The survey focused on 2,500 IT leaders from the US, UK, Germany, France, and Japan. The findings spotlight both the enthusiasm surrounding AI’s potential benefits and the significant challenges that organisations encounter in implementing AI technologies.

The survey revealed a compelling enthusiasm for AI’s potential advantages, with three out of four IT leaders expressing optimism about its capabilities. These benefits spanned from amplified employee efficiency to automated cybersecurity solutions.

A striking 67 percent of the respondents revealed that they are intensifying investments in AI technologies to harness these advantages. Nonetheless, the survey’s results also exposed a sense of hesitancy arising from implementation uncertainties, the readiness of existing hardware, and technology stacks.

Matthew Unangst, Senior Director of Commercial Client and Workstation at AMD, said: 

“There is a benefit to being an early AI adopter. IT leaders are seeing the benefits of AI-enabled solutions, but their enterprises need to outline a more focused plan for implementation or risk falling behind.

Open software ecosystems, with high-performance hardware, are essential, and AMD believes in a multi-faceted approach of leveraging AI IP across our full portfolio of products to the benefit of our partners and customers.”

Of the organisations prioritising AI deployment, 90 percent reported experiencing heightened workplace efficiency. This underscores the idea that early AI adoption could yield a competitive edge in productivity and performance.

AMD’s survey outcomes indicated that establishments which defer AI adoption could potentially jeopardise their market standing.

To address these challenges and offer solutions, AMD is concentrating on the development of AI-capable solutions across its product portfolio. This includes the cloud, edge computing, and endpoints.

While IT leaders harbour optimism about the possibilities that AI presents, there exists a critical need for well-defined implementation strategies.

AMD’s survey indicates that those enterprises that act swiftly and purposefully on AI adoption could potentially reap substantial benefits, while those that lag behind may encounter challenges.

Find a full copy of the report here (PDF)

(Photo by paolo candelo on Unsplash)

See also: UK Deputy PM: AI is the most ‘extensive’ industrial revolution yet

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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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