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Key takeaways
AI adoption is increasing, but most consulting firms aren't capturing business value. While 40% of professional services firms now use AI organization-wide, only 18% measure AI ROI, meaning most firms can't prove whether AI is improving margin, utilization or revenue.
The real challenge isn't AI adoption, it's operational execution. AI creates delivery capacity, but without visibility, effective resource management and intentional redeployment, efficiency gains disappear into non-billable work and operational friction.
Consulting firms need to answer one strategic question: if AI created 10% more delivery capacity tomorrow, where would that capacity be deployed? Firms with a clear answer are more likely to convert AI into growth and competitive advantage.
High-performing firms combine AI adoption with operational discipline. The strongest performers don't just implement AI, they measure outcomes, redeploy freed capacity to client work, and turn efficiency into higher margins and new revenue opportunities.
Measuring AI's business impact is now a leadership priority. Firms should track AI-generated capacity, how much converts into billable work, where growth is constrained, and whether AI investments are delivering measurable commercial returns.
There's a question that consulting leaders don't ask themselves enough: if AI created 10% more delivery capacity in your teams tomorrow, would you actually know where to deploy it?
That provocation sat at the heart of the session I ran at Leaders in Consultancy Munich, and judging by the room, it landed.
What's the paradox no one wants to admit?
Consulting firms are in an awkward position. Clients are paying them to navigate AI transformation, to redesign operating models, retrain workforces, monetize efficiency gains. And yet most of those same firms are among the last to convert their own AI efficiency into measurable margin, utilization, or revenue.
It's the cobbler's children problem, scaled to a multi-billion dollar industry.
The firms that close this gap fastest won't just be better run. They'll have a structural margin advantage over everyone still catching up.
What do the numbers actually tell us?
According to the Thomson Reuters 2026 AI in Professional Services Report, 40% of PS firms now use AI organization-wide, up from 22% year on year. That's meaningful adoption. But only 18% actually measure AI ROI. The other 82% are, as I put it, flying blind.
And the third number? It doesn't even exist yet. Ask most firms what percentage of their AI time savings actually became billable last year, and you'll get silence. Not because the answer is bad, but because nobody's tracking it.
So where do the gains go?
If AI is creating real capacity and that capacity isn't showing up in revenue or margin, it's disappearing somewhere. I map it as a value destruction chain with three familiar culprits:
No visibility. Capacity creation goes unmeasured and unmanaged, so firms can't see where the savings went in the first place.
Operational drag. Whatever efficiency does emerge gets absorbed by slow staffing, resource conflicts, approval delays, and scope creep.
No redeployment. Freed hours simply fill with non-billable work, and capacity never converts to client value. This is the most silently damaging of the three.
None of these are AI problems. They're operational problems that AI just makes more expensive to ignore.
Where does your firm actually sit?
In my session, I introduced a useful two-by-two to cut through the noise. On one axis: AI adoption. On the other: the operational ability to convert that efficiency into margin.
Firms in the top-right — high adoption, strong operational discipline — are the operationalized winners. AI efficiency is converting to margin and new revenue. That's where everyone wants to be.
Most firms are somewhere else. "Capacity rich, strategy poor" is increasingly common: AI is working, but leaders haven't figured out what to do with the output. "Efficiency but no payoff" describes firms with strong operations but lagging AI adoption — they have the pipes but not the fuel. And laggards, sitting at low adoption and low conversion, are facing what Wayne called existential risk.
What questions should you take back to your team?
I closed the session with a breakout discussion built around six questions that are worth putting to your own leadership team:
Are you seeing AI create real capacity, or is it still theoretical?
What are your clients asking you to deliver right now that you're not fully resourced for?
If capacity increased 10% tomorrow, where would you deploy it?
Where is growth currently constrained?
What's preventing efficiency gains from showing up in your utilization or margin?
How, exactly, are you measuring the business impact of AI today?
They're not comfortable questions. That's the point.
The firms that leave events like this and actually answer them, not in a strategy deck, but in their resourcing decisions and delivery model, are the ones that will have a structural advantage twelve months from now. The capacity is there. The question is whether you're building the operating model to use it.
FAQ
What percentage of professional services firms measure AI ROI?
Only 18% of professional services firms measure AI ROI, despite 40% now using AI organization-wide, according to the Thomson Reuters 2026 AI in Professional Services Report.
Why doesn't AI adoption automatically improve margin?
AI creates delivery capacity, but that capacity only converts to margin with visibility, effective resource management, and intentional redeployment. Without those, efficiency gains disappear into non-billable work and operational friction.
What is the "value destruction chain" Wayne Murphy describes?
It's the three-part pattern behind lost AI efficiency: no visibility into where capacity was created, operational drag that absorbs whatever efficiency does emerge, and no redeployment plan, so freed hours default back to non-billable work.
How do I know where my firm sits on AI adoption versus operational conversion?
Plot your firm against two axes: AI adoption and operational ability to convert efficiency into margin. Firms high on both are the operationalized winners. Firms high on adoption but low on conversion are capacity rich but strategy poor.
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