
Enterprise AI returns still trail spending.
57% of firms fail to outpace costs.
Value is shifting from models to integration, governance, and workflow.
What happened
57% of organizations still struggle to generate returns that outpace their AI spending, according to Domino Data Lab CEO Thomas Robinson. He argues AI must move beyond desktop productivity tools into mission-critical core processes.
Why it matters
The gap between AI in production and measurable business outcomes is widest in high-consequence industries like pharma, financial services, and national security. Robinson warns that counting seats or tokens measures spending, not earnings, and that cost-reduction framing caps upside.
What to watch
41% of organizations are piloting or scaling agentic AI without proper governance. Domino's answer is a three-layer model: a policy engine, continuous monitoring, and a human accountable for outcomes.
Ask the AI about this article →
The interview with Thomas Robinson points to a fundamental shift in how enterprises should measure AI success. Rather than counting seats or tokens, leaders need to tie AI directly to revenue, innovation, and cost cutting. This is especially critical in sectors like pharma and finance, where errors have high stakes. Robinson's assertion that "judgment is better than hallucination" underscores the limits of current reasoning models and the need for human oversight. His observation that model providers are building forward-deployed engineering teams suggests that the real value lies not in the models themselves but in their integration into business workflows. This implies that the competitive advantage will shift to those who can effectively govern and implement AI in mission-critical processes.
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