Ben Thompson’s Paradigm Shifts and the Winner’s Curse highlights a brutal truth: the very strengths that make incumbents dominant in one era often become the shackles that hold them back in the next. This is not just a lesson in tech history. It is a warning flare for today’s professional services leaders.
For decades, consulting, legal, and advisory firms have built empires on human capital, billable hours, and hard-earned reputations. A new breed of competitors is emerging. These are AI-native professional services firms, built from the ground up with algorithms rather than org charts as the core engine. They do not play by your rules.
Real-World Examples: How AI Is Already Impacting Professional Services
- McKinsey & Company has deployed around 12,000 AI agents to assist consultants with tasks like data analysis and presentation preparation, helping them shift toward outcome-based work, which now makes up about 25% of their revenue. LexisNexis, Wall Street Journal
- EY launched its EY.ai Agentic Platform, deploying 150 AI agents to support 80,000 tax professionals with tasks like data collection and compliance. The firm sees AI as a productivity enhancer that could enable growth without cutting headcount. Business Insider
- PwC is reworking training for junior accountants who now oversee AI-run audit tasks rather than execute routine work. They have also introduced “assurance for AI” services to help clients manage responsible AI use. Business Insider
- Crete Professionals Alliance, backed by Thrive Capital, plans to invest $500 million in AI-powered roll-ups, integrating tools for data mapping and audit memo writing to enhance accounting efficiency. Reuters
- UBS is focusing AI efforts on boosting productivity, with about 60% of its efforts targeting onboarding, KYC, and internal chatbots. Their internal AI assistant “Red” is now used by 52,000 employees. Financial News London
- Legal sector: Top Australian law firms, including Baker McKenzie and Clayton Utz, use generative AI tools such as Harvey and RelativityOne for contract analysis and legal research. Human lawyers review and validate all AI outputs. The Australian
- A Reuters podcast highlights how AI is reducing time-consuming tasks such as research across law, audit, and consulting, but also threatens traditional billable-hour models. Reuters
The Paradigm Shift: From Human Hours to Autonomous Intelligence
This is the moment when the rules change. For decades, the traditional model has run on human expertise applied client by client and hour by hour. Much of that work has been presented as highly bespoke and uniquely tailored, but in reality it often draws from pre-existing playbooks, templates, and solution sets that are lightly customized for each engagement. The perception of deep customization has been part of the value proposition, even when the underlying methods are largely standardized.
AI is beginning to break this illusion. The shift is away from labor-intensive delivery, often masked as handcrafted expertise, toward scalable agent-based autonomous intelligence. Instead of a team of humans manually adapting familiar solutions, AI agents can ingest a client’s specific context, synthesize relevant patterns from vast data sets, and generate responses or solutions that are genuinely unique in structure, scope, and speed.
In this new model, scalability is not about hiring more associates to serve more clients. It is about orchestrating fleets of specialized AI agents that can operate in parallel, adapt instantly to new inputs, and continuously improve as they learn from each engagement. The economics and the client experience both change. Solutions arrive faster, are more precisely aligned to the problem at hand, and can be iterated in real time rather than across billing cycles.
Traditional + AI vs. AI-Native: A Side-by-Side Look
| Dimension | Traditional Firm Using AI | AI-Native Firm |
|---|---|---|
| Core Model | AI enhances human work | AI is the engine, with humans as supervisors |
| Client Delivery | AI supports humans in research and drafting | AI produces deliverables, humans provide trust and context |
| Pricing | Billable hours with some fixed-fee experimentation | Subscription, outcome- or usage-based from day one |
| Talent | AI skills added to human-led roles | Roles around AI system design, governance, integration |
| Scalability | Capped by human capacity | Scaled by compute power and data access |
| Culture | Risk-averse and legacy-bound | Experiment-driven, nimble, innovation-focused |
A traditional firm “with AI” remains tied to its legacy model. An AI-native firm has engineered its escape from that orbit entirely.
Why Change Is Not Optional
Disruption theory shows incumbents fall not because they cannot see the future, but because acting hurts their current model. The billable-hour structure is a prime example. AI reducing junior hours hurts near-term economics even if the long-term upside is massive.
Delay is dangerous. AI-native firms may start small, but they improve fast and climb the value chain rapidly. By the time they rival traditional firms in quality, their cost base will be so much lower that competing feels like swimming upstream.
The Playbook for Leaders
If you are part of the C-suite in a professional services firm, you have a choice: treat AI as a tool to make the old model faster, or make it the foundation for a new model. That means:
- Reimagine roles so humans emphasize judgment, trust, and strategic creativity.
- Shift pricing to reflect outcomes delivered, not hours spent.
- Build internal AI-native teams that can move fast and ship without legacy constraints.
- Own AI governance and ethics as a competitive differentiator.
The Inflection Point
The next decade will test whether traditional firms can compete with those born into the AI era. The advantages are there. Established firms bring trust, client relationships, and domain expertise. AI-native challengers bring speed, scalability, and cost-efficiency.
The winners will be those who fuse the trust and insight of the old world with the scale and velocity of the new. Standing still is not an option.
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