After the first wave fizzled, manufacturers realized that each market—or at least each cluster of markets—needs a more tailored approach. This led to the rise of regional templates and archetype-based models. Instead of forcing one global design onto everyone, companies began grouping markets by structural similarities—e.g., “emerging markets” vs. “established markets”—and implemented more targeted solutions. Simultaneously, smaller RGM software vendors emerged, offering specialized packages that were faster to implement and sufficient for simpler markets. For example, a tier-2 or tier-3 subsidiary might choose a lightweight cloud-based trade promotion tool, while the large tier-1 markets kept using heavyweight, enterprise-scale solutions. Did It Solve Everything? • Better Fit: These smaller or regional solutions were indeed more user-friendly for local teams. • Limited Differentiation: Companies still struggled to differentiate their strategies. The solutions covered common processes but needed expensive custom development to implement unique or advanced capabilities. • Upgrade & Integration Chaos: Each time the software was upgraded, the custom code risked breaking. Integrating these tools with third-party systems (or with corporate data warehouses) was often complex and costly. While the second wave improved initial adoption, it still left major gaps in flexibility and scalability.

By around 2020, I noticed a new trend in the RGM consulting arena: major consumer goods players deciding, “If off-the-shelf solutions won’t meet our needs, we’ll just build our own.” Frustrated by recurring integration headaches and expensive customizations, some companies poured resources into internal development teams to create bespoke RGM software from the ground up. Pros & Cons of Going In-House • Tailored Fit: Internally built tools could precisely align with unique business processes—no compromise on specialized features. • High Cost & Maintenance: Developing and maintaining enterprise-grade software is rarely a core competency of a consumer goods business. Many projects ran over budget and took far longer than expected. • Scalability Issues: What worked in a single division or country often failed to roll out globally—especially when region-specific logic was baked into the code. • Technology Churn: In-house solutions risked becoming obsolete quickly, because new requirements like AI-driven analytics were hard to integrate if the internal team lacked those specialized skills. Ultimately, many of these custom-built platforms stalled after a few years, underscoring that “build it yourself” can be just as precarious as depending on a giant vendor—just in different ways.

Today, RGM is entering an AI-driven era. Advanced analytics, machine learning algorithms, and predictive models promise to resolve many longstanding conflicts: standardization vs. flexibility, speed vs. depth, and global consistency vs. local nuance. SAP: RGM in the Age of “Business AI” After years of focusing on classic Trade Promotion Management, SAP has repositioned to a next-generation, cloud-based suite sometimes referred to as SAP RGM. According to SAP, these offerings are increasingly infused with AI (often called “Business AI”), developed in partnership with specialized firms like Enterra Solutions. The goal: • Real-Time Analytics: Assess promotions, price changes, and ROI on the fly. • Smart Recommendations: Suggest “best next actions” based on large historical datasets. • Integrated Operations: Connect the insights seamlessly with financial and supply chain processes in the SAP ecosystem. In practice, these AI modules are mostly decision-support tools rather than fully autonomous systems. The Revenue or Key Account Manager still makes the final call, but AI algorithms serve up validated forecasts and optimization scenarios in real-time.

A number of niche RGM software vendors have also embraced AI—sometimes more aggressively than the large suites. They offer specialized features like advanced demand sensing, automated promotion optimization, or even near-autonomous decisions. • Enterra’s Revenue Growth Intelligence System: Celebrated by certain trade optimization groups, it can autonomously optimize an entire trade plan (price, promotion, pack format, etc.). • Visualfabriq / Blacksmith AI: Both emphasize machine learning forecasts to better predict baseline volume and measure promo uplift. • Aforza with AVA: Known for providing real-time trade promotion insights, Aforza’s “AVA” uses an AI-driven engine to deliver context-based recommendations directly to field reps. This can speed up promotional decision-making in diverse market settings. • Buynomics: Uses virtual-shopper simulations to anticipate consumer behavior and identify psychological price thresholds or new pack opportunities. These players typically offer quicker innovation cycles and deeper specialization in specific use cases. Their main challenge? Integration. Larger companies often need robust connectivity between best-of-breed RGM modules and core ERP or supply chain systems. That’s where SAP or other enterprise vendors have an edge. Where does that leave SAP? In broad terms, SAP provides an integrated enterprise backbone, so AI outputs can feed directly into planning, finance, or logistics. Smaller vendors push the envelope with cutting-edge algorithms but may need more effort to integrate. The result: many enterprises choose a hybrid approach—using SAP as the central platform and layering specialized AI modules where needed.

From my vantage point as an RGM consultant, AI holds the promise of at last bridging the gap between standardizationand differentiation. An AI-driven engine can learn from large pools of data across multiple markets and still provide hyper-local recommendations. It’s a dynamic system that evolves over time—unlike the static global templates of the past.

The journey of RGM—through ambitious global templates, regional archetypes, and even fully bespoke in-house solutions—reveals a recurring lesson: there is no single perfect approach for everyone. We are now at a pivotal moment where AI can potentially unlock the flexibility we’ve always needed, without compromising on efficiency or scale. Yet, as in all previous waves, careful planning, robust data governance, and strong leadership remain critical to success. Having consulted for decades in this space and guided companies of all sizes, I believe the AI era could be our most impactful shift yet. If implemented thoughtfully, AI can finally reconcile the global vs. local tension and deliver measurable, lasting revenue growth. The key is to combine strategic clarity, smart technology choices, and the right partnerships. That’s where I come in—at XLever, we help businesses define not just the right RGM strategy, but also the best path to operationalize it. Because in the end, technology should serve your strategy, not the other way around.