Reckitt has placed its AI leadership inside a shared services model, treating the capability as infrastructure that serves marketing, procurement and R&D rather than as a standalone function. For a consumer goods company of that scale, the choice matters. AI roles in this structure are delivery roles, measured by whether the marketing team actually uses the output and whether procurement sees cost improvement.
We have spoken with candidates who assume AI work in large corporates means building models in relative isolation, with business adoption as someone else’s problem. A shared services structure demands something different. The AI team becomes accountable to internal customers who have specific problems and tight timelines. The technical work remains demanding, and the person doing it also needs to explain trade-offs to a procurement director who wants to know why the model flagged one supplier contract and missed another.
Candidates who have delivered something end-to-end in a commercial setting tend to fit this structure well. We placed a senior analytics lead into a similar environment last year, and the hiring manager was explicit about the requirement: someone who had sat through a budget review defending their own work to sceptical stakeholders.
The shared services framing also affects how companies measure AI investment. Reckitt’s approach ties value back to specific functions, which makes it easier to justify headcount when procurement can point to a measurable gain. For candidates, that often means roles are more stable than standalone AI labs that face existential budget questions every cycle.
If you are looking at AI roles in large consumer goods or manufacturing companies, ask where the function reports. The reporting line tells you more about the day-to-day work than the job title does.
Prompted by reporting from Diginomica.