CEOs Are Investing Billions in This RGM Technology (Are You Being Left Behind?)

Key Takeaways

✔ Why Traditional RGM Falls Short – Learn why conventional analytics and AI tools struggle to keep pace with today's fast-moving market landscape.

✔ How Agentic AI for RGM Changes the Game – Discover how intelligent, autonomous agents optimize pricing, promotions, and portfolio strategies in real-time.

✔ Top Use Cases of Agentic AI in RGM – Explore how leading organizations are leveraging agentic AI for market mix modeling, dynamic price pack architecture, and trade promotion optimization.

✔ The Business Impact of an Agentic AI Strategy – Understand how agentic AI accelerates decision-making, improves promotional ROI, and enhances pricing effectiveness.

✔ How to Get Started – Learn the key steps to successfully integrating Agentic AI for RGM without disrupting your existing analytics ecosystem.

Is Agentic AI in Revenue growth Management a need or a want?

Let's examine the facts. Despite multimillion-dollar investments in RGM capabilities, more than 80% of CEOs are dissatisfied with their current RGM results, as only about 1 in 10 brands systematically achieve growth in share, category leadership and profit.

Now with companies expected to invest over $2.5 billion in AI and machine learning technologies, they naturally expect more than the basic "which promotion to run in which store and at what price" analysis that traditional AI systems currently offer. The actual question is: how do all these pieces fit together to maximize overall business performance?

Now of course this traditional analytics is important for strong analytical foundation. Because these existing analytics has stored in them years of SKU-level data, promotional lift analyses, and retailer-specific insights. And we are not saying that they don’t deliver results anymore because relying on them has delivered reliable results that drive tangible results in CPG revenue growth management (RGM).

But now the game has changed.

What really changed in RGM?

Consumer preferences shift overnight, competitive moves happen in real-time, and the explosion of digital channels has created unprecedented complexity. Even the most advanced traditional analytics are struggling to keep pace.

Why? Because these traditional and some AI-based RGM systems – weren't designed for the velocity and complexity of today's market. These systems—built on regression techniques, time-series forecasting, and static optimization algorithms—have incrementally improved decision quality but remain fundamentally reactive rather than proactive, episodic rather than continuous.

This is where agentic AI enters not as a replacement, but as a transformative enhancement to your existing RGM capabilities.

Implementing Agentic AI in RGM isn’t just about deploying a new technology—it requires a solid foundation as emphasized by Marcelle Cruz, Sr. Director – Revenue Growth Management Capabilities (Latin America) at PepsiCo.

By building upon your current analytics infrastructure and infusing it with autonomous, intelligent agents, you can unlock an entirely new dimension of value. Traditional AI in RGM gives you insights and recommendations. Building upon it with Agentic AI gives you an ecosystem. An ecosystem consisting of specialized intelligent agents that plan, reason, and act with increasing autonomy within their domains.

Building Blocks of Value: Top three Agentic AI use cases in RGM

Let me walk you through how leading CPG companies are implementing these capabilities to create measurable financial impact across the core RGM domains.

1. Market Mix Modeling with Reasoning Agents

Market mix modeling has always been an important part of revenue growth and with 69% to 84% of the CMOs facing constant pressure to do more with less. Hence it’s natural for CMOs to use marketing mix modeling to make data-based budget allocation decisions.

Though we already have AI playing major role in better their MMM decision making process but many RGM professionals are experiencing a growing sense of disappointment with current AI implementations. Here's why:

1. Black Box Solutions: Many current AI-powered MMM tools operate as inscrutable systems. They provide outputs without explaining the reasoning, making it difficult for RGM teams to trust or properly implement the insights.

2. Static Modeling Paradigms: Despite using more sophisticated algorithms, today's AI-based MMM still operates within traditional modeling frameworks that run periodically rather than continuously.

3. Limited Integration: Most AI solutions for MMM function as standalone tools, disconnected from other critical RGM systems like pricing, promotion, and supply chain planning.

4. Insight Without Action: Even the most advanced AI-based MMM tools typically stop at providing insights or recommendations, leaving the execution and adaptation to human teams.

The core problem isn't that AI isn't powerful enough—it's that traditional AI approaches aren't designed to handle the interconnected, dynamic nature of modern marketing environments.

But a multi-agent architecture transforms this into an intelligent budget allocation system that autonomously optimizes revenue growth.

2. Dynamic Portfolio Optimization with Agentic Price Pack Architecture

Price Pack Architecture is an important RGM level, yet many companies still fail to fully leverage it. The issue isn't data or tech, it's systemic. Think about how Price Pack Architecture typically works in most organizations:

The analytics team runs quarterly analysis to identify opportunities. They create beautiful slide decks with recommendations. Those recommendations go through multiple rounds of review with marketing, sales, and finance.

Then comes implementation. The pricing team updates price lists. The sales team negotiates with retailers. The trade marketing team adjusts promotions. The supply chain team reconfigures production. Each step introduces delays and inconsistencies.

By the time the new architecture hits the market, competitors have already reacted to the initial conditions that prompted the changes. Consumer perceptions have shifted. Cost structures have evolved. It's like trying to drive by looking only in the rearview mirror – and doing it with a committee where everyone has a different idea of where you should go.

This is where agentic AI fundamentally changes the game. Instead of just providing better analysis or faster calculations, it creates an intelligent system that orchestrates the entire PPA process from insight to execution to learning. What makes agentic PPA different isn't just the technology – it's how it transforms the human workflow around Price Pack Architecture.

How this transforms PPA as part of RGM practice is by providing

1. Speed and Responsiveness

Traditional PPA cycles take 12-16 weeks from insight to implementation. Agentic systems compress this to days or weeks by removing coordination bottlenecks and automating execution planning.

2. Dynamic Optimization

Rather than static architectures that quickly become outdated, agentic systems continuously refine the architecture based on real-world feedback and changing market conditions.

3. Cross-Functional Alignment

Instead of sequential handoffs between departments, the agentic system creates a shared understanding and coordinated execution across the entire organization.

4. Learning Organization

Every implementation becomes a learning opportunity, with insights automatically incorporated into future decisions – creating an ever-improving system.

3. Trade spend optimization with Agentic AI

Now when it comes to trade spend optimization it’s literally on the the cusp of a revolutionary transformation. Now as we look towards 2025 and beyond, one thing is certain – Agentic AI will fundamentally reshape how organizations approach trade spend, moving from today's reactive tools to truly anticipatory, autonomous systems.

Here are the emerging trends that will define this evolution:

- Autonomous Negotiation Agents will simulate thousands of potential retailer negotiations before human teams engage, identifying optimal trade term structures for each account

- Dynamic Spend Reallocation will occur in real-time as agents detect market shifts, automatically adjusting promotion parameters within pre-approved guardrails

- Cross-Portfolio Harmonization will eliminate the common problem of conflicting promotions, as agent networks coordinate calendars across brands and categories

This evolution represents nothing less than a complete reinvention of how trade spend is managed. Organizations that move early to adopt these agentic approaches will establish sustainable competitive advantages through superior trade investment efficiency and effectiveness.

Next step for Agentic RGM

Now the good news is that you are not at level zero. And agentic AI is sure to deliver. However, it is important to keep in mind that at the end of the day, it’s a tool and not a miraculous fix. Successful implementation requires a commitment to high-quality data, human expertise to interpret AI insights, and an ethical approach to AI use. Hence introducing AI in a phased approach allows for controlled integration and ongoing optimization.

Companies looking to strengthen their RGM programs with AI and analytics will have some homework to do. There are many excellent solutions in the market, but not all are suitable for all organizations. Firms may find they also lack internal expertise and access to data sources.

Hence, they may need to partner with specialized providers like Polestar Solutions who understand the nuances of the industry, have sound analytical domain expertise, and have the tools to provide quick and accurate results to gain the upper hand to accelerate their “growth”.