AI + analytics = value? (1/2) Domo's Chief AI Officer on what organizations really need to measure
Organizations are measuring AI wrong. Domo's CAO argues productivity gains and model costs miss the real question: does the AI work actually improve business outcomes?
Why it matters
As enterprises deploy agentic AI at scale, measurement frameworks remain stuck on throughput and infrastructure costs. An industry leader argues that verification, data quality, and outcome attribution are the metrics that should drive budgets — but few organizations track them.
The key facts
8 to knowBen Schein, Domo Chief AI Officer
Measurement gap: productivity and model costs dominate, but miss verification, context, data quality, and business-outcome attribution
Framework gap: no industry standard for measuring whether AI-generated work actually improves outcomes
Implication: enterprises may be over-investing in AI initiatives that don't deliver measurable value
Domo Chief AI Officer Ben Schein identifies measurement blind spots: verification, context, data quality
Organizations focusing on productivity and model costs as primary metrics
Argument: AI-generated work quality and business outcome correlation largely unmeasured
Part 1 of 2 — deeper analysis promised in follow-up
Go to the source
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Publisher excerpt: Domo’s Ben Schein argues that organizations need to look beyond productivity and model costs to account for verification, context, data quality, and whether AI-generated work actually improves business outcomes.