For retailers, there is considerable hype about the application of AI in the grocery store. Swiftly CTO and co-founder, Sean Turner, explains to Digital Journal at what’s actually working.
Digital Journal: AI is everywhere in retail right now. What do you think most people are getting wrong about its impact on grocery and convenience?
Sean Turner: Most people think AI in retail is about chatbots, recommendations, or some big shift in how people shop. That is not where the real impact is happening.
The bigger shift is how AI is changing the way the business actually operates. In grocery and convenience, the challenge has never been a lack of data. It is the gap between data, decision-making, and execution. Teams are still managing promotions, vendor coordination, and campaign performance across disconnected systems and a lot of manual work.
That is where AI is having a real impact. It is helping retailers move from insight to action. It is enabling more precise targeting, faster decision-making, and automating the workflows behind how promotions are planned, executed, and measured.
At the same time, it is improving the shopper experience in practical ways. Retailers can personalize offers more effectively, make smarter marketing decisions, and reduce the guesswork around what shoppers actually respond to.
What most people are getting wrong is expecting a visible, overnight transformation. The real impact of AI in grocery is more practical and operational. It is about helping retailers run a better, faster, more efficient business, which ultimately shows up as a more relevant and valuable experience for the shopper.
DJ: If most shopping is still happening in-store, where does AI actually show up in the experience today?
Turner: Most of the value shows up behind the scenes, but shoppers feel it in very real ways. The app feels more relevant. The offers make more sense. Promotions are more timely. The digital circular is easier to use and actually helps plan a trip. That is AI at work, even if the shopper never thinks about it that way.
For retailers, it is about connecting digital signals to in-store behavior. AI helps teams decide which offers to put in front of which shoppers, when to surface them, and how to measure whether those interactions actually drove a trip or a purchase.
That is where AI is showing up today. It is not changing the core in-store experience, but it is shaping everything around it. It is helping retailers make smarter decisions, deliver more relevant interactions, and connect digital engagement to real-world outcomes in a way that improves both the shopper experience and business performance.
DJ: Where is AI delivering real, measurable value for retailers and brands today?
Turner: The clearest value is in precision and execution.
On the marketing side, AI helps retailers and brands move beyond broad promotions and generic targeting. They can deliver more relevant offers, reduce wasted spend, and tie campaigns more directly to real purchases. That matters because in this industry, margin for error is small.
The other area where we are seeing real impact is in how the business operates day to day. AI is helping teams move faster by reducing the amount of manual work required to manage promotions, coordinate with partners, and track performance. Instead of pulling data from multiple systems, chasing updates, and making constant adjustments, teams can rely on AI to surface what matters and help execute on it more efficiently.
That shift drives real gains in productivity and speed. It allows retailers and brands to streamline how work gets done, respond more quickly to changes, and focus more of their time on decisions that actually move the business forward.
The strongest use cases today are the ones that tie all of this back to measurable outcomes. Better conversion, more efficient spend, faster execution, and improved operational efficiency. That is where AI is delivering real value today.
DJ: How is AI changing the way shoppers discover products and what challenges does that create for brands?
Turner: AI is moving product discovery earlier in the decision process. More of that discovery is happening before a shopper ever gets to the shelf, through search, recommendations, personalized offers, and digital planning tools.
That changes the game for brands. If a shopper is being guided toward a smaller, more relevant set of options earlier in the journey, brands have fewer chances to win attention through broad visibility alone. Discovery becomes more selective and more data-driven.
For brands, the challenge is not just being present. It’s being relevant in the moments that actually influence consideration. That is where data, targeting, and measurable activation start to matter a lot more.
DJ: Why is data ownership becoming even more critical in an AI-driven retail landscape?
Turner: Data ownership becomes more important as AI plays a larger role in how retailers market, measure, and make decisions. The reason is simple: AI is only as useful as the data behind it. If a retailer does not control its customer and transaction data, it has limited visibility into what shoppers are actually doing and less ability to shape the experience around that.
It also directly impacts the customer relationship. If too much of that data and intelligence sits with a third party, retailers risk being disintermediated. They lose control over how they engage with shoppers, how value is delivered, and who ultimately owns that relationship. In an AI-driven market, that is a meaningful competitive risk.
Privacy is another important factor. Retailers need to be able to use data in a way that is secure, compliant, and trusted by the shopper. That becomes much harder when data is fragmented or controlled by external platforms.
For both retailers and brands, the advantage comes from owning and activating trusted first-party data in a way that improves performance while protecting the customer relationship. That is why data ownership is becoming such a critical foundation for using AI effectively in retail.
DJ: Where do you see the biggest opportunity for AI to improve efficiency and reduce manual work across retail operations?
Turner: The biggest opportunity is in the work that still depends on too much manual coordination across teams, systems, and partners.
Retailers and wholesalers are still spending a lot of time pulling data, reconciling reports, managing vendor communication, adjusting promotions, and tracking follow-up across disconnected systems. That work is time-consuming and it slows the business down.
This is where AI can have a much bigger impact than most people realize. It can do more than surface insight. It can help automate the routine work around execution, whether that is streamlining workflows, generating communications, identifying issues earlier, or helping teams move faster on decisions that affect merchandising, promotions, and supply chain coordination.
That is where AI starts to shift from being a reporting tool to being an execution layer for the business.
DJ: Looking ahead, what should retailers and brands prioritize to successfully use AI in grocery and retail media?
Turner: First, they need a strong data foundation. If the data is fragmented, delayed, or hard to use, AI is not going to solve the problem. It will just add complexity on top of it. The companies that are seeing real progress are the ones with a connected view across customers, transactions, and performance.
Second, they need to stay focused on practical use cases. The most value comes from applying AI to real problems like improving targeting, optimizing promotions, reducing manual work, and making better decisions faster. It is less about experimentation and more about execution.
Third, they need to connect how the business operates. AI works best when it is not siloed across tools or teams, but instead brings together marketing, operations, and data in a way that allows teams to move faster and act with more precision.
The retailers and brands that will be most successful are the ones that treat AI as a way to improve how the business actually runs, not just how it is analyzed. That focus on connected data, practical use cases, and faster execution is what will ultimately determine who gets real value from AI.
