What Happens After the AI Launch?
An AI demo can look brilliant in five minutes. Then real customers start using the product. They push it into workflows you never anticipated. They expect it to work reliably. And they quickly find out whether it solves a problem big enough to become part of how they work β or whether it becomes just another AI experiment they tried and abandoned.
At TechCrunch Disrupt 2026, leaders from Anthropic, Gamma, and Clay will take the AI Stage for a session titled βWhat Anthropic Sees When Enterprises Actually Deploy Claude.β
The conversation will bring together two sides of AI deployment: the patterns Anthropic observes across enterprise implementations of its Claude models, and the firsthand experience of founders building AI products that people actually use.
From Demo to Daily Workflow
Most conversations about enterprise AI focus on what companies could do with the technology. Far fewer focus on what happens once deployment is real: the integration headaches, the reliability expectations, the cost and latency trade-offs, and the organizational change required to make AI stick.
That gap between potential and production is where the hard lessons live. In 2026, enterprises are no longer asking whether to adopt AI. They are asking how to move from a patchwork of pilots to systems that run in production, handle sensitive data, and deliver measurable ROI.
This session will explore what separates the AI projects that scale from those that stall β and what that means for product teams, platform vendors, and enterprise buyers alike.
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