New functions

Chief AI Officer, is it really useful to have him in the company?

The evolving role of the CAIO, the challenges in implementing AI and the skills needed for business success

21 July 2025

5' min read

The acronym, CAIO, probably conjures up thoughts not exactly akin to the true nature of this emerging managerial figure, currently among the most discussed (and often misunderstood) in the executive landscape. We are talking about the Chief AI Officer, a function present in almost half of the companies in the FTSE 100 and with a percentage of new hires in the last year alone exceeding 40% (according to a study by the British research company Pltfrm). There is, however, a double anomaly in this race to grab these 'specialists'.

The first is reflected in the premature action that many companies are taking in order to position themselves as AI experts in the boardroom and towards the market, without however adequately defining the tasks, tools and responsibilities of this figure. The second sees several companies with both a Chief AI Officer and a Chief Data Officer on their staff, others that have simply renamed their CDOs to CAIOs, and still others where CDOs are absorbing the responsibility for the adoption of artificial intelligence without providing further support or clarity. The result? Such a confusing leadership structure can create conflicting priorities and internal friction, slowing the progress of technology and diluting accountability. What does a CAIO need to achieve real results and reduce the risk of thwarting their organisation's AI efforts? We talked about it (exclusively) with Francisco Mateo-Sidron, Senior Vice President and EMEA Head of Cloudera.

The advent of CAIOs, at the moment, seems to bring more problems than benefits. Why have big companies invested in this figure without having built the foundations for it to work well?

Many companies are embarking on advanced artificial intelligence strategies, but often their IT teams are still faced with fragmented and outdated data systems that are definitely not designed for artificial intelligence. To overcome this discrepancy between business objectives and data infrastructure, and with the primary goal of getting real results from AI, these same companies are investing in the figure of the Chief Artificial Intelligence Officer. In reality, the basis of an effective AI strategy is data, and an organisation's confidence in the results produced by this technology is directly proportional to the confidence placed in the input data. It is therefore not just a question of visibility: having reliable data is the key to analysing and processing information and turning it into knowledge to understand business performance and how to improve it. But without a unified database and proper governance, it is difficult to scale artificial intelligence technologies successfully and in compliance with regulations.

What are the most common mistakes companies make when introducing this figure?

First of all, it is crucial to make sure that the roles of the Chief Artificial Intelligence Officer and the Chief Data Officer are well defined: while the former focuses on obtaining concrete results from AI, the latter is responsible for the data environment that supports such initiatives. In practice, however, these roles often overlap and, when responsibilities are unclear, tensions can arise with regard to resources and direction, undermining the success of joint initiatives. To avoid this risk, companies must introduce this figure with a strategic intent and a clear vision, not just introduce a new title.

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What do managers who want to take on this role not have to do?

Those considering this position should first understand if there is a disconnect between the business objectives and the data infrastructure, which could slow down the implementation and lead to fragmented and non-scalable projects. This role can only function effectively if the organisation is structurally and culturally ready for AI. If governance and scalability are not already established, hiring a single person will certainly not fill these gaps. It is therefore essential to question the current data environment and the operational maturity of the company in depth. The role of the CAIO is not to speed up artificial intelligence initiatives but to have the real opportunity to drive change.

Is it a truly strategic function or is it still too 'young' to bring concrete value?

The CAIO is at the helm of the company's AI strategy, and holds an inherently strategic function within the organisation. His objective is to ensure that artificial intelligence efforts generate tangible business value, going beyond mere experimentation. I believe that the real value of the Chief Artificial Intelligence Officer lies in his leadership ability. He or she is responsible for defining the AI strategy and communicating it effectively to all business stakeholders, identifying the most relevant use cases and shaping the strategy accordingly according to the company's overall objectives. Last but not least, as the initiator of AI initiatives, the CAIO guides teams through the digital transformation process and analyses the implications of change on individuals, teams and the entire company.

Who are the ideal candidates for this position today? Data scientist, innovation manager, ... or a new profile to be trained?

The most effective CAIOs do not necessarily need to have in-depth machine learning skills. On the contrary, many of them are brilliant communicators with solid business and operational experience, capable above all of aligning artificial intelligence with concrete objectives. Upgrading skills is now a top priority for many organisations, and recent research by AWS found that 56 per cent of companies already have a Generative AI training plan in place, with a further 19 per cent planning to develop one by the end of the year. This approach is entirely justified: we have seen a staggering speed in the development and adoption of AI and, therefore, embracing AI literacy and promoting ongoing skill development will result in greater success for business operations.

Do these competencies complement (or overlap) those of a CIO or Chief Data Officer?

If there is something that sets CAIOs apart, it is their ability to effectively translate data science into business strategy. Their success will be dictated by their experience in asking the right questions, challenging business customs and transforming technical potential into concrete business results. Achieving this requires a diverse skill set: strategic communication, impactful leadership, the ability to take direct responsibility for results, and adherence to ethical standards. The competencies of a CDO are focused on governance, compliance and data management, and therefore must possess a solid understanding of data management principles, including security, governance, modelling and quality. Chief Artificial Intelligence Officers, on the other hand, must be able to act as true 'CEOs of AI', and thus strategic leaders who drive innovation in this field and generate value for the organisation.