Following Kroger and Dollar General, Target has just appointed its first Chief AI Officer (CAIO). Two years ago this was a title that barely existed in the industry, and it’s now spreading faster than any executive role in recent memory — with one exception.
That exception should give us pause. In 2022 the Chief Metaverse Officer was retail’s hot new hire, and luxury brands especially rushed to fill it. Hardly any of those jobs exist today. Retail has even run this experiment once before: Levi Strauss put AI into a C-suite title back in 2019, years before it was fashionable, and when that executive left in 2023 the title went with her, with AI folded back under the Chief Digital and Technology Officer. And the wobble has already begun this time round as Lululemon’s first Chief AI and Technology Officer left in August after less than a year in a newly created role. So it is fair to ask whether the new wave of appointments will go the same way.
I don’t think it will, but not for the reason you might expect. The metaverse role answered demand that never actually materialized. AI assistants, by contrast, are already recommending products, comparing prices and completing purchases on shoppers’ behalf. There is no doubt the role responds to something real. The harder question, in retail specifically, is whether the Chief AI Officer owns the right things and this starts with product data.
Avoiding a Portfolio of Pilots
On paper, a Chief AI Officer at a large retailer could touch every part of the organization: how a shopper discovers a product, how they convert, how the order is delivered and how service handles what goes wrong. That breadth is exactly the danger. A role responsible for everything tends to end up with a portfolio of pilots, a seat on steering committees, and no operational ground of its own.
Lessons from a Black Friday
I spent my early career leading direct-to-consumer and ecommerce for global consumer electronics brands. One Black Friday I saw a brand run out of stock, yet its Facebook ads kept sending shoppers to product pages with nothing left to sell. Meanwhile the Google ads showed the regular price instead of the promotion. And the extra inventory secured at the last minute to plug the gaps went live under SKU titles no shopper could decipher.
No single team caused it. The product listings belonged to the direct-to-consumer team. But the feeds behind them, the data telling every channel what the brand sold, at what price, in what quantity, had been set up at different times by the ecommerce team, the development team, global marketing and an external agency. Everyone was working from the same product data but nobody owned it — and because each team was measured on different KPIs, nobody was rewarded for fixing it.
What AI Agents Have Changed
Human shoppers are, in most cases, willing to forgive errors. They hit an out-of-stock page, sigh, look elsewhere and sometimes come back later. An AI agent shopping on someone’s behalf will not. A feed price that doesn’t match the checkout price, a missing attribute, a stale stock flag are discrepancies a person would never notice but they are enough for an agent to drop the purchase and move on to a competitor whose data holds up.
The same logic applies earlier in the journey. LLMs recommend products based on the data they can read. If descriptions are incomplete, labels inconsistent, or pricing and inventory out of date, the sale is gone before the retailer ever knew the shopper existed.
The First 90 Days
None of this means the Chief AI Officer’s remit should shrink to product data; the role is rightly bigger than that. But the data layer is where a new CAIO can prove the role has teeth. Ninety days spent mapping every team, system and agency that touches the product data, and every channel that data feeds, would tell them more about their organization’s AI readiness than any pilot. The harder step comes after: asking for the authority to fix what the map reveals, rather than settling for a view of it. An organization that refuses has created a symbolic role and it will find out at the worst possible moment — usually a Friday in late November.
What They Inherit Can Define the Role
The bigger truth is that the majority of retailers will never have a Chief AI Officer. The title is a luxury of scale, but the clarity behind it shouldn’t be. At a recent industry event about AI in commerce, I’d have bet fewer than half the ecommerce leaders in the room could draw their own tech stack, and it is hard to direct AI across an organization nobody can sketch. So the question underneath the title is one every retailer should answer when considering making a CAIO hire: who owns your product data right now? If your organization has a clear answer, you may not need the role. Without one, no hire will fix that alone.
For those who do make the hire, the first conversation should start with two questions. What are we doing to get our products recommended — and sold — through LLMs? And what would it take for our checkout to be ready when one agent is buying from another? A CAIO who inherits well-owned product data can get to work on both from day one. One who inherits nobody’s data will spend their first year finding that out. And in a few years, when someone asks whatever happened to the title, the answer will be the same as last time. It was never given anything to own.
Jessica Laan is Chief Revenue Officer at Channable, bringing over 15 years of commercial and brand-side international experience in eCommerce to the company’s go-to-market strategy. Before joining Channable, Jessica held international roles in sales, marketing and ecommerce at global consumer electronics brands.
