The enterprise AI payoff shifts beyond models to mission-critical workflows
Enterprise AI capabilities are improving almost everywhere, yet the returns still trail the spending. The technology is reaching production, but it often stops short of the business process where revenue, innovation and risk actually live — a gap that is now reshaping how enterprises measure AI success.
That gap is widest in industries where a wrong answer carries real consequences, such as pharmaceutical research, financial services and national security. Much of the past few years of adoption has been aimed at individual desktop productivity rather than core operations, and 57% of organizations still struggle to generate returns that outpace their spending, according to Thomas Robinson (pictured), chief executive officer of Domino Data Lab Inc.
“A lot of the application we’ve seen over the past few years has been about putting the tools in an end-user compute context, so giving users a desktop tool to draft emails or make marketing copy,” Robinson told theCUBE. “That’s not where companies make and lose their revenue. It’s not where they do their innovation, and it’s not where they drive cost cutting. At the end of the day, we think the AI needs to be applied in the high consequence, mission critical, core processes of the business.”
