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Anthropic, Gamma, and Clay share what happens when enterprises actually deploy AI at TechCrunch Disrupt 2026
An AI demo can look brilliant in five minutes. Then customers start using the product. They push it into workflows you didn’t anticipate. They expect it to work reliably. And they quickly find out whether it solves a big enough problem to become part of how they work — or becomes another AI experiment they tried and abandoned.
At TechCrunch Disrupt 2026, leaders from Anthropic, Gamma, and Clay will come together on the AI Stage for “What Anthropic Sees When Enterprises Actually Deploy Claude.” The conversation will bring two sides of AI deployment: the patterns Anthropic sees across enterprise implementations and the firsthand experience of founders building AI products people actually use.
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Most conversations about enterprise AI focus on what companies could do with it. Anthropic Head of Applied AI Cat de Jong starts somewhere more interesting: what happens after deployment.
De Jong works directly with enterprises putting Claude into critical workflows. At Disrupt, she’ll explore where deployments succeed, where they stall, and what separates organizations extracting real value from those still running pilots 18 months later.
If you’re selling AI into the enterprise, those patterns matter. De Jong’s experience offers a firsthand look at what changes when AI moves from experimentation into critical workflows and why some organizations get to production while others don’t.
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Anthropic can see patterns across enterprise deployments. Gamma Co-Founder and CEO Grant Lee brings another perspective to the conversation: what it looks like from inside a company building an AI product and getting people to actually use it.
Gamma has grown its AI-powered platform from an alternative to traditional presentation software into a broader visual communication tool. TechCrunch reported in March that the company was approaching 100 million users as it expanded its AI tools into marketing assets and other forms of visual content.
That kind of adoption gives Lee a useful vantage point on the questions at the center of this session: What makes an AI product useful enough for customers to keep coming back? What changes once people start using it in ways you didn’t anticipate? And how do you turn powerful AI capabilities into a product that solves a problem people actually have?