Here's a question I'd genuinely like you to answer honestly - to yourself, not to me. If I asked you to place your firm on a five-stage AI adoption curve right now, from "not started" through to "strategically embedded," where would you put it?
Now the follow-up: where would someone who'd spent two weeks inside your firm actually put it?
I ask because there's a gap forming between those two answers across almost every mid-market professional services firm I speak to. And it's not vanity - managing partners aren't deliberately inflating their position. The signals are genuinely confusing. You've had AI on the strategy day agenda for two years. A few partners are using ChatGPT daily. Someone ran a pilot last quarter. There's a line in the business plan about "leveraging AI for competitive advantage." From the inside, that feels like progress. From the outside - and I say this with genuine respect for how difficult this stuff is - it often looks like Stage 2 dressed up as Stage 4.
That matters because the actions appropriate at Stage 2 are actively counterproductive at Stage 4, and vice versa. Misdiagnose where you are, and you'll resource the wrong things, govern the wrong risks, and wonder why nothing's landing.
McKinsey's 2025 State of AI report found that 78% of organisations use AI somewhere in their operations, 71% regularly use generative AI - but only one-third have successfully scaled it across the organisation. That distribution tells you something important: most firms are somewhere in the middle, and the middle is a much bigger and more ambiguous place than anyone's comfortable admitting.
So let me walk you through a framework we've developed from working with mid-market professional services firms over the past couple of years - law firms, consultancies, accountancy practices, financial advisory businesses. It's a five-stage model. It's not precise. It's not meant to be. But it's specific enough that you should be able to read the descriptions and know, within a stage, where your firm actually sits.
Stage 1: Unaware or unengaged. AI isn't on the firm's agenda. Either it's actively dismissed - "our clients pay for human judgement, not algorithms" - or it simply hasn't surfaced as something requiring leadership attention. No AI tools are deployed. No AI appears in strategic planning. The prevailing view is that client relationships provide sufficient competitive protection and that AI is something for tech companies to worry about.
If this is you, that's fine. I'll come back to it.
Stage 2: Curious. AI is on the agenda but not yet in practice. Partners are asking about it at strategy days. A handful of people - usually the younger associates or one tech-forward partner - are using ChatGPT or Copilot for personal productivity. There's a general sense that "we should probably do something" but no specific plan, no budget, and no named person responsible. Data quality hasn't come up yet. Governance hasn't been discussed. The firm's AI position is essentially: interested, but watching.
Stage 3: Experimenting. Some structured AI activity is underway. At least one formal pilot has been run - whether it succeeded or not. Someone has been given responsibility for AI exploration, even if it's bolted onto their existing role. There's been at least some conversation about data readiness. At least one use case has defined success criteria, even if the results were underwhelming. The firm is learning, sometimes painfully, what works and what doesn't.
Stage 4: Operational. At least one AI application is producing measurable value in production. Not "we think it's helping" - actual documented productivity improvement. An AI governance framework is in place. Internal AI literacy is being actively developed. The firm's AI programme is informing strategic decisions about hiring, technology investment, and which services to develop. AI has moved from an experiment to a capability.
Stage 5: Strategic. AI is embedded in the firm's competitive positioning. It comes up in client conversations as a genuine differentiator. AI infrastructure decisions are informing platform and data investment at the firm level. There's a continuous improvement rhythm - quarterly, typically - that compounds capability over time. AI isn't a project; it's part of how the firm operates and competes.
Right. So where did you land?
The stage descriptions are useful, but they're broad. What tends to be more telling is the behavioural indicators - the specific, observable things happening (or not happening) inside a firm at each stage. These are the bits that make the self-assessment honest rather than aspirational.
If you're at Stage 2, one or more of these will be true: AI gets discussed at board level but no action plan has resulted. There's no AI budget line anywhere in the firm's financials. The actual state of your internal data - its structure, its accessibility - hasn't surfaced as a topic. A written policy about how and where AI can be used in client work hasn't been discussed. Individual partners or associates are using AI tools, but there's no firm-level coordination of what they're using or how.
I was in a conversation with a managing partner about six months ago - a mid-sized consultancy, around 200 people - and he told me, quite confidently, that the firm was "well into its AI journey." I asked him three questions: does the firm have an AI budget? Has anyone assessed the quality of the firm's data? Is there a written policy about AI use in client deliverables? No, no, and "we've been meaning to." That's Stage 2. Solidly, respectably Stage 2. But he'd placed himself at Stage 4 because several partners were using AI tools daily and they'd talked about it at every strategy day for eighteen months.
Talking about AI is not the same as doing AI. I know that sounds harsh, but it's a distinction that matters enormously when you're deciding what to do next.
If you're at Stage 3, the indicators shift: a pilot has been run, even if it underdelivered. A specific named person - not necessarily in a dedicated role - has responsibility for AI exploration. At least one use case has a defined outcome measure, meaning you decided in advance what success would look like and measured against it. There's been some conversation about data requirements, even if it was uncomfortable.
Stage 3 is where things get properly interesting. And also where they tend to get stuck. The short version of why AI pilots often disappoint: it's usually not the technology. The use case was too ambitious for the data available, or the success criteria were vague, or the pilot ran too long without clear checkpoints. A disappointing pilot at Stage 3 is still Stage 3. You've learned something. The question is whether you learn the right lessons from it.
If you're at Stage 4, the bar is higher: at least one AI application is being used by more than 20% of relevant users on a weekly basis. There's a written AI use policy. AI performance is reviewed quarterly - not just "is it working?" but "what value is it producing and where should we invest next?" AI-related capability is showing up in your recruitment conversations, either because you're hiring for it or because candidates are asking about it.
If you're at Stage 5 - and very few mid-market professional services firms are here yet, so don't feel bad - AI capability is referenced in client-facing materials. AI investment decisions are being made at partnership level. AI maturity is assessed as part of the firm's strategic planning cycle, not as a separate technology conversation.
The majority of mid-market professional services firms that describe themselves as "exploring AI" or "piloting AI" are at Stage 2 or early Stage 3.
They have AI in their strategy documents. They have individuals using AI tools. They might have the beginnings of a pilot. But they don't have measurable operational outcomes. They don't have a governance framework. They don't have sustained internal capability development. They have activity, but not yet capability.
This is not a failure. This is the normal, expected position for firms that are eighteen to twenty-four months into the AI conversation. The technology is still evolving rapidly. The use cases in professional services are genuinely harder to implement than in, say, e-commerce or content marketing. The regulatory and ethical considerations are real. Being at Stage 2 in mid-2025 is not embarrassing - it's honest.
What is problematic is being at Stage 2 and resourcing your AI programme as though you're at Stage 4. That's where the damage happens.
But we're not a laggard. We've had AI in our strategy for two years, we've run pilots, and our partners know it's a priority. We're further ahead than most.
I hear you. And I'm not saying you're a laggard. I'm saying the distance between "partners know it's a priority" and "we have an AI application producing measurable value in production" is larger than it looks from the inside. Having AI in your strategy is a Stage 2 activity. Having it in your operations is a Stage 4 activity. Both are valid. But they require completely different actions, different governance, and different investment.
If you want a structured way to assess where your firm sits across all the relevant dimensions - data readiness, governance, infrastructure, culture - there's a companion readiness checklist worth working through. It maps directly onto this stage framework and is designed to make the self-assessment specific rather than general.
Stage 1 to Stage 2: establish awareness. One senior leader - ideally someone with genuine influence over partnership decisions - takes personal responsibility for understanding what AI means for the firm specifically. Not AI in general. Not AI for tech companies. AI for a mid-market professional services firm with your specific client base, regulatory environment, and operational model. They read. They talk to peers at other firms. They talk to us, or to someone like us. And within three months, they present a clear-eyed, jargon-free view to the partnership about what the opportunity looks like and what the risks of inaction are.
That's it. That's the transition. One person, three months, one honest presentation. Not a strategy. Not a pilot. Just awareness with enough specificity to move the conversation forward.
Stage 2 to Stage 3: start a structured pilot. Select one specific use case. Not three. Not "let's explore the possibilities." One use case, chosen because the data is available, the process is well-understood, and the potential impact is measurable. Define success criteria before the pilot begins - this is the bit that gets skipped most often, and it's the reason most pilots end with a shrug rather than a decision. Run it for four to six weeks with honest measurement. If it works, you've got evidence. If it doesn't, you've still got evidence - just different evidence.
I worked with a 300-person professional services firm last year that chose automated proposal generation as their first use case. Sensible choice - the data was largely internal, the process was well-documented, and the time savings were easy to measure. Sixty percent reduction in proposal turnaround time. About twelve hours of senior consultant time saved per week. That's not theoretical. That's Stage 3 moving towards Stage 4 with a clear evidence base.
Stage 3 to Stage 4: achieve operational use. This is the hardest transition, and it's where most firms stall. I'll be honest - I've got this wrong myself. We worked with a financial advisory firm about eighteen months ago where I was confident the pilot results justified a full operational rollout. The use case was strong, the numbers were good, and I pushed for moving quickly. What I underestimated was the governance gap. There was no written policy about AI use in client-facing work, and the team assumed that because the pilot had gone well, the rules were obvious. They weren't. Within six weeks of broader rollout, a junior analyst used the tool in a way that wasn't technically wrong but wasn't what the partners had in mind either. Nothing catastrophic - but it created exactly the kind of organisational scepticism that makes every subsequent AI conversation twice as hard. We had to spend three months rebuilding confidence that should have been built before we deployed.
The lesson: based on pilot results, make a proper budget commitment to operational deployment, assign a named owner whose performance is partly measured on AI outcomes, and set up the governance framework before broader rollout, not after. The governance piece is non-negotiable.
Stage 4 to Stage 5: achieve strategic integration. Integrate AI maturity into the firm's strategic planning process. Make AI capability part of the external proposition - not in a gimmicky way, but in a genuine "this is how we work now and here's what it means for clients" way. Establish the quarterly rhythm that compounds capability over time. This is where something like our WHNN® framework becomes relevant - it creates a recurring cycle of review, prioritisation, and commitment that stops AI from being a one-off project and starts making it a way of operating.
A word of realism, though: Stage 5 is not the right goal for every firm. For most mid-market professional services firms in 2025, Stage 4 - at least one AI application producing measurable value, with proper governance and active capability development - is the appropriate operational target. Stage 5 is for firms where AI is genuinely core to competitive differentiation. If that's not you, that's fine. Don't over-index on a destination that doesn't match your context.
I know some of you are thinking: surely we can just jump straight to the good stuff? And I understand the impatience. You've been hearing about AI for two years. Your competitors are making claims about their AI capabilities. The pressure to show progress is real.
But here's what actually happens when a firm tries to jump from Stage 2 to Stage 4 without the Stage 3 foundations in place.
They select a use case that's too ambitious for their data. Because they haven't done the Stage 3 work of understanding what their data can actually support, they pick something that sounds impressive in a partnership meeting but requires clean, structured, accessible data that doesn't exist. The pilot - sorry, the "deployment" - underwhelms. Partners who were sceptical feel vindicated. Partners who were enthusiastic feel burned.
Or they deploy an AI tool into client-adjacent work without a governance framework. Because they skipped the Stage 3 conversation about policies and boundaries, something goes sideways. Maybe it's minor - an AI-generated document with a hallucinated citation that gets caught in review. Maybe it's less minor. Either way, it creates what I've started calling "AI scar tissue" - organisational memory of the time AI went wrong, which makes every subsequent investment harder to approve and every subsequent conversation more defensive.
The five stages aren't arbitrary. Each one builds the organisational capability that the next stage requires. Stage 3 experimentation isn't a delay - it's the necessary validation of whether your use cases fit your data, your processes, and your culture before you commit operational budget. Skipping it doesn't save time. It creates the governance failures and the scar tissue that slow everything down afterwards.
The five-stage model is a diagnostic tool, not a grading system. If you read the descriptions and think "we're somewhere between Stage 2 and Stage 3" - that's a perfectly valid and useful position to identify. In fact, it's probably the most common position I encounter. The firm has moved beyond pure curiosity, some structured activity is starting, but the pilot isn't complete or the results aren't yet clear.
The point isn't to land neatly on a stage number. The point is to be honest about which set of actions is appropriate for where you actually are. If you're between Stage 2 and Stage 3, the right move is to complete the transition into Stage 3 - pick the use case, define the success criteria, run the pilot. Not to leap ahead to the governance frameworks and strategic integration that belong to Stage 4.
If you want a structured assessment of where your firm actually sits on the adoption curve - across all five dimensions, with a specific recommendation for the next stage - book an AI maturity assessment. And if you want the five-stage framework as a reference to share with your partners, download the AI adoption curve framework. It's designed to give you a shared language for the conversation about where you are and what to do next. Because in my experience, half the problem isn't that firms don't know what to do - it's that the partnership can't agree on where they're starting from.