Organizations Need 3 Data Sources to Measure AI ROI as Budgets Triple
Updated
Updated · InfoWorld · Jul 23
Organizations Need 3 Data Sources to Measure AI ROI as Budgets Triple
3 articles · Updated · InfoWorld · Jul 23
Summary
Most companies still cannot tell which AI projects are profitable because provider billing shows token spend, not which customer, feature or outcome generated the cost.
Accurate AI ROI requires stitching together 3 inputs—normalized cost data, business data and application-layer telemetry captured before each model call leaves the app.
That telemetry must log 6 elements, including request tracing, feature attribution, agent steps, retries, model choice and business outcomes; none of it appears on AI invoices.
Agentic AI makes the gap more urgent because one user request can trigger dozens of model calls, fallbacks and tool invocations, with aggregate costs surfacing only weeks later.
The report argues building this internally is often a trap: production systems must handle millions of events per hour and constant schema changes, while boards want ROI visibility now, not in 18 months.
What hidden costs, four times larger than model fees, are secretly making your AI initiatives unprofitable?
As AI spending skyrockets, are enterprises building valuable products or just massively unprofitable workflows?
Will startups solving AI’s cost crisis become the next critical layer in the enterprise tech stack?
AI ROI in 2026: Why Surging Enterprise Investment Still Fails to Deliver Measurable Value—and How to Fix It
Overview
As AI spending continues to surge in mid-2026, organizations are accelerating investment and deepening integration across the enterprise, driven by perceived gains in productivity and decision-making. CFOs now see AI as a core capability, supported by growing AI literacy and modernization of legacy systems. However, despite this momentum, many companies struggle to demonstrate measurable ROI, as substantial investments often fail to deliver tangible benefits like revenue growth or cost reduction. This paradox highlights the urgent need for robust measurement frameworks and disciplined attribution to ensure that AI truly delivers value, rather than remaining an expensive experiment.