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Forbes reported on Lanai's 2026 AI Labor Report released June 9. The survey of 200 U.S. technology executives found 92 percent track AI-generated work financially while only 2 percent record more than half of it as outcomes. Ninety percent of organizations lack a dedicated function for AI return on investment.
ForbesLanai released its 2026 AI Labor Report on June 9, 2026, documenting a wide gap between tracking and recording the financial effects of AI-generated work, Forbes reported. Ninety-two percent of technology executives said their organizations track the financial impact of AI-generated work. Two percent said more than half of that work is recorded as a business outcome.
Ninety percent of organizations lack a single dedicated function responsible for tracking how AI delivers return on investment. Seventy-nine percent of technology executives worry their AI budgets will be cut because spending cannot be tied clearly to revenue or profit. Fifty-three percent of executives estimate most automated work runs through unmonitored shadow applications.
Eighty-seven percent of organizations credit AI-assisted output entirely to the human employee, sometimes or always. Eighty-eight percent have no formal methodology for attributing business outcomes to AI. Forty-three percent assume that if AI was involved it contributed to outcomes.
Thirty-eight percent rely on educated guesses. Twelve percent have a defensible answer when finance asks about AI attribution. All organizations still require human review after AI generates work. None reported fully autonomous workflows.
McKinsey's 2025 State of AI survey found nearly 88 percent of organizations now use AI regularly, yet only 39 percent attribute any EBIT impact to it and just 6 percent qualify as high performers capturing significant enterprise value. BCG's 2025 research found just 5 percent of companies achieving AI value at scale and 60 percent reporting little to no material impact.
Lexi Reese, co-founder and CEO of Lanai and former COO of Gusto, called the pattern AI labor orphaning.
One finance team using Lanai's Token Tuner measured two groups running identical workflows for 30 days. One group's bill came to $52,015 and the other's to $13,007. Fixing the default model choice saved roughly 5 percent of the team's annual token spend.
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