New Survey Reveals Enterprise AI Breakthroughs Advancing, But Business Impact Still Out of ReachDomino’s 2025 REVelate report shows safe AI adoption is accelerating, yet measurable ROI remains a challenge for most organisationsRevX 2025 – New York City – 8 May, 2025 – Domino Data Lab, provider of the leading Enterprise AI Platform trusted by the largest AI-driven companies, released its 2025 REVelate report, revealing a major move: organisations are going beyond AI experimentation into large-scale production. However, short-term return on investment (ROI) has taken a back seat as leaders prioritise infrastructure, governance, and scalable tools that support long-term strategic value over hype. While 88% of organisations report improved ability to move AI from experimentation to production, nearly 60% expect less than 50% ROI in the rapidly changing areas of machine learning (60%) or Gen AI (57%).“It’s clear that the AI hype cycle has peaked. Enterprises are done experimenting, and they’re racing to scale, but the payoff is still out of reach,” said Thomas Robinson, Chief Operating Officer at Domino. “Our data validates what leaders know about and followers are learning: aligned leadership, flexible infrastructure, and integrated governance drive a straight line to AI business impact. These aren’t technical upgrades — they’re business imperatives that will define who sees real returns from AI.”Key findings from this year’s report include:Governance is the top priority: When it comes to AI execution, integrated AI/ML governance is the top capability required (66%) by enterprises, followed closely by tooling (49%), and hybrid cloud AI infrastructure (44%)

Christopher McSpiritt, Head of life sciences at Domino Data LabData science teams in the life sciences industry are experiencing a significant change that will transform how clinical data is analyzed, opening the door to new possibilities for innovation. The transition isn’t happening overnight, but such incremental approaches are necessary to ensure success in such a highly regulated environment.For decades, SAS was “it” in life sciences. If you wanted to be a data scientist in the industry, you had to know SAS. However, recently there has been a move toward open-source tools among organizations under growing pressure to cut costs and innovate faster. But open source comes with its own set of challenges—especially for such a tightly regulated industry. For the transition to work, teams must strike a careful balance between the dependability of established legacy systems and the adaptability that open-source tools bring