AI That People Actually Use: Zoher Karu on Personalization, Trust, and Building AI at Scale

<p>Everyone is talking about AI. Far fewer people have spent decades actually building AI and data systems inside some of the world’s largest organizations.</p><p>In this episode of Signal &amp; Noise, Brett House and Rio Longacre sit down with Zoher Karu, Head of AI at Taelor, to separate AI hype from what it actually takes to create measurable business value.</p><p>Zohar brings an unusually broad perspective. His career has taken him through McKinsey, Sears, Citi, eBay, Blue Shield of California, and now Taelor—an AI-powered men’s clothing rental service attempting to combine machine intelligence with human styling expertise. </p><p>Across those very different businesses, Zohar argues that the same lesson keeps resurfacing: the technology is rarely the hardest part. </p><p>The conversation starts with one of enterprise AI’s least glamorous truths: bad data doesn’t disappear because you put an LLM on top of it. As Zoher puts it, AI can simply give you “bad answers faster.” Data governance, business processes, organizational knowledge, and change management remain foundational.</p><p>From there, the discussion gets practical. Zoher explains how Taelor is attempting to teach machines something surprisingly difficult: taste. Matching clothes to a person requires understanding not just size and style, but weather, occasion, context, individual preferences, previous feedback—and even whether two individually appropriate pieces of clothing actually work together. That becomes a window into a much bigger conversation about the future of personalization. </p><p>Generative AI dramatically expands the amount of customer context businesses can process, how quickly they can respond to new signals, and the number of individualized experiences they can create. Instead of choosing among three versions of an email, brands could theoretically generate an almost infinite number of variations for individual customers.</p><p>The discussion also tackles the uncomfortable economics of enterprise AI. Companies are spending enormous amounts on models, infrastructure and tokens—but are they actually redesigning the business processes required to capture the ROI? </p><p>Zoher argues that automating pieces of an existing workflow may deliver incremental efficiency, while the much larger opportunity comes from asking whether that workflow should exist at all. </p><p>Finally, the conversation explores what may become one of the most important issues in enterprise AI: context. Agents can access data, but data alone doesn't contain all the rules, judgment and institutional knowledge humans use to make decisions. Capturing that tacit business knowledge—and making it available to AI systems—could become a critical source of competitive advantage and intellectual property.In this episode:</p><p>* Why dirty data can derail even sophisticated AI</p><p>* Why AI transformation is really organizational transformation</p><p>* The gap between AI spending and measurable ROI</p><p>* Why simply automating existing processes isn't enough</p><p>* How AI is changing personalization and recommendation systems</p><p>* How Taelor combines human stylists with machine intelligence</p><p>* Why context and business knowledge matter as much as models</p><p>* Whether AI is actually eliminating jobs or simply changing them</p><p>* Why change management may be the biggest barrier to enterprise AI</p><p>* The continuing importance of human judgment in increasingly autonomous systems</p><p><br /></p><p>The companies that win the AI race may not be the ones with the most sophisticated models. They may simply be the ones that figure out how to build AI that people actually use. </p><p>#ArtificialIntelligence #AI #EnterpriseAI #GenerativeAI #AgenticAI #Personalization #CustomerExperience #DataStrategy #DataGovernance #MachineLearning #DigitalTransformation #AITransformation #ChangeManagement #MarTech #RecommendationEngines #FutureOfWork #SignalAndNoise #Podcast</p>