Andrew Chin has spent nearly three decades working at AllianceBernstein, along the way taking leadership positions involving the management of $829 billion in assets, overseeing quantitative research, and spending time with clients.
This diverse expertise made Chin a perfect candidate when the asset manager created the chief AI officer (CAIO) role a year ago.
“It’s easier for me to imagine reimagining our workflows with AI,” says Chin, who previously served as chief risk officer and chief data scientist. AllianceBernstein CEO Seth Bernstein and chief operating officer Karl Sprules asked him to step into the CAIO role to focus all of his energy on AI.
But what CAIOs don’t fully agree on is whom they should report to. There’s a wide range of options, including the CIO, CEO, chief technology officer, and chief operating officer. This decision varies greatly, based on the company’s AI maturity journey, how much the technology is transforming product development, and to what level it affects workflows from the CEO to the frontline worker.
Chin reports to the COO, because he was adamant that the CAIO cannot report to a technology C-suite leader. “For AI, the focus has to be on the outcomes, not on the tools,” he says.
“I think Alex looks to me to understand the ways to utilize AI to give the maximum customer benefit,” says Srivastava.
Srivastava says the project developed quickly because of a decision that he and other senior leaders at the company made to create smaller, more nimble teams and reduce onerous meetings. “Just let them write code and experiment with customers,” says Srivastava of Intuit’s product development approach.
He also lauded Intuit’s investment in GenOS, a proprietary generative AI operating system that was built on AWS, as critical infrastructure that allowed the project to move forward in an efficient manner.
“You don’t really need a chief AI officer if you are only talking about AI making some changes for productivity,” says Xu. “I don’t need to tell people, ‘Use ChatGPT.’ That’s a given.”
As it pertains to workflow changes, Xu operates under the assumption that every engineer at Gen Digital is already using AI-powered code editor Cursor and similar tools. That speeds up work and means that the old rules requiring product managers to schedule biweekly or bimonthly check-ins with engineers didn’t make much sense. Gen Digital is changing its approach: For one project now in development, product managers are meeting with the engineers in as little as two days.
“AI is going to do coding blazingly fast; sometimes it’s good, sometimes it’s less good, depending on the context,” says Xu. “How do you get 2x, 5x, or 10x productivity out of it? You have to have someone look at things more strategically, step back and zoom out, and reshape how a company does things.”
“My initial conversation with Wes [Schroll], our CEO, really got me excited about this role,” says Gundu. “There are so many aspects of this core business that require AI to be able to scale.”
Arora also leads the integration of data, analytics, and AI across all of the insurance and investment management company’s business divisions, coordinating closely with division presidents, as well as Principal’s shared services model across marketing and sales, to ensure that all data from different software systems are funneled into a centralized ecosystem.
He also coordinates upskilling efforts with the HR team, including an ongoing project to offer a specific education plan for 130 of Principal’s top executives. Globally, all employees will take training courses on data, AI, and learning how to prompt. The training launched in August at offices in North America and Europe, soon to be followed by Latin America and Asia-Pacific in September.
“It’s a pretty aggressive plan,” says Arora. “This is not going to be once and done. We’re going to continue to enhance our posture as new things come into play and as we make progress towards the maturity of our data and AI journey.”
