The Chief AI Officer (CAIO) is a fast-growing and increasingly important C-suite role. While engineering leaders focused on artificial intelligence (AI) have long been influential at certain, often research-led organizations, the dramatic democratization of large language models (LLMs) and generative AI tools have created the need for more engineering leaders who understand how these tools can be best and safely deployed.

What is a Chief AI Officer?

The Chief AI Officer is the executive responsible for setting an overall AI strategy that is aligned with the organization's business goals. That could mean research and development (R&D) efforts, deploying AI to improve products and services, or to increase operational efficiencies. 

For Dr. Mark Daley, CAIO of Western University in Ontario, every CAIO, regardless of organization, should have “a very clear understanding of the aspirations – and concerns – of stakeholders and enough technical acumen to be able to match those to the current (and near future) capabilities of technology.” He sees the CAIO as responsible for “bridging the gap between technical AI capabilities and business or institutional needs, ensuring AI technologies are implemented optimally.”

The function of a CAIO can be incredibly varied and often misunderstood, and depends a lot on the specifics of the organization. At an AI focused start up, the CAIO is likely to be one of the most important figures in the company and will be responsible for key product development decisions. While in a large multinational enterprise, they may have to be more consultative, reporting into the chief technology officer (CTO) or chief information officer (CIO). 

What’s the difference between a CTO and a Chief AI Officer?

Both the CTO and CAIO are important technology leadership positions within an organization, but there are a few key differences between them. 

The CTO is a well-established role, typically responsible for research and development, product engineering, and the overall technical architecture of the whole company. Their main job is to align the technology the company develops and uses with the businesses goals of the organization as a whole. This can mean making sure the product has the features its users need, developing internal tools that can streamline operations, and looking for ways that technology can boost revenue and increase sales. 

AI will now be an important facet of all of those discussions and decisions, but it is still just one part of an organization’s overall tech stack. It can absolutely be used to meet many organizations’ business goals, but it’s unlikely to be deployed in isolation. This is why the CAIO will often report to the CTO, rather than operate independently. They will work together to develop an AI strategy that fits with the overall technology strategy for the organization, then the CAIO will oversee any engineers working on AI projects. 

Of course, as the role of the CAIO officer is relatively new and a lot of the standards and expectations around it haven’t developed yet, the exact relationship between the CTO and CAIO will vary from company to company. 

Chief AI Officer roles and responsibilities

Hopefully you now have a rough idea of what the purpose of a CAIO is, but what are their more specific roles and responsibilities?

Akshay Sharma, CAIO at Lyric.ai, an AI healthcare payments company, sees the role starting with “formulating and leading the AI strategy,” and stretching all the way across “alignment of AI initiatives with business objectives, fostering AI innovation, managing AI talent, overseeing AI governance, ethics, and compliance, and integrating AI technologies into products and services to enhance efficiency and improve customer experience in the tech landscape.”.

At a big picture level, the CAIO is responsible for:

• Strategic AI planning. The CAIO has to keep on top of new technological developments, as well as consider how AI can be best deployed to meet business goals. 
• AI development and quality control. The CAIO has to ensure that any AI tools that the organization develops or deploys are fit for purpose. This often means collaborating with the different engineering teams to successfully integrate any AI models or features. 
• Internal AI implementation. Many organizations will only ever use AI to improve internal processes. The CAIO will be responsible for the safe rollout of these tools, set guardrails and policy, and educate teams on how best to use them. 
• Legal and regulatory compliance. The CAIO is responsible for ensuring that any AI implementation is compliant with any legal or regulatory demands, particularly around the handling of sensitive customer data. 

Adam Lieberman, CAIO at financial services firm Finastra, tends to focus on the latter safety elements of the role, saying that a CAIO is “responsible for the architecture and infrastructure, and the legal and governance policies around data and artificial intelligence. [They also] educate the entire enterprise around AI, review use-cases, and bring about the delivery of production grade AI models.”

How to become a Chief AI Officer

Because the role is so new, there isn’t yet a typical pathway to becoming a CAIO. While a bachelor’s degree is almost certainly essential, it doesn’t necessarily need to be in the field of artificial intelligence. Similarly, CAIOs will be experienced engineering leaders, but the exact nature of their experience may vary widely. 

As a result, the Chief AI Officer needs sufficient technological knowledge to follow the latest AI developments and is able to supervise the teams tasked with implementing them. They must also have a deep enough understanding of the business to recognize where the technology can be most effectively employed.

Experience working with machine learning and artificial intelligence is going to be hugely beneficial for a CAIO candidate, ideally with a strong background in computer science, programming, data science and statistics, and similar technical fields, as well as lots of experience leading a team. However, given the sudden rise in the popularity of AI, it seems that many CAIOs are experienced engineering leaders with enough technical knowledge to get up to speed fast and understand the broad strokes of modern generative AI tools, rather than a dedicated AI research background.

Daley at Western University in Ontario says a good CAIO doesn’t necessarily need to be able to build a model themselves, but that they should understand enough about the key technologies and be sufficiently capable of managing a team that they could come up with an abstract plan for how it could be done.

Another key aspect of a CAIO’s role is interdepartmental collaboration. They need to be a team player, with experience working across different teams to bring complex interdisciplinary projects to fruition. Similarly, given the need to align the organization’s AI strategy with its business goals, the more experience a CAIO candidate has with business processes and operations, the better. Experience working with customer-facing sales and marketing teams, or a degree like an MBA are likely to contribute strongly here.

An exciting and lucrative role

Finally, as a relatively new role, there isn’t a huge amount of data available about CAIO salaries. On Glassdoor, a single reported salary in the United States suggests that the average total compensation for a CAIO is $380,486, with a base pay of between $128,000 and $240,000 and $156,969 to $293,008 in additional compensation. Expect pay to be similar to other technical leadership roles like a chief technology officer, which Glassdoor more confidently says has an average salary of $276,000, with the total compensation typically being somewhere between $207,000 and $387,000.

The CAIO job is a new and exciting role for forward looking engineering managers. It’s likely to only become more important over the coming years, so if you’re keen to take on the role, now is the time to start plotting your career trajectory. Good luck!