Who wants to be a chief AI officer? A new career path emerges

In the year ahead, nine out of 10 organizations are expected to hire talent with generative AI expertise, with a quarter of organizations forecasting that at least half of their new hires will need this skill. In addition, six in 10 companies now have a chief AI officer (CAIO) to guide the process.

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These are the findings from a new survey of 3,739 executives and professionals, released by AWS. Almost all respondents (92%) indicated their organizations intend to recruit for new roles that require generative AI expertise. For a quarter (26%) of those respondents surveyed, at least half of the new positions in their organization will demand generative AI skills. 

Some industries are even taking a more aggressive approach to AI hiring. In the IT sector, 35% of organizations plan to make generative AI skills a key requirement for at least half of their new roles, while 28% of organizations in the manufacturing sector will follow suit.

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At least 60% of respondents reported their companies have already appointed CAIOs, and another 26% are planning appointments by 2026. You may think these AI leadership positions are limited to large organizations, but 64% of the businesses in the survey had fewer than 250 employees. Many AI leadership positions are probably part of the roles of people with other job titles. 

“AI leadership will be crucial moving forward — CAIOs are being added to the C-suite to oversee integration, risk management, and value creation,” the AWS authors stated. “As generative AI adoption continues to expand across industries, organizations are adapting to meet the growing demand for consolidated AI leadership to drive impactful organization-wide generative AI transformation. This also opens up new corporate career opportunities for AI-skilled talent within organizations.” 

Along with skills, data is also an issue — and AI leaders have their work cut out. Over the past year, their organizations ran an average of 45 experiments with generative AI. However, only an average of 20 experiments (44%) are expected to go into production. 

Talent shortages and the need for clean data are potential roadblocks. More than half of respondents (55%) from organizations engaged in generative AI experiments said a paucity of skilled talent is the greatest barrier preventing them from taking generative experiments into production. Other key obstacles included the perceived high costs of development (48%) and biases and hallucinations (40%). 

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Companies are focused on upskilling and hiring to attempt to bridge the generative AI talent gap. To accelerate deployment, 56% of organizations have already developed generative AI training plans, and a further 19% will do so this year. However, around half of the respondents said they had a limited understanding of employees’ generative AI training needs, which hinders the creation of robust learning and development plans. 

Almost half of IT decision makers (45%) selected generative AI tools as their top budget priority in 2025, exceeding other IT spending categories, such as security tools (30%). Only 13% were primarily focused on obtaining compute power. 

Ease of integration was a key criterion for most organizations adopting generative AI solutions. IT leaders who operate in environments with a high level of regulatory oversight and strict compliance requirements also valued advanced capabilities (56%) and robust privacy and security features (48%). Change management was also a concern — nearly one-quarter of organizations will still lack formal transformation strategies by 2026.