How AI Is Reshaping Expertise, Economics, Governance, and Power
Most discussions about artificial intelligence in professional services focus on technology.
The conversation typically revolves around copilots, agents, automation, productivity gains, and workforce implications. Firms announce new AI initiatives. Vendors launch new capabilities. Leadership teams discuss efficiency improvements and future operating models. The dominant question is often relatively simple: how much work can AI eliminate?
That question matters.
But it may not be the most important question.
Because artificial intelligence is increasingly changing more than how work is performed. It is changing how expertise is delivered, how value is created, where costs accumulate, how institutions are governed, and where economic power increasingly resides. The implications extend far beyond productivity. They reach into the foundations of how professional-services firms have operated for decades.
For most of the modern history of the industry, expertise was scarce. Firms recruited talented graduates, trained them through apprenticeship models, accumulated institutional knowledge, and sold access to expertise through audits, consulting engagements, legal opinions, tax advice, and advisory services. The economics of the profession reflected that reality. Partnership structures, leverage models, utilization metrics, promotion systems, and compensation frameworks all evolved around the scarcity of expertise.
Artificial intelligence increasingly challenges that assumption.
Not because expertise disappears.
But because expertise is beginning to change economic form.
As expertise becomes embedded inside models, workflows, platforms, and infrastructure, professional-services firms are entering a structural transition that increasingly resembles earlier shifts around software, cloud computing, shared-service organizations, and platform businesses. The technology itself matters. The economic consequences may matter more.
This page brings together my research on how AI is reshaping the economics, governance, operating models, and institutional structures of professional-services firms.
The Four Structural Shifts
1. Expertise Is Becoming Infrastructure
For decades, organizations accessed expertise through people.
If a company needed legal advice, it hired lawyers. If it needed tax expertise, it hired tax specialists. If it needed strategic insight, it hired consultants. Expertise was delivered through professionals. The economics therefore followed labor economics. More demand required more professionals. More professionals required more hiring, training, and management.
Artificial intelligence increasingly changes that relationship.
Organizations can now access growing portions of expertise through platforms, workflows, APIs, models, and intelligent systems. Research can be generated through AI. Legal analysis can be accelerated through AI. Tax scenarios can be evaluated through AI. Audit procedures can increasingly be supported through AI. Human judgment remains essential, but expertise itself is becoming increasingly separable from the expert.
This mirrors a pattern seen repeatedly throughout technology history. Business processes became software. Software became a service. Computing became a utility. Capabilities that once sat inside organizations increasingly became infrastructure delivered through platforms. The same process may now be happening to expertise itself.
The implications are significant. Once expertise begins behaving like infrastructure, the economics of the industry built around expertise begin changing as well.
Related work:
2. The Economics Move Down the Stack
Most firms evaluate AI through the layer closest to the client.
They see consultants creating presentations faster. Auditors completing testing procedures more efficiently. Tax specialists generating analyses in minutes rather than hours. Lawyers reviewing contracts with AI support. These productivity gains are visible, measurable, and easy to understand.
The problem is that the visible layer often represents only a small part of the underlying economic system.
Every AI-generated output depends on models, data environments, orchestration layers, governance mechanisms, security controls, workflows, integrations, and infrastructure investments sitting underneath the user experience. The consultant sees the answer. The institution carries the infrastructure required to produce the answer.
This creates a surprisingly familiar pattern. Productivity gains become visible immediately. Infrastructure requirements emerge more gradually. Costs do not disappear. They move.
The AI Cost Stack provides a framework for understanding where AI economics increasingly sit inside professional-services firms:
- Human Cognition
- Machine Cognition
- Orchestration
- Governance
- Capital
As AI adoption expands, costs increasingly flow down the stack even while value remains concentrated near the top.
Related work:
3. Platform Economics Collide with Partnership Economics
The largest professional-services firms increasingly talk about AI platforms, reusable intelligence, integrated delivery environments, shared knowledge systems, and centralized operating capabilities.
Yet most continue to operate through governance structures originally designed for localized relationship businesses.
This creates a growing tension.
AI naturally rewards scale. It rewards standardization. It rewards reuse. It rewards centralized ownership of platforms, data, workflows, and intellectual property. Partnership economics often reward something different. Local profitability. Partner autonomy. Annual distributions. Utilization. Service-line optimization. Member-firm independence.
As long as expertise remained closely tied to local delivery, these tensions were manageable.
Reusable AI systems change the equation.
Once intelligence becomes embedded inside shared platforms, difficult questions emerge. Who owns the capability? Who funds development? Who captures the benefit? Which service line receives credit? Which member firm pays for the investment? How should value be distributed when the same intelligence is reused across hundreds or thousands of engagements?
These are not technology questions.
They are governance questions.
And increasingly, they sit at the center of the industry’s AI transformation.
Related work:
4. Platform Gravity: Why Control Increasingly Matters More Than Delivery
The previous three shifts naturally lead to a fourth question.
If expertise becomes infrastructure, and if infrastructure increasingly sits inside platforms, who controls the platform?
Historically, influence inside professional-services firms often followed client relationships, local economic performance, partner ownership, and delivery capability. As shared systems become more important, those sources of influence increasingly compete with something new: control over infrastructure.
- Technology platforms.
- AI environments.
- Knowledge systems.
- Delivery centers.
- Data architectures.
- Workflow engines.
- Governance frameworks.
The more activity moves onto shared systems, the more value, workflows, decision-making authority, and institutional influence tend to accumulate around the owners of those systems.
This is what I describe as Platform Gravity.
The concept is simple. Shared platforms increasingly become centers of economic and organizational gravity. As more professionals depend on them, more workflows move onto them. As more workflows move onto them, more data accumulates. As more data accumulates, the platforms become more valuable. Over time, influence increasingly shifts toward whoever controls the infrastructure layer.
Artificial intelligence may accelerate this dynamic dramatically.
As expertise itself becomes increasingly embedded inside platforms, ownership of the platform layer may become one of the most important sources of competitive advantage inside professional-services firms. What initially appears to be a technology decision gradually becomes a governance, economics, and power question.
Related work:
Key Questions for Boards
The most important AI questions increasingly have little to do with technology.
Instead, boards should be asking:
- Where is expertise becoming infrastructure?
- Which AI capabilities should the firm own?
- Which capabilities will increasingly be rented?
- Where is platform gravity accumulating?
- How will AI affect partnership economics?
- How will AI affect the leverage model?
- Which capabilities remain scarce once expertise becomes abundant?
- How will the firm continue developing judgment?
- Where will value ultimately accumulate inside the industry?
- Who controls the infrastructure on which future delivery increasingly depends?
These questions sit at the intersection of AI, governance, economics, operating models, and institutional strategy.
And they may ultimately matter more than the next generation of AI tools themselves.
Explore the Core Frameworks
Economic Reality
Modern professional-services firms increasingly depend on global delivery systems, centralized platforms, AI investment, shared infrastructure, compliance layers, and operational environments sitting outside the engagement itself. As a result, visible engagement economics and actual firm-level economics increasingly diverge, creating structural blind spots around profitability, scalability, and value creation.
Key concepts:
The AI Cost Stack
Artificial intelligence is often discussed as a productivity story. Increasingly, it may be an infrastructure story. As expertise becomes embedded in models, workflows, governance systems, and shared platforms, costs increasingly shift away from delivery teams and toward machine cognition, orchestration, governance, and capital. Understanding where those costs move may become more important than measuring how many hours disappear.
The Two-Speed Firm
Many professional-services firms increasingly operate as hybrid organizations containing multiple economic systems under the same brand. One part still behaves like a traditional partnership optimized around utilization and local profitability, while another increasingly behaves like a platform business built around centralized investment, scalable delivery, AI-enabled operations, and industrialized infrastructure.
Platform Gravity
As professional-services firms become increasingly dependent on shared AI environments, delivery platforms, workflow orchestration systems, cybersecurity infrastructure, and centralized operational environments, institutional influence increasingly shifts toward whoever controls the infrastructure the organization depends on operationally.
The Professional Services Transformation Paradox
Professional-services firms are exceptionally good at transforming clients, yet many struggle to transform themselves. Internal transformation increasingly collides with partner autonomy, governance fragmentation, utilization pressure, local economics, and platform standardization challenges, causing many programs to drift despite substantial investment and executive attention. Download Visual
About My Work
My work focuses on helping boards and senior leadership teams understand the structural, economic, operational, and governance forces reshaping professional-services firms.
This includes:
- private equity in professional services
- operating-model transformation
- AI and platform economics
- governance
- economic visibility
- delivery infrastructure
- and structural risks inside global professional-services firms
My services:
Contact
If this is relevant to your organization, feel free to get in touch.
Email: henrico.dolfing@roughtrailventures.com
Mobile: +41 79 326 4763
LinkedIn: http://ch.linkedin.com/in/henricodolfing