Enterprise AI has moved beyond experimentation at many companies, but getting projects into production and then scaling them across a business remains uneven. Riviera Partners’ latest survey reflects that divide.
Some 43% of organizations said AI was already scaling across the business or fully embedded. But only 35% said more than 60% of their initiatives reached production, while 13% said nearly all initiatives successfully scaled.
Riviera Partners, an executive search firm, points to how technology teams are organized as one factor associated with that execution gap. Its 2026 “Future of Tech Leadership” report examines technology structure alongside leadership engagement and governance integration.
The research surveyed 958 qualified technology leaders between June 8 and June 26. Most respondents, 86%, were in the U.S., with 13% in Europe and 1% in Canada.
Where execution breaks across teams
Riviera measures technology structure by how Product, Data and Engineering functions report. Overall, 49% of organizations had highly unified structures. Within Riviera’s maturity model, that rose to 92% among Advanced organizations, compared with 12% of Emerging organizations. None of the Advanced organizations were fully siloed, compared with 45% of Emerging organizations.
The comparison comes with an important limitation: technology structure is itself one of the criteria Riviera uses to define its maturity groups, so the 92%-to-12% gap is not an independent test of whether unified reporting improves AI execution.
A separate production measure points in the same direction. Among organizations with fully siloed technology functions, only 22% said they moved more than 60% of AI initiatives into production, the lowest rate in the study. One-third of organizations also said AI initiatives most often stalled when they expanded beyond the initial team, product or use case.
Riviera argues those transitions require Product, Data, Engineering, Security and Governance teams to make coordinated decisions on architecture, access, risk and deployment.
An AI title does not remove the handoffs
Riviera said organizations had not converged on a single AI ownership model, although chief technology officers were the primary owners at 61% of organizations. Chief AI officers accounted for 20% and chief product and technology officers for 17%. The share with a chief AI officer slipped from 22% in 2025 to 20% in 2026.
The company said appointing a dedicated AI executive can improve accountability and strategic focus, but the role alone cannot remove fragmented reporting lines, unclear decision rights or cross-functional handoffs.
Riviera’s analysis separates executive ownership from the reporting structure beneath it: who leads AI may be settled while the teams responsible for building and deploying it still operate across organizational boundaries.