Most Companies Can't Track AI's Financial Contribution

Large enterprises face a fundamental accounting problem: AI systems are performing substantial work that never appears in their financial records, performance metrics, or systems of record.

A report from Lanai surveying 200 U.S. technology executives at organizations with 1,000+ employees reveals the scope of what CEO Lexi Reese calls "AI labor orphaning." While 92% of tech leaders say their organization tracks AI-generated work's financial impact, only 2% report that more than half of that work is actually recorded as a business outcome, according to details first reported by Forbes contributor Güney Yıldız.

The gap isn't a measurement quirk—it represents incomplete financial data at the core of enterprise operations. "If AI is doing a meaningful slice of the work but never shows up in the ledgers, how confident can you be in your P&L, your headcount plan, or the org chart you use to run the organization?" Reese said in the report released June 9.

Why it matters

This isn't an IT problem—it's a boardroom crisis. Companies are making budget, hiring, and restructuring decisions based on financial data that omits an entire category of labor. With 79% of executives worried about AI budget cuts due to unmeasurable value, the inability to demonstrate ROI threatens continued investment precisely when AI adoption is accelerating.

Shadow AI creates invisible workflows

The research found that 53% of executives estimate most automated work runs through unmonitored shadow applications—tools employees adopt independently because they deliver productivity gains the organization demands. Rather than security breaches to eliminate, these represent high-performing workflows that enterprises have blinded themselves to by attempting to ban rather than measure the behavior.

The performance management distortion runs deep. Some 87% of organizations credit AI-assisted output entirely to human employees, at least sometimes. Promotion decisions and bonus pools are built on work where machine contribution is invisible—not because anyone is hiding it, but because no system exists to record it differently. Only 12% of organizations have a defensible methodology for attributing business outcomes to AI, while 43% simply assume AI contributed if it was involved, and 38% rely on educated guesses.

Accountability without authority

The structural problem extends beyond visibility to decision rights. Ninety percent of surveyed organizations lack a single dedicated function responsible for tracking AI ROI. Accountability is scattered across finance, IT, operations, and business units, with no clear owner when the CFO demands proof of value.

Reese points to a concrete example from a customer using Lanai's Token Tuner product. Two groups in the same finance team ran identical workflows for 30 days with the same output quality. One group's bill totaled $52,015; the other's came to $13,007—a difference driven solely by which default AI model each happened to use, a choice no one had made deliberately. Fixing that single default saved roughly 5% of the team's annual token spend.

Boards hold technology leaders accountable for AI return on investment while denying them the levers to deliver it: the power to set standards across business units, reallocate spend from failed pilots, and redesign workflows where AI changes how work gets done. Reese describes this as "accountability without power."

The report suggests enterprises need leaders with actual authority over intelligence flows—both human and machine—across the organization, not just responsibility for keeping servers running. Without attribution methodology built before budget reviews force the issue, companies are operating on numbers that omit an entire category of worker.

The findings were detailed in Lanai's 2026 AI Labor Report and reported by Güney Yıldız for Forbes.