Target SVP says its real AI moat isn't the models — it's everything built around them

Target SVP Siobhán Mc Feeney says the AI models her company runs aren't what gives Target its edge — everything built around them is. "There's a lot in it. That to us is the moat," Mc Feeney said at VB Transform 2026. "The models are great, and they're important. They're just not sufficient to be the competitive advantage." That discipline shows up early in how Target decides whether to build an agent at all. Mc Feeney was blunt, even "controversial" by her own admission, about the current AI moment: every enterprise wants AI agents, but not everything needs one, she said. Agents earn their autonomy over time rather than getting it by default, she said — a principle that runs through everything Target has built around them. Mc Feeney said the goal is to make sure agents are aimed at the problems that drive the most value for Target's guests. "We want to make sure we're investing in the right places," she said. Being deliberate about agents Agents are becoming part of Target's underlying architecture, increasingly connecting signals, systems, and decisions across supply chain, replenishment, and demand forecasting. Mc Feeney framed it as retail's oldest promise — the right product, in the right place, at the right time — delivered at scale. But her team has been deliberate about building AI agents, beginning with the simplest, most obvious question: What is the problem they're trying to solve? This leads to several follow-on questions: Does that problem need an agent? If it does, what type of agent? An orchestrator? A super agent? A domain-specific agent? Or is what you're calling an "agent" actually just a tool? "You define that upfront, and this may sound a little process-heavy, then you have to register and certify your agent," Mc Feeney said. Because a solution may

Target SVP Siobhán Mc Feeney says the AI models her company runs aren't what gives Target its edge — everything built around them is. "There's a lot in it. That to us is the moat," Mc Feeney said at VB Transform 2026. "The models are great, and they're important. They're just not sufficient to be the competitive advantage." That discipline shows up early in how Target decides whether to build an agent at all. Mc Feeney was blunt, even "controversial" by her own admission, about the current AI moment: every enterprise wants AI agents, but not everything needs one, she said. Agents earn their autonomy over time rather than getting it by default, she said — a principle that runs through everything Target has built around them. Mc Feeney said the goal is to make sure agents are aimed at the problems that drive the most value for Target's guests. "We want to make sure we're investing in the right places," she said. Being deliberate about agents Agents are becoming part of Target's underlying architecture, increasingly connecting signals, systems, and decisions across supply chain, replenishment, and demand forecasting. Mc Feeney framed it as retail's oldest promise — the right product, in the right place, at the right time — delivered at scale. But her team has been deliberate about building AI agents, beginning with the simplest, most obvious question: What is the problem they're trying to solve? This leads to several follow-on questions: Does that problem need an agent? If it does, what type of agent? An orchestrator? A super agent? A domain-specific agent? Or is what you're calling an "agent" actually just a tool? "You define that upfront, and this may sound a little process-heavy, then you have to register and certify your agent," Mc Feeney said. Because a solution may