The hire and the number behind it
Morrisons announced on September 28 that Mohsen Ghasempour will join as the grocer's first chief AI officer, effective October 1, 2026, reporting directly to CEO Rami Baitiéh. Ghasempour arrives from Kingfisher, the home improvement group that owns B&Q and Screwfix, where he held the same title and led AI strategy across the group's European operations. He previously held senior technology roles at The Hut Group, giving him direct experience on both the retail operations and e-commerce technology sides of the business.
The number that makes this hire legible is one that came out of Kingfisher's own reporting this year: AI-driven personalization work under Ghasempour's function generated approximately 100 million pounds in incremental sales, a 16% increase attributable specifically to that initiative. That is not a pilot result or a projected figure. It is a realized revenue number large enough that a direct competitor went and hired the executive who delivered it, which is as clear a signal as retail CIOs get about where boards now expect AI investment to show up on the P&L, rather than parked indefinitely in a slide about future potential.
Why the mandate is customer growth, not just efficiency
Baitiéh's own framing of the role matters here. He described the hire as reflecting Morrisons' commitment to 'embracing the opportunities in data and AI at pace,' and the responsibilities attached to the role span customer growth, colleague empowerment, and operational efficiency, in that order. Putting customer growth first in a chief AI officer mandate is a deliberate choice, and it is a different brief than the cost-reduction and process-automation mandate that has defined most retail AI roles created over the past two years.
That ordering tracks directly with what Ghasempour actually delivered at Kingfisher. Personalization that drives incremental revenue requires a fundamentally different data and AI architecture than fraud detection or supply chain automation does, because it has to operate on live customer behavior at the point of purchase rather than on batch data reviewed after the fact. Hiring the executive who proved that model works elsewhere is a faster path to replicating it than building the capability internally from a standing start.
Building centralized, not bolted on
Ghasempour's brief at Morrisons is explicitly to build a centralized data science and AI function, rather than inherit or consolidate an existing one scattered across departments. That is a meaningful architectural choice. Most large retailers, Morrisons included historically, have built AI capability as a series of departmental pilots, loyalty, pricing, supply chain, each running its own data pipeline and its own vendor relationships, and each competing for the same scarce engineering talent. Centralizing that into a single function under one executive is a bet that the returns from AI come from cross-functional data access, not from point solutions stitched together after the fact.
It also concentrates accountability in a way departmental pilots never do. When AI capability is distributed, a disappointing pilot in one department rarely becomes a board-level conversation, and a successful one rarely gets the cross-functional resourcing it needs to scale beyond its original use case. When it sits under a single chief AI officer reporting directly to the CEO, results are visible and attributable in a way that creates real pressure to replicate the Kingfisher outcome on a comparable timeline, not an indefinite one, and that same visibility cuts both ways if the function underdelivers.
What this says about the retail AI talent market
This hire is also a data point about where retail AI talent is actually flowing. Ghasempour was not pulled from a consultancy or a technology vendor, the traditional sourcing pools for this kind of role. He was pulled directly from a competitor with a quantified, board-credible result attached to his name, in a sector where that kind of direct, named competitive poaching at the executive level has historically been rare. That is a more expensive and more targeted hiring strategy than posting a chief AI officer role broadly, and it signals that Morrisons' board was not willing to bet on potential when a proven operator with a specific, replicable playbook was available on the open market.
For retail and commerce-tech leaders watching the talent market, this raises the bar on what a credible chief AI officer candidate now looks like. Technical fluency and vendor relationships are table stakes. What differentiates candidates going forward is a track record of AI work that moved a real revenue or margin number at scale, documented well enough that a competitor's board can evaluate it independently, the way Morrisons' evidently did with Kingfisher's personalization results.
The implication for your own AI leadership structure
If your organization still treats AI leadership as a function embedded inside IT or data engineering, this hire is a reason to revisit that structure. A chief AI officer reporting directly to the CEO, with an explicit customer-growth mandate rather than a cost-reduction one, is quickly becoming the structure that ambitious retail and commerce organizations are converging on, and it is the structure that produced the 100 million pound result Morrisons is now trying to replicate.
The harder question for most boards is whether they are willing to recruit for that role the way Morrisons did, by paying a premium for a proven operator with a specific quantified outcome rather than promoting internally or hiring on technical credentials alone. Given how publicly these results are now being reported and compared across competitors, that premium is likely to keep rising for the executives who can actually point to a number like Kingfisher's.
