The strategy gap that’s costing services firms their AI returns

Services firms are adopting and deploying AI widely: 57% of services industry leaders said their firm is scaling AI across multiple functions — nearly eight points above the cross-industry average. Firms are using AI to accelerate research, draft deliverables and manage workloads — driven more by competitive pressure and regulatory demands than by direct client requests. 

Thirty-one percent of services leaders cite regulatory changes as the top external AI pressure, nine points above the cross-industry average. Only 18% say the same of customer expectations, well below the 25% average. In a client-facing industry, that gap is worth naming. 

The benefits of these AI investments are immediate: faster delivery, reduced rework and lower strain on teams. Stopping to define a strategy before scaling can feel like a competitive risk. But to meet regulatory, competitive and client expectations, defining what all that activity is meant to produce matters, and many professional services firms haven’t. 

The question that matters most: what problem AI is solving?

There are two fundamentally different types of AI investment in services — and most firms are conflating them. Some AI investments are aimed at delivering work faster, more consistently or with less strain on teams. Others touch pricing, scope realization and how value is perceived by clients. Pursuing both at once without defining the priority produces the pattern the data reveals: 48% of services leaders report accelerated innovation as a measurable benefit, 17 points above the cross-industry average. Only 50% report improved efficiency, 13 points below average. 

AI innovation without strategic direction produces capability, but profitability requires AI investments to be driven by commercial goals. That’s why strategy is the prerequisite for AI to generate enterprise value.

“For services firms, AI strategy has to be tied to the economics of the business model. That means defining not only the use case, but also the KPI it should move, the governance needed to support it, and the leadership owner accountable for results,” said Zac Taylor, Partner in Grant Thornton’s AI, Data and Technology Practice. “Without that discipline, firms may generate activity, but not measurable enterprise value.”

The disconnect between AI experimentation and ROI becomes even more clear when firms lose sight of what clients expect AI to deliver.

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To determine whether a particular AI use case will move the needle for their firm, leaders should ask three questions:

1. Does it solve a high-value business problem? Focus on use cases tied to revenue growth, margin improvement, client experience or risk reduction. Use cases built around technology capability alone rarely justify their investment. 
2. Can it be adopted at scale? Assess data availability, process maturity, integration requirements, and whether employees and clients will actually use it. 
3. Can we measure the impact? Define clear KPIs upfront, such as productivity gains, reduced cycle times, improved win rates, higher client satisfaction, or lower delivery costs. 

“We often think about AI use cases through a value/feasibility/adoption lens,” Kohm said. “The strongest opportunities are those that deliver meaningful business value, are technically achievable with available data and systems, and can be embedded into everyday workflows to drive sustained outcomes.”

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For professional services firms, every AI strategy must include:

1. Strategy and alignment: Prioritize use cases that improve client outcomes, engagement economics and talent productivity. 
2. Governance and risk: Place heightened focus on confidentiality, regulatory compliance, professional judgment and model transparency. 
3. Data and technical foundation: Maintain access to high-quality firm knowledge, methodologies and client data while maintaining strict security controls. 
4. Value identification and prioritization: Beyond back-office efficiencies, focus on use cases that enhance proposal development, research, delivery quality and client insight. 
5. Operating model and ownership: Clearly define accountability between business leaders, practitioners, risk functions and technology teams. 
6. Readiness and adoption: Invest heavily in change management and skills development, as value is realized only when professionals integrate AI into how they work and serve clients. 

The commercial-vs.-operational question is the diagnostic that separates firms building AI strategy from firms building AI activity. To determine where their AI strategy is aligned, services firms should review their most significant AI investments from the past 12 months under that lens. If leaders can't answer it definitively for each one — or if the answers differ across the leadership team — that gap is worth closing before building AI into more workflows.