The consumer packaged goods (CPG) industry is standing at a pivotal moment, in a landscape shaped by inflation, geopolitical instability and supply chain fragility, according to the Promotion Optimization Institute (POI).
In addition, consumers have become more value-driven, digitally fluent and channel agnostic than ever. CPG organisations are evolving into media platforms, blurring the lines between trade, marketing and digital commerce. Meanwhile, sustainability, personalisation and speed-to-value are becoming non-negotiable imperatives for both CPG retailers and customers.
Amid these changes, revenue growth management (RGM) continues to be a key strategy to adopt and, according to Dina Zhou, senior industry advisor for retail and consumer goods at Microsoft, “it cannot be a tactical lever but the engine for profitable growth”.
RGM helps business to optimise revenue and profitability by aligning pricing, promotion, distribution and trade terms. Think of it as a guide to help CPG organisations answer four key questions: what products should we sell? At what price? Through which channels? With what promotional support?
However, legacy systems, siloed insights and incremental thinking continue to hold organisations back from optimising their use of RGM.
Zhou states that RGM remains “under-leveraged in many organisations”. According to POI’s 2025 State of the Industry report, 43 per cent of companies are still operating at a descriptive analytics level. Furthermore, more than half of teams lack the capabilities to support pricing and trade strategy, and nearly half of sales teams are continuing to replan promotions from previous years.
“This disconnect between strategy and execution underscores the need for transformation,” says Zhou. “To thrive, leaders must reimagine RGM as a dynamic, intelligent and agentic capability.”
In a 2023 Microsoft blog post, Zhou and her fellow executives said the idea that generative AI could give more employees access to RGM tools and insights was a “bold prediction”. Not only has that prediction now come true, but a new contender has also emerged to change the game for the CPG industry: agentic AI.
“Agentic AI introduces a new paradigm; systems that not only analyse and recommend, but also act autonomously,” says Zhou. “These agents are contextual, continuous and adaptive, which in RGM can facilitate real-time scenario planning, pricing agility and promotion orchestration. Agentic AI bridges the gap between strategy and field execution, moving beyond dashboards to decisioning.”
Dina Zhou, senior industry advisor for retail and consumer goods at Microsoft
In its 2025 Work Trend Index, Microsoft introduced the concept of the ‘Frontier Firm’; an organisation that rapidly absorbs and applies new technologies, flattens decision hierarchies and scales insights across the enterprise. According to Zhou, agentic AI is “at the cornerstone of this transformation” and is enabling firms to shift from reactive to regenerative operating models.
“AI is already embedded into many RGM functions, but in a siloed way and for specific purposes such as merging data sets to make predictions of future trends,” she explains. “However, we have witnessed that organisations are still in need of more real-time insights. This is where agentic AI can make a difference.”
Solutions for change
Microsoft provides the technology foundation, with products such as Microsoft Azure, Dynamics 365 and Fabric. Meanwhile, the Microsoft partner ecosystem brings the RGM domain expertise and implementation experience. Through this collaboration, they help CPG firms unify data, automate insights and orchestrate decisions across pricing, promotion and trade investment.
For example, The Xtel AI Platform harnesses the power of AI to help CPGs manage complex data, address intricate questions, develop sophisticated commercial strategies and optimise enterprise planning and execution in real-time.
According to Piet Surmont, global head of strategy and analytics at at Xtel, AI agent personas in RGM act as a “team of consultants”.
“The user interacts with a multi-agent system, and each agent specialises in tasks, such as impact analysis of price changes, and collaborates to provide real-time, actionable answers,” he explains. “Max.AI unifies sales by providing a strong data foundation, explainable AI, deep business context and a powerful planning engine. It breaks down negotiations into sub-goals such as price optimisation and margin security, then delivers actionable strategies.”
Powering innovation in RGM
As the RGM journey continues to evolve, Marco Casalaina, vice president of core AI at Microsoft, offers his perspective on how generative and agentic AI are reshaping the landscape of machine learning, data science and data management automation.
Casalaina sees the future of RGM unfolding at the intersection of generative AI and classical machine learning. This convergence is transforming predictive analytics, with new generative models for time series forecasting and anomaly detection opening doors to deeper business insights.
He emphasises that data science agents, capable of automating feature engineering and model setup, are streamlining the creation of predictive models. For consumer goods companies, this innovation promises to scale analytics efforts and unlock new efficiencies.
At the heart of this transformation is the role of conversational data agents.
“Data agents can allow you to chat with your data, which is useful by itself, but the real value of them is to endow downstream agents with data,” he explains. “It lets you encapsulate the data querying logic so your other agents can focus on the jobs they need to do.”
The roadmap for adoption
When it comes to agentic AI adoption, Praneet Aneja, chief of staff and product strategy at Fractal subsidiary Asper.AI, says the process in RGM is a journey which will evolve in three stages.
The first, and current, stage is RGM as a copilot. By building RGM domain agents that can generate insights on the data and AI model outputs provided, RGM experts can become more productive and handle a bigger mandate, while driving more value for markets where there is no dedicated expert.
The second stage focuses on collaborative intelligence. Training the agents to understand the enterprise functions such as marketing, sales and supply chain will help to drive better alignment between cross-functional teams, increasing speed of trust and alignment. This will help to unlock more productivity and improve RGM mastery.
Using Microsoft technology as a foundation, CPG companies can unify data, automate insights and orchestrate decisions across pricing, promotion and trade investment
The third and final stage focuses on autonomous decisions. “This will be the stage where agent design and training will extend to include deep research on external or enterprise information, reasoning models, fine tuning of domain agents, perform modelling and simulation for different scenarios,” says Aneja. “This will start with low-critical and high-volume decisions, with success leading into mid- to high-critical decisions and unlock direct business impact on gross margins, volume and sales.”
A call to action
Before an organisation can take the leap and start using agentic AI though, it must first have a solid foundation to work from.
“The promise of generative and agentic AI is immense, but fears remain regarding black-box decisions, data quality and organisational resistance to AI-led change,” says Zhou. “It is critical for organisations to develop responsible AI principles, be transparent and implement change management to realise the full potential of these technologies.”
To build a solid base for agentic AI, organisations must have the right technology stack, security and governance measures, a strong data foundation, clear use cases, an experimental mindset and employees who are well-equipped to work with the technology.
Organisations must ensure the AI can only act within authorised boundaries and drive awareness and training to encourage employees to embrace the tools and techniques in their work. A clear buy versus build philosophy is also vital because it will help to avoid sunk cost and lost time further down the line.
So, what’s the next step for CPG retailers? RGM needs to evolve from a siloed function to a cross-enterprise capability. According to POI’s 2025 RGM Insights, 56 per cent of organisations need to improve trade promotion and pricing optimisation, with 49 per cent citing pricing a key lever for RGM efficiency.
The call to action is simple. “AI is not just a tool, it’s a teammate,” says Zhou. “The winners will be those who build agentic systems, democratise decision-making and lead with agility, not legacy. Now is the time to recalibrate your strategy, rewire your systems and reimagine what’s possible with RGM in the age of agentic AI.”
Discover insights from these partners and more in the Autumn 2025 issue of Technology Record. Don’t miss out – subscribe for free today and get future issues delivered straight to your inbox.