How AI-native companies turn workflows into operating capability

OpenAI's Enterprise Signals report highlights an expanding gap in AI usage speed and depth, noting that top-tier firms now generate 8.3 times as many output tokens per active user compared to typical firms. This widening metric reflects a broader operating shift where leading organizations connect AI agents to company context and tools to handle substantive, repeatable work. To illustrate how enterprises can apply these patterns, OpenAI points to three startups integrating AI agents into core operations: Basis, Clay, and Exa Labs. Basis transformed its accounting onboarding process by turning a proven sequence into a reusable skill handled by Codex, cutting first-day onboarding from two hours to 30 minutes. Clay implemented a system where a persistent workspace and dedicated subagents update scattered deal context overnight, allowing a coordinating agent to produce a daily priority list that saves a go-to-market engineer an hour of inbox triage each night. Exa Labs deployed Codex to monitor web search integration opportunities, gather context, create pull requests, and run tests before human review. These examples demonstrate that successful AI implementation requires consistent structures, persistent context, shared evidence, and human judgment at decision points. OpenAI advises enterprise leaders to choose consequential value surfaces, define clear outcomes and metrics, write precise agent job descriptions, build supportive human systems, and make experimentation visible and reusable by packaging successful practices into skills, Plugins, or shared workspaces.