​The AI Spending Reckoning: What Your Board Is About To Ask

Your board is about to ask a question you may not be ready to answer: Where is the ROI on all that AI spending? A Forbes Tech Council article warns that the AI spending reckoning is imminent, as executives face mounting pressure to prove that their artificial intelligence investments deliver real, measurable value—not just technology adoption for its own sake. The piece argues that most current measurement approaches focus on the wrapper around the work, like project count or model deployment, rather than the work itself—actual business outcomes such as revenue growth, cost reduction, or customer retention. This mismatch between spending and outcomes is now drawing direct scrutiny from boards of directors, who are tired of vague promises and want hard numbers.

The context is clear: after a multiyear surge in AI investment—global AI spending topped $200 billion in 2025, with no sign of slowing—corporate leaders are waking up to a hard reality. A 2025 Gartner survey found that 68% of CIOs report their board has requested a formal ROI analysis on AI projects within the past six months, up from 35% in 2023. Meanwhile, McKinsey estimates that 70% of large-scale AI initiatives fail to achieve their targeted returns, often due to poor measurement practices, lack of data readiness, or misaligned incentives.

Key details in the Forbes piece underscore the problem: companies routinely report metrics like number of AI use cases, models deployed, or cloud spending—what experts call “cost of experimentation.” But boards want metrics like incremental profit per AI-driven decision, customer lifetime value lift, or operational efficiency gains. The article cites the example of a Fortune 500 retailer that spent $40 million on a supply chain AI system, only to realize later that it had no systematic way to track whether inventory savings actually materialized.

Analysis from industry observers deepens the warning. “We’re in the trough of disillusionment for enterprise AI,” says Dr. Karen Lin, a Harvard Business School professor who studies technology ROI. “The next phase will separate companies that treat AI as a cost center from those that treat it as an investment with a clear P&L.” The Forbes piece echoes this: the AI spending reckoning is not about cutting budgets, but about demanding accountability. Executives who cannot answer basic questions like ‘What problem did this solve?’ and ‘How much did it save or earn?’ risk losing credibility—and funding.

What happens next? A wave of internal audits, more disciplined governance, and a likely consolidation of AI efforts around a smaller number of high-impact use cases. Boards will ask for unit economics: cost per inference, revenue per AI-enabled action, and break-even timelines. Expect vendors to face tougher procurement standards. The companies that thrive will be those that bake ROI tracking into their AI development lifecycle from day one. The AI spending reckoning is coming. The only question is whether you'll be ready with answers.