Squeezed from Both Sides: Professional Services in the Age of AI

The professional services industry has entered a new phase as AI becomes mainstream in daily work. A new Thomson Reuters Institute report finds that organization-wide use of generative AI has nearly doubled, with 40% of professional service firms now using GenAI, up from just 22% a year ago ([1]). Over 80% of current users engage with these tools at least weekly, and over 90% expect AI to be central to their workflows in five years ([2]). This surge in adoption marks a critical mass – AI is no longer a futuristic pilot project but part of core operations, signaling what one industry leader calls a “strategic phase” of AI integration.

However, the rapid embrace of AI is accompanied by new uncertainties. Even as usage grows, measuring success remains a challenge. Only 18% of professionals say their organizations track the return on investment of AI tools ([3]), and even fewer assess AI’s impact on broader business outcomes like client satisfaction. This suggests many firms are diving headlong into AI without a clear handle on whether these technologies are actually boosting performance or profitability.

At the same time, professionals themselves are torn between excitement and anxiety over AI’s growing role. While 66% of practitioners support using generative AI in their daily work and are optimistic about its potential, many also foresee significant disruption ahead ([4]). There is mounting concern that AI could threaten jobs, erode the billable hour model, and even redefine traditional advisory roles. In short, AI’s arrival at scale presents both opportunity and ambiguity – promising new efficiencies and capabilities, but also raising existential questions about the future of work in consulting, law, and accounting.

It’s not just the firms embracing AI – their clients are as well, and this client-side adoption is starting to erode demand for traditional advice. In the public sector, the U.S. General Services Administration recently demanded that its consulting vendors slash costs, calling their initial AI-enabled savings proposals “wholly insufficient…insulting” ([1]). In the corporate world, in-house teams have quickly moved from experimenting with AI to rolling it out at scale. A Deloitte Legal study found that 61% of legal departments are now actively deploying AI, with just 2% reporting no AI use at all – a stunning drop from 76% just two years ago ([2]). In fact, 10% of corporate legal teams say AI is already fully embedded in daily workflows ([3]). Many are even exploring advanced "agentic" AI systems that can automate multi-step tasks without human prompts, further reducing their reliance on outside counsel or consultants.

Armed with generative AI, clients can accomplish analysis and planning tasks internally in a fraction of the time and cost. As one industry observer notes, when clients realize "it only takes five minutes to build a SWOT analysis" with AI, the traditional consulting playbook falls apart ([4]). Routine deliverables that once justified hefty fees can now be generated by AI in seconds, changing what clients are willing to pay for. This new reality is driving clients to demand more value for money. In legal services, 78% of general counsel say cost reduction is the top benefit they expect when their law firms use AI ([5]) – implicitly pressuring firms to pass on efficiency gains rather than pocket them. Simply put, clients are starting to question why they should pay by the hour for work that AI can do in an instant.

This outside-in pressure is forcing professional service providers to rethink their pricing models and scope of work. Some clients are reducing the volume of work they outsource, choosing in-house AI and analytics teams instead of hiring consultants or lawyers for every project. Others continue to use external advisors but are pushing for alternative fee arrangements, outcome-based contracts, and proof of technology use. The net effect is a squeezing of the traditional high-margin, labor-intensive model from the client side: unless firms can demonstrate superior value – through specialized expertise, judgment, or technology – they risk seeing their clients take more work in-house.

In response, the major players in consulting, law, and accounting are aggressively infusing AI into their services and operations. The Big Four firms, long built on armies of junior staff, are now investing billions in AI tools to automate research, analysis, and routine tasks. KPMG, for example, has reportedly committed $2 billion over five years to integrate AI across audit, tax, and advisory work, aiming to generate an extra $12 billion in AI-enabled revenue ([1]). Just last week, KPMG was named an OpenAI "Elite Partner," giving its 270,000+ professionals access to OpenAI’s latest generative models and tooling ([2]). Earlier this year, KPMG also partnered with Anthropic to deploy its Claude AI assistant to all 276,000 employees in 144 countries via a global platform for tax and deal advisory work ([3]). As KPMG’s global CEO described, the goal is to “bring AI directly into the flow of enterprise processes, enabling faster decisions, more intelligent automation and simpler user experiences” for both its teams and clients ([4]).

Others are making similar moves. Deloitte has developed in-house AI systems like "Zora" to automate invoice processing and financial analytics ([5]). EY’s global workforce is augmented by 150 AI "agents" serving 80,000 tax professionals, collectively handling millions of compliance cases and tasks that used to require legions of junior accountants ([6]). PwC, which made a $1 billion AI investment in recent years, now uses AI tools such as GL.ai to handle auditing of financial records like journal entry testing and fraud detection, once time-consuming manual work for junior auditors ([7]). Major law firms, too, are embracing AI-driven platforms: for instance, Ropes & Gray recently adopted an "agentic" AI called Celeste integrated into its deal management system to accelerate legal research and client service across the firm ([8]).

While these technologies promise huge efficiency gains, they are also reshaping firm operations and staffing. Internal research suggests consulting firms can now staff smaller project teams and still accomplish the same work, thanks to AI-enhanced productivity ([9]). In practice, this is translating into a slowdown in hiring and even workforce reductions in support and junior ranks. McKinsey announced plans to cut 25% of its non-client-facing roles due in part to AI-driven productivity improvements ([10]). Across the Big Four, entry-level recruitment is declining; graduate hiring in some service lines is down 44% year-over-year as automation takes over many junior tasks ([11]). In big law, firms like Baker McKenzie have eliminated hundreds of support staff roles in research, knowledge management, and administration, explicitly citing new AI tools that can now handle work "previously performed by these teams" ([12]). The immediate impact for incumbents is a leaner leverage model – but also questions about how to cultivate new talent and maintain client service quality in the age of AI.

The AI revolution is not only enabling existing firms to streamline operations – it’s also giving rise to entirely new kinds of professional service providers. In the legal arena, a wave of AI-native startups is challenging the traditional law firm model. One standout example is Norm: a three-year-old company that has built an AI-driven law firm from scratch. Backed by major investors and now valued at $1.2 billion after a fresh $120 million funding round ([1]), Norm’s approach is radically different. The firm’s platform combines artificial intelligence with experienced attorneys to deliver services like contract analysis, compliance, and due diligence at scale. Its affiliated practice, Norm Law LLP, uses in-house developed AI agents to handle much of the work, while seasoned lawyers oversee the output and focus on complex strategy and client counseling ([2]). By leveraging AI, Norm operates with far fewer junior staff and promises faster turnaround. Crucially, it prices services on an outcome-based model rather than billing by the hour, directly passing efficiency gains to clients ([3]). This client-aligned pricing flips the traditional incentive structure on its head – unlike legacy firms that profit from longer engagements, Norm is rewarded for delivering results more efficiently ([4]).

Such ventures are not limited to law. In tax and accounting, even established players are introducing AI-powered platforms that could reduce the need for external advisors. Just this week, LexisNexis announced a collaboration with a tax consultancy to integrate its “Protégé” generative AI into the Tolley+ tax research platform, aiming to automate complex tax advisory support for accounting firms ([5]). And in management consulting, new tech-driven services promise to offer AI-generated strategic analysis on-demand, potentially bypassing traditional consultancies. The rise of these platforms and startups – from legal AI co-pilots like Harvey, to accounting automation tools, to independent AI consultants – underscores that the next generation of professional services may be delivered as much by algorithms as by MBAs and JDs. These upstarts often operate with lower overhead and novel fee models (including subscriptions and fixed fees), putting additional pressure on incumbents to innovate or acquire to keep up.

Amid the race to automate, the most profound question for professional services is what remains uniquely human. AI’s encroachment on routine “grunt work” is creating what some analysts call a growing "professional judgment gap." As AI takes over tasks that used to train junior consultants, accountants, and lawyers, those entering the profession lose crucial opportunities to develop practical experience and decision-making skills ([1]). In effect, the traditional pyramid model of talent development is cracking: artificial intelligence is wiping out the entry-level work that once produced the next generation of expert advisors ([2]). If that foundational training ground disappears, firms face a looming talent crisis – who will cultivate the high-level judgment and nuanced expertise that clients pay top dollar for?

Paradoxically, even as AI threatens the old apprenticeship model, it elevates the importance of human insight at the highest levels. With advanced tools handling analysis, calculations, and first drafts, senior professionals are shifting into roles of strategic oversight and quality control. Thought leaders suggest the "future lawyer isn’t a document reviewer" but rather a "symphony conductor" skillfully orchestrating AI outputs, data, and domain knowledge to solve complex problems ([3]). In an AI-saturated environment, a consultant’s or lawyer’s value will hinge less on cranking out reports or code and more on asking the right questions, interpreting AI-generated insights, and exercising ethical and strategic judgment that machines cannot. This is the new human edge – the ability to contextualize and guide AI-driven work to deliver outcomes that truly matter.

For firms, this means reimagining roles, career paths, and value propositions. Technical and AI literacy is now seen as a critical skill for professionals – 96% of legal leaders say tech and AI skills are becoming essential for lawyers ([4]). Yet, despite heavy tech investment, 84% of organizations admit they haven’t updated roles or workflows to reflect AI’s presence ([5]). Leaders must therefore act decisively to bridge this gap: by redesigning training programs, rethinking leverage models, and defining new career paths that cultivate judgment in the age of automation. As AI handles more of the measurable work, expert human judgment – the deep contextual understanding, creativity, and empathy that consultants and lawyers provide – becomes the definitive differentiator ([6]). Forward-looking firms will pivot to harness AI as a tool to amplify their advisors’ cognitive strengths, not replace them, ensuring that they remain indispensable partners to clients navigating an increasingly complex, AI-enabled business landscape.