- Only a third of executives can confidently estimate indirect AI costs, including human oversight, rework and governance
- Most organizations cannot directly match AI spending with successful business outcomes
- Just one in five organizations calculates AI Yield, which captures the verified value of an AI-assisted outcome after accounting for its full safe-delivery cost
- As AI adoption scales, only 56% of organizations are confident they can forecast future costs
Mumbai/Paris, November 12, 2026 – AI costs are now a concern for 53% of executives according to a new ‘snapshot’ report by the Capgemini Research Institute, based on a September 2026 survey of 500 CXO-level senior executives from large organizations. Beyond direct cost such as tokens, executives lack visibility into the full cost of safe AI deployment, including human oversight, rework and governance. To address this, businesses are both tightening cost controls and exploring multi-model strategies. As organizations scale AI and prepare for more agentic use, the research highlights a need for greater focus on the business value generated by AI transformation.
A lack of cost visibility across the enterprise risks serious financial impact
In the past 12 months, 59% of executives say their organization’s AI-related expenditure was significantly higher than expected or budgeted. Among the organizations affected, expenditure was 24% above expectations or budget on average, and around one in five organizations (21%) overshot their AI budget by more than 30%. Concern about AI costs has risen over the past year, and only 56% are fully confident they can forecast how AI costs will change as adoption scales.
The report points to a lack of cost visibility across the enterprise. Half of executives say AI spending is approved without adequate visibility into the full cost. Organizations also lack confidence in accurately estimating the full cost of their existing AI initiatives, particularly indirect costs such as human oversight, rework and governance.
The financial impact can be serious for those affected. Two thirds of organizations that overshot their AI budget faced negative financial consequences, such as lower free cash flow or a reduction in discretionary spending. In that context, CFOs are now more involved in approving major AI investments.
“As AI moves deeper into business workflows, organizations need to stop treating its cost as a technology or procurement issue and start managing AI as a portfolio of economic outcomes,” said Volker Darius, CEO of Capgemini Invent. “Cost discipline alone will not create a profitable AI estate, just as value creation without cost control will not be sustainable. Organizations need to measure cost, quality and business value together, use the most appropriate model for each task, and account for the human oversight required to deliver outcomes safely. By continuously measuring AI Yield, organizations can make more informed capital-allocation decisions and scale AI with confidence.”
Multi-model strategies lead AI portfolio plans
In response to unexpected AI spend, over half (53%) of organizations increased expenditure control, either by tightening budgets or controls for the affected initiative, introducing organization-wide AI spending controls, or reducing the initiative’s scale or scope, Only one in five rethought tools or processes, including which activities should be AI-led rather than human-led, or changed the AI solution, models, configuration or model routing.
Beyond these immediate responses, more than three in five executives expect their organization to adopt a multi-model strategy over the next 12 months, dynamically selecting models based on cost, capability, latency, risk, and other business requirements. Executives expect greater use of lower-cost proprietary models where they are sufficient, as well as small or specialized models for specific tasks. Nearly three in five organizations also plan to expand their use of open-weight models.
AI Yield brings successful business outcomes into focus
Although two thirds of executives say they are confident they can verify the business value generated by AI-assisted outcomes, most organizations cannot directly match AI spending with successful business outcomes – either because they do not formally measure them, because they track costs or usage but not successful business outcomes or because they track them separately. Demonstrating sufficient value to secure approval and funding for AI investments remains particularly challenging, with 57% citing it as their biggest obstacle.
Only one in five organizations calculates AI Yield, a metric that captures the verified value of an AI-assisted outcome after accounting for its full safe-delivery cost. The business value most often cited is employee productivity or released capacity followed by operating-cost reduction and faster processes or cycle times. Less than a third (32%) cite revenue growth, including growth from new products, services or business models.
The metrics used to manage AI economics are therefore expected to change. Today, 60% primarily use total AI expenditure against budget, but only 44% expect to prioritize it in the next 6-12 months. First-pass success rate[2] is set to become the top-priority metric, and cost per completed task or workflow is also expected to be prioritized. These measures put successful outcomes, rather than AI consumption alone, at the center of AI economics.
Capgemini’s AI Compass
This ‘snapshot’ report follows the first issue of The AI Compass, Capgemini’s new paper on AI economics. The Era of AI Yield: Mastering Tokenomics to Maximise Enterprise Value explores how organizations can move beyond AI consumption and cost metrics to measure AI Yield: the verified value of AI-assisted outcomes, minus the full cost of achieving them safely. The publication sets out the implications for CEOs and technology leaders, from capital allocation and governance to model routing, workflow engineering and human oversight.
To access the full ‘snapshot’ report: https://www.capgemini.com/insights/research-library/ai-economics/
Methodology of the report
In September 2026, the Capgemini Research Institute surveyed 500 CXO-level senior executives from organizations with annual revenues above USD 1 billion, across 10 countries (United States, China, India, United Kingdom, France, Germany, Japan, Netherlands, Italy and Spain) and 13 industries. Respondents primarily represented AI strategy and leadership, platforms and engineering, value realization, technology finance and AI cost management. The survey was complemented by two in-depth interviews with industry experts.[1] Open-weight models are AI models whose parameters (“weights”) are publicly available, enabling organizations to customize and deploy them with greater flexibility and control over cost.[2] First-pass success rate refers to the percentage of AI-assisted tasks, workflows or outcomes completed successfully on the first attempt without rework, escalation, correction or additional human intervention.
