I was having coffee with a managing partner at a 200-person consulting firm a few months back - good bloke, sharp, clients love him. We'd been talking for about twenty minutes about something else entirely when he said, almost as an aside: "James, I've had four vendors in this month telling me I need an AI strategy. I've sat through the demos. I still couldn't tell you what any of it actually means for a firm like ours."
And then, before I could answer, he said: "Also, I told the last one I was already doing it. Just to get them to leave."
That made me laugh. But it also stuck with me, because it's the most honest version of a conversation I keep having. He wasn't behind. He wasn't ignorant. He was exhausted by noise and couldn't find the signal. And that's where most of the senior leaders I speak to are right now - somewhere between a vague sense that they should be doing something and mounting pressure to produce a plan, with very little clarity about what practical AI adoption actually looks like for a firm their size.
So, here's my attempt at a plain-English answer. Not a sales pitch. Not a "the robots are coming" warning. Just what AI adoption actually means at your scale, where it helps, where it doesn't, and how to start without making an expensive mistake.
There are three positions I see most often, and recognising which one you're in matters more than people think.
The first is: "We should probably be thinking about this." You've seen the headlines. Clients have mentioned it. A few people in the firm are using ChatGPT on the quiet. But there's no formal conversation happening, and nobody's been asked to lead on it. Aware, not active.
The second is: "We've had some demos and I'm not sure what to do next." Vendors have been in. Someone internally has championed a tool or two. There might even be a small budget earmarked. The demos all looked impressive and none of them felt obviously right, and you're stuck in the gap between interest and action.
The third is: "The board wants an AI strategy by Q2." This one comes with a deadline and usually some political weight behind it. A major client asked about your AI capability. A competitor made noise about theirs. Either way, someone senior has put this on the agenda and you need to respond with something more than a slide deck.
Here's my honest take: position two is the dangerous one. Position one is fine - you're watching, learning, not spending. Position three at least has urgency to force a decision. But position two is where firms spend £150k on a pilot they didn't understand, produce nothing useful, and then spend the next two years being sceptical about every subsequent AI conversation. I've seen it happen more than once. The vendor was impressive. The brief was vague. Nobody asked the right questions before the invoice arrived.
Part of the confusion is that "AI" is doing way too much work as a term. It covers everything from the autocomplete on your phone to systems that can draft legal opinions. When someone says "we're using AI," they could mean anything from "Dave in accounts uses ChatGPT to write his emails" to "we've built a proprietary model trained on twenty years of client data."
So let me break it into four practical categories. Not technology categories - use categories. What AI can actually do in a professional services firm, in language that makes sense over a coffee.
Removing repetitive tasks from human workflows. Document processing, data entry, scheduling, routine client communications - the stuff that eats your fee earners' time without generating any billable value. A law firm I spoke to recently reckoned their associates spend roughly 15% of their working week on administrative tasks that follow predictable patterns. That's basically Friday afternoon every week, gone.
Finding patterns in your data. AI can identify trends in client behaviour, flag anomalies in operational performance, or surface market signals that would take a human analyst weeks to find. The catch - and it's a big one - is that this only works if your data is in reasonable shape. If your CRM is a graveyard of half-completed records and your knowledge management lives in a shared drive nobody's organised since 2019, then analysis tools will give you confident-looking answers built on sand.
Helping people communicate. Drafting, summarising, translating, reformatting. This is the category most firms are already experimenting with, whether they've formalised it or not. Someone in your firm is already using ChatGPT or Copilot to draft proposals, summarise meeting notes, or turn a dense technical document into something a client can actually read. The productivity gains here are real and immediate. They're also the easiest place to start, which matters more than people think.
Organising institutional knowledge. This is the one that genuinely excites me, and it's probably the least well understood. Every professional services firm has decades of accumulated expertise locked inside people's heads, buried in file systems, scattered across email threads, or sitting in SharePoint folders nobody's opened since 2021. Knowledge management AI can surface relevant precedents, past proposals, and internal research in seconds rather than hours. For a consulting firm or law firm where the answer to "have we done something like this before?" currently requires asking three partners and hoping one of them remembers - that's not a small thing.
This is the framework I keep coming back to in conversations with managing partners. There are three distinct levels of AI adoption, and the firms that get into trouble are almost always the ones that skip to level two or three without properly doing level one.
Level one: Using tools. ChatGPT. Copilot. Sector-specific tools like Harvey for legal. Low cost - a few hundred pounds per seat per year in most cases. Low risk. Minimal infrastructure requirement. High individual productivity gain. This is where almost every firm should start. And if you're at position one on the confusion spectrum, this is where you should stay for a while. Let people use the tools. See what sticks. Watch where the productivity gains actually show up. Build genuine internal experience before you spend any real money.
Level two: Integrating AI into your workflows. This is where you connect AI capability to your existing systems and processes. Instead of an individual using ChatGPT to draft a proposal, the proposal system itself has AI built in - pulling from your CRM data, your previous proposals, your pricing history, producing a first draft that's already 80% of the way there. Meaningfully harder than level one. It requires clean data, defined workflows, and some technical implementation. But it's where the compound gains start to appear, because you're not just making individuals faster - you're making the whole firm faster.
The right time to move to level two is when you've spent enough time at level one to know which use cases actually matter for your specific firm. Not before.
Level three: Building custom AI capability. Proprietary models trained on your data. Custom applications built for your specific workflows. Bespoke AI systems that give you a genuine competitive advantage. This requires significant investment in data, infrastructure, and ongoing governance. And I want to be direct: for most mid-market professional services firms, level three is not where you should be spending your time or money right now. The firms I've seen try to leap straight to custom AI capability without solid foundations at levels one and two have universally been disappointed. We do AI readiness assessments, and the honest answer for most firms is: you're not ready yet, and that's fine, because there's a huge amount of value to unlock before you get there.
McKinsey's own data backs this up - only one-third of organisations are successfully scaling AI across their business, despite a headline adoption figure of 71%. The gap between "we're using AI" and "AI is changing how we operate" is enormous, and most of that gap is firms that jumped ahead without doing the groundwork.
Right. You've read the framework and you're thinking: Fine, level one. But what specifically should I do on Monday morning?
Fair question.
The highest-value, lowest-risk first applications for a mid-market professional services firm are almost always in communication assistance and knowledge management at the tool level. And I'll make that even more specific.
I was talking to a partner at a mid-sized accountancy firm last month - Sarah, been in practice for fifteen years, deeply sceptical about technology in general. She'd started using Copilot for drafting client letters, mostly because a colleague had nagged her into it. I asked how it was going. She said: "Honestly? I thought it would be rubbish. It's not rubbish. I'd say I'm getting four hours back a week, maybe more." Then she paused and said: "Which is annoying, because I've been complaining about not having enough time for years."
Four hours a week. Half a day, every week, for the cost of a Microsoft 365 subscription upgrade. No custom build. No infrastructure investment. No vendor demo. Just a tool she was already paying for, used properly.
The bit that matters beyond the individual productivity story: starting here creates visible early value. And visible early value is the currency that buys you permission to do more. When a sceptical partner sees a colleague producing better first drafts in half the time, the conversation about "should we invest in this?" changes. You don't have to argue the theoretical case anymore.
It also builds genuine capability. People learn what AI is good at and - just as importantly - what it's not good at. They develop an instinct for when to trust the output and when to check it. That organisational learning is what makes level two actually viable. Without it, you're trying to integrate a technology that nobody in the firm really understands into processes that nobody's examined critically.
I need to say this plainly, because the hype cycle has created expectations that are going to hurt people.
AI cannot replace professional judgement in complex, high-stakes decisions. It can inform those decisions. It can surface relevant information faster. It can draft options for a human to evaluate. But the moment you treat an AI output as a final answer in a situation that requires expertise, contextual understanding, and professional accountability, you've made a serious error. The technology is impressive. It doesn't know what it doesn't know. In professional services, knowing the limits of your knowledge is half the job.
AI cannot substitute for the relationship and trust that drive client retention and new business. Your clients stay with you because they trust you, because you understand their business, because they can pick up the phone and get honest advice. The firms that frame AI as a replacement for human relationships rather than a way to free humans up to spend more time on relationships have misread the opportunity entirely.
AI cannot compensate for poor data quality. This is the trap I see most often. AI applied to bad data produces confident-looking bad outputs - which is genuinely worse than no output at all, because people trust it. If your CRM hasn't been properly maintained, if your client data is scattered across six different systems with no single source of truth, then AI will cheerfully analyse all of that mess and give you answers that look authoritative and are completely wrong.
And AI cannot deliver value without human involvement in its deployment, oversight, and governance. This is not a plug-it-in-and-walk-away technology. In a regulated environment - which covers most of the firms we work with - that's not optional. It's a compliance requirement.
Only 7% of CFOs report seeing high ROI from AI investments, according to Forrester's 2024 survey. I'd bet a significant chunk of the other 93% set expectations the technology was never going to meet.
I'd be doing you a disservice if I skipped this. In professional services, AI adoption isn't just a technology decision - it's a political one.
Some partners are worried about client confidentiality - and they should be, because putting client data into public AI tools without proper governance is a genuine risk. Some are worried about job displacement - not for themselves, usually, but for their teams, and the question of what AI means for junior roles in professional services is a real one that deserves honest engagement rather than hand-waving. Some are simply exhausted by technology initiatives that promised transformation and delivered PowerPoint. That last group, I have some sympathy for.
The firms navigating this well are the ones that acknowledge these concerns openly rather than dismissing them. "We understand the confidentiality implications and here's specifically how we're addressing them" lands very differently from "Don't worry about it, the tool is secure."
Deloitte's 2025 research shows 66% of organisations reporting productivity gains from AI - but that still leaves a third that aren't seeing those gains. In my experience, the difference isn't the technology. It's whether the organisation did the internal work to create the conditions for the technology to succeed.
If you're a managing partner or operations leader at a mid-market B2B service firm, here's what I'd tell you if we were sitting across a table.
Start at level one. Properly. Pick a team, give them access to Copilot or a relevant tool, and watch what happens over 90 days. Don't try to measure ROI on day one - you're building capability and generating learning, and that has value even if the spreadsheet doesn't capture it yet.
Get your data house in order. Not perfectly - perfection is the enemy of progress. But if you can't answer basic questions about your client base or your pipeline without asking three people and cross-referencing two spreadsheets, you're not ready for level two.
Have the internal conversation honestly. Don't pretend AI is going to transform everything overnight. Don't dismiss the concerns your partners raise. And don't let a vendor set the agenda for a conversation that should be led by someone who understands your firm, your clients, and your culture.
And if you're in position three - the board wants an AI strategy by Q2 - resist the temptation to produce something ambitious that you can't deliver. A clear-eyed, honest strategy that says "here's where we are, here's where we're starting, and here's what we'll know in six months" is infinitely more valuable than a 40-page document full of promises. I've seen those documents. I've seen what happens to the people who wrote them when the promises don't materialise.
The opportunity is real. The right first step is almost certainly smaller, cheaper, and more useful than whatever the last vendor told you.