the diagnostic is that it lets you compare People against the rest of the business. Most companies have wildly uneven AI capability across functions. Engineering at "adaptive," Marketing at "capable," People at "unacceptable." If you do not know where you are relative to your peers in the business, you will either over-promise or get out-flanked.
A simple frame, borrowed from capability maturity work in adjacent fields:
- Unacceptable. Refuses or ignores AI tooling.
- Capable. Uses AI for individual tasks: drafting, summarising, light analysis.
- Adaptive. Embeds AI into core workflows with human-in-the-loop checkpoints.
- Transformative. AI changes the operating model, not just the tasks.
You do not need every function at "transformative." You need to know where each one is, and to set the right next step for each, this quarter.
The trap of skipping
Skipping the diagnostic feels like speed. It is the most expensive form of slow. Every workflow built without reading the function first carries a small bet, that the data is good enough, that the process is clear enough, that the team will adopt, and most of the bets lose.
Read first. The building is faster afterwards, every time. The first concrete build, in almost every case we have seen, is setting up the team's AI workspace so that subsequent workflows have somewhere to live. From there, the domain map tells you where to point the build effort, and the move from prompts to systems tells you what the work looks like once you start.
The point of the diagnostic is not to grade the team. It is to make the next move obvious.
What this connects to
Auto-recommended next reads in the People Ops cluster, ranked by shared concepts and headings:
- Designing the AI-native People team
- An AI enablement operating model for People leaders
- AI governance for People teams
- From prompts to systems
Common questions
- Why diagnose readiness before building anything?
- Because the cost of skipping the diagnostic is not subtle. It shows up four months later as a half-built workflow nobody adopted, a tool the team works around, a champion who burned out trying to drag the function somewhere it was not ready to go. Reading first, building second. Always. The building is faster afterwards, every time.
- What is the difference between tooling maturity and strategy maturity?
- Tooling maturity is what your team can physically do with AI today: prompt well, build a Custom GPT, wire an automation, design an end-to-end workflow in n8n, deploy a small agent. Strategy maturity is whether AI is doing anything that matters: tactical drafting, embedded in core processes, or transforming how strategic People work happens. Most teams are mismatched. Tooling outruns strategy, or strategy outruns tooling. The first job of the diagnostic is to see the gap honestly.
- What axes should we actually assess?
- Six. None of them about the model. Data hygiene: is there one source of truth or three? Process clarity: can the team draw each major workflow on a whiteboard in five minutes? Tool fragmentation: how many tools does the average workflow touch? Curiosity distribution: who has already played with AI and who has not? Sponsor presence: is there a named senior leader who will defend the champions' time? Risk posture: how does the company think about AI risk?
- Which axis matters most?
- Sponsor presence. Without a named senior leader who will publicly say "this is part of how we work now" and defend the time the champions take, every workflow build will be the first thing dropped when a quarter gets hot. Better predictor than budget, tooling, or capability.
- What does the diagnostic actually produce?
- A short, honest description of where the function stands on each axis, and what that implies about the order of the work. Not a score out of ten. Not a rolled-up maturity badge. A six-line summary that makes the build order obvious: start where the data is good and the process is clear, use the curious people as initial champions, set up governance early if the risk posture demands it, leave the murky areas for phase two.
If this resonated, there's more.
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