Why CPG's Biggest AI Returns Are Going Untapped

MOST CONSUMER-PACKAGED GOODS companies now have an AI pilot running somewhere in the commercial organization. The harder question isn't whether to invest. It's whether the money is going anywhere near where it would actually move the P&L.

Almost every pilot looks like progress from the inside: a content tool ships, a chatbot goes live, a sales team gets a coaching assistant. Activity is easy to see; impact is not, and the two have quietly come apart. The gap traces to one choice companies keep making: they fund narrow point solutions instead of end-to-end AI enablement across the commercial process.

That's the core finding from the Consumer Industry Commercial AI study, a new Blue Ridge Partners survey of more than 300 executives, managers, and specialists across public and PE-owned consumer companies conducted in collaboration with the AI market research and intelligent survey firm, GroupSolver.

The strongest-performing use cases (commercial planning and analytics, RGM, and AI-driven innovation) deliver revenue growth improvements of 16% to 40%, yet fewer than 8% of companies invest in them, and 92% are missing the AI use cases that would move the needle most.

The numbers are striking. AI-powered customer onboarding delivers a 41% lift in customer lifetime value, the highest return measured, yet fewer than 8% pursue it. High-impact adopters also spend less: $15 million on average for commercial technology and AI, versus $20 million for those that don't. Executives expect commercial AI to deliver EBITDA impact 1.3 to 2.1 times higher than supply-chain or finance AI investment, and faster.

Why CPG is different

Consumer companies are, in theory, well positioned to win with commercial AI: sales, marketing, and innovation run on more standardized processes than manufacturing or logistics, and most already have clean CRM data in place. That structure makes end-to-end AI enablement, not a patchwork of point tools, the natural strategy for CPG. CPG doesn't have an opportunity problem. It has a targeting problem.

Yet the ten most common commercial AI use cases today (content, sales coaching, segmentation, ad creative) are each adopted by roughly 8% to 12% of companies and deliver modest returns. The pattern is nearly inverse: the more common the use case, the lower the return.

The real cost of playing It safe

Executives were candid about why. Point solutions are faster to stand up and easier to defend to a board, and typically run on a single, clean data source. Higher-return use cases require integrating pricing, promotion, retailer, and elasticity data across functions, a harder lift that demands cross-functional cooperation most organizations aren't structured to deliver. One automates a single step; the other rewires how pricing, promotion, and go-to-market decisions get made together. There's also “AI theater,” piloting where results are easy to show the board rather than where they're largest. And there's a real asymmetry in risk: a pricing misfire can echo for years, while a failed marketing test is quietly retired.

What the leaders do differently

The highest performers experiment with AI agents over single-purpose tools, expect AI to grow their workforce rather than shrink it, and are more likely to buy proven tools than build their own. In short, leaders organize around end-to-end AI enablement, not a growing collection of point solutions.

Closing the gap

None of this means abandoning current pilots; content tools and sales coaching platforms still deliver real value. But if the next AI dollar gets allocated the way the last one did, toward whatever is easiest to greenlight, the gap between activity and impact will keep widening. The companies pulling ahead weren't running the most experiments; they concentrated money on a few harder, messier initiatives and said no to the ones that weren't moving revenue.

Four takeaways for your next budget conversation:

1. Benchmark spend against impact, not activity.
2. Prioritize end-to-end AI enablement over point solutions. The cross-functional reach that makes them harder to greenlight is what makes them the highest-return bet.
3. Build toward scale from the start; a pilot that only works in one region isn't a strategy.
4. Expect to spend less, not more. The best performers were more disciplined, not bigger spenders.

CPG has a narrow window to compound an advantage in commercial AI before competitors catch up. The companies that close the gap won't be the ones spending most; they'll be the ones funding the harder, less demo-friendly use cases. The winners won't be measured by how many point solutions they've deployed, but by how much of the commercial process they've connected end to end.

Carrie Shea, Managing Director and Consumer Practice Leader, leads the Consumer-Driven Industries practice at Blue Ridge Partners, a management consulting firm focused exclusively on accelerating profitable revenue growth. With over 25 years of experience, she specializes in using data, analytics, and AI to unlock growth opportunities for consumer and distribution businesses. She has held senior roles at Kearney, Ipsos, IRI/Circana, and PwC. Read the full Consumer Industry Commercial AI study white paper here.