How Is AI in Revenue Growth Management Changing the RGM Growth Playbook?

How do you build a transformational Revenue Growth Management Strategy from the ground up?

A transformational Revenue Growth Management Strategy does not start with AI. It starts with commercial clarity.

Harry Ergan’s starting point was simple: “Many teams are not inheriting a mature RGM machine. They are building one. That changes the work. You cannot automate a machine that does not yet have trusted parts.”

Before a business can scale recommendations, automate decisions or move faster, it needs a foundation people trust. The question is not how quickly RGM can become more sophisticated. The question is what needs to be in place before that sophistication becomes useful.

That is why the framework below should not be treated as a theoretical model. It is a practical sequence for building trust before AI, analytics or automation can scale.

This is where The Four-Pillar RGM Scale Framework comes into picture.

• Fundamentals: create one commercial baseline
  
  Before elasticity models, AI pricing optimization or promotion optimization, the business needs one answer to basic questions. What are we selling? To whom? At what net price after trade investment? At what actual profitability?
  
  In many enterprises, those answers sit across sales, finance, marketing and supply chain. Sales sees volume. Finance sees margin. Marketing sees brand equity. Revenue Growth Management has to connect the full commercial system.
• Visibility: expose the decisions behind performance
  
  Promo ROI trackers, margin waterfalls and pricing dashboards only matter when they expose decisions that need to change. Where is the business over-discounting? Which promotions are truly incremental? Where are different customers being treated the same despite different economics?
• Commercial guardrails: give teams structured freedom
  
  This is where RGM has to avoid becoming the pricing police. The role is not to slow the business down. It is to give teams enough structure to move faster without making inconsistent calls.
  Pricing corridors, customer tiers, pack-price architecture and trade investment rules help teams know where to flex, where to hold and when to escalate. Good guardrails protect margin without removing commercial judgement.
• Tangible value creation: prove RGM through decisions
  
  RGM earns credibility when the business can point to one or two decisions it would not have made otherwise. A promotion that looked strong on volume but destroyed margin. A price move that protected profit without damaging share. A pack architecture change that improved mix. The point is not to show that the model is sophisticated. The point is to show that RGM helped the business make a decision it would not have made otherwise.
  
  The point is simple. The RGM machine has to be trusted before it can be automated. That is also the broader role of connected Revenue Growth Management solutions: not another reporting layer, but a clearer way to connect pricing, promotion and profitability decisions.

Once the RGM machine is trusted, why does Enterprise Revenue Growth Management still struggle with adoption?

Trust in the numbers is only the first step. The harder test is whether RGM thinking shows up inside everyday commercial decisions.

This is where Colin McQuay’s “people and process” point matters. The issue is not that technology has no role. It is that strong analytics can still sit unused when teams lack context, question the recommendation, or receive the insight after the decision has already moved forward.

That is why Enterprise Revenue Growth Management cannot stay dependent on a small expert team producing analysis for everyone else. It has to become part of how commercial teams plan, challenge and act.

Three adoption shifts stood out.

• Data democratization
  
  Data democratization is not about giving every team another report. It is about making Revenue Growth Management Analytics usable where decisions happen.
  
  The account team planning a promotion should understand the trade-off. The pricing team should see guardrails before customer conversations. Marketing should know how portfolio choices affect pack roles and mix. Finance should see the link to margin and value capture.
• RGM embedded in workflow
  
  Colin’s operating model shift was clear: RGM has to move from “do it for me” to “I’ve planned this, can you look at it?”
  
  That is the moment RGM stops being a service desk and starts becoming a commercial capability. The function is still needed. But its value shifts from doing every analysis to raising the quality of decisions across the organisation.
• Integrated commercial capability
  
  Nuno Alexandre pushed this further. The growth plan and the RGM plan cannot stay separate. They need to become synonymous.
  
  When they stay separate, the business creates two calendars, two sets of assumptions and two versions of accountability. When they come together, RGM becomes part of how the business defines growth, not just how it reviews performance.
  
  This is also where RGM programmes often struggle. Central analytics can be strong, but value is lost when insights are not connected to local market context, ownership and action.

If adoption depends on trust, which AI use cases in Revenue Growth Management are worth scaling now?

AI in RGM is not one use case. It is a set of practical interventions that reduce the distance between insight and decision.

The roundtable did not treat AI as a replacement for commercial judgement. The more useful view was narrower and stronger: AI should make complex RGM decisions easier to question, test and act on.

Use Case 1: Demystifying the black box

Generative AI can help users “chat” with their data. Instead of receiving a recommendation without context, a sales or commercial user can ask how the recommendation was built, why it worked, and what assumptions sit behind it.

That matters because trust has always been one of the biggest barriers in Revenue Growth Management Analytics. If users can question the recommendation and understand the logic, they are more likely to act on it.

Use Case 2: High-speed scenario planning

Scenario planning is where AI for Pricing Strategy becomes immediately relevant.

Harry referenced the ability to run pricing scenarios across 60 SKUs in 15 markets overnight. That kind of speed changes the rhythm of commercial decision-making. Teams can compare revenue, volume, margin and mix impact before they enter customer negotiations, not after.

Use Case 3: Speed to insight

Nuno’s point on speed and agility is important here. Price-mix composition, promo diagnostics and post-event analytics often lose value because they arrive too late.

This is one of the clearest ways AI is transforming Revenue Growth Management. Commercial teams do not always need more theory. Teams need a clear read on what changed, what caused it and how to adjust before the next cycle starts.

Use Case 4: The agentic future

Agentic AI is the ambitious use case. Autonomous agents could flag under-delivering promotions, identify weak signals and suggest adjustments before the cycle closes.

Harry saw this as close. Nuno’s caution was equally important: poor data does not become strategic just because AI sits on top of it.

That caution now has wider market backing. Gartner predicts that more than 40% of agentic AI projects will be cancelled by the end of 2027 because of escalating costs, unclear business value or inadequate risk controls. For RGM, the lesson is direct: agentic AI cannot sit ahead of the operating model it is meant to improve.

After AI use cases, what Revenue Growth Management best practices still decide whether RGM scales?

Expert Insight: Why people and process still decide RGM scale The talent flywheel: Harry’s point was not about hiring for analytics alone. Better RGM work attracts better RGM talent. When the function influences pricing strategy, portfolio roadmaps, customer negotiations and annual planning, it builds stronger commercial capability. Preparation before decisions are final: RGM needs to enter before the decision is made, not after it is validated. Moving upstream into planning, price-pack strategy, innovation, commercialization and customer investment changes the quality of the decision. The unicorn profile: Nuno described the rare RGM mix well: strategic view, commercial instinct, analytical depth, influence and the courage to challenge constructively. That combination is not about personal growth. It is about RGM growth.

So what do Revenue Growth Management Trends show about the future of RGM?

The Future of Revenue Growth Management is not a jump from dashboards to autonomous Revenue Growth Management best practices still come back to capability, not headcount alone.agents. That sounds attractive, but it skips the hard middle. The roundtable made the progression clearer: first visibility, then influence, then scale. AI can accelerate each stage, but it cannot replace the work required to earn credibility.

Stage one: Reporting

This is where most RGM functions begin. They explain what happened: pricing results, promotion performance and margin analysis. Finance often embraces this first because it creates visibility. But the function still sits downstream of the decision.

Stage two: Moving upstream

This is where real change begins. RGM starts shaping the next quarter instead of explaining the last one. It helps build the promo calendar rather than review it later. It sets commercial guardrails before customer negotiations. It becomes part of annual planning, innovation, pricing architecture, portfolio decisions and customer investment.

Stage three: Democratization

Once RGM has credibility, it can scale across the organisation. RGM thinking becomes embedded in sales, finance, marketing and operations. The central RGM team does not disappear. It becomes more focused on governance, capability, decision quality and the few choices that shape the growth trajectory.

Harry’s line captures the risk perfectly: “You can be completely right and completely irrelevant at the same time if the people making decisions do not trust the framework.”

That is the RGM mandate in the age of AI. Build the baseline. Build trust. Build guardrails. Embed the function into planning. Then scale AI.

This is where Polestar Analytics’ AI Pulse Suite connects back to the RGM agenda in a practical way. PricePulse, PromoPulse and ProfitPulse help bring pricing analytics, promotion diagnostics, portfolio intelligence and margin visibility closer to the commercial decision flow. The role is not to replace RGM judgement. It is to make trusted RGM thinking easier to apply across teams.

AI will change RGM. But only when RGM is strong enough to absorb it. That is the real direction behind Revenue Growth Management trends 2026.

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