Sixty-four percent of companies don’t have a dedicated Chief AI Officer in place, according to the “Risky Business: Identifying Blind Spots in Corporate Oversight of Artificial Intelligence” report released by Chicago-based law firm Baker McKenzie (based on responses from 500 U.S. based C-level executives). In the absence of a Chief AI Officer, AI oversight typically falls under the domain of the CTO or CIO. Moreover, the same report cited that just 41% of corporate boards have an expert in AI on them. So, what is it about the Chief AI Officer role that’s got people talking? Is it time for organizations to hire one or is that still a few years off in the distance? What are the key roles and responsibilities of the Chief AI Officer job title? We’ll unpack that here.
Managing AI Risk
Sticking with the theme of data points, in Deloitte’s latest State of AI in the Enterprise report, 50% of organizations that are attempting to scale up their AI projects over time cited both “managing AI-related risks” and “lack of executive buy-in” as key impediments to actually being able to scale their AI initiatives. It is true that without clear leadership for AI initiatives, successful AI implementation and scaling might not ever be realistic.
A few years ago, we imagined what some potential directions of the theoretical Chief AI Officer role could go, and the one that is really rearing its head today is the connection between AI and legal/risk. Given the rapidly evolving regulatory environment, this person could be the owner of the company’s governance and data privacy standards, making sure that they are enforced at all levels of the company while also looking out for new risks. As self-service analytics continue to take root on the business side of the house, this person would be tasked with ensuring proper governance policies while also maintaining ownership of machine learning interpretability, trust, and ethics.
In our blog overview from a Dataiku Product Days session, Sulabh Soral, Chief AI Officer at Deloitte Consulting, U.K. said,
An exec that understands the language of AI and is able to study the larger implications of the technology will be extremely valuable. A defined strategy and contextualization of AI in terms of business processes that permeates the entire organization is key. Everyone needs to be able to look at measurements and determine how they should be stacked in order to support an AI project from beginning to end. This understanding and knowledgeable ambition needs to come from the top down.”
Evangelizing to Leadership
Now, while every leader in the organization should invest time in understanding AI and its potential impact on the business, the Chief AI Officer can set themselves up for success by setting up a series of workshops for the executive team to coach them on the key tenets of advanced analytics and eliminate any lingering misconceptions. They can ask questions such as:
- Does pursuing AI or advanced analytics pose any threats for the company?
- What are the opportunities to use these technologies to improve existing processes?
- How can they be used to generate new business opportunities?
- What is the risk of not leveraging AI within the company or for some specific function?
Even though the role is centered around AI-backed technology, the right fit will be someone who can easily build and maintain relationships with other executives that may not be in tech-first roles (i.e., CFO, CHRO) and work cross-functionally with business units and functional teams.
Evolving From Another Role to Bridge Tech + Business
In an HBR article, AI expert Andrew NG said, “To the majority of companies that have data but lack deep AI knowledge, I recommend hiring a Chief AI Officer or VP of AI. Some Chief Data Officers and forward-thinking CIOs are effectively taking on this role.” Many organizations might already have a Chief Data Officer, Chief Data Science Officer, or Chief Analytics Officer in place, so it’s fair to say that any of these existing roles can expand and grow into a Chief AI Officer position, leveraging their original expertise and taking it a step further to implement and scale AI-driven technology across the enterprise. In the same article, Ng recommends the following traits to look out for when hiring a Chief AI Officer:
- Strong technical understanding of AI and data infrastructure
- Strong intrapreneurial skills
- The ability to attract and retain AI talent (and successfully manage AI teams)
Overall, the Chief AI Officer role might differ from a CDO that hails from an IT or technical data science background because (s)he should be confidently able to make sure AI gets applied across silos and is democratized beyond just central data teams and into the business.