The Reality of Enterprise AI
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.
Leaders from Anthropic, Gamma, and Clay are examining the two sides of AI deployment: the patterns Anthropic observes across enterprise implementations and the firsthand experience of founders building AI products that gain traction.
Image Credits:TechCrunch
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What happens after the AI launch?
Most conversations about enterprise AI focus on initial capabilities. Anthropic Head of Applied AI Cat de Jong focuses on the post-deployment phase, analyzing where implementations succeed, where they stall, and what separates organizations extracting real value from those still running pilots.
De Jong works directly with enterprises putting Claude into critical workflows, offering a firsthand look at the transition from experimentation into production.
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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How do you get people to use what you’ve built?
Gamma co-founder and CEO Grant Lee provides the perspective of a company building an AI product and driving user adoption. Gamma has evolved its AI-powered platform from a presentation tool into a broader visual communication suite.
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.
This adoption provides insight into the core challenges of AI product development: ensuring utility, adapting to unexpected user behaviors, and solving actual customer problems.
Building an AI product is one thing. Getting people to make it part of how they work is another. Secure your pass to Disrupt and hear what Gamma has learned along the way.
What happens when AI becomes part of the workflow?
Clay co-founder and CEO Kareem Amin brings the perspective of a founder building AI into GTM (go-to-market) workflows. Clay provides infrastructure for data aggregation and agentic workflows.
Clay was one of the launch applications integrated into Claude, placing Amin at the center of discussions regarding where AI is genuinely useful and how it fits into existing enterprise systems.
That puts Amin close to the questions this session will explore: where AI is genuinely useful, how it fits into existing workflows, and what happens once customers start depending on it.
Together, these leaders compare Anthropic’s broad view of enterprise patterns with the specific, practical experiences of founders pressure-testing AI products in real-world environments.
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Beyond the demo
While an impressive demo can showcase potential, the true test occurs when customers rely on the technology. The combination of Anthropic’s enterprise-wide data and the founder-led perspectives of Gamma and Clay highlights the requirements for building sustainable AI products.
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Image Credits:Eric Slomonson, The Photo Group
