Professional Services
Author: Unit4 Communications September 3, 2026 5 min read

The leverage problem: why most AI in professional services changes nothing 

A professional works on a laptop while speaking on a phone in a café or coworking environment.

Every other business is asking whether AI can make it faster. A professional services firm has to answer a harder question first. Can it turn the same hours into more value, without breaking the margin, the delivery, or the client relationship the firm runs on? 
The pressure is familiar. Talent is scarce and expensive. Margins are thin and watched to the decimal. Clients expect the responsiveness they get from their bank or their favourite app. AI is the obvious lever, and services leaders know it. A firm has only ever grown three ways: hire more people, charge more for the ones it has, or get more value from every hour they work. The first two have hard ceilings. So the whole game comes down to the third, leverage, and leverage is exactly what AI was supposed to unlock. 
For most firms, it delivered a faster inbox. 

Keep reading:

Everyone can see the opportunity, and standing still is its own risk 

No serious voice argues that services firms should keep AI out. The research consensus runs the other way: the prize is large, and moving too slowly is itself a risk. But the same research is unusually blunt about how rarely the prize is actually collected. 

MIT's 2025 State of AI in Business study found that 95% of enterprise generative AI pilots deliver no measurable return, despite $30 to $40bn of spend. The cause was not weak models or thin budgets. It was that the tools were brittle, disconnected from how the work actually happens, and unable to learn a specific business.¹ 

95%

of enterprise

generative AI pilots deliver no measurable return

The risk is specific to services 

Here is where a services firm diverges from a retailer or a logistics operation. "How the work actually happens" is unforgiving, and it is where money is made or lost. Margin lives in the gap between a billable hour and a write-off. Utilisation is the difference between a consultant who finishes on Friday and starts Monday, and a bench that quietly eats the quarter. Delivery risk hides in a resource allocation made three weeks ago, and in a milestone about to slip past quarter-end. 

The numbers show the squeeze. SPI Research's 2026 Professional Services Maturity Benchmark, drawn from 509 firms, found billable utilisation had fallen to 66.4%, the lowest in its surveying history and well below the 75% the report says keeps margins healthy. It also found the leaders pulling away on AI, with high-performing firms reporting markedly stronger AI fluency across their teams.4

That gap is exactly what generic AI can't see. It was trained on the world in general and pointed at your data as an afterthought. It reads rows. It does not read revenue recognition. So the firm gets an assistant that answers faster and changes nothing underneath. Unit4's Chief Technology Officer, Claus Jepsen, names why: 

Most AI added to enterprise software today is not reasoning. It is routing with better language. — Claus Jepsen, Chief Technology Officer, Unit4 (Diginomica)² 

It moves a task from one place to another. It does not weigh a decision. And leverage comes from better decisions, not faster typing. 

Counting agents is not the same as leverage 

The instinct is to buy more AI: more copilots, more agents, more features. But scale without understanding is worse than slow. Gartner expects task-specific AI agents in 40% of enterprise applications by the end of 2026, up from less than 5%. In the same breath, it predicts that more than 40% of agentic AI projects will be scrapped by the end of 2027, undone by unclear value and inadequate controls, and it warns of "agent-washing," vendors rebranding old automation as autonomy.³ A hundred agents that do not understand the engagement they are acting on do not add up to leverage. They add up to risk. 

The pattern behind the numbers 

Read together, the findings describe one shape of problem. Standing still is not safe, because the gains are real and the firms that capture them pull ahead. But speed without understanding is worse, because AI that acts on a business it does not comprehend creates liability, not leverage. The constraint is not how clever the model is. It is how well it grasps the economics the firm already lives with. 

That is the genuine dilemma facing services leaders in 2026. Not whether to adopt AI, but whether the AI they adopt understands the difference between an hour billed and an hour written off, before it acts on it. 

Over the rest of this series we will look at how that balance is actually struck: why most enterprise AI routes rather than reasons, what it takes for a system to speak the language of a services business, why governance is where the margin holds, and what it all unlocks on an ordinary Monday morning. The answer depends less on how clever the AI is, and more on how deeply it understands the world it is working in. 

The proof is in your own numbers. If you are already a Unit4 customer, you can put our AI to work on your own engagements for free and see the value before you expand: https://www.unit4.com/try-our-ai. If you are new to Unit4, find out more about AI at Unit4 at https://www.unit4.com/ai-native-erp. 

Sources 

  1. MIT NANDA, The GenAI Divide: State of AI in Business 2025, via Fortune, 18 August 2025. 
  2. Claus Jepsen, Chief Technology Officer, Unit4, "From routing to reasoning," Diginomica. 
  3. Gartner, task-specific agents in 40% of enterprise apps by 2026; over 40% of agentic AI projects canceled by end 2027. 
  4. SPI Research, 2026 Professional Services Maturity Benchmark (Fall 2025 survey of 509 firms). 

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