Multi-Model AI Budgets: Measure Cost per Accepted Result

As corporate expenditure on new technology continues to climb, a new multi-model AI strategy is becoming the standard for large organizations. According to a recent report from the Capgemini Research Institute, in the survey, nearly six in ten executives reported that their AI spending significantly exceeded initial budgets. This financial pressure is forcing a move away from reliance on singular, expensive platforms toward a more flexible multi-model AI strategy that balances performance with cost-efficiency.

Multi-model AI Strategy: The Hidden Costs of Enterprise Deployment

The primary driver behind these budget overruns is a lack of visibility into indirect expenses. While direct costs like tokens are easy to track, many organizations fail to account for the labor-intensive requirements of human oversight, governance, and necessary rework. In fact, only one-third of surveyed leaders feel confident in their ability to estimate these hidden costs. This oversight has led to a situation where 21% of companies have exceeded their AI budgets by more than 30%, often resulting in reduced free cash flow or cuts to other discretionary spending.

Multi-model AI Strategy: Why Organizations Lack AI Yield Visibility

A critical issue identified in the report is the low adoption of ‘AI Yield’ metrics. Only one in five organizations calculates this figure, which measures the actual value generated by an AI-assisted outcome against the total cost of safe delivery. Without this data, firms often struggle to match spending with tangible business outcomes. By adopting a multi-model AI strategy, companies hope to create a more resilient architecture that allows them to swap models based on specific task requirements rather than being locked into high-cost, one-size-fits-all solutions.

Managing AI as an Economic Portfolio

Volker Darius, CEO of Capgemini Invent, suggests that the industry must stop treating AI as a standard procurement issue. Instead, he advocates for managing AI as a portfolio of economic outcomes. This means that as organizations mature, they must prioritize cost discipline alongside value creation. A multi-model AI strategy enables this by allowing teams to utilize smaller, specialized models for mundane tasks while reserving expensive, high-capacity models for complex, high-value operations. This tiered approach is essential for long-term sustainability.

The Bottom Line for Future Scaling

• 59% of executives reported AI costs higher than expected.
• Among affected organizations, reported expenditure averaged 24% above expectations or budget.
• Only 56% of organizations are confident in future cost forecasting.
• CFOs are increasingly involved in vetting major AI investments.

Ultimately, a multi-model AI strategy represents a transition toward maturity. As companies scale their agentic use cases, the ability to control costs will determine which organizations actually benefit from the technology versus those that simply burn through capital. Future growth depends on moving beyond hype and focusing strictly on verified economic value.

For additional reporting, see Indiatechnologynews.in Reporting.

Source: Capgemini research announcement.

Calculate cost per accepted result

Capgemini’s announcement describes a September 2026 survey of 500 senior executives at large organizations. Its reported overspend should not be treated as a forecast for every small project. A multi-model design also introduces evaluation, routing, monitoring, and maintenance work.

For example, a hypothetical pilot costing 600 units of currency and producing 200 accepted results costs 3 units per accepted result. The example is arithmetic, not a measured benchmark. A cheaper token price is not automatically a cheaper accepted outcome.