CAIO: What to Study and What Does a Chief AI Officer Do in a Company?

Artificial intelligence is no longer just a technological consideration; it has become a strategic business decision. Companies need to determine where AI can deliver value, how much to invest, which risks to take and, above all, what return they can expect on that investment. This growing need has led to the emergence of a new role within senior leadership: the Chief AI Officer (CAIO).

The role is growing particularly rapidly. A global IBM study involving 2,000 CEOs found that 76% of the organisations surveyed had a Chief AI Officer in 2026, up from 26% the previous year. In Spain, a further IBM study conducted in 2025 found that 72% of the CAIOs surveyed reported directly to the CEO or COO.

These figures highlight the true nature of the position: a CAIO is not simply a senior programmer, but an executive who bridges artificial intelligence and business strategy. Their responsibility is to turn technological possibilities into scalable, well-governed initiatives that deliver measurable results.

What Is a CAIO (Chief AI Officer), and Why Are Companies Creating This Role?

The Chief AI Officer (CAIO) is the executive responsible for leading an organisation's artificial intelligence strategy. They determine how to integrate AI into the business, oversee AI governance and set priorities to prevent individual departments from developing disconnected initiatives without a shared strategic direction.

The rapid growth of generative AI has accelerated the need for this role. Marketing teams may want to automate content creation, Operations may seek to optimise processes, Human Resources may introduce new tools, and Finance may adopt predictive models. Challenges arise when each department invests independently, without shared criteria for costs, security, data management or return on investment.

The CAIO provides an organisation-wide perspective, determining which use cases are worth investing in, which should be scaled up and which should be discontinued.

Alongside these business needs, regulatory requirements are also shaping the role. The EU Artificial Intelligence Act establishes different obligations depending on the type and risk level of AI systems, with a phased implementation timeline. Since August 2026, certain transparency obligations, among others, have come into effect, and competent authorities have begun enforcing various provisions of the regulation.

The Role of the Chief AI Officer: Where AI Is Applied and Who Is Accountable for ROI

The CAIO's primary responsibility is to turn artificial intelligence into measurable business value. Having access to advanced tools or launching pilot projects is not enough: AI initiatives must solve specific problems and justify the resources invested in them.

This is why the role combines technology, strategy, finance and management:

• Identifying AI use cases with the potential to generate revenue, reduce costs or improve productivity.
• Prioritising AI investments based on their expected impact and feasibility.
• Defining performance indicators to assess whether projects are delivering results.
• Coordinating technical teams and business departments to translate business needs into practical, deployable solutions.
• Establishing governance standards for security, privacy and regulatory compliance.
• Deciding whether to develop in-house solutions or work with external providers.
• Driving internal adoption by training employees and managing organisational change.

This focus on return on investment is particularly important. IBM reports that, in its study, organisations with a CAIO achieved a 5% higher return on their AI investments than those without one.

Results can be measured in different ways depending on the project, including time savings, cost reductions, increased sales, employee productivity or improvements in customer experience. The CAIO must be able to translate these technological outcomes into business metrics that senior management can understand.

Not a Programming Role: Why the CAIO Is an Executive Position

One of the most common misconceptions is that the CAIO is simply a company's most senior AI programmer.

A Machine Learning Engineer or Data Scientist may design models, work with large datasets, evaluate algorithms or develop technical solutions. The CAIO, by contrast, oversees the broader business strategy within which these professionals operate.

This does not mean that a CAIO can afford to lack technical knowledge. They need to understand how AI models work, the role data plays, the limitations of different solutions and the potential risks involved. However, their primary responsibility is making strategic decisions, rather than writing the code that puts those decisions into practice.

This explains why AI training for executives is becoming increasingly important. Senior leaders do not necessarily need to become developers, but they do need sufficient technical understanding to ask the right questions, interpret specialists' recommendations and make well-informed decisions.

Differences Between CAIO, CIO, CTO and CDO

The introduction of the CAIO adds a new position to a technology leadership structure that already includes several executive roles. Their responsibilities may overlap in some companies, but each has a different primary focus.

• The CIO (Chief Information Officer) is typically responsible for information systems and ensuring that the organisation's IT infrastructure meets its business needs.
• The CTO (Chief Technology Officer) generally focuses on technology strategy, technical architecture and, depending on the company, the technological development of products and services.
• The Chief Data Officer (CDO) primarily focuses on data, including its quality, availability, governance and use across the organisation.

The CAIO focuses specifically on how artificial intelligence can be used to transform processes and deliver business results.

In practice, the boundaries between these roles depend on each company's organisational structure. In some organisations, the CAIO reports directly to the CEO; in others, they may report to another senior technology executive. What matters is that AI requires coordination between technology, data and business teams, rather than departments working independently.

How Much Does a Chief AI Officer Earn?

In Spain, a Chief AI Officer earns between €110,000 and €220,000 gross per year, with an average salary of €154,000, according to the Pearson Carter Data & AI 2026 Salary Guide.

As this is still a relatively new executive role, salary benchmarks are not as well established as those for other senior leadership positions. Furthermore, the same job title can encompass very different responsibilities, so the available figures should be viewed as market estimates rather than a standard salary.

Salary differences depend primarily on factors such as:

• Company size and revenue.
• Number of markets and business units under their responsibility.
• Budget allocated to artificial intelligence.
• Size of the team managed.
• Industry and level of regulatory oversight.
• Decision-making authority and position within the organisational structure.
• Bonuses, incentives and variable compensation.

As a result, a Head of AI leading a specific technical team and a CAIO overseeing the global AI strategy of a multinational company may fall into very different salary brackets, even though both work with the same technology.

Two Career Paths to Becoming a CAIO: From Business Leadership or a Technical Background

There is no single career path to becoming a Chief AI Officer. The hybrid nature of the role means that two main routes into the position can be identified.

- The first route is through business leadership. These professionals come from backgrounds in strategy, operations, digital transformation or business unit management. They understand how businesses operate, manage budgets and teams, and know how to evaluate investments. However, they need to develop a solid understanding of artificial intelligence.

For these professionals, the challenge is to understand the possibilities and limitations of AI without necessarily becoming programmers. Executive education can help bridge the gap between business experience and emerging technological capabilities.

- The second route is through a technical background, particularly STEM careers. This includes data scientists, engineers, Machine Learning specialists and technology leaders who have gained experience managing complex projects.

In this case, the challenge often lies on the other side of the equation: moving from understanding how technology works to understanding how to lead a business. Finance, leadership, budget management, strategy, negotiation and executive communication become increasingly important skills.

What to Study to Become a Chief AI Officer

There is currently no mandatory qualification required to become a CAIO. The most suitable educational path will largely depend on a professional's background and the skills they need to develop.

1. For business professionals looking to understand how to integrate AI into corporate strategy, the Master in AI Applied to Business offers training focused on practical use cases and the application of artificial intelligence within organisations.
2. Those seeking to develop more advanced technical expertise can pursue the Master in Artificial Intelligence & Machine Learning for Business, combining knowledge of AI and Machine Learning with their practical business applications.
3. For professionals specialising in data, analytics and decision-making, the Global Master in Business Analytics and Data Strategy provides an opportunity to deepen their understanding of how to use data strategically, an essential foundation for developing artificial intelligence projects.
4. Finally, experienced professionals from technical backgrounds who need to strengthen their financial, strategic and leadership capabilities may find an Executive MBA a valuable way to develop these skills.

The professional profile emerging from these two career paths is neither exclusively technical nor purely business-oriented. A CAIO needs to understand artificial intelligence well enough to govern it and understand business well enough to generate a return on investment. This combination is precisely what is establishing the CAIO as a new position within senior executive leadership.