The enterprise AI payoff shifts beyond models to mission-critical workflows
SiliconANGLE Chad Wilson ● Covered by 5 sources
AI is moving into production, but most companies still aren’t seeing the payoff. The money shows up in core workflows, not in email drafts and marketing copy.
Based on reporting by SiliconANGLE, Chad Wilson — read the original for the full story.
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Enterprise AI is getting better almost everywhere. The returns, though, are still lagging the spend, and that is pushing companies to stop treating AI as a desktop convenience and start asking whether it can move the needle inside the business itself.
That shift matters most where mistakes are expensive. Pharmaceutical research, financial services and national security all sit in the category where a wrong answer is not just awkward; it can be costly or dangerous. Thomas Robinson, chief executive of Domino Data Lab, said 57% of organizations still struggle to make returns outrun spending. A lot of early adoption, he argued, has gone into end-user tools for tasks like drafting emails or generating marketing copy. Useful, maybe. Mission critical, no.
Robinson’s bigger complaint is how companies measure success. Seats, tokens and raw adoption tell leaders what they’re paying for, not what they’re getting back. If AI is framed only as a cost-cutting tool, the ceiling is obvious: the most you can save is 100% of a cost line. The companies getting ahead, in his view, are thinking about new products, future innovation and revenue growth that may show up a year to five years out.
Trust is the other bottleneck. Robinson said about 41% of organizations are piloting or scaling agentic AI without the governance to control it. Domino Data Lab’s answer is a three-layer setup: a policy engine that controls projects from start to finish, continuous monitoring and tracing in production, and a human accountable for the result. He also said judgment still beats hallucination, and that so-called reasoning models are not there yet.
The real tell is where the market is heading next. Robinson expects value to move away from the models and toward integration, governance and workflow. The model vendors themselves are already signaling the same thing by building forward-deployed engineering teams to help customers actually implement the tech. The flashy part sold the dream. The boring part is where the bill gets paid.
My take — AI-written commentary, not fact-checked reporting
This is the AI story that matters, and it’s much less glamorous than the demo reel. Model bragging rights are cheap; wiring AI into a risky workflow without setting the place on fire is the hard bit. The industry keeps discovering that “more model” is not a business plan, which is almost comforting in its predictability.
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