What AI in Retail and Consumer Goods Can Teach Every Industry

It’s easy to look at AI-powered changes in consumer businesses—retail, ecommerce, FMCG and think, “That doesn’t apply to us.”

But look closer. The real lesson isn’t in the tech itself. It’s in the way these businesses are applying it: deliberately, commercially, and with one goal—faster, smarter decisions that improve outcomes.

The packaging might be different. But the logic behind it applies just as much to healthcare, professional services, logistics, or tourism.

The Competitive Advantage Isn’t the Tool. It’s How You Use It

Consumer companies didn’t win by being the first to adopt AI. They won by being the first to apply it with intent.

Think about what’s happening behind the scenes:

• AI is cutting forecasting errors by half in global retail supply chains.
• It's reducing customer churn by flagging silent drop-offs before they’re visible in dashboards.
• It’s powering autonomous decision systems—not just chatbots, but demand prediction, pricing adjustments, and fraud alerts.

A large global consumer goods company used AI to reconfigure its supply chain planning. Rather than reacting to orders and historic trends, they introduced a predictive system that could account for marketing campaigns, real-time demand shifts, and transport constraints.

This system allowed the business to reduce fulfilment times, increase shelf availability, and reallocate stock more efficiently without expanding warehouse capacity or headcount. 

The takeaway isn’t “use AI.” It’s: what operational question are you trying to answer better than before? That’s where AI becomes commercially useful.

Why Every Sector Should Pay Attention

Most sectors outside of retail are still testing AI in isolation trying out a tool here, automating a workflow there. There’s often no clear link between these experiments and the strategic goals of the business.

Consumer industries show what it looks like when AI use is stitched into a company’s commercial engine. Not every experiment worked. But the ones that did were scaled because they:

• Solved a problem tied to revenue, margin, or cost reduction
• Could be deployed without overhauling core systems
• Had a sponsor who understood both the tech and the business case

You don’t need to be Amazon to start.

You need clarity on where AI can shift an outcome that matters.

The First Move Is Not the Model. It’s the Business Case.

You can’t copy what Nike or Unilever did line by line. But you can follow their approach: treat AI like a business decision, not a pilot project.

That starts with building a proper case.

If your business is serious about moving from curiosity to real value with AI, then this is where we begin: aligning commercial goals with practical AI use cases that can be tested and scaled.

That’s exactly what we do in the AI Business Case Workshop.

No fluff. No jargon. Just the questions that help you decide where to start, what’s feasible, and what’s worth the investment.

Join the workshop to get clarity on how AI fits your business.