State of ModelOps: 90% expect a dedicated budget within 12 months, 80% say risk-management is a key AI barrier

Ryan Daws is a senior editor at TechForge Media, with a seasoned background spanning over a decade in tech journalism. His expertise lies in identifying the latest technological trends, dissecting complex topics, and weaving compelling narratives around the most cutting-edge developments. His articles and interviews with leading industry figures have gained him recognition as a key influencer by organisations such as Onalytica. Publications under his stewardship have since gained recognition from leading analyst houses like Forrester for their performance. Find him on X (@gadget_ry) or Mastodon (@gadgetry@techhub.social)


The first annual State of ModelOps report highlights some interesting trends about the real-world adoption of AI in enterprises.

Independent research firm Corinium Intelligence conducted the study on behalf of ModelOp and aims to summarise the state of model operationalisation today.

Stu Bailey, Co-Founder and Chief Enterprise AI Architect at ModelOp, said:

“As the report shows, enterprises increasingly view ModelOps as the key to ensuring operational excellence and maximising value from their AI initiatives, in the same way that DevOps, ITOps, and SecOps have for the development, IT, and cybersecurity sectors.”

According to the survey of 100 AI-focused executives from F100 and global financial services companies—each enterprise has an average of 270 models in production.

Despite the rapid uptake, 80 percent report that difficulty in managing risk and ensuring compliance is a key barrier to adoption. With increasingly strict AI regulations – such as those being drafted by the EU – this figure could increase without robust solutions.

Improving the enforcement of AI governance processes is noted by 69 percent of respondents as a key reason for investing in a ModelOps platform

Bailey explains:

Experience has shown that creating AI models is only half the battle. Operationalising models – getting them into production, keeping them functioning properly and within guidelines for compliance and risk, and demonstrating their business value – is the next frontier as organisations mature and scale their AI initiatives.”

Data scientists at the surveyed organisations are using an average of 5-7 different tools for developing models—highlighting the potential for streamlining operations. Just 25 percent rate their existing processes as “very effective” for inventorying production models.

76 percent of respondents say the cost reductions associated with a ModelOps platform is a “very important” benefit for such an investment. 42 percent describe it as crucial.

Skip McCormick, Data Science Fellow at BNY Mellon, commented: “ModelOps is the next logical step after DevOps. We’re looking for a systematic way to make sure that the models we’re putting into play actually do what they should do.”

Overall, 90 percent of respondents expect to have a dedicated ModelOps budget within 12 months.

(Photo by Kevin Ku on Unsplash)

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