Everyone wants to talk about what is new in AI. But in retail and CPG, the biggest challenges are not new at all. People ask me all the time:
“What are the biggest areas of focus you’re seeing from retailers and brands right now?”
And honestly, I have to smile a little.
Demand forecasting. Personalization. Right product, right place, right time. These are not new problems. I was wrestling with these same challenges 10 to 20 years ago at Nestlé and ConAgra. And the truth is, most companies are still grappling with them.
What has changed, dramatically, is our ability to solve them. Today, AI can process far more variables, from weather and geolocation to shopper behavior and channel signals, and make personalization far more scalable, precise, and actionable than it was even a few years ago.
For years, the gap between having data and doing something meaningful with it was wide. We had instincts, spreadsheets, and a lot of meetings trying to turn information into action. Today, AI and machine learning are collapsing that gap faster than most organizations realize.
How AI is Rewriting the Retail Playbook
This theme came through clearly in a recent conversation I had with Sean Forgatch, our Practice Lead for Retail, CPG & Travel at Lovelytics, and Rob Saker, Global VP of Consumer Industries GTM at Databricks.
One of the ideas that resonated most with me was the importance of the last mile.
The real breakthrough is not just the platform or the model. It is getting the right insight to the right person fast enough to actually change what happens next.
So what does that actually look like in practice?
It’s not one model or one dashboard. It’s a set of focused, purpose-built capabilities that bring intelligence directly into the decisions teams are making every day, from brand and category to supply chain and operations.
We’ve been thinking about this as a practical framework for where AI can show up across the enterprise, not to replace people, but to surface the right signals at the right time so better decisions happen faster. The image below highlights a few ways to think about where AI can create value across retail and CPG.
Example: Embedding AI into the Decision Cycle
One way to think about where AI can create value across retail and CPG
This is where the shift becomes real. Moving from talking about AI to embedding it into the day-to-day decisions that actually drive performance. What makes this especially exciting to me is that this opportunity is not limited to one team or one use case. It is showing up across the retail and CPG enterprise, from demand forecasting and brand performance to trade promotion, assortment, supply chain, and operations.
When you start to look at AI through that lens, it becomes clear that the conversation is much bigger than experimentation. It is about identifying where intelligent decision support can create measurable value and then designing solutions that actually fit how the business works.
AI is making that possible in ways we simply could not have imagined twenty years ago.
This topic feels more relevant than ever for retail and CPG as companies work to move beyond AI experimentation and start creating real business value. If any of this resonates, I’d encourage you to listen to our recent conversation.
