The arms race in RGM: Why AI will separate winners from the rest
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Revenue Growth Management is not new. What’s new is that AI and computing power have finally caught up with the scale and complexity of commercial data. That convergence is changing what RGM can deliver: improved store-level optimization, more effective promotion strategies, and pricing decisions that balance growth, regulation, and equity.
In the latest episode of Reimagine with AI, the discussion explores how this shift is reshaping RGM for CPG organizations and why AI is fast becoming the decisive factor separating leaders from the rest.
This episode features Michael Healy, Head of Revenue Growth Management at BAT, with prior leadership roles across AI-driven pricing and global RGM programs at PepsiCo, AB InBev, and Reynolds. Drawing on decades of experience building and operating RGM engines within some of the world’s largest consumer brands, Michael examines what it truly takes to transition from RGM ambition to AI-driven execution in complex, highly regulated environments.
Four lessons defining the next phase of RGM
1. Data is the new arms race
A common misconception is that AI creates insight on its own. In reality, it amplifies whatever foundation already exists. Fragmented or stale data does not improve with AI; it scales the problem. As a result, RGM is becoming an arms race around data: enterprises that can build clean, harmonized, and scalable commercial data foundations the fastest will dominate RGM performance in the years ahead.
2. Store-level RGM is finally feasible
Historically, RGM teams could not run tens or hundreds of thousands of store-level scenarios. The computational and operational constraints were simply too high. That constraint has now been removed. Algorithms can evaluate store-specific pricing, assortment, and promotion scenarios at scale, equipping sales teams with real-time, actionable recommendations. What once felt impossible is now operationally viable.
3. AI strengthens retailer relationships
AI-driven promotion optimization is often misunderstood as a lever for squeezing partners. In practice, its greatest value lies in reframing retailer conversations. Instead of leading with static promo plans, manufacturers can arrive with data-backed calendars designed to grow the retailer’s category versus competitors. The conversation shifts from negotiation to collaboration, strengthening long-term partnerships rather than weakening them.
4. Misconceptions remain the biggest risk
Many leaders assume AI-based pricing and promotion programs are fast, turnkey, and inexpensive. They are not. Without intentional investment in data readiness, algorithms, and sales enablement, initiatives stall before impact is realized. With those elements in place, AI-driven RGM can unlock millions in incremental revenue and materially improve execution quality.
Why AI-led RGM has reached an inflexion point
Several forces are converging to make AI-driven RGM central to CPG growth strategies:
- Promotion spend remains one of the largest levers on the P&L: AI introduces efficiency and precision into promotion planning and execution without weakening retailer trust or collaboration, making it possible to improve returns on one of the most material commercial investments.
- Regulatory constraints are intensifying: Pricing and promotion decisions must increasingly balance growth with affordability, compliance, and brand equity. This requires human judgment to remain firmly in the loop, even as AI scales decision-making.
- Long-held assumptions are being challenged: Data is increasingly disproving conventional wisdom around discount depth, promo cadence, and portfolio-first optimization. AI enables the systematic testing of these assumptions, rather than relying on intuition.
How leading CPG organizations are responding
Organizations advancing AI-led RGM are differentiating themselves by systematically operationalizing AI across data, commercial execution, and decision-making to embed it directly into pricing and promotion workflows rather than treating it as a standalone capability. In practice, this shift shows up in a consistent set of actions:
- Auditing and strengthening their data foundation before scaling models
- Equipping sales teams with store-level recommendations, not just analytics outputs
- Using AI to support category-level optimization alongside portfolio strategies that strengthen retailer collaboration
- Running pilots to test long-held assumptions around pricing and promotions
- Investing institutionally to build trust in AI outputs through transparency, experimentation, and proof points
The broader implication for RGM
AI does not compensate for weak foundations; it makes them visible. Enterprises with aligned data, operating processes, and commercial teams can scale RGM with a level of precision that was previously unattainable. Those operating on fragmented systems and intuition risk accelerating existing inefficiencies. The next phase of RGM leadership will belong to organizations that treat RGM as both a data discipline and an operating model transformation by arming commercial teams with algorithms while keeping human judgment, governance, and compliance firmly at the centre.
You can listen to the entire podcast on Apple Podcasts and Spotify.
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