What is a CAIO — and what should they know?

Though it’s a relatively new title, the role of chief artificial intelligence officer (CAIO) is gaining prominence at organizations deploying generative AI (genAI) technology — whether they’re moving deliberately or plowing ahead quickly.

By last October, 11% of midsize and large organizations had already filled a CAIO role, according to research firm IDC — and another 21% were actively seeking one. Just over half of 97 CIOs surveyed last fall said their organization had plans to have an individual leader responsible for AI and about half of those CIOs expect that person to be part of the C-Suite, IDC said.

Newly hired or appointed CAIOs “are not only part of an organization’s C-suite, but they are expected to be one of the most strategic members of the organization,” IDC said in its report.

As organizations chase efficiency and the productivity promise of AI, the CAIO title is expected to emerge on LinkedIn and other social media feeds, according to Forrester Research Analyst Zeid Khater. In fact, the role could soon surface in one out of eight executive leadership teams. 

In a recent Forrester survey, 12% of companies said their CAIO is primarily responsible for the overall enterprise AI strategy; only 2% attributed that responsibility to a chief data officer (CDO). “This doesn’t mean that CDOs are on the verge of extinction,” Khater wrote in a blog post. “Data is still a vital and often unleveraged resource within organizations due to challenges around quality, governance, and access.”

He urged companies to “ensure your AI and data leaders are in lockstep to spin data straw into insights gold. The CAIO brings technical knowledge, while the CDO provides quality data. It’s a powerful partnership for AI success.”

One big factor every CAIO will have to consider is cost; deploying AI models is expensive because cloud providers and proprietary genAI use cases require a lot of computing power — high-end, expensive computing power. And the chips that power learning and inference processes in large language models can cost thousands of dollars. (Nvidia makes most of the GPUs for the AI industry, and its primary data center workhorse chip costs $10,000; the company’s lock on the AI chip market is, however, being challenged by others who hope to undercut it with lower chip prices.)

All federal agencies will have CAIOs

It’s not just private companies looking to hire. In March, US President Joseph R. Biden Jr. gave all federal agencies two months to appoint CAIOs who be responsible for promoting AI innovation, coordinating with other agencies, and managing risks associated with the technology. The 60-day deadline highlighted the urgent need for governance as AI continues its meteoric adoption.

“While AI is improving operations and service delivery across the Federal Government, agencies must effectively manage its use,” Biden’s memo said. “The risks… result from any reliance on AI outputs to inform, influence, decide, or execute agency decisions or actions, which could undermine the efficacy, safety, equitableness, fairness, transparency, accountability, appropriateness, or lawfulness of such decisions or actions.”

Twenty-four federal agencies had appointed CAIOs by the May 30 deadline. In all, the Biden administration plans to hire 100 AI professionals by this summer and is requiring all federal agencies to establish AI governance boards to coordinate adoption efforts and establish rules for the use of AI and genAI.

“The decision to institutionalize the role of CAIOs demonstrates a clear acknowledgment of AI’s strategic significance,” Joel Meyer, former deputy assistant secretary of the US Department of Homeland Security, said in a recent Fedscoop article. (Meyer led the creation of DHS’s AI Task Force.)

Lt. Gen. John Shanahan, who co-authored the article with Meyer and was the nation’s first director of the Department of Defense Joint Artificial Intelligence Center, said one CAIO responsibility “is to identify low-hanging fruit. AI pilots can be chosen thoughtfully to demonstrate hypotheses that can then be affirmed in each department’s AI strategy. These quick wins can build momentum for broader AI strategy implementation.”

Because federal agencies were given latitude to define the organization under CAIOs, there’s a lot of variety between them in terms of authority, budgets, and what how the role would be executed, according to Amy Jones, US Public Sector AI Market Lead with Ernst & Young.

“Day to day responsibilities [are] pretty varied,” she said. “I think a CAIO’s success would be agency literacy. We all use the internet and email every day, and that requires literacy on both how to use them safely and securely and also how to use them optimally.”

The same is true for genAI technology.

Data quality matters

One known hurdle for genAI rollouts is the quality of data used to train LLMs. As the saying goes: garbage in, garbage out. It’s both challenging and costly to obtain high-quality, unbiased, and representative data, according to Andrew Rabinovich, who recently took the new position as Head of AI at freelance job platform Upwork.

According Rabinovich, key considerations for new CAIOs looking to deploy AI include:

• Careful planning and consideration for how the technology will deliver real customer impact rather than moving forward just for the sake of it or to keep pace with the hype cycle.

• A clear understanding of business objectives and specific customer pain points to solve with AI before launch. 

• Evaluating and ensuring the quality and reliability of the AI models being created, whether home-grown or by a third-party provider.

• Ensuring that LLMs are trained on diverse and representative datasets to avoid bias, while consistently monitoring for iterative improvements.

“Ensuring data cleanliness and accuracy often requires extensive pre-processing, which is both time-consuming and resource-intensive — and that’s if you even have access to the right datasets,” Rabinovich said.

CAIOs and others tasked with overseeing AI deployments play an essential role in “shaping an organization’s strategic, informed and responsible use of AI,” he said. “There are many responsibilities baked into the role, but at its core, it’s about steering the direction of AI initiatives and innovation to align with company goals. AI leads must also create a culture of collaboration and continuous learning.”

“All teams across all functions within an organization should be thinking about how they can collaborate on AI projects, experiment with the technology and explore how to equip their teams with the right knowledge, skills and tools to harness AI,” he said.

At Upwork, Rabinovich is overseeing the company’s use of a GPT-4 LLM to create an AI-powered platform called Uma, which powers features such Best Match insights, aimed at helping businesses find the best potential person for a job by identifying relevant insights like top proposals, client reviews, and skill alignment with the job post.

For genAI to function reliably, CAIOs will need to figure out how to utilize AI and data optimization techniques for improved efficiency, data quality, and ethical considerations. “On paper, [you need] baseline compliance — making sure they [LLMs and genAI] are within regulatory and policies, creating [your] own policy within the agency that’s specific to the mission, [and] identification of inventory of use cases,” Jones said.

Rabinovich agreed. The quality of data used to train AI models is an important aspect of the development process, but it can be hard to obtain high-quality, unbiased, and representative data, he noted.

“Ensuring data cleanliness and accuracy often requires extensive pre-processing, which is both time-consuming and resource-intensive — and that’s if you even have access to the right datasets,” Rabinovich said.

A dedicated CAIO or one with shared duties?

Jenn Kosar, a partner at PricewaterhouseCoopers (PwC), said while most organizations have not yet designated CAIOs as an official C-suite role and title, from a functional perspective a significant number of organizations are filling the role today without the title. Most often, the position is one notch below a CIO, she said.

“Today, we often seen CTOs and CISOs taking this [genAI responsibility] on,” Kosar said. “And that may be OK for where we are today. But the strategic [planning], the change management, the innovation, the ability to take an organization through a transformation — these are really critical skills to the success of this role.

“Unfortunately, what we’re seeing in most instances it’s not a full-time job. In other words, they [CAIOs] have other roles. We believe it should be a dedicated role. They’re being held accountable for how an organization is moving forward with AI.”

While CAIOs might not always be seated at the C-suite table, those who are there are keenly focused on genAI and its potential to drive efficiencies and profits. Without an executive guiding those deployments, achieving the performance and ROI organizations seek will be tough, she said.

“It’s hard to imagine how pieces come together and how you’d bring together so many players,” Kosar said, noting that PwC has more than a dozen LLM-backed tools running internally to power AI tools and products in virtually every business unit.

“You have to have the ability to do short-term and long-term planning and balance the two and stay focused on innovation,” she continued. “At the same time, you need to recognize the pace of change while not getting distracted by the latest shiny object.”

Getting AI right is important because of how much it will be a part of everyday life by the end of the decade, Rabinovich said. By 2030, he believes virtually everyone will interact with AI and the tech will perform in roles varying from personal assistants and tutors to therapists and accountants — even lawyers.

“AI will help humans uplevel and enhance societies, because it’ll enable humans to focus on solving ever more complex problems,” Rabinovich said.