Enterprise AI is rapidly becoming a material operating expense. Yet many organizations still struggle to answer one fundamental question: Are AI investments creating enterprise value at a rate that justifies their cost? AI Yield provides a framework for answering that question by connecting AI spending to verified business outcomes.

There is one true metric for the profitability of enterprise AI: AI Yield – the verifiable value of AI-assisted enterprise outcomes, minus the cost of achieving them safely with human oversight.

The economics that supported the first wave of enterprise AI are changing. As the market matures, the question is shifting from how quickly organizations can adopt AI to whether the value it creates can justify what they spend.

The cost of AI is engineered before it is purchased. The price of a token tells only part of the story. Context, model choice, caching, tool calls and agent loops all shape what an AI-assisted workflow ultimately costs. Two organizations paying the same unit price can therefore arrive at very different economics.

But cost alone is not value. A token tells you what was consumed. It does not tell you what was achieved. Performance, reliability, and the level of human oversight required can matter far more to the business case than the price of an individual token.

That changes the conversation for enterprise leaders. Instead of optimizing AI around consumption or headline token prices, they need to trace every material AI investment to a verified outcome, use case by use case and workflow by workflow. Cost Per Verified Outcome provides a way to make that connection visible.

AI Yield = Verified business value – Fully loaded costs of successful delivery

The strategic question is no longer simply, “What does our AI cost?” It is: “How much verified value does every AI-assisted outcome create?”

Three significant factors changed in enterprise AI in early 2026, and their effects have compounded:

Effective subsidy

That funded enterprise AI is being withdrawn

Cost of a token

Depends less on the price list and more on how workflow is engineered

Value a token

Creates is not closely correlated with what it costs – performance and human oversight play a major part too

Enterprise leaders must ask whether every dollar or euro can be traced to a verified outcome, process by process, workflow by workflow, person by person.

However, conventional cost management and traditional cloud techniques cannot measure the combination of these factors accurately.

The AI Compass is Capgemini’s three-part PoV series on the economics and business transformation shaping enterprise AI authored by Capgemini Invent experts.

Mastering Tokenomics:

The keys to AI Yield show how leaders can govern AI as a capital-allocation decision, connect investment to verified outcomes, and improve value across every workflow. It is designed for you to learn how to govern AI as a capital-allocation decision, with every investment tied to a verified outcome.

What AI Yield and this report means for the CEO

For the CEO, AI Yield is about capital allocation, not technology. Without controls, AI costs affecting margins can grow without board scrutiny. Protecting margins requires a balance of value creation with cost discipline. This paper equips CEOs with the key questions they need to answer, before the board asks them.

AI costs are an executive issue requiring clear accountability. Organizations must maximize value per token through efficient architecture, governance and model selection. Lower token prices alone rarely reduce costs because usage tends to grow.

For leaders responsible for delivering AI, AI Yield depends on reliable measurement and sound architecture. That means choosing models based on evidence and balancing frontier LLMs with domain-specific, locally hosted SLMs. This report delivers our view on how to achieve this.

Cost control without value creation produces cheaper but still unprofitable AI estate – cost discipline alone cannot solve the AI cost challenge.

We use the Capgemini AI Economics Framework to model this, then translate what it means for CEOs, CIOs, CTOs, and CAIOs.

As AI moves from experimentation to a material business investment, leaders need a more rigorous way to evaluate value creation. The most effective organizations are shifting the conversation from AI consumption to AI economics by asking five critical questions:

  • Which AI use cases generate measurable business value?
  • What is the cost per verified outcome?
  • Where is human oversight improving outcomes and where is it creating inefficiency?
  • Which combination of models, workflows, and architecture delivers the highest yield?
  • How much AI spending can be directly linked to business outcomes?

The answers to these questions determine whether AI becomes a source of sustainable productivity and margin improvement, or simply a growing operating expense.

Efficiency must be designed in

In the report’s modeled deployment, running a qualifying workload on owned infrastructure costs 88–94% less than purchasing the same volume from frontier model providers. And, hosted open-weight capacity may be more economical when control requirements do not justify ownership.

Own the routing policy

Different workloads need different models. Routine, high-volume tasks can be routed to efficient open-weight or domain-specific models, while complex, high-stakes work may demand frontier models. The choice should be driven by measured performance, context, and consequence, not preference or price alone.

Stop managing AI as a token bill

The better question is not “How do we buy cheaper tokens?” It is, “Which combination of model, workflow, oversight and architecture creates the highest yield?”  Focusing only on price per token may cut spending while reducing reliability and business value. AI Yield is the verified, tracked value from an AI-assisted outcome, minus the full cost of achieving it safely, including necessary human oversight.

Enterprise AI is moving from experimentation to industrialization and from industrialization to economics. Leaders now need to prove what each material workflow costs, what outcome it produces and what value it creates.

AI Yield turns AI economics into an operating discipline. Through Capgemini’s AI Economics Stress Test, organizations can assess use cases across models, routing, human oversight, volatility and risk, and outcome economics. The Tokenomics Framework puts that insight into practice, connecting Cost Per Verified Outcome with evaluation gates, telemetry and named accountability. Together, they create the discipline to make AI investment measurable, governable, and grounded in the outcomes it delivers.

Measure the Economics of Enterprise AI

Learn how AI Yield helps executives connect AI investment, governance, infrastructure, and operating performance to verified business outcomes.

Download AI Compass: Mastering Tokenomics