OpenAI has published case studies showing how three companies—Basis, Clay, and Exa Labs—have restructured workflows around AI agents to improve core business functions. Basis used AI to streamline customer onboarding; Clay integrated AI into account management and customer communication; Exa Labs built AI-driven developer integrations. In each case, the companies redesigned processes rather than simply bolting AI onto existing workflows.

The pattern emerging is that AI's highest-value applications come from rethinking workflows from first principles, not automating existing processes. Companies achieving measurable improvements have generally made explicit decisions about which human roles shift toward oversight and judgment, and which tasks become purely automated.

What This Means for Your Business

If your organization has deployed AI pilots that underperformed, the lesson here is likely that you're optimizing the wrong process. Start by identifying high-friction, high-cost workflows, then ask whether restructuring the workflow around AI capabilities (rather than incrementally improving the existing process) creates step-change efficiency gains. Pilot teams should include both operational staff and stakeholders responsible for downstream processes.