What do you need more -- a chief AI officer or better data? [Q&A]

According to recent research nearly half of FTSE 100 companies now have a Chief AI Officer (CAIO) -- with 42 percent of those hires made in just the past year.

Companies are clearly rushing to signal their AI credentials at board level, but is this a meaningful shift, or simply another wave of hype-led decision making? We spoke to Francisco Mateo-Sidron, SVP and head of EMEA at Cloudera, who believes that a CAIO alone can’t drive real results if enterprises don’t have data that’s built on solid foundations.

BN: What’s the current landscape of AI adoption in businesses, and where do most organizations stand in their AI journey?

FMS: The adoption of AI in businesses is far from a one-size-fits-all approach; it varies based on factors like industry, company size, and digital maturity. There are clear trends, however. According to Forbes, 68 percent of large enterprises in the UK have adopted at least one AI tool, compared to 33 percent of mid-sized companies and 15 percent of small businesses have done the same. Sectors like IT, finance, telecom, and legal are leading the charge, while industries like hospitality, healthcare, and retail are slower to embrace AI.

While AI is gaining traction in business operations, many organizations remain in the early stages -- experimenting with early use cases and analysing where AI can add the most value.

BN: How does a CAIO differ from a Chief Data Officer (CDO) or Chief Technology Officer (CTO)?

FMS: The role of a CAIO can vary significantly between organizations, especially when AI responsibilities overlap with those of existing roles like the CDO or CTO. In some companies, a CAIO may step into a space already managed by a CDO, leading to blurred responsibilities and potential friction. In others, the CDO may absorb AI-related duties without additional support.

Typically, CAIOs are responsible for developing an AI strategy that aligns with broader technology goals, while CDOs focus on data governance. However, the lack of clear role definitions can create tension, as both positions often vie for control over data and governance. Successful AI adoption doesn’t happen by adding a piecemeal job title. Organizations need to ensure clear ownership of the data lifecycle aligns with the business processes, enabling them to drive real value from AI.

BN: Can data quality reduce the immediate need for a dedicated CAIO, or do both go hand-in hand?

FMS: Like any transformative technology, AI requires strong leadership to steer its implementation and ensure it delivers results. A CAIO can add real value by promoting AI adoption, aligning it with business goals and ensuring their organization is ready for AI adoption. However, with AI, none of that matters without high-quality data.

A strong data foundation is essential. Without this, CAIOs will be at a disadvantage from the outset, and their impact will diminish. To truly unlock value, organizations must ask themselves: Do we have full visibility of our data lifecycle? Are we applying governance and security consistently, wherever our data resides? Is our architecture flexible enough to support AI at scale? And importantly, are we culturally and operationally prepared to integrate AI in a way that delivers value? Strong data and strong leadership go hand-in-hand -- both are necessary for AI to succeed.

BN: Should the focus be on hiring a CAIO first, or investing heavily in data infrastructure and quality initiatives?

FMS: While hiring a CAIO is important, prioritizing data infrastructure must be the first step towards adoption. AI both creates and consumes a massive amount of data, often across cloud and on-prem environments, which needs careful management. Many organizations underestimate the complexity of handling data at this scale. Without clear visibility of data -- where it lives, how it flows, who controls it, and how it’s governed -- you can’t trust AI’s output.

A modern data architecture that enables the unification of data is critical for AI to function effectively. Once these foundations are able to support AI, a CAIO can step in to guide AI strategy, aligning technical capabilities with business objectives.

BN: Is the CAIO role destined to disappear as AI becomes more pervasive, what’s the end goal?

FMS: Like AI itself, the role of CAIO will continue to evolve, but it’s not going anywhere. We’ve already started to see the conversation around AI move on from GenAI to Agentic, and CAIO’s need to adapt to these changes. As AI becomes more embedded in business operations, the need for leadership to drive AI strategy, ensure alignment with business goals, and enforce responsible use will only grow. So, the role will shift as organizations scale their AI capabilities.

This means there's no definitive 'end goal' for AI adoption -- it's an ongoing journey. But regardless of the direction AI takes, organizations need to ensure they can bring AI to all of their data, wherever it resides. With that foundation, and guided by the thoughtful and flexible leadership of a CAIO, organizations can stay ahead of new use cases.

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