Agentic AI in CPG: Real-World Use Cases and What’s Next

Key Insights -

✔ Why CPG Needs Agentic AI – Understand the limitations of traditional automation and generative AI in the CPG industry and how Agentic AI in CPG is driving smarter, autonomous decision-making. Learn about its impact on the consumer-packaged goods sector and why leading brands are making the shift.

✔ Use Cases of Agentic AI in CPG – Explore four major areas where Agentic AI is transforming the industry, from inventory management to sales force effectiveness.

✔ Next Steps for CPG Businesses – Learn how to assess your current processes, identify high-impact areas for AI adoption, and implement use cases of Agentic AI to gain a competitive edge.

Is Your CPG Ready for Agentic AI?

Generative AI is already delivering results (With the global Generative AI in CPG market projected to hit $5.4 billion by 2033); And it’s clear that companies are seeing value. But here’s the catch: While Generative AI excels at creating and analysing, it can’t autonomously manage the complex, interconnected decisions that define CPG operations.

Think about it - When it comes to consumer goods industry, we're juggling 30-day payment terms against 120-day inventory cycles, while racing to meet 24-hour delivery expectations; Hence it’s not just the insights that you need; You need systems that can act on them. (And since you are here we assume that you’re already familiar with impact of agentic AI powered autonomous agents that can reason, plan and execute complex tasks).

Let’s take a classic port congestion and transportation bottleneck scenario. Now when you see the two AI models approach the problem, you will see -

Now after looking at the example, one thing is for sure. The impact of agentic ai on the consumer-packaged goods sector is going to be huge. And this becomes clearer when we look at industry pioneers like PepsiCo moving beyond traditional workflows towards fully autonomous processes.

So, the real question isn’t why Agentic AI, but where to deploy it first. Which core CPG processes in your business are primed for transformation?

To help you in your decision-making process- let’s check out top use cases of agentic ai in cpg to get us started.

Top 4 use cases of Agentic AI in CPG to watch out for

There are many possible use cases for cpg industry with Agentic AI- ranging across customer experience, inventory management , revenue growth management, vendor assessment, and sales force effectiveness.

Now for the better understanding let’s drill down into few of these agents to see Agentic AI in cpg in action.

Use cases 1: Customer Experience and Service

Agentic AI has been used across customer service for different purposes.

Now considering the omnichannel setup we are in, having a high return rate is automatically correlated with your customer being dissatisfied because of poor service. (1 in 10 consumers believe businesses can deliver a seamless omnichannel experience. And poorly synced order and fulfillment channels contribute to this significantly.)

Now in this case – A product return request ecosystem can make a big different.

When you look at the framework, you’ll see how agentic AI - Instead of just processing returns, this system identifies recurring issues, alerts quality control, suggests better alternatives, and prevents future dissatisfaction by taking it a step further by -

• Recognize that this is a customer's third return of the same product


• Analyze product reviews to identify a potential design flaw


• Proactively alert quality control


• Suggest a different product variant based on the customer's usage patterns


• Initiate a feedback loop to prevent similar issues for other customers

Use case 2: Inventory Management

One of the areas where agentic AI shines is Inventory management. So, to understand it better, let’s take an example of simple inventory drop scenario. When a inventory drop is detected, a simple person will sit along, and how will they react to it. They will call upon a meeting, they can get into the meeting to plan what are the next steps, and then they come up with the plan and then they can place the order.

But with the agentic AI bot which we are working on – this changes. (Bonus points for it being CTO approved!) Here's how Ankit Rana , CTO – Polestar solution see agents solving the situation with the given framework.

Now as you further drill it down , you’ll see that systems continuously analyses multiple data points in real-time for authentic decision making, such as:

• Sales velocity: Identifying sudden spikes in demand.


• External factors: Considering market trends, social media buzz, and competitor pricing.


• Supply chain constraints: Factoring in supplier lead times and logistics disruptions.


• Customer behaviour: Predicting potential buying patterns based on past interactions.

Use case 3 : Revenue Growth Management

While we were working on developing our RGM agents we realized that - AI, particularly generative AI, has made strides in optimizing promotional calendars and suggesting price adjustments, it still faces a major challenge: multi-system paralysis. As a result, RGM teams spend hours manually coordinating across different systems and departments, delaying execution and missing critical opportunities for revenue optimization.

Now what Agentic AI does is, it eliminates these bottlenecks by autonomously integrating and orchestrating pricing, trade, and supply chain decisions. Here’s how it works:

• Continuous Price-Volume-Mix Monitoring: AI tracks sales trends, pricing impact, and proposes market shifts in real time.


• Trade Compliance Checks: Before suggesting a price move, it verifies contractual obligations to prevent violations.


• Supply Chain Validation: AI ensures production and inventory levels can support pricing changes before execution.


• Automated Promotion Adjustments: It dynamically updates promotional calendars and trade spend allocations to maintain profitability.

And this not only frees up RGM teams to focus on strategy rather than execution. The future of RGM isn't just about better decisions - it's about autonomous value creation at a scale and speed previously unimaginable.

Use case 4: Vendor Selection and Assessment

Now as simple as it sounds, one wrong vendor decision can lead to hidden losses like wasted resources, failed on time delivery leading to delayed production or service delivery or even impaired decision making within the organization.

Now here’s how our Vendor Quote analysis and comparison agents worked -

So the agent automatically identifies suitable vendors and raises requests for quotes. It analyses multiple criteria, including price, quality, delivery timelines, and vendor reliability, to select the most suitable vendor.

Business Impact we’ve seen

Now after seeing agentic AI in action, one this is for sure – Agentic AI is here to stay and is already changing the way we work. But is it soon enough to say – “This is an Agentic AI world, and we are living it?” not yet. But are we on a path of it? One hundred percentage!

The Path Forward for Agentic AI in CPG

CPG companies must take proactive steps to integrate intelligent, autonomous systems into their operations. To stay ahead in a rapidly evolving market, you should prioritize the final outcomes you want. ( Is It "Customer Centricity,""Customer Focus," "Enterprise Operations," or "Strategic Innovation" ). Check out our industry use case quadrant for better understanding.

The time to act is now. The future belongs to companies that adapt quickly and embrace the power of intelligent, self-managing systems. It’s no longer about analysing data in isolation—it’s about using that data to make informed, real-time decisions that drive business growth with precision and speed.