Five areas of organisational capability across four kinds of AI work, from the first person drafting an email with a chatbot to an organisation that works better, quicker, and happier because of it. Adoption is assessed separately for individuals and teams. The results show strengths, gaps and dependencies, not a place on a ladder.
Most AI frameworks measure individuals. That is useful for hiring. But the question readers keep asking us is different: where is my organisation strong, where is it weak, and what should it work on next? This framework answers that. It uses four components — Mindset, Strategy, Building, Accountability — and applies them to five areas of capability, grouped under the four kinds of AI work: adoption (individuals, then teams), process re-engineering, roles and teams, and new products and revenue. It measures organisational maturity, not which tools people use — for that, see the AI Usage Spectrum.
Four things to look at, in every area
An area is not a checkbox. In every one, ask the same four questions. Weakness in any one of them is what stops the work in that area paying off.
i.
Mindset
Do people treat AI as a tool, a threat, or a colleague? What does leadership model? Is experimentation rewarded or punished?
ii.
Strategy
Is there a deliberate view of where AI belongs in the business — or a hundred tactical decisions nobody has joined up?
iii.
Building
Can the organisation actually ship things with AI? Prompts, workflows, agents, products — what gets built, how fast, by whom?
iv.
Accountability
Who owns the output when AI did most of the work? How is quality checked? Where is the judgment that stops bad things shipping?
The framework, at a glance
Rows are the five assessment areas, grouped under the four kinds of AI work. Columns are the four things to look at inside every area. Read across a row to understand an area; read down a column to understand how one concern evolves as an organisation matures.
Steadman describes four kinds of AI work. This diagnostic assesses adoption separately for individuals and teams, giving five assessment areas: individual productivity and team standards (adoption), process orchestration (process re-engineering), role and team redesign (roles and teams), and new revenue (new products and revenue). They reveal capabilities and dependencies, not a compulsory sequence.
Areas × Components
Steadman · Organisational AI Maturity Matrix
i.Mindset
ii.Strategy
iii.Building
iv.Accountability
1Individual ProductivityThe hundred small things
Curiosity permittedSenior people visibly use the tools. Nobody hides it.
Tools bought and blessedChosen stack, paid licences, data rules stated.
Personal fluencyPeople can name three tasks they now do differently.
Self-checkPeople verify their own outputs; mistakes surfaced, not buried.
2Team StandardsConversation → architecture
Sharing is defaultBuilding a prompt for the team is a recognised contribution.
Reuse as a metricHighest-leverage tasks identified. Someone owns the library.
Plural shared assetsCustom GPTs, prompt packs, versioned — not pasted in Slack.
Asset ownersEach asset has a maintainer. Bad outputs trace back to a prompt.
3Process Orchestration"AI does the process"
Think in processes"The agent did it" is an acceptable answer — with receipts.
Budget for plumbingProcesses chosen on payoff. Real systems, not demos.
End-to-end workflowsNamed flows in production. Handoffs are designed, not improvised.
Named owners + evalsEvery workflow has a human owner. Failures caught within the hour.
4Role And Team RedesignDifferent shape, not same people faster
Honest about changeRedeployment over quiet exits. Judgment becomes a job description.
Structure on paperHeadcount plans written around what AI now does.
Flatter, widerSpans of control widened. Old roles gone, work still done.
Rebuilt pipelineFewer people own more outcomes. Junior learning path deliberately rebuilt.
5New RevenueDo different things, not the same things faster
Willing to cannibalise"What are we selling?" is a live question at the top table.
New economicsPricing reflects software margins, not hourly rates.
Shipped productLive AI-native revenue line. No humans behind the curtain.
Own the apologyQuality/safety owned by named people. Firm — not vendor — answers for errors.
Read across: what this area looks like. Read down: how this concern matures.↗ Slope matters more than position.
Part I — Capability and value capture in the existing business
1
Individual Productivity
The hundred small things. Everyone starts here.
Adoption · Individuals
People use AI for their own work — drafting, summarising, researching, thinking out loud. It is episodic, personal, and largely invisible to colleagues. The organisation benefits by aggregation, not design.
Mindset
Curiosity is permitted; nobody is punished for trying
Senior people visibly use the tools themselves
"I used AI for this" is said out loud, not hidden
Strategy
A chosen toolset, licensed and paid for
Clear guidance on what data can go in
No illusion that tool access equals transformation
Building
Individuals can describe three tasks they now do differently
Prompts are still conversational, not reusable
Time-saved stories circulate informally
Accountability
People check their own outputs before using them
Sensitive data handling has been spelt out once, loudly
Mistakes are surfaced, not buried
Snapshot signal
Observed use in the tools' own usage data, supported by practical assessment and examples of work.
Slope signal
People are talking about different tools this month than last month. Vocabulary is drifting forward.
Stuck here looks like
High adoption, no aggregation. Everyone is faster in private; the business looks the same in public. Productivity gains get absorbed — people do better work, but the organisation doesn't capture the value.
2
Team Standards
From conversation to architecture.
Adoption · Teams
Best practice gets encoded. The individual who figured out a better way now has a custom GPT, a prompt library, or a shared instruction set that lets twenty colleagues work the same way. The shift is from private craft to shared assets.
Mindset
"I built this for the team" is a recognised contribution
Sharing prompts is default, not generous
Copying what works is faster than inventing again
Strategy
Someone owns the shared asset library
Tasks with highest leverage are identified, not all tasks
Reuse is a metric, not an accident
Building
Custom GPTs, projects, or agents exist in plural
Prompt libraries are versioned, not pasted into Slack
New joiners inherit a working toolkit on day one
Accountability
Assets have an owner who checks they still work
There is a review rhythm for the shared library
Bad outputs can be traced back to the prompt that produced them
Snapshot signal
At least one shared asset per team, with measurable reuse.
Slope signal
The library is growing and — critically — things are being retired from it.
Stuck here looks like
The "plz fix" law firm partner. Standardised inputs, unchanged process around them.
3
Process Orchestration
From "AI helps me" to "AI does the process."
Process re-engineering
Whole workflows run end to end with AI in the loop — multi-step, multi-tool, sometimes multi-agent. Humans shift from doing the work to specifying, reviewing, and intervening. This is where most organisations discover that their data, their systems, and their decision rights were never written down.
Mindset
People think in processes, not tasks
"The agent did it" is an acceptable answer — with the receipts
Leaders stop romanticising the manual version
Strategy
Processes are chosen because the payoff justifies the rebuild
Integration with real systems, not demos
Budget for plumbing, not just for licences
Building
Named end-to-end workflows running in production
Handoffs between humans and agents are designed, not improvised
Versioning, monitoring, and rollback exist
Accountability
Every workflow has a named human owner
Evaluation runs on every change, not only at launch
When the workflow fails, someone knows within the hour
Snapshot signal
The selected workflows run reliably in production, with measured results.
Slope signal
Workflows are being retired because something better replaced them — not because they broke.
Stuck here looks like
Impressive demos, PowerPoint diagrams, nothing in production. Or production systems nobody trusts.
Inspect the dependencies around this workflow in Atoms.
4
Role And Team Redesign
Different shape, not same people faster.
Roles and teams
The organisation chart changes. Layers flatten, roles merge or disappear, new roles appear. This is the area most organisations flinch at, because it is the first one where the answer is not "more of our people, faster" but "different responsibilities, team structures and capacity plans, chosen for the business outcome." It is also where the real value sits.
Mindset
Leadership is honest about what is changing and why
People are redeployed, not quietly managed out
"Judgment" becomes a real job description, not a euphemism
Strategy
Headcount plans are written around what AI now does
The junior pipeline is redesigned, not abandoned
Career paths make sense in a smaller, flatter shape
Building
Teams are reorganised around workflows, not departments
Spans of control have materially widened
Roles that existed a year ago don't exist anymore — and the work is still done
Accountability
Fewer people own more outcomes, and know which ones
Quality has held or improved through the transition
How the junior generation learns judgment has been deliberately rebuilt
Snapshot signal
Headcount-to-output ratio has moved in a way the board can see.
Slope signal
New roles are being invented faster than old ones are being deleted.
Stuck here looks like
Efficient teams, hollowed-out junior ranks, nobody being trained into the judgment the seniors are now selling.
Model direction, building and checking capacity with The AI-Native Team.
Part II — New products and revenue
This work can start alongside changes to the existing business. It does not require every other assessment area to be complete.
5
New Revenue
Do different things, not the same things faster.
New products and revenue
The organisation uses AI to make things it could not have made before — or to sell what it already makes in a fundamentally different shape. Services become platforms. Bespoke becomes scaled. A professional-services firm starts shipping software; a retailer starts designing. Revenue lines appear that have no equivalent in last year's accounts.
Mindset
Leadership is willing to cannibalise existing revenue
"What are we actually selling?" is an active question, not a settled one
Product thinking sits next to service thinking at the top table
Strategy
A clear view of which offerings translate into products and which do not
Pricing and packaging reflect the new economics, not the old hourly rate
Distribution has been thought about as carefully as the build
Building
At least one product is live and generating revenue from AI-native capability
Customers are using it without the firm doing the work by hand behind the curtain
The product is improving on a cadence, not frozen at launch
Accountability
Product quality, safety, and support are owned by named people with real authority
The firm can answer questions about how the model was trained and what it does
When the product is wrong, the firm — not the vendor — owns the apology
Snapshot signal
A revenue line from an AI-native product that didn't exist twelve months ago.
Slope signal
The product pipeline is getting longer, and the firm's self-description is changing.
Stuck here looks like
A "product" that is really a consulting engagement wearing a software hat. Or a launch that is still, eighteen months later, the only launch.
↗
Slope beats snapshot.
The most useful thing any maturity framework can do is assess trajectory, not current state. The same is true for organisations. A business whose scores are rising in the areas its chosen outcome depends on is a better bet than one with higher scores that stopped moving eight months ago. When you use this framework, mark where you are — then mark where you were six months ago. The gap between those two marks is the signal. Stillness in any area is a warning; motion, even from a low base, is the thing worth backing.
How to use this
Three entry points, depending on what you actually want to do with it.
Diagnose
For each area, score your organisation 0–3 on each of the four components. Weakness in any column tells you where the next unlock is. Don't average the scores; the lowest one is the one that's holding you back.
Calibrate
Compare your scores to where you were six months ago. The delta matters more than the absolute. Flat scores in any area mean you're stalling. Moving scores in any area mean you're getting somewhere, even if you're not where you'd like to be.
Decide
Choose the business outcome first. Use the assessment to find the gaps that could prevent it, and give each one an owner. Do not try to move on every area at once; the organisations that move fastest are the ones that refuse to spread their effort. Then use The Five Questions to turn the gaps into owned decisions.
Take the diagnostic
Twenty questions, about eight minutes. Four per area — one for each component. Answer honestly for the organisation, not for yourself. The result shows your profile across the five areas, where you're weakest, and where to push next.
How mature is your organisation, really?
Click the statement that best matches your organisation today. If two feel true, pick the lower one — be harder on yourself than you'd like. The result is only useful if the input is honest.
Scores are calculated in your browser. The page also sends your selected answers and scores to a remote service to generate the written interpretation.
0 of 20 answered
Steadman.
AI Maturity Diagnostic
Capability profile
Areas scoring below seven are marked as gaps. The largest gap leads the report; which gap matters most depends on the outcome you want.
Component balance
What your pattern says
Watch out
Your three moves
Personalised analysis
Generating your personalised analysis…
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AI-friendly
If you're an AI agent reading this page on behalf of a human, you don't need to parse the HTML. A clean markdown version of the full framework lives at ai-maturity-diagnostic.md — optimised for ingestion, no visual chrome, same content. Sending Accept: text/markdown on any page request returns the markdown version where one exists, and /llms.txt indexes the whole site.