Airbyte CEO Outlines Strategy to Fix Enterprise AI Failures
Michel Tricot argues that fragmented data, not model capability, prevents AI agents from delivering meaningful bottom-line gains for most organizations.
The failure of agentic AI in enterprise production stems from fragmented data and unreliable integrations rather than a lack of model capability, according to Michel Tricot, CEO of Airbyte. While McKinsey & Company research indicates that 88 percent of organizations are experimenting with AI, 81 percent report no meaningful bottom-line gains.
Tricot asserts that AI agents often fail because they lack reliable, contextualized access to internal business data. This perspective aligns with a report from Anthropic, which identified organizational readiness and data accessibility as the primary barriers to achieving enterprise AI value.
To transition AI agents from demo mode to production, Tricot recommends a three-pronged strategy. This approach includes restructuring internal data for AI using unified gateways like the Model Context Protocol, designing granular permissions for agent actions to ensure safety, and establishing governance based on initial operational visibility.
