When tariffs disrupted Sur La Table's supply chain, the marketing team navigated a constantly moving target. Inventory costs shifted week to week, forcing constant recalibration of pricing and promotions. Teams continuously reissued offers, rebuilt assets, and refreshed campaigns to stay in step with shifting conditions on a near-rolling basis. To keep up, the company leaned on AI to create capacity. It worked for output—and in the process, surfaced a critical tension many marketers now face because, without a clearly defined strategic foundation, AI can amplify the wrong work just as fast as the right work.
Blake Renee Bakanoff is the former VP of Creative and Operations at Sur La Table. Her career is rooted in accountable direct marketing, including a tenure at Publicis Groupe managing highly variable, data-informed creative for global brands. She later served as Chief Creative Officer at eBay, where she centralized the creative function and led a global rebrand. At Sur La Table, she helped build an AI-enabled creative production hub that changed how the team operated under pressure. That experience of evaluating tools by business impact shapes how she thinks about the line between efficiency and effectiveness. "A lot of leaders are looking for a silver bullet to adapt to this disruption. They're hoping that scale is going to save the day. But what I saw was that, in many cases, it risked scaling the wrong work," Bakanoff says.
Speed trap: "Because CEOs are nervous about headwinds, it's very attractive to think they can have a portion of the workforce and still have this outcome they want. But it is literally the difference between that efficiency vs. effectiveness tradeoff," Bakanoff says. The pattern is predictable. Leadership sees cost pressure, AI offers a way to maintain output with fewer resources, and the early wins reinforce the approach. But productivity without direction produces more of whatever already exists, whether or not it was working in the first place. The question that often goes unasked is whether the work being produced is actually driving results or just filling a pipeline.
Missing the magic: The people most naturally drawn to AI tend to be the most data-oriented members of the team. Bakanoff watches those early adopters build real confidence with the tools, moving faster and producing more. But over time, she notices a tradeoff. "Individuals who are very data-driven over-rotate and rely too much on the system. There’s a risk of losing the insight layer. Where is that human truth? Where is that distinction in the thing that we're communicating?" Data tells a team what happened. Insight tells them why it matters. When AI handles the assembly, the temptation is to skip the interpretive step entirely, and the work starts to lose the texture that makes it resonate with an actual audience.
Early results from top-of-funnel awareness campaigns can look strong as teams produce more variations at speed. But platforms are adjusting to the influx of repetitive formats, and undifferentiated content starts to work against the brands producing it. Reach drops as algorithms learn to discount sameness, both across brands and within a single account. Without a distinct point of view, the output that initially felt like a competitive advantage quietly becomes the thing pulling performance down.
Accidental architects: The most meaningful gains came when the team started using AI for strategic thinking, not just production. "It started with ChatGPT and copy bots plugging in brand strategy and purpose to create capacity. But what happened was it taught those same writers how to architect a strategy and develop a messaging hierarchy. That's where we saw the biggest lift in thinking. I'm proud of our resilience and adaptive mindsets at Sur La Table," Bakanoff says. As the team grew more fluent with the tools, writers who had been using AI to draft copy faster started internalizing how the tools organized information, and that changed how they approached the work itself. With AI as a partner, Bakanoff could move faster on product launches, testing approaches, and expanding her own thinking. The upstream work, how a brand frames problems and checks for blind spots, turned out to be the true needle-mover.
But an upstream strategy only works when teams know what they are building toward. The brands most insulated from AI-generated fatigue tend to possess a strong, consistent sense of identity. When a company knows its "why," that origin naturally guides decisions about what kind of work to produce and what to filter out. Patagonia is a consistent example. Its 2011 "Don't Buy This Jacket" campaign urged consumers to reconsider purchasing during peak shopping season, and its Worn Wear program encourages customers to repair and resell gear rather than replace it. Every decision, from donating a portion of sales through its 1% for the Planet pledge to transferring ownership of the company to the Holdfast Collective, sounds like something Patagonia would do. That clarity becomes a filter for AI. Brands without that foundation tend to default to optimization as a strategy in itself, leaning on AI to refine what already exists rather than questioning whether it should exist at all.
The mirror problem: Marketers get more from AI by paying attention to how the tools affect behavior. The systems are designed to be helpful, returning polished copy or images in seconds. That smoothness can be disarming. As teams get comfortable with the speed of AI output, they can lose the habit of interrogating it. "If your AI partner is hyping you up, run. It's like dating a yes-man. If you ask them how you look in these jeans, they're going to say you look great. You don't want that partner," Bakanoff says. The instinct to accept what AI produces at face value is natural, but it is also where the work starts to flatten. Teams that treat AI as a collaborator rather than a mirror get sharper results because they are still thinking.
AI becomes genuinely useful when it stops acting as an echo chamber. It works best when it prompts the moments that actually improve the work, the reactions that no amount of production can replicate. "We're getting mesmerized, and we're not checking our work anymore. You always have to inspect what you expect. The opportunity is to bring that human quality back to the forefront; the shift isn’t about using AI more. It’s about using it differently."
