--- title: Organisational AI Diagnostic source: https://steadman.ai/newsletters/david/ai-maturity-diagnostic.html published: 2026-04-05 summary: Five assessment areas of organisational AI capability, grouped under the four kinds of AI work, with a 5x4 matrix, a 20-question diagnostic and result notes across Mindset, Strategy, Building, and Accountability. --- # Where is your organisation on the AI journey? 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](/newsletters/david/ai-usage-spectrum.html). --- ## 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 x Components Matrix | Area | Mindset | Strategy | Building | Accountability | |-------|---------|----------|----------|----------------| | **1. Individual Productivity** — *The hundred small things* | **Curiosity permitted.** Senior people visibly use the tools. Nobody hides it. | **Tools bought and blessed.** Chosen stack, paid licences, data rules stated. | **Personal fluency.** People can name three tasks they now do differently. | **Self-check.** People verify their own outputs; mistakes surfaced, not buried. | | **2. Team Standards** — *Conversation to architecture* | **Sharing is default.** Building a prompt for the team is a recognised contribution. | **Reuse as a metric.** Highest-leverage tasks identified. Someone owns the library. | **Plural shared assets.** Custom GPTs, prompt packs, versioned — not pasted in Slack. | **Asset owners.** Each asset has a maintainer. Bad outputs trace back to a prompt. | | **3. Process Orchestration** — *"AI does the process"* | **Think in processes.** "The agent did it" is an acceptable answer — with receipts. | **Budget for plumbing.** Processes chosen on payoff. Real systems, not demos. | **End-to-end workflows.** Named flows in production. Handoffs are designed, not improvised. | **Named owners + evals.** Every workflow has a human owner. Failures caught within the hour. | | **4. Role And Team Redesign** — *Different shape, not same people faster* | **Honest about change.** Redeployment over quiet exits. Judgment becomes a job description. | **Structure on paper.** Headcount plans written around what AI now does. | **Flatter, wider.** Spans of control widened. Old roles gone, work still done. | **Rebuilt pipeline.** Fewer people own more outcomes. Junior learning path deliberately rebuilt. | | **5. New Revenue** — *Do different things, not the same things faster* | **Willing to cannibalise.** "What are we selling?" is a live question at the top table. | **New economics.** Pricing reflects software margins, not hourly rates. | **Shipped product.** Live AI-native revenue line. No humans behind the curtain. | **Own the apology.** Quality/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 ### Area 1: Individual Productivity **Tagline:** The hundred small things. Everyone starts here. 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. --- ### Area 2: Team Standards **Tagline:** From conversation to architecture. 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. --- ### Area 3: Process Orchestration **Tagline:** From "AI helps me" to "AI does the process." 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](https://steadman.ai/newsletters/david/atoms.html). --- ### Area 4: Role And Team Redesign **Tagline:** Different shape, not same people faster. 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](https://steadman.ai/newsletters/david/ai-native-team.html). --- ## 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. ### Area 5: New Revenue **Tagline:** Do different things, not the same things faster. 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](https://steadman.ai/newsletters/david/five-questions.html) to turn the gaps into owned decisions. --- ## The diagnostic quiz Twenty questions, about eight minutes. Four per area — one for each component. Answer honestly for the organisation, not for yourself. Pick 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. ### Area 1: Individual Productivity **Q1 (Mindset): How do senior leaders in your organisation talk about using AI themselves?** * (0) They don't use it, and it shows * (1) They say it's important; unclear if they use it personally * (2) Most of them use it and say so openly * (3) They demo their own use in team meetings and it's contagious **Q2 (Strategy): Which of these best describes your AI tooling situation?** * (0) Ad hoc; people bring their own, sensitive data goes God-knows-where * (1) A tool has been licensed, but there's no guidance on use * (2) Chosen stack, paid licences, and clear rules on what data can go in * (3) All of the above, plus visible tracking of who actually uses what **Q3 (Building): If you asked a random employee 'what are three things AI has changed in how you work?', you'd most likely get...** * (0) A blank stare or 'I don't really use it' * (1) One generic example ('I use it for emails sometimes') * (2) Three specific examples with genuine craft behind them * (3) Three examples plus what they tried and abandoned **Q4 (Accountability): When an employee uses AI to produce something and it turns out to be wrong, what happens?** * (0) Nobody notices, or the person is blamed without learning * (1) There's awkward silence and informal 'be careful' mutterings * (2) The person checks their own outputs; mistakes are surfaced openly * (3) It's treated as valuable feedback and the lesson circulates ### Area 2: Team Standards **Q5 (Mindset): When someone on your team builds a useful prompt, custom GPT, or instruction set, what's the norm?** * (0) They keep it to themselves — no reason to share * (1) They share if asked, but nobody knows what exists * (2) Sharing is the default; teams expect it * (3) Building reusable assets is a recognised contribution in reviews **Q6 (Strategy): Does your organisation know which tasks have the highest leverage for AI standardisation?** * (0) No — it's random what gets built and what doesn't * (1) Somebody has a rough view, not written down * (2) Yes — a deliberate list exists and drives investment * (3) Yes, and it's reviewed quarterly with a named owner **Q7 (Building): How are your shared AI assets (custom GPTs, prompts, templates) managed?** * (0) They aren't — things get pasted into Slack and lost * (1) There's a folder somewhere, patchy maintenance * (2) Plural assets exist, versioned, and new joiners inherit them * (3) All of the above, plus things get retired when they stop working **Q8 (Accountability): If an output from a shared prompt causes a problem, can you trace it back?** * (0) No — we wouldn't know which prompt produced what * (1) In theory, if we dug into it * (2) Yes — assets have owners and a review rhythm * (3) Yes, and the review rhythm has caught real issues recently ### Area 3: Process Orchestration **Q9 (Mindset): How do your people talk about work they want to get done?** * (0) As tasks — 'I need to do X' * (1) A mix; occasionally someone frames it as a process * (2) As processes — 'how could this whole thing run?' * (3) And 'the agent did it' is a normal answer, with receipts **Q10 (Strategy): When you think about AI investment, what's the budget shape?** * (0) All licences, no plumbing * (1) Licences plus some experimentation money * (2) Explicit budget for integration and workflow build * (3) Process rebuilds are chosen on expected payoff and measured after **Q11 (Building): How reliably do the AI workflows chosen for your business priorities run in production?** * (0) No production workflow yet * (1) A pilot people don't fully trust * (2) Reliable in production, with measured results * (3) Reliable in production, with monitoring and recovery in place **Q12 (Accountability): If a production AI workflow fails silently tomorrow morning, when do you find out?** * (0) When a customer or colleague complains, if ever * (1) Within a day or two, manually * (2) Within the hour — someone is on the hook for it * (3) Within minutes, via evals that run on every change ### Area 4: Role And Team Redesign **Q13 (Mindset): Is leadership being honest with people about what AI is changing in the organisation?** * (0) No — it's 'just a tool to help you' messaging * (1) Partially — the direction is hinted at but not spelt out * (2) Yes — the direction of travel is explicit * (3) Yes, and redeployment plans are visible, not euphemistic **Q14 (Strategy): Have role, hiring and capacity plans changed to capture the intended benefit?** * (0) No — we're planning as if nothing changed * (1) Some tweaks at the edges * (2) Yes — the plan looks materially different * (3) Yes, and career paths make sense in the new shape **Q15 (Building): Compared to a year ago, how has team structure changed?** * (0) Same people, same org chart * (1) Some reshuffling; nothing structural * (2) Responsibilities and team capacity have changed around the redesigned work, with an explicit plan for the benefit * (3) Teams are organised around workflows, not departments **Q16 (Accountability): How is the junior pipeline — how juniors learn judgment — being handled?** * (0) It isn't — we just hire fewer juniors * (1) We worry about it but haven't acted * (2) We've deliberately rebuilt how juniors learn judgment * (3) And it's working — quality is holding through the transition ### Area 5: New Revenue **Q17 (Mindset): Is leadership willing to cannibalise existing revenue to launch something AI-native?** * (0) No — protecting the current book is the priority * (1) In conversation, not in decisions * (2) Yes — decisions have been made that hurt in the short term * (3) Yes, and product thinking sits next to service thinking at the top table **Q18 (Strategy): Have you rethought pricing and packaging around AI economics?** * (0) No — we still sell time, materials, or seats the old way * (1) We've talked about it * (2) Yes — pricing reflects the new unit economics * (3) Yes, and distribution has been thought about as carefully as the build **Q19 (Building): Do you have an AI-native product in market — one that didn't exist a year ago and isn't humans in a trench coat?** * (0) No * (1) Something that looks like one but is actually manual behind the curtain * (2) Yes — live, generating revenue, customers use it without us doing the work * (3) Yes, and the pipeline behind it is getting longer **Q20 (Accountability): If your AI product produces a harmful or embarrassing output tomorrow, who owns the response?** * (0) Unclear — probably legal or 'the vendor' * (1) Someone, but authority and process aren't well defined * (2) Named owners with real authority; the firm answers, not the vendor * (3) All of that, plus we can explain how the model was trained and what it does --- ## Scoring Each question scores 0-3. Each area has four questions, giving an area score out of 12. Each component appears once per area, giving a component score out of 15. **Your profile:** five area scores, each out of twelve, shown grouped under the four kinds of work. Areas scoring below seven are gaps. The report leads with the largest gap and lists the others; it does not place the organisation on a single rung, and a strong score in a later area is not penalised for a gap in an earlier one. **Capability profile chart:** Five bars, each scored out of 12, grouped under the four kinds of work; areas scoring below seven carry a "gap" marker. **Weakest-component diagnosis:** The component with the lowest cross-area sum — the systemic under-investment — along with a tailored "next move" prescription. --- ## Result notes by largest gap **Starting line (no area scores seven or more, and the total is 12 or less):** You're at the starting line. Your scores are low in every area. That isn't a failure; it's where everyone begins. The risk is staying here by default while the market moves. Get the adoption basics in place: visible leadership use, a blessed toolset, data rules, and a culture where trying things is safe. **Largest gap: Individual Productivity.** Individual use of AI is the weakest part of your profile. Everything else rests on it: shared standards, rebuilt workflows and new products all need people who can use the tools well. Look at the answers that scored low here. Visible leadership use, a chosen toolset with clear data rules, and permission to try things are the usual fixes, and they are cheap. **Largest gap: Team Standards.** Turning private craft into shared assets is the weakest part of your profile, so the organisation is probably not capturing what its best individuals can do. Look at the answers that scored low here. Encoding what the best people do into prompts, templates and instructions with owners and a review rhythm is how the gains compound across teams rather than staying personal. **Largest gap: Process Orchestration.** Running work end to end with AI in the loop is the weakest part of your profile. This is where the economics change, so it is the gap that matters most if the number you want to move is about throughput, speed or cost. Look at the answers that scored low here. Two or three real workflows chosen by working back from that number, with integration, data, security and an operational owner planned from the start, is the usual route. **Largest gap: Role And Team Redesign.** Changing the organisation's shape around what the tools now do is the weakest part of your profile, which usually means the capacity created is being absorbed into busier days rather than reaching the bottom line. This is the gap most programmes leave open. Look at the answers that scored low here. Deciding what the created capacity is for, and changing responsibilities, team structures and capacity plans to match, is the work. **Largest gap: New Revenue.** AI-native products and revenue are the weakest part of your profile. That is not a failure if the outcome you want sits in the existing business, and this work does not have to wait for the other areas to be complete. Look at the answers that scored low here. If growth is the number you want to move, test what customers need, what fits the strategy and what can work commercially before building. **Capable across the board (every area seven or more):** Every area scores seven or more, which very few organisations can say. The question now is which number you want to move next, and whether the roles and teams around the work have changed enough to capture what the tools create. Retake this in six months; the slope matters more than the score. The three moves for this profile: name the next number and its owner; choose two or three changes by working back from it; retake the diagnostic in six months, measuring business outcomes rather than the score at the ceiling. Each note is followed by the strongest area and its score (unless every area scores zero), and by any other areas scoring below seven (or "No area scores below seven"). The notes describe the weakest part of the profile; the specific answers behind it are what to read next. ## What your pattern says Alongside the note, the report names patterns in the answers: Early days, The Talkers (belief ahead of building), The Planners (strategy ahead of building), The Risk Factory (building ahead of accountability), Builders without a map (building ahead of strategy), Top-down push (strategy ahead of mindset), Strong individuals, weak organisation, Uneven profile (a spread of seven or more between areas, which matters only if the outcome you want depends on a weak area), Balanced progress, and No dramatic imbalances. Any question scored zero is listed under Watch out. Three moves follow (this month, this quarter, this half), chosen by the weakest component and the largest gap. A personalised written analysis is then generated from the answers. ## Next-move prescriptions by weakest component **Mindset is your weakest column.** No amount of tooling fixes this — it's a leadership job. Pick three senior people and have them publicly use AI in meetings this month. Reward people who share, not hoard. **Strategy is your weakest column.** You're moving tactically without a plan. Write down the three highest-leverage processes you want AI in. Name an owner. Ignore everything else until those three work. **Building is your weakest column.** You have intent but not delivery. Find the one person who's actually shipped an AI workflow, protect their time, and have them build one more with a colleague looking over their shoulder. **Accountability is your weakest column.** This is the quietest but most dangerous gap — it's where reputational damage lives. Before building anything else, make sure every AI-touched thing has a named human owner and a way to catch it failing. --- ## Related tools Use the [AI Value Map](https://steadman.ai/newsletters/david/ai-value-map.html) to see where the value sits and how much you're capturing. Read [From What's True to What to Do](https://steadman.ai/newsletters/david/ai-from-whats-true-to-what-to-do.html) for the argument behind the four kinds of work. Then [The Five Questions](https://steadman.ai/newsletters/david/five-questions.html) to turn the gaps into owned decisions. If you would like to talk it through, [discuss the change behind these gaps](https://steadman.ai/#contact). ## Re-use Scores are calculated in your browser. The page also sends your selected answers and scores to a remote service to generate the written interpretation. Free to use, cite, and adapt. Please credit Steadman. If you build on it, we'd like to hear about it — david@steadman.ai.