Who is “Chief AI Officer” and do companies need this role?

As the digital age matures, organisations across the globe are embracing artificial intelligence (AI) as a major driver of innovation. However, as AI evolves at a breakneck pace, with its increasing role in business strategy, operations, and governance, a critical question has surfaced: do companies already need a dedicated Chief AI Officer to oversee their AI initiatives? Is this the right moment to introduce the new role?  

The debate around the need for a specialised C-level executive to manage AI innovation, ethics, risk, has been at the forefront of multiple corporate and regulatory discussions.  According to Forbes, the rise of the role of Chief AI Officer is seen as a natural response to the increased complexity and impact of AI across industries.  

Leaders in risk management, technology, and law have been discussing the changing nature of AI governance and whether a new approach to this, including introduction of a dedicated specialist is necessary.  

The emergence of the Chief AI Officer role is not just a theoretical discussion but a growing reality for many businesses. One of the panel discussions at the recent #RISK conference in London opened with a simple question: How many organisations in the room had already appointed a Chief AI Officer? Still, only a small number of hands were raised, reflecting the uncertainty.  

As AI technology becomes more integrated into business processes, the traditional roles of Chief Technology Officers (CTO), Chief Data Officers (CDO), and Chief Privacy Officers (CPO) are being stretched to cover AI-related issues. But the consensus is far from clear on whether AI governance requires a new, standalone C-suite position, and the conversations further reveal varying perspectives. 

For instance, some panelists at the conference, like Tia Cheang, Director of IT Data and Information Services at Gallagher, questioned the need for this new role as such. She argued that AI, while transformative, has long been managed within the data and technology frameworks, suggesting that a senior director or VP-level role might be more appropriate. “I don’t necessarily think there is a gap in that space for a chief-level AI officer,” Cheang noted, pointing out that AI is often just an extension of existing data and IT responsibilities, and this role could be spread between several senior level tech roles. 

Other speakers, however, emphasised the significance of AI strategy and governance, especially in organisations where AI is a major component. Sanja Hukovic, Group Director and Head of Model and AI Risk Management at the London Stock Exchange Group (LSEG), said that as AI becomes more embedded in critical business functions, the need for dedicated oversight grows: “AI governance isn’t just about technology, it’s about understanding the risks, managing bias, and ensuring transparency and accountability across the organisation.” 

The need for comprehensive AI governance frameworks, including risk assessments and ethical guidelines, was echoed by several participants. Hukovic suggested that organisations could build on existing governance models, such as those developed for GDPR compliance, but warned that AI introduces new unique challenges: “You have to review AI risk independent of privacy,” she said, urging companies to establish multidisciplinary teams to address the complexities of AI governance. 

A significant portion of the discussion focused on the regulatory landscape, particularly the implications of the EU AI Act. There is no doubt that regulation plays a crucial role in shaping AI governance but concerns still exist about the challenges of complying with emerging AI-specific laws. Nish Imthiyaz, Global Legal Counsel for Privacy, AI, and Digital Regulations at Vodafone, spoke about the similarities between AI governance and the journey organisations already underwent to comply with GDPR. “There are parallels,” Imthiyaz said, “but AI is different in fundamental ways. We need AI governance capabilities, whether or not that means appointing a Chief AI Officer.” 

Regulatory pressure, particularly from the EU AI Act, may compel companies to formalise AI governance roles. However, the experts predominantly cautioned against rushing to create a CAIO role simply to meet compliance requirements. Instead, they advised companies to carefully consider their AI maturity, and the specific risks AI poses to their specific business models before making organisational changes. 

Instead of limiting AI knowledge to specialised specific roles like a Chief AI Officer, Oisín Boydell, Chief Data Officer at Corlytics, stressed the value of integrating this knowledge across all roles within a company providing employees from all departments and units with a fundamental understanding of AI’s potential and threats: “As AI plays an increasingly important role within companies, not to mention society in general, all staff across all functions should be equipped with at least a baseline understanding of AI – its opportunities as well as its risks. Companies that are successful in leveraging AI tend to be good at instilling this knowledge across all teams, so that novel AI use cases as well as potential pitfalls can be identified by those who best understand the business.”  

This approach enables teams, who are most familiar with the business, to identify innovative AI applications and possible challenges, rather than isolating AI knowledge within the Data Science team, which, while highly skilled in execution, may lack close alignment with specific business needs: “It’s about empowering all staff, rather than boxing off AI as purely the preserve of the Data Science team who may be experts in its implementation but are often at arm’s length from detailed business needs and requirements,” he continued.  

Echoing the points voiced by other experts in the industry, Oisín confirmed that there is no one-size-fits-all approach to a dedicated role of Chief AI Officer as every company is different in terms of AI maturity, and how AI is being utilised varies. It may be an integral aspect of a core product offering or may be used as part of an internal process, as well as the industry, the types and sources of data a company is working with – differ. And in its turn, all these aspects and variables have an impact on the complexity of AI governance.   

For example, at Corlytics, this nuanced understanding shapes the company’s approach where data and AI strategies are closely aligned. “As Chief Data Officer I oversee our AI strategy and governance, with the support of our CTO from the data infrastructure and security perspectives. Our unique data assets, such as our annotated, global regulations library, regulatory taxonomy and data integration across the full regulatory risk value chain – from horizon scanning to regulations to controls and policies – in conjunction with our in-house legal experts, power all of our AI solutions across the platform. Having an integrated and joined up view of data and AI, and the close relationship between them, allows us to deliver accurate, reliable and trustworthy AI driven solutions,” Oisín Boydell explained.   

While opinions on the exact role vary, the need for AI leadership is undeniable. Whether this leadership comes in the form of a Chief AI Officer or through existing roles depends on the organisation’s size, industry, and AI strategy. For companies heavily reliant on AI for operational efficiency or customer experience, the AI Officer could provide the necessary focus and accountability to navigate the complex world of AI governance. For other organisations, AI governance might best be handled by expanding the responsibilities of existing leaders in technology, data, or privacy. The ultimate goal is not to create new titles but to ensure that AI is managed effectively, ethically, and in compliance with evolving regulations. It looks like the role of a Chief AI Officer is still in its infancy, perhaps it is still 3-5 years early to become a standard. Its necessity will vary depending on an organisation’s unique needs, its use of AI technology and its position in the AI maturity curve. What is clear, however, is that AI governance and leadership are critical components of any AI strategy, and companies must adequately address these challenges to remain competitive and compliant in the rapidly evolving digital landscape. 

In co-authorship with Oisín Boydell, Chief Data Officer at Corlytics