AI Economics Review

Most Firms Focus on AI Use Cases. Very Few Understand the Economics Underneath Them.

Professional-services firms are investing heavily in artificial intelligence.

Partners are experimenting with copilots. Service lines are launching AI-enabled offerings. Delivery teams are automating workflows. Technology groups are deploying new platforms. Boards are asking when productivity gains will start appearing.

The discussion is usually framed around capabilities.

• What can AI do?
• How many hours can be saved?
• Which processes can be automated?
• Which services can be transformed?

Those are important questions.

But they are not economic questions.

The harder question is whether AI is actually improving the economics of the firm.

In many cases, costs do not disappear. They move.

Labor costs may decline in one part of the organization while spending increases elsewhere through technology platforms, data infrastructure, governance requirements, model oversight, cybersecurity controls, implementation programs, and organizational transformation efforts.

As a result, firms can experience substantial AI adoption while struggling to understand whether they are creating economic value.

That is where the AI Economics Review starts.

The AI Cost Stack

Most discussions about AI in professional services assume a relatively simple equation:

AI reduces effort.
Reduced effort lowers cost.
Lower cost improves profitability.

Reality is more complicated.

As AI becomes embedded into professional-services firms, costs increasingly shift across multiple layers of the organization rather than disappearing altogether.

The result is what I describe as the AI Cost Stack.

The framework highlights five layers that increasingly shape the economics of AI-enabled professional-services firms.

Human Cognition

Partners, managers, specialists, and client-facing professionals who provide expertise, judgment, relationships, trust, and accountability.

Machine Cognition

Large language models, AI agents, copilots, foundation models, and other AI capabilities performing cognitive work traditionally executed by humans.

Orchestration

Workflows, automation, process redesign, prompt engineering, data integration, operating-model adaptation, and the coordination mechanisms required to make AI productive at scale.

Governance

Risk management, compliance, cybersecurity, quality control, model oversight, regulatory requirements, auditability, and defensibility.

Capital

Technology investments, cloud infrastructure, data platforms, licensing costs, implementation programs, and ongoing operating expenditure required to support AI adoption.

The central question is therefore not whether AI reduces effort.

The central question is whether the savings generated in one layer exceed the new costs created in the layers underneath it.

That is exactly what the AI Economics Review is designed to assess.

Background

The AI Economics Review emerged directly from observing recurring tensions across professional-services firms as artificial intelligence moved from experimentation into the operating core of the firm.

Many organizations still evaluate AI primarily through productivity metrics, use-case adoption, or projected labor savings. Yet as AI becomes embedded into service delivery, knowledge management, risk management, workflow orchestration, compliance, and client-facing processes, the economic picture becomes significantly more complex.

The central challenge is that AI often removes costs in one part of the organization while creating new costs elsewhere. Labor requirements may decrease, while investments in infrastructure, governance, model oversight, cybersecurity, data architecture, platform integration, and organizational transformation increase simultaneously.

As a result, many firms struggle to answer a seemingly simple question:

Is AI actually improving the economics of the firm, or merely changing where costs sit?

The AI Economics Review was developed to help boards and executive teams answer that question through a structured economic lens rather than a purely technological one.

These themes are explored further in:

• The AI Cost Stack: Why Professional Services Firms Are Looking for AI Savings in the Wrong Place
• When Expertise Becomes Infrastructure: How AI Is Changing the Economics of Professional Services
• The Professional Services AI Paradox: How the AI Platform Economy Is Colliding with the Partnership Model
• The Cost Reality: Why Front, Middle, and Back Office Economics Don’t Add Up
• The Two-Speed Firm: Why Professional Services Firms Are Quietly Splitting Into Multiple Economic Systems Under One Brand
• The Platform Gravity Problem: Why Control Over Shared Systems Increasingly Shapes Power Inside Professional Services Networks
• The Silent Engine: How Global Delivery Centers Are Rewiring Professional Services Firms

What We Assess

The AI Economics Review examines AI through an economic and operating-model lens rather than a technology lens.

AI Cost Stack Assessment

• Which layers create the largest AI-related costs?
• Where are savings actually appearing?
• Which costs remain hidden?
• Are AI investments creating firm-level value or simply shifting costs between functions?
• How sustainable are the economics once governance and infrastructure requirements are included?

Economic Visibility

• Where AI investments are currently being made
• Where benefits are expected to emerge
• Hidden cost layers
• Cost transfers between organizational units
• Impact on contribution margin and profitability

Operating Model Impact

• Effects on service delivery
• Impact on global delivery centers
• Changes to leverage models
• Workflow redesign requirements
• Scalability constraints

Governance and Risk

• Model governance
• Regulatory requirements
• Quality assurance
• Defensibility
• Auditability
• Risk-management implications

Strategic Implications

• Long-term economics of AI adoption
• Implications for partnership structures
• Capital requirements
• Competitive positioning
• Future operating-model scenarios

Deliverables

The review typically includes:

Executive Assessment

A board-level summary of findings, observations, and recommendations.

AI Economics Diagnostic

An assessment of the current AI cost structure and economic impact across the organization.

Cost Stack Analysis

A structured view of costs and value creation across the five layers of the AI Cost Stack.

Strategic Discussion

Implications for governance, operating models, investment priorities, and long-term competitiveness.

Who This Is For

The AI Economics Review is designed for:

• Executive Boards
• Supervisory Boards
• Managing Partners
• CEOs
• CFOs / COOs
• CIOs
• Digital Transformation Leaders
• Strategy Leaders

Particularly within:

• Accounting Firms
• Consulting Firms
• Audit Firms
• Tax Firms
• Legal Firms
• Other Professional-Services Organizations

Why This Matters

Many firms are currently asking:

How do we implement AI?

The more important question may be:

How does AI change the economics of the firm?

The answer increasingly determines investment decisions, operating models, governance structures, delivery strategies, and long-term competitiveness.

The firms that understand these economics early will have strategic options.

The firms that focus only on productivity metrics may discover that the cost structure underneath them has already changed.

Contact

If your firm is currently reassessing operating-model economics, scaling centralized operations, investing heavily into AI or platform delivery, or undergoing major transformation, feel free to get in touch.

Email: henrico.dolfing@roughtrailventures.com
Mobile: +41 79 326 4763

LinkedIn: http://ch.linkedin.com/in/henricodolfing