The Chief AI Officer (CAIO) has become one of the fastest-growing executive roles in the corporate world: according to an IBM survey published in May 2026, 76% of global organizations have already appointed someone to this position, against just 26% in 2025. In Latin America, the term is starting to appear in job descriptions at multinationals and tech unicorns, but still very few professionals know exactly what it involves.

In this article we explain what a CAIO does, how it differs from other executive roles, what skills you need to get there, and what the salary ranges are in LATAM.

What a Chief AI Officer is and why it exists

The Chief AI Officer is the top person responsible for the artificial intelligence strategy within an organization. It's not a purely technical role: it's a bridge between technology and business. The CAIO defines which problems are attacked with AI, supervises the implementation teams, ensures that AI projects are aligned with corporate objectives, and manages the ethical and regulatory risks that the use of models entails.

The position emerged because organizations realized that AI is not an IT project: it's a strategic decision that impacts all departments. Having someone in the C-suite exclusively focused on AI accelerates adoption and reduces implementation errors.

CAIO vs CTO vs CDO vs CIO: what's the difference

The confusion between these roles is common. Here's a clear distinction:

• CTO (Chief Technology Officer): responsible for the company's general technological architecture. Focuses on infrastructure, platforms, and stack decisions. AI is part of their agenda, not all of it.

• CDO (Chief Data Officer): responsible for data governance, quality, and strategy. They are the owner of the data that feeds the AI models, but not necessarily of the models themselves.

• CIO (Chief Information Officer): manages internal information systems. Historically more operational than strategic.

• CAIO (Chief AI Officer): has an exclusive focus on AI strategy. Defines the roadmap, prioritizes use cases, supervises AI/ML teams, and acts as a spokesperson before the board and regulators.

In smaller organizations, the CTO can take on the CAIO role. In large corporations with multiple business units, having a dedicated CAIO is already a standard.

What a CAIO does day to day

The responsibilities vary according to the industry and the size of the company, but there is a common core:

• Define the AI roadmap for 1, 3, and 5 years

• Prioritize the use cases with the greatest return on investment

• Supervise Data Science, ML Engineering, and AI Research teams

• Manage relationships with model providers (OpenAI, Anthropic, Google, etc.)

• Ensure regulatory compliance (especially relevant with the European AI Act already in force)

• Communicate AI progress to the board and investors

• Build an AI culture within the organization

According to a 2025 McKinsey report, companies with a dedicated AI leader are twice as likely to scale their AI projects beyond the pilot stage. Without a CAIO, projects get trapped in experimentation.

Skills you need to be a CAIO

The CAIO profile is hybrid. You don't need to be a model researcher, but you do need to understand how they work. The key skills are:

Technical skills (not implementation, but understanding)

• Understand how LLMs, vision models, and agent systems work

• Read and evaluate model benchmark results

• Know the data architectures needed to train and deploy AI

• Handle concepts of MLOps and LLMOps

Strategic and leadership skills

• Communicate complex AI concepts to non-technical audiences

• Manage multidisciplinary teams (data, engineering, product, legal)

• Build business cases for AI investments

• Lead the organizational change management that AI adoption entails

Regulatory and ethical skills

• Understand the framework of the European AI Act and its impact on global companies

• Manage bias, privacy, and security risks in AI systems

• Develop responsible AI use policies for the organization

If you want to start building these competencies, in CoderLibrary you'll find a detailed analysis of how big tech companies are investing in AI infrastructure, which gives you context of the ecosystem in which a CAIO will operate.

How much a CAIO earns in LATAM

The salary data for this role in the region is still scarce, but ranges can be drawn based on similar positions in the tech C-suite:

• Argentina (multinational companies or unicorns): between USD 4,000 and USD 12,000 monthly, depending on the size of the company and whether the payment is in dollars.

• Mexico and Colombia: the ranges are similar in positions reporting to the CEO or board.

• LATAM at global companies (remote work): the ranges can exceed USD 15,000/month for CAIOs with demonstrated experience in scaling AI projects.

The role still doesn't have a stabilized salary rate because the supply of qualified talent is very low. Whoever reaches the market first with the right profile has an enormous advantage.

How to prepare to become a CAIO

There is no single path, but there are concrete steps that accelerate the journey:

1. Build a technical base in AI: you don't need to be an ML Engineer, but you do need to understand how the models work, what limits them, and how they are deployed in production.

2. Acquire experience in leading tech teams: the CAIO manages people, budgets, and stakeholders.

3. Specialize in data strategy: AI without data is impossible. Understanding the chain from capture to modeling is a differentiator.

4. Learn about AI regulation and ethics: the European AI Act already impacts companies in LATAM. Whoever knows the regulatory framework is more valuable.

5. Build a track record of AI projects with business impact: concrete use cases with measurable ROI are the best portfolio for this role.

Recommended Coderhouse courses

If you want to build the technical and strategic profile that a CAIO needs, these Coderhouse programs are a concrete starting point:

• Introduction to Artificial Intelligence Course: ideal for understanding the fundamentals of AI models, their capabilities, and their limits, without needing an advanced programming background.

• AI Engineering Course: to go deeper into how AI systems are built and deployed in production, key to supervising technical teams as a CAIO.

• AI Marketing Career: for those who come from the business world and want to integrate AI into the growth strategy, an ideal complement for the CAIO profile at consumer companies.

Frequently asked questions

Does the CAIO replace the CTO?

No. They are complementary roles. The CTO manages the company's entire technological architecture, while the CAIO has an exclusive focus on the artificial intelligence strategy. In large organizations, they coexist. In smaller companies, the CTO can temporarily take on the CAIO role.

Do you need a PhD in AI to be a CAIO?

Not necessarily. Many CAIOs have training in business, engineering, or computer science, combined with practical experience in AI projects. What matters is the ability to connect technology with business, not to do model research.

Are the 76% of companies that appointed a CAIO all large corporations?

According to IBM's May 2026 report, the phenomenon occurs mainly in medium and large companies (more than 500 employees). However, the most relevant data is the speed of the change: it went from 26% to 76% in one year, which indicates a massive and immediate adoption across all company sizes.

How long does it take to prepare for this role?

It depends on the starting point. Someone with tech leadership experience can position themselves for the role in 1 to 2 years of intensive AI training. Someone without a technological background needs more time to build the conceptual base the role requires.

Does the CAIO role exist at companies in LATAM or only abroad?

It already exists in LATAM. Multinationals with operations in Argentina, Mexico, Colombia, and Brazil are creating these positions locally. Tech unicorns in the region are also starting to appoint AI leaders with C-suite powers, although not always with the formal title of CAIO.