Decision Engineering: AI's first win in consumer goods is in the pricing room

AI's first durable win in consumer goods did not show up on the shelf. It is showing up in the least glamorous room in the building: the one where trade promotion and pricing get decided.

What PepsiCo actually did

Look at what PepsiCo is doing. It is pushing AI into revenue growth management: optimizing trade spend, sharpening price-pack architecture, and using first-party data to target promotions and "next best action" at the store level. In April it even put a global VP in charge of artificial intelligence and what it calls "Decision Engineering".

Sit with that phrase for a second, "Decision Engineering". A ninety-billion-dollar company just called out where the value is, and it is not the model.

Why pricing is the real prize

Here is why pricing unlock is real; trade promotion is one of the largest lines on a consumer-goods P&L and, for decades, one of the most wasted. The reason was never bad intent. It was math no human team could hold: every SKU, every pack, every account, every week, against every competing price. RGM ran as a quarterly committee debate over a handful of scenarios. AI makes it continuous and granular, and the number it moves is net revenue realization, not volume for its own sake.

Now the part most decks end up missing.

If everyone rents the same optimizer and points it at the same syndicated data, RGM commoditizes. Two competitors converge on the same prices and the same promotions and compete margin to the floor. Adoption alone is not an edge here. It is a faster route to average.

And optimizers do exactly what you tell them. Aim one at short-term trade ROI and it will deliver short-term trade ROI, quietly spending down what it cannot see: brand equity, price tolerance, the loyalty that lets you hold a premium next year.

The edge is the decision you engineer

So the edge is not the engine. Everyone can rent the engine. The edge is the proprietary read on how your shoppers actually respond, and the tradeoff rules you engineer into the system: what you refuse to discount, where you defend premium, how you weigh margin against volume against loyalty on every line. That is the decision. That is the moat.

I have spent twenty years in that room. The board question on pricing this year is not "are we using AI in RGM." Everyone will be using it. It is narrower and more uncomfortable: which tradeoffs are we encoding, and what will we never let the optimizer do?

What is the one pricing decision in your business you would never hand to the machine? Answer that honestly, and you already understand Decision Engineering.

Part of a larger thesis: the Destination Problem, on why, as execution goes abundant, judgment becomes the moat.

First shared on LinkedIn. Join the discussion there.