For Glean, an AI-powered work assistant that connects to organizations’ business applications, that means connecting to enterprise knowledge systems while preserving permissions and governance. For Cognition, maker of the AI coding agent Devin, it means creating infrastructure that allows agents to understand code bases, validate work and proactively assist development teams. For Decagon, which builds AI-powered customer service agents, the challenge is delivering accurate, low-latency interactions at enterprise scale.
“Even if you have 99% performance, that remaining 1% at enterprise scale is 10,000 times a day” that the AI may be hallucinating, said Ashwin Sreenivas, Decagon’s co-founder and president.
As a result, companies are investing heavily in safeguards, testing frameworks and specialized models designed for specific tasks, rather than relying solely on a single frontier model.
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Why AI Success Depends on More Than a Single Model
One recurring theme throughout the discussion was the growing importance of model orchestration.
Rather than standardizing on a single foundation model provider, panelists described environments where dozens of models may be used for different tasks, balancing performance, latency and cost.
Jeff Wang, president of new enterprise at Cognition, said his company evaluates models continuously and routes workloads based on performance and economics. Token costs, he noted, have become one of the most common topics in executive conversations.
Similarly, Glean supports multiple frontier and open-source models while automatically selecting the most appropriate option for a given task. Decagon takes an even more specialized approach, Sreenivas said, using teams of smaller models that each perform a specific function, such as gathering information, generating responses or detecting errors.
The result is an AI stack that increasingly resembles a coordinated system of agents rather than a single monolithic model.
That complexity extends beyond customer-facing products. Panelists said their own organizations are aggressively using AI internally and measuring usage patterns to identify high-value applications.
Sreenivas described how Decagon analyzes internal AI consumption and studies how top users are achieving productivity gains. In many cases, employees are using AI to create highly personalized customer briefings, automate administrative work and streamline customer engagement processes.
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