Do You Really Need a Chief AI Officer?
The right answer depends on the strategic importance and maturity of AI in your company.
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In the rapidly evolving landscape of artificial intelligence, organizations are grappling with how best to harness its transformative power. Some are choosing to create a new senior management position with enterprisewide oversight of AI activities. In March 2024, for example, the Biden administration mandated that all U.S. federal agencies appoint a chief AI officer (CAIO) to oversee AI activities and minimize related risks. An August 2023 survey of 965 global IT decision makers at midsize to large companies found that 11% had already hired a CAIO and a further 21% were actively seeking to fill the position.
This role, meant to drive a cohesive approach to implementing AI across an organization, comes with compelling arguments both for and against its creation. As a group, we have an informed point of view on the potential value of CAIOs; four of us have been chief digital officers (CDOs) across a variety of sectors, including pharmaceuticals, technology, consumer goods, and industrial products and services. As CDOs, we experienced many of the same challenges and opportunities that CAIOs are now facing.
The Case for Appointing a Chief AI Officer
AI, with its potential to transform business operations, customer experiences, and market offerings, is key to meeting the management mandate to be nimble and innovative in seeking competitive advantage. Arguments that organizations should have an executive role dedicated to AI point to the following benefits.
Reduced AI fragmentation. Like digital projects before it, AI often suffers from a lack of executive-level oversight, resulting in solution fragmentation, underinvestment in strategic initiatives, and missed opportunities for scaling. A dedicated CAIO with a mandate to explore and exploit AI across the organization can help to steer focus and prioritization from the highest level. Left unchecked, most organizations will expand AI projects and offerings in an uncoordinated manner, leading to a mishmash of fragmented and overlapping projects that fail to realize their full potential. The appointment of a cross-functional CAIO can bring these initiatives under a single strategic umbrella, leading to streamlined operations, enhanced innovation, and significant cost savings.
Many of these same anticipated benefits led organizations to appoint CDOs, like us. For example, a global fast-moving consumer goods company had big ambitions to expand e-commerce as part of its board-mandated growth strategy. Marketing was charged with delivering this strategy and launched a variety of programs with different approaches, budgets, and, ultimately, levels of success. Chaos ensued. One of us was brought in to formulate a global approach and orchestrate its implementation across the company. At the time, over 100 existing digital initiatives, pilots, and proofs of concept were identified, most of which were small, incremental in scope, and focused on testing new technologies rather than solving business challenges. In fact, after the review, 90% of the projects were stopped, and the remaining projects — which were consistent with the global strategy — were elevated.
In another case, having a central digital and data function enabled a large pharma company to create or identify what it called lighthouse projects, which served as reference examples for similar projects. The CDO was positioned to set up governance processes to balance grassroots innovation with standardized data and systems across the company.
One of us designed, trained, and piloted an AI chatbot alongside the team supporting one market at a large consumer brand. The objective was to help customers more easily navigate a recently launched e-commerce website. The chatbot resulted in significantly higher sales, as well as an enhanced customer experience. Following the pilot, the technology was scaled to 20 markets, some of which had independently developed their own suboptimal solutions. Many of the markets wouldn’t have been able to build a solution on their own, and none could have scaled it to other markets.
Transformational change. Since many AI projects are run within single divisions or functions, they tend to be incremental in scope. A CAIO can elevate the level of ambition to work on AI projects that have the potential to disrupt the organization’s goals and business model. In this sense, the CAIO can act as a partner for the CEO and management team when a step change is required to explore radical innovation and help design and promote an updated corporate strategy for board approval.
Take the example of a health technology company that wanted to build a new commercial offering by changing the way that highly technical medical information was made available to patients. The chief AI officer and his team built an AI platform that would scan medical research for complex terms like disease names, symptoms, and drugs, and translate them into language that patients could more easily understand. This process enabled the platform to guide people seeking information to relevant answers and treatment options. Having strong executive oversight was helpful because the project was expensive and had an uncertain payback, as is the case with many AI projects.
Centralized management of AI risks. While there is a lot of focus on how AI can improve performance, it comes with attendant risks. Some of these are legal risks, such as violations of data privacy or intellectual property protections, the potential for discrimination, and requirements to explain decisions such as a denial of credit. There are also strategic risks, such as the impact of AI on employment and competitiveness, ethical questions on usage, and cybersecurity concerns. The creation of a centralized role to manage these risks can unlock the potential of AI in a compliant and ethical manner across the enterprise.
Indeed, this is a core focus of the U.S. federal government’s March mandate. Under the order, CAIOs are required to assess AI impacts, conduct real-world tests, regularly evaluate AI-related risks, train staff members, and give notice of any AI solution that could have a significant impact on public rights or safety. With the emergence of legislation such as the European Union’s AI Act in early 2024 as well, a focus on regulatory risks is likely to rise in the coming years.
A reduction of internal deficiencies. In some cases, the expertise and experience that a CAIO brings can address internal gaps regarding AI understanding and application. For example, there may be limits to how well internal functions, like corporate IT or data, can deliver AI solutions effectively. Generative AI, in particular, is a relatively new technology that requires in-depth knowledge and expertise, as well as an understanding of external vendor ecosystems linked to data, models, and infrastructure.
Most of us were brought in as CDOs because leaders were frustrated with the pace and scope of digital innovation. In one case, it was unclear who owned digital, and since no one took full responsibility, digital projects significantly underperformed compared with industry benchmarks. Indeed, the ability to provide learning and development support is an important aspect of recruiting a CAIO, as these individuals can help to upskill other executives on AI-related technologies, opportunities, and risks. Organizations must have an aligned understanding of AI that goes beyond the buzzwords and sensationalist headlines that bombard many executives. As CDOs, most of us set up corporate training programs on digital transformation and disruption, and CAIOs can do something similar with AI.
The Case Against Appointing a Chief AI Officer
Despite many potential benefits, appointing a chief AI officer is not without its drawbacks. Cross-functional roles like those of CAIOs — and, indeed, CDOs — are notoriously difficult to navigate.
Cross-functional conflict and overlap. The introduction of a CAIO can create tension within the existing C-suite, particularly with roles such as CIO, CTO, chief operating officer, and, if there is one, the CDO (with the D representing digital and/or data). This overlap could lead to conflicts over jurisdiction, resources, and strategic direction, potentially stifling innovation rather than fostering it.
The challenge of maintaining a comprehensive overview of all AI initiatives across an organization also poses a significant hurdle. Without full visibility, a CAIO might inadvertently encourage the very fragmentation and overlap they are meant to resolve.
In particular, the success of AI initiatives relies heavily on the support of corporate IT systems. A CAIO will find it challenging to implement AI solutions effectively without the cooperation and collaboration of the IT department and data owners across an organization.
At the same large pharma company mentioned earlier, one of us saw what happened when it added a CDO to the management team. The move created overlap with the data science and IT organizations, which in turn resulted in a cost increase, triggering questions from the finance organization around the value of data and digital. In the next inevitable cost-cutting exercise, many digital experts were let go and others were moved to IT.
An overemphasis on AI as an enterprise solution. By the very nature of the role, a CAIO will emphasize AI solutions over potentially simpler, more cost-effective options. For example, a company might invest heavily in AI-driven customer service solutions when enhancing existing non-AI processes could provide similar benefits at a fraction of the cost. CAIOs could be tempted to pursue AI for the sake of AI rather than in the service of business objectives.
Of course, if AI is managed by a corporate technology function, such as the IT department, a similar situation may result. Thus, it’s important for a CAIO to not only have a commercial focus but also be open to alternative solutions beyond AI.
Additional costs. The financial implications of creating a new C-suite position, along with the necessary supporting infrastructure, can be significant. The benefits provided by the role simply may not be sufficient to compensate for the costs of establishing and operating it.
As CDOs, we’ve learned that it’s critical to have a strong business case that’s based on tangible value to the business, like new revenue, cost savings, or a reduction in complexity. In one case, the CDO office expanded to more than 100 full-time equivalents, which quickly turned all business case calculations negative. Costs are fine as long as they can be offset by revenues or savings elsewhere. Maintaining financial rigor is particularly important as the thrill and excitement around AI, and the CAIO role, inevitably diminishes over time.
Balancing the Equation
The decision to appoint a chief AI officer should not be taken lightly. Organizations must carefully weigh the potential benefits of having a dedicated leader for AI initiatives against the risks and costs. We see the following factors as having the largest bearing on whether to appoint a CAIO.
The strategic importance of AI. In organizations where AI can have a major impact on competitiveness, the scales may be tipped in favor of appointing a CAIO. For example, organizations in the technology or financial services sectors, where data management and insights are key sources of differentiation, may see significant benefits from coordinating AI initiatives centrally.
Similarly, organizations that can leverage AI to build new sources of competitive advantage may benefit from an executive role to oversee AI development and deployment. For example, the education and retail sectors may be transformed by new AI tools and business models. Smaller businesses looking to capitalize on the current excitement around AI may also benefit from a CAIO.
The timeline. In cases where appointing a CAIO is advisable, the role should not become a permanent position, in our view. Rather, it should be a fixed-term appointment with a specific brief to build a set of enterprise AI capabilities that will ultimately be handed over to the business and/or technology organizations. Indeed, we see the need for a CAIO linked to the maturity of AI within an organization, as shown in “CAIO Applicability Mapped to AI Maturity.” We’ve adapted these stages of maturity from Gartner’s AI maturity model.
CAIO Applicability Mapped to AI Maturity
The positive impact of a chief AI officer is linked to an organization’s stage of AI maturity. CAIOs can exert maximum impact in stages 2, 3, and 4 via the reduction of AI fragmentation, ruthless prioritization of objectives, and active scaling of AI solutions across the organization. The effectiveness of CAIOs drops significantly in cases of both low (Stage 1) and high (Stage 5) AI maturity.
Some organizations today are in the early stages of working with AI. They are aware of the technology but are doing little with it. We refer to this as Stage 1. We don’t see hiring a CAIO as benefiting companies at this early stage.
Many organizations today are in Stage 2, where they are actively experimenting with AI, but in a fragmented manner with multiple initiatives spread out across the company. As these experiments become more numerous and mature, we see the potential for a CAIO start to emerge. The role of the CAIO at this stage is primarily to capture and map the portfolio of AI activities so that there is visibility around what is currently being done. During this second stage, gaps and areas of overlap should become apparent.
The periods in which a CAIO can provide the most benefit, in our view, are stages 3 and 4. A CAIO can guide the process in Stage 3, where AI initiatives move from experiments to selective implementation. Critically, the CAIO needs to prepare the groundwork for widespread integration of AI across the organization. This process involves preparing the data for use by AI systems.
As CDOs, we encountered many issues with poor data formatting, standardization, and quality. The quality of AI outputs is directly linked to the quality of the data the AI systems use. In our experience, “fixing the basics” around data is a lot more challenging, expensive, and time-consuming than most executives expect. In this regard, the CAIO needs to coordinate closely with the chief data officer and other data owners within the organization.
As this integration continues and starts to scale throughout the organization in Stage 4, the CAIO role is still potentially beneficial. At this stage, the political acumen of the CAIO becomes as important as their operational role. Scaling AI initiatives will inevitably mean canceling many local projects and replacing them with enterprise-scale AI solutions. These changes will surely encounter local resistance.
However, once AI has been integrated and become an accepted way of working across a large part of an organization, in Stage 5, we see the need for a CAIO diminish. Indeed, it may be counterproductive to maintain the role when AI has become part of the normal course of business.
Setting Up a CAIO for Success
Once a company has made the decision to appoint a CAIO, there are a number of factors that experience tells us should be in place to avoid underperformance or failure.
First, the role should have a clear mandate. In our view, this step is often vaguely described in statements such as “help us build and deliver an AI strategy” or “make us a leader in AI.” However, a clear mandate means very well-defined objectives, including deliverables and associated timelines. It’s also helpful to align expectations with the CEO and board and to agree on how performance should be measured, along with the timing for results.
One of us worked at a fast-moving consumer goods company where the leadership team engaged in a six-month project to redefine the business strategy. It recognized the pivotal role of digital and decided to appoint a CDO. Executives and department leaders were excited about the new digital strategy and very engaged in the CDO onboarding process, but there were red flags. Though they welcomed digital technologies and expressed their full support for the CDO, it extended only to their own divisional or market projects and priorities. We could imagine a very similar situation for a CAIO.
Second, the responsibilities between the CAIO and other executive roles, particularly the CIO, CTO, and CDO (if there is one), need to be very clearly delineated. Before hiring a CAIO, companies should allocate significant time and energy to clearly defining the responsibilities, budgets, and governance in relation to other top executive roles.
Third, the CAIO will need significant resources to build and deploy an enterprise-level AI capability. These resources should come in the form of staff members with technical AI expertise and funds to accelerate appropriate initiatives within the organization. The budget and other resources should be fixed and transparent. Indeed, experience has shown us that a reallocation of resources and a loss of funding after the honeymoon period can doom a CDO.
Fourth, the profile for a CAIO role should prioritize strong communication, political, and change management skills. Because the role is cross-functional, the CAIO will need to lead via influence, sometimes without the benefit of formal authority. An executive seat and a reporting line to the CEO can help, but even this will not overcome structural resistance and corporate change “antibodies.”
Hiring a chief AI officer may be a popular trend today, but such a role is not required for all organizations. The default approach for most companies, in our opinion, should be to leverage AI either within existing structures, via dedicated cross-enterprise governance, or via partnerships with external providers. For many organizations, integrating responsibility for AI into existing roles, such as the CIO, CTO, or CDO, could provide the focus that AI initiatives need, without the potential drawbacks of adding another chief officer to the mix.
However, as we’ve outlined, there are situations where hiring a chief AI officer makes sense. Indeed, the debate over whether to hire a CAIO reflects broader questions about how organizations manage opportunities and risks and navigate the complexities of digital and data transformation. While technology continues to evolve, so will the strategies for managing its implementation. What remains clear, however, is the need for leadership that can guide AI initiatives with a strategic vision, whether through a dedicated CAIO or by other means. As organizations chart their course in an AI-driven landscape, the decision on how to manage this transformative technology will be a critical determinant of their future success.
