Video: Watch Jennifer’s full Love Generation session, AI-native retail: What will ‘great’ look like?
Retail has spent the last few years talking about, and trying to get to grips with, AI. There have been pilots, proofs of concept, productivity hacks, copilots, agents, and more. But despite the excitement, many retailers are still struggling to answer the ultimate question: Where is the value actually coming from?
According to Voyado’s latest research, 95% of retailers are using AI in some form, but only 5% feel they’re seeing a meaningful return on their investment. And this gap was the starting point for Jennifer Roebuck Stephen’s session at this year’s Love Generation.
Drawing on experience as a retail leader, investor, and AI practitioner, Jennifer offered a grounded perspective on where retailers are succeeding, where they’re getting stuck, and why the businesses seeing results are often focused on different problems than the rest of the market.
Everyone is using AI, but very few are transforming with it
Most organizations have already introduced AI tools into daily workflows. Teams are summarising documents, generating content, analyzing data, and experimenting with new ways of working. Yet many of those activities remain disconnected from measurable business outcomes.
As Jennifer pointed out, the market is moving quickly. Consumers are already using AI to research purchases, discover products, plan trips, and complete tasks. Meanwhile, many retailers are still debating governance models, technology stacks, and internal approval processes.
But the challenge is no longer access to technology, but figuring out how to embed it into the business in ways that create value.
The biggest barrier isn’t technology
One of the most striking themes from Jennifer’s session was how often organizational structure becomes the bottleneck.
Many retailers have appointed AI leads, established governance groups, and launched innovation initiatives. Yet progress remains slow because teams are still operating within structures designed for a different era, meaning approval chains get longer, projects sit in queues, and capability becomes concentrated in a handful of specialists.
Meanwhile, businesses moving fastest are distributing AI capability throughout the organization and giving teams permission to solve problems directly.
This shift matters because AI adoption is ultimately about changing how decisions are made, how work gets done, and how quickly organizations can respond to new opportunities. Technology is what enables that change, but it’s people who determine whether it happens.
Why the unsexy use cases win
There can be a tendency to focus on the exciting side of AI:
- Virtual shopping assistants
- Hyper-personalized experiences
- Agentic commerce
- Autonomous customer journeys
While these developments are important, they’re only part of the story. And Jennifer’s argument was that many retailers are overlooking the areas where ROI is currently showing up fastest, such as:
- Stock forecasting
- Operational workflows
- Customer service
- Knowledge management
- Pricing
- Internal reporting
- IT support
These cases aren’t particularly glamorous, and most of them probably wouldn’t appear in keynote headlines. Yet they’re often where businesses see the first meaningful financial returns.
It’s a simple lesson at its core. Start with real business problems instead of technology ambitions.
Retailers that focus on removing friction from existing processes often create value more quickly than those pursuing large-scale transformation programs from day one.
AI is changing how customers discover brands
The other major theme from Jennifer’s session was discoverability.
For years, retailers have optimized for search engines, marketplaces, and social platforms. Now, a growing number of consumers are beginning their journeys inside AI-powered environments. They are asking questions, requesting recommendations, researching products, comparing brands, and seeking advice.
As AI becomes a new layer between customers and retailers, visibility becomes increasingly important.
Jennifer highlighted a trend that should catch the attention of every retailer. Brand performance in AI environments is increasingly shaped by reputation signals that businesses have spent years building online. Think reviews, press coverage, video content, social conversations, community discussions, and third-party recommendations. All of these influence how AI models understand and describe brands.
For retailers, that creates an interesting challenge, because success in AI discovery environments will depend on many of the same fundamentals that have always mattered: trust, reputation, relevance, and customer experience.
The difference is that those signals are now being interpreted by machines as well as people.
What the 5% are doing differently
The retailers seeing meaningful returns from AI are not necessarily the ones with the most advanced tools. More often, they’re the ones approaching the technology with greater focus. They are investing in strong foundations, empowering teams to experiment, and connecting AI initiatives to measurable business outcomes rather than treating them as isolated innovation projects.
That may not be the most attention-grabbing story in retail right now, but it is one of the most useful.
Throughout her Love Generation session, Jennifer returned to the idea that AI transformation is ultimately about people, behaviors, and ways of working. The businesses pulling ahead are making AI part of how their organizations operate, rather than confining it to a handful of specialists or side projects.
The opportunity is enormous, but retailers don’t need to solve everything at once. In many cases, the best place to start is with the operational frustrations, repetitive tasks, and inefficiencies that already exist. As Jennifer put it, those less glamorous challenges are often where the fastest returns can be found.
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