Retail didn't have an AI adoption problem this year. It had a data readiness problem — and most organizations still haven't agreed on who's responsible for fixing it.

Everyone bet on the model. For two years, retail poured budget, headlines, and board attention into AI itself — the copilots, the agents, the pilots. It was the obvious place to look, because it was the newest, most visible piece of the puzzle.

But the industry is now converging on a quieter, less flattering realization: the models were mostly fine. What held everything back were the decisions made — or avoided — about the data beneath them: who owns it, what gets prioritized, and what gets funded before the next flashy use case.

This isn't a technical session, and it isn't a product session — no reference architectures, no platform walkthroughs. It's a closed-door leadership conversation about the organizational choices that determine whether data debt gets paid down or quietly compounds. Come ready to benchmark honestly against peers facing the same gap, and leave with a clearer view of the one or two decisions — not tools — that would unlock the most value fastest.

• Who in your organization actually owns the decision to prioritize data work over the next AI use case — and do they have the authority to make it stick?
• What's the AI use case your team wants to build but can't, because no one signed off on fixing the data first?
• If every retailer in this room were to compare honestly, where would the industry actually land?