Target’s AI push hinges on shopper trust and clear recommendations

Trust is the conversion test

Retail’s latest AI lesson is simple: shoppers do not just want a fast answer, they want a believable one. Retail Dive’s May 20 coverage of AI referrals points to the same pattern teams have been seeing across ecommerce: when people understand why a tool recommended a product, a merchant, or a next step, they are more likely to stay engaged. When the reasoning feels fuzzy, confidence drops fast, and so do the odds of a purchase.

That matters at Target because the company is no longer treating AI as a back-office experiment. It is trying to weave it into shopping, advertising, store operations, and the digital journey at the same time. For team members on the floor, that means AI success will not be measured by flashy demos. It will be measured by whether guests arrive with recommendations that actually make sense, whether those items are in stock, and whether the store can back up what the app is suggesting.

The trust problem is bigger than one recommendation gone bad. Bain and Gartner research cited by Retail Dive found that 7 in 10 consumers did not realize they had used generative AI while shopping, and three-quarters expected disclosure when AI was part of the interaction. That gap between invisible automation and visible trust is exactly where Target’s digital teams, store leaders, and front-line workers will feel the strain.

From store companion to shopper companion

Target has already made a sizable bet on generative AI inside its stores. In June 2024, the company said it would roll out Store Companion, a GenAI-powered chatbot, to team members across nearly 2,000 stores by August. Target said the tool would answer process questions, coach newer workers, support store operations management, and help team members respond to guests more quickly and efficiently.

That first wave of AI use is important because it shows where Target thinks the value begins: with speed, consistency, and less time spent hunting for information. If a new team member can get a policy answer faster, or a leader can solve an operations question without slowing the floor, AI can reduce friction instead of adding it. But the same logic applies to the customer side. If the system is going to suggest a product, it has to be right about the product details, the price, the use case, and the store’s ability to deliver.

Target’s March 4, 2025 investor presentation made the bigger business case clear. The company said its strategic plan aimed to drive more than $15 billion in sales growth by 2030, with a focus on digital experience, speed, reliability, and a blended physical, digital, and social shopping experience. In other words, AI is not a side project. It is part of the turnaround math.

That direction sharpened in March 2026, when Target said it planned to invest an incremental $2 billion in 2026, including more than $1 billion in additional capital expenditures and $1 billion in additional operating investments. The company said the plan explicitly includes accelerating technology, including AI, to make shopping easier and more personalized. For Target workers, that means the company is backing the technology with real money, but also raising the stakes for execution.

Why the shelf still decides the outcome

The app may start the trip, but the store still has to finish it. If AI nudges a guest toward an item, the recommendation has to survive the trip from screen to shelf. That means product data has to be clean, signage has to be accurate, reviews have to be useful, and team members have to know enough to confirm or correct what the algorithm said.

That is where front-line trust becomes a workforce issue, not just a product issue. A recommendation that is slightly off can turn into a disappointed guest, a substitution request, a return, or an abandoned cart. It can also turn into a longer conversation at guest services, a more complicated pickup order, or an associate having to explain why the app surfaced one item when the aisle tells a different story.

For store teams, the practical burden is familiar even if the technology is new. You still need the right item in the right place, with the right label and the right inventory count. AI does not erase the old retail basics. It makes those basics more visible because the guest now expects the app, the shelf, and the employee to agree with one another.

Target is pushing AI outward, not just inward

The company’s consumer-facing AI launches show how quickly this is moving beyond internal tools. In November 2025, Target announced AI-powered Gift Finder and list-to-cart scanning features for holiday shopping, along with the ability for consumers to discover and shop Target inside ChatGPT. Later that month, Target said shoppers would be able to find and buy products through ChatGPT, which is a major shift in how the company can reach customers outside its own app.

By January 2026, Target said shoppers would soon be able to browse product listings and buy directly from Target in Google’s AI Mode and Gemini app through the Universal Commerce Protocol, which Target co-developed with Google. That matters because it puts Target into third-party AI environments where the brand has less control over the conversation and even less control over how the recommendation is framed.

Then in February 2026, Target and Roundel said they were testing clearly labeled ads in ChatGPT. OpenAI’s ads lead said the company was trying to keep ads separate, distinct, relevant, useful, and aligned with user trust. That test cuts to the core issue for retail media teams: if shoppers cannot tell when advice becomes advertising, the whole experience can feel manipulative rather than helpful.

What this means for Target workers

The work now falls into a few clear buckets:

• Digital teams need to make sure product data, inventory signals, and recommendation logic are accurate enough to support a more personalized shopping experience.

• Store leaders need to prepare for guests who arrive with an AI-backed shopping plan and expect the store to match it.

• Front-line team members need tools that explain not just what to say, but why a recommendation makes sense, especially when a guest challenges it.

• Operations teams need fewer mismatches between the app, the shelf, and the back room, because every mismatch weakens trust.

The broader lesson is not that AI will replace retail judgment. It is that retail judgment becomes more important when AI is part of the path to purchase. Target can spend billions on speed, personalization, and new tools, but the real test will happen in ordinary moments, when a guest scans a list, checks a recommendation, walks to the aisle, and decides whether the company got it right.

If that answer feels clear and credible, AI helps move the sale forward. If it does not, the floor team inherits the fallout, and that is where the technology either earns its keep or loses the customer.