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The Pricing Playbook: RGM in the Age of AI

When pricing decisions get faster, weak commercial judgment gets exposed even faster

The Illusion of Integrated RGM

Most CPG organizations do not struggle because they lack pricing activity. They struggle because pricing, promotions, assortment, and pack architecture are still too often managed through separate tools, separate teams, and separate assumptions. That creates a decision system that looks analytical on the surface but breaks down when markets, competitors, and shoppers move at the same time.

This session is useful because it does not treat AI as a layer of automation on top of existing RGM routines. It argues for something more demanding: better models, more integrated decision-making, and a stronger commercial operating model that can turn richer insight into better choices.

What You'll Learn

Stop treating price elasticity as a complete answer when it is only a simplification. Better pricing decisions depend on models that reflect how shoppers switch, how portfolios interact, and how market context changes the outcome.
Use AI to improve decision quality before chasing productivity gains. The larger commercial upside comes from making more profitable choices, not from shaving time off analysis alone.
Build RGM as an integrated decision muscle, not a collection of disconnected interventions. Pricing, promotions, assortment, and pack decisions create value when they are evaluated together.
Expect agentic AI to coordinate specialist tools rather than replace commercial judgment. The practical near-term model is orchestration, with people still accountable for high-value decisions.
Assume AI will widen performance gaps where execution and data quality are already uneven. Access to advanced analytics matters, but the real advantage goes to organisations that can act on insight faster and more effectively.

Meet the Speaker

Ingo Reinhardt

Co-founder and Managing Director at Buynomics

Before Buynomics, Ingo was a Senior Director with Simon-Kucher & Partners, a global leader in pricing. He holds a Ph.D. in Management from the University of Cologne and Master's degrees in Management and Mathematics. Ingo was a PostDoc at the University of Oxford and published in the Strategic Management Journal.

Session Highlights

AI will not fix weak RGM by itself.
The central claim is not that an AI agent will determine the right price on its own, but that it will help teams use specialist tools more effectively. In practice, that means orchestration around pricing, portfolio, and market analysis rather than full automation of commercial decisions. [11:48]

The bigger prize is not efficiency. It is decision quality.
The session makes a clear commercial argument that even modest improvements in decision accuracy are worth more than most efficiency gains available to RGM teams. Better models matter because getting too far away from the real demand response quickly destroys value. [12:24]

Traditional pricing tools were built for simplification, not for today’s decision environment.
Price elasticity remains useful, but the session challenges the idea that one number can represent how a market actually behaves. Once portfolio effects, competitor responses, and changing price points matter, the simplification starts to constrain the decision rather than support it. [15:13]

RGM maturity is as much an organisational issue as an analytical one.
The evolution described here is not only about adopting better models. It is about moving from fragmented decision rights across functions into a more central RGM capability, then equipping that team with tools that let it evaluate the levers together. [29:24]

Integrated tools only create advantage when teams are set up to use them together.
There is a strong link in the session between operating model and technology. Bringing pricing, promotions, and assortment choices into one view only matters if the organisation is also designed to make joint decisions rather than compensate for one team’s choices with another team’s fixes. [31:12]

AI is unlikely to level the field completely.
While broader access to advanced analytics will help more teams raise their baseline, the bigger gains will go to companies with stronger data, stronger supply chains, and a greater ability to execute on insight. In that sense, AI is likely to magnify existing advantages more than erase them. [40:38]

Q&A

Is price elasticity still useful, or has it become too limited for modern RGM?
It is still useful, but only within limits. Ingo’s view is that elasticity-based models can work for small changes, yet they become less reliable when portfolio effects, competitor reactions, or multi-product changes are involved.

Are companies already letting agentic AI execute pricing decisions directly?
Not in the high-stakes way many people imagine. The position in the session is that companies are using these systems to support decision-making, structure options, and surface trade-offs, while people still retain control over major commercial calls.

Will agentic AI sit on top of existing TPM and TPO tools, or replace them?
The current expectation is orchestration, not replacement. The more realistic near-term model is that agents will use the functionality of these tools and connect them more effectively, rather than making them disappear altogether.

Can this kind of modelling support innovation and new product pricing, or only optimise existing ranges?
The answer given is yes, it can support innovation, especially when new products are recombinations of existing features, brands, sizes, or product attributes. Where something genuinely new enters the market, the model can also be informed by additional sources such as surveys.

Where should leaders focus first if their organization is still early in AI adoption?
The session argues for starting with high-value use cases that can show tangible results quickly. Early wins help build internal momentum, make the case for broader adoption, and reduce the risk of AI remaining stuck in experimentation.

Will AI make RGM teams more technical, or less?
The argument here is that the technical burden should reduce as better tools take over more of the analytical heavy lifting. That shifts the centre of gravity of RGM toward strategy, judgment, and stronger contribution to senior commercial decision-making.