Agentic AI Won't Fix CPG Growth If Your Data Is Still in Pieces

Agentic AI is the next big promise for CPG commercial teams. A recent McKinsey survey suggests most of them are not ready for it yet, and the reason is not the technology. It is the data underneath.

In For CPG companies, agentic AI's real payoff is growth (October 2026), McKinsey surveyed 41 senior commercial leaders at global CPG companies in the United States and Europe. Most of those companies have more than $10 billion in annual revenue. The findings are telling.

Commercial teams are buried in busywork

More than half of the leaders surveyed said drafting a single customer plan takes over five days. 38 of the 41 said end-to-end approval cycles run longer than three weeks.

Most of that time goes to gathering data, reconciling it, building reports and assembling decks. It is work that needs little of the team's expertise, and it leaves less time for what drives growth: category strategy, negotiation and execution with retailers.

AI should be changing this, but it mostly isn't yet. Only 12 of the 41 respondents have embedded AI into core workflows or scaled it across the organisation. The rest are still at the ad hoc or pilot stage.

The problem underneath: data in pieces

McKinsey's diagnosis is direct. Most commercial organisations are trying to layer AI on top of data that is fragmented, inconsistent or simply not connected across functions.

That limits what any agent can do. An AI asked to recommend a promotion cannot weigh the risk of an out-of-stock if it never sees inventory. It cannot explain a drop in sell-through if sell-out lives in one system, sell-in in another and field execution in a spreadsheet.

Where the foundation does exist, McKinsey sees real results. In performance management, AI that tracks KPIs, detects variances and recommends actions can cut the human effort required by roughly half.

The real prize is growth, not cost

The article's central point is one we hear from customers every week. Most CPG companies are using AI to cut costs and speed up existing work. But investors reward top-line growth more than margin expansion, and that growth depends on how well the levers work together.

McKinsey's examples make it concrete. A retail media campaign built around a promotion loses most of its punch if the promoted item is out of stock. A deep discount draws shoppers to the shelf, but if the product isn't there, they leave with a competitor's.

Growth comes from connecting sell-in, sell-out, pricing, promotions and field execution, not from optimising each one in isolation.

Start with the data behind your biggest decisions

McKinsey's final recommendation to commercial leaders says it all. Before chasing more AI use cases, fund the work of cleaning and connecting the two or three data sources tied to your highest-value decisions. Then pair a commercial owner with the technical team behind each source, so the effort stays tied to a business decision.

That is exactly the foundation SalesData is built to provide. It gives you one view of the full sales cycle, from sell-in to sell-out: shipments to customers, distribution, field execution and sell-out from retail partners, all in one place. Your commercial, supply chain and finance teams, and your AI, work from the same truth.

Is your commercial data ready for agentic AI?

If your team still spends days assembling a customer plan before anyone can discuss it, the answer is probably not yet. Talk to us to see how SalesData connects the data your next AI initiative will depend on.

Sources

For CPG companies, agentic AI's real payoff is growth, McKinsey & Company, 9 October 2026.