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How Retailers Are Reducing Tool Sprawl To Unlock Faster Execution: With Snowflake's Head of Retail GTM
Leslie Lorenz, Head of Retail GTM, Americas at Snowflake, is focusing on data foundations as the key to scaling AI.
You have to slow down and focus on data fundamentals to lay the groundwork for scaling AI data capabilities and prepare for agentic commerce. But stopping to build that foundation takes time and money, which many retailers are hesitant to do as retail is such a low-margin business. That's the tension.
Retail’s core data challenge of getting clean, unified customer data into one place hasn't changed. What's changed is the urgency behind it. As agentic AI, new commerce channels, and AI-assisted shopping experiences gain traction, solving that problem is no longer optional. Many brands are accelerating investment in personalization and automation, aiming to respond to increasingly fragmented consumer behavior. But in practice, layering new technology onto disconnected systems often adds complexity rather than clarity. The companies seeing results are taking a different approach. Instead of relying on new algorithms alone, they're prioritizing the underlying data infrastructure that makes those tools effective.
Leslie Lorenz, Head of Retail GTM, Americas at Snowflake, has seen this challenge repeat across multiple waves of ecommerce transformation. Snowflake provides a cloud-based data and AI platform that enables enterprises to store, analyze, and share data at scale, giving Lorenz a front-row view into how retail organizations manage their data. Prior to Snowflake, Lorenz served as Head of Data and Analytics at lululemon and Chief Data Officer at Chord Commerce, where she worked directly with the systems and constraints marketing teams navigate every day. From that vantage point, she sees a clear pattern: retailers that invest in a unified data foundation can effectively activate AI, while those that don’t tend to layer new tools onto fragmented systems, creating more silos instead of resolving them.
"You have to slow down and focus on data fundamentals to lay the groundwork for scaling AI data capabilities and prepare for agentic commerce. But stopping to build that foundation takes time and money, which many retailers are hesitant to do as retail is such a low-margin business. That's the tension," Lorenz explains. For leaders operating on thin margins and limited headcount, that trade-off is real. Stepping back to fix backend infrastructure can feel risky when teams are under pressure to keep pace with new tools and channels. Lorenz’s view is that the upfront investment creates leverage. Once the data plumbing is connected, teams can move faster and get more done without the constant friction that slows everything down.
Chasing tails: The expanding software landscape is creating a different kind of constraint. As more tools enter the market, teams are forced to move data between platforms for personalization, measurement, and activation, increasing the risk of errors and fragmentation. Instead of simplifying the path to a unified customer view, that constant movement often reinforces silos and slows progress. The result is a growing operational burden, with marketers spending more time managing vendors than executing strategy. "There are more tools being introduced than the amount of time you have to actually set up the infrastructure you need," Lorenz observes. "You're almost chasing your tail."
Flipping the script: Retailers are beginning to flip the model. Rather than sending data out to multiple tools, they’re keeping it centralized and pulling capabilities into the environment where the data already lives. By limiting movement, teams can reduce fragmentation and build on a more consistent foundation. For Lorenz, the payoff is practical: less time spent managing infrastructure and more time focused on execution. "You're driving capabilities across AI, modeling or semantics within technology you've already purchased. Fundamentally, the reduction in operational overhead allows for marketers to actually focus on the business unlock of driving effective marketing activation, not spending countless hours munging through data," she explains.
When a solid foundation is in place, the benefits show up across the entire customer lifecycle. Connecting marketing data with inventory, supply, and channel performance gives teams a clearer view of customer value and how to engage across touchpoints. With better visibility into what’s working, marketers can adjust campaigns without waiting on additional analysis or data reconciliation. In that environment, AI becomes less about external automation and more about internal acceleration, helping teams interpret and act on data. "Imagine we built a campaign calendar for a product line six months ago that was designed for a specific purpose," says Lorenz. "As we begin to activate on that campaign, we can more easily understand and react to changes in campaign performance in line with the speed and evolution in the marketing. Now we can say, 'It's not relevant anymore, we want to drive another type of campaign', and turn that around extremely quickly."
So what, then what?: For Lorenz, the shift isn’t just about how data is stored, but how it’s used across the organization. What started as centralized data has evolved into broader access, and as a result, more active engagement. That change shows up in how marketers interact with information. Instead of relying on static dashboards, they can ask questions in plain language and get answers tied directly to their own data. The result is a shorter loop between performance and action, making it easier to adjust campaigns, refine targeting, or rethink creative on the fly. "Engagement with data is being democratized," she notes. "It's the next phase of what I call the 'so what, then what?' We used to get a dashboard that displayed the numbers. Now you can actually dig into what happened, how it happened, and why it happened."
Flexibility is becoming a baseline requirement for marketing infrastructure, especially in a market that can shift quickly. Recent experimentation with agentic commerce made that clear. As brands moved to build around early in-agent shopping experiences, some committed heavily to a single direction. When those experiences pulled back, alongside OpenAI’s evolving shopping integrations and a wave of retailer-side changes, many teams were forced to rethink their plans in real time. The episode highlighted how quickly a high-potential opportunity can change, and how risky it is to anchor core infrastructure to any one emerging trend. "As the market is shifting towards agentic commerce and evolving shopping journeys, retailers are also to a certain extent at the will of how the market and consumers will react to technological advances and AI enhanced shopper journeys," says Lorenz. "They have to be ready to react to consumer sentiment, and potentially alter what they are building toward as a result."
As new AI-driven experiences continue to emerge, the underlying data foundation needs to support change without forcing teams to unwind months of tightly coupled work. Lorenz stresses the importance of a more flexible operating model, one where core customer data remains in place and tools, including agents, can be added, removed, or reconfigured as the market evolves. In that environment, adaptability becomes a built-in capability rather than a reactive fix. "Without knowing what's going to happen, one of the best things you can do is to prepare your organizational data foundation to strategically build towards capabilities that will drive ROI, and allow for the ability to quickly react to industry shifts and changes as they come," she concludes.
