Essential Guide: Pathways to Becoming an AI Leadership Executive in Tech Companies

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With so much focus on artificial intelligence (AI), the question of who will lead AI at an executive level is becoming increasingly pertinent. Some of this responsibility is likely to be encapsulated by the rise of the chief AI officer (CAIO). Regardless of the title given to an AI leader, however, such individuals are needed to promote the responsible and productive use of the emerging technology.

So, is CAIO or AI leader a worthy career aspiration, and when you get there, how many toes will you step on?

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Research suggests a consensus that CAIO is a worthy role – 98% of 700 IT executives in a new survey released by Iron Mountain believe a CAIO can accelerate generative AI adoption. Around a third (32%) of organizations currently have a CAIO on board, which is expected to grow to 94% over time.

The research recognizes that the wide availability of generative AI tools has created a form of shadow AI: “End users wield AI without the education, guidance, discipline, and control data scientists and other experts have long brought to the AI value versus risk equation,” according to co-authors Debra Slapak and Jill Shoup, both with Iron Mountain.

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Leading AI efforts doesn’t necessarily mean having the title of CAIO. It’s essentially “a functional role, not a title role,” Andy Thurai , principal analyst with Constellation Research, told ZDNet. “Where the role is placed depends on the organization and the sponsor. It can be pretty much placed anywhere, even reporting to the CEO.”

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It’s likely that adjacent roles, such as chief information officer , chief digital officer , chief data office r, and chief analytics officer, will also be charged with seeing AI implementation through from start to finish.

However, a CAIO role would have a broader reach, Thurai argued. “They are spread across the organization horizontally. They deal with governance, security, compliance, legal, risk management, business, and line-of-business units that generally never get exposed to the other tech side.”

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While leading AI inherently requires a deep understanding of emerging technology, potential CAIOs should have an even stronger foundation in the business. “They don’t have to be data scientists, which would probably work against the goals of this role,” Thurai said. “This role is not a technical role and shouldn’t be.”

Ideally, the CAIO or AI leader will assume the following responsibilities:

Manage expectations for AI - With democratized generative AI products, such as ChatGPT , people assume “AI is so easy that anyone can implement any solution in a matter of days, without realizing the liabilities and consequences [for] their organization,” Thurai observed in a report co-authored with R. Ray Wang of Constellation Research. “A CAIO will balance the opportunities with the risks. For example, AI projects need standardized security, privacy, governance, auditing, legal, and risk management.”
Make sure resource needs are orchestrated - The CAIO or AI leader needs to work across the organization, “so that talent, training, and implementation capabilities are in place to accelerate generative AI adoption,” Slapak and Shoup said.
Measure impact with finance and operations teams - “CAIOs must deliver quantifiable results. Leaders are expected to track ROI calculations, valuation of business graphs, and value exchange in data collectives,” Thurai and Wang said.
Ensure ethical and legal practices are followed - A CAIO or AI leader will help ensure “the generative AI models used by their organization are reliable, fair, and transparent,” Slapak and Shoup said. This focus also includes overseeing legal implications that stem from “uncertainties regarding copyright and ownership of content created by generative AI.”
Orchestrate design, creation, testing, and deployment of AI with IT leaders - “From AI strategy and model design to technology-vendor and services-provider selection, CAIOs will work closely with their technology counterparts to determine the short, medium, and long-term strategy,” Thurai and Wang said.

Given the frantic pace of AI adoption in businesses, it’s time for managers and professionals to step up and ensure emerging technologies deliver value for the money spent. AI leaders will also need to ensure hasty implementations of AI do not take their businesses down an erroneous or even dangerous path.