AI is everywhere in retail conversations right now.
It’s in boardrooms. It’s in vendor decks. And it’s all over the agenda for NRF 2026, where nearly every track touches AI in some way — operations, supply chain, personalization, customer experience, and the store itself.
That level of focus sends a clear message:
AI isn’t optional anymore.
But it also creates confusion.
Retail leaders are being told AI will transform everything — forecasting, labor, pricing, engagement, even how customers shop. At the same time, many teams are still dealing with basic realities: inconsistent store networks, disconnected systems, and technology stacks that were never designed for this level of intelligence.
So where does that leave AI today?
Some of it is delivering real value.
Some of it is still more promise than practice.
And knowing the difference matters.
Where AI Is Actually Helping Retailers Today
Despite the hype, AI is working in retail — just not always in the ways people expect.
Quiet Improvements in Operations
The most successful AI use cases right now aren’t flashy. They live behind the scenes.
Retailers are using AI to improve:
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- Demand forecasting
- Inventory planning
- Replenishment decisions
- Supply chain visibility
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These tools help teams react faster and make better calls with the data they already have. They don’t replace people. They reduce guesswork.
That’s why so many NRF sessions focus on predictive planning and operational intelligence. Retailers aren’t chasing moonshots — they’re trying to run tighter, more consistent operations across hundreds or thousands of locations.
In many cases, if AI is doing its job, customers never notice it.
And that’s a good thing.
Helping Store Teams Instead of Burdening Them
Another place practical AI in retail is showing real value is on the frontline.
Scheduling tools, task management systems, and exception alerts are getting smarter. Instead of overwhelming associates with dashboards, AI helps surface what matters most right now.
Done well, this:
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- Reduces firefighting
- Improves labor efficiency
- Gives store teams clearer priorities
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What it doesn’t do is eliminate the need for people.
The retailers seeing progress are using AI to support human decision-making, not replace it. When associates trust the tools, adoption follows. When they don’t, AI becomes just another system they work around.
Personalization That’s Practical, Not Perfect
Personalization is one of the loudest AI use-cases in retail — and one of the most misunderstood.
In reality, most successful personalization today is incremental.
AI helps:
- Improve product recommendations
- Tailor promotions
- Support loyalty programs
- Provides store associates better insight so they can offer more personalized service
It’s not about predicting every customer’s next move. It’s about being a little more relevant, a little more consistent, at scale.
The retailers making this work have one thing in common: clean, connected data. Without that, AI doesn’t improve the experience — it exposes the gaps.
Where the Hype Still Gets Ahead of Reality
For all the progress, there are still areas where expectations don’t match what most retailers can realistically deliver today.
Fully Autonomous and “Agentic” AI
You’ll hear a lot about agentic AI — systems that act independently, adapt on their own, and make decisions without human input.
It’s an interesting concept. It’s also not where most retailers are today.
In practice, many teams are still:
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- Piloting narrow use cases
- Struggling with system integration
- Navigating data governance and security concerns
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The gap between a compelling demo and a scalable rollout is still wide. That doesn’t mean these ideas won’t mature — but for most retailers, they’re part of a longer roadmap, not a near-term solution.
AI Built on Shaky Foundations
AI is only as good as the environment it runs in.
If systems are siloed, networks are inconsistent, or store-level setups vary widely, AI struggles to deliver consistent value. This is why so many industry conversations — including at NRF — keep circling back to data platforms, infrastructure, and integration.
It’s not glamorous work. But it’s necessary.
AI doesn’t fix foundational problems.
It makes them more visible.
Experience-First AI That Ignores Operations
Retail has always been good at chasing customer-facing innovation. But AI tools that focus only on the experience layer often fall apart when operations can’t support them.
Real-time recommendations, dynamic pricing, and AI-driven engagement all depend on:
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- Reliable networks
- Integrated systems
- Consistent execution at the store level
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Without those basics, even the smartest tools struggle in the real world — especially during peak periods.
That’s why many retailers are shifting focus back to fundamentals before layering on more intelligence.
What NRF Signals About What Comes Next
If there’s one takeaway from the sheer volume of AI sessions at NRF, it’s this:
AI is becoming table stakes.
The next phase won’t be about who adopts the most tools. It will be about who can make them work — consistently, across locations, without breaking daily operations.
That means:
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- Fewer experiments for experimentation’s sake
- More focus on execution
- Greater emphasis on integration and reliability
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Retailers that succeed with AI won’t treat it as a standalone initiative. They’ll treat it as part of how the business runs.
A More Practical Way to Think About AI
AI isn’t a silver bullet.
It’s also not a fad.
It’s a set of tools that can deliver real value when expectations are grounded and foundations are solid.
The retailers seeing progress today aren’t chasing every new promise. They’re:
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- Fixing what’s underneath
- Choosing use cases carefully
- Making sure AI helps people do their jobs better
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As AI continues to mature, the divide won’t be between retailers who “use AI” and those who don’t. It will be between those who can deploy it reliably, at scale — and those still trying to layer intelligence onto unstable systems.
The most successful retail organizations won’t be the ones talking the loudest about AI.
They’ll be the ones quietly making it work.