To spread AI use beyond engineering, use a 'pull' rather than 'push' strategy. By having engineers interact with AI agents in public forums like Slack, other departments organically see the benefits and processes, overcoming skepticism and encouraging participation without a top-down mandate.

Despite the hype, advanced AI tools like autonomous agents won't reach scale in enterprises until late 2027, lagging startup adoption by 2-3 years. Even non-technical departments at major tech companies are still focused on basic chatbot usage, highlighting a significant gap in adoption speeds.

OpenAI's own AI adoption strategy involves creating small, dedicated teams for each business vertical (e.g., finance, sales). These teams deeply understand the domain to build custom AI skills and UIs. Crucially, they maintain a human-in-the-loop to be accountable for all final decisions, like approving code merges.

Engineering is the ideal starting point for 'self-driving' initiatives not just due to tech-savviness, but because software development has clear, verifiable outcomes (e.g., a bug fix works or it doesn't). This contrasts with subjective domains like marketing, making it a fertile ground for initial experiments.

Linear's data reveals that non-engineering roles, particularly PMs, show the largest increase in using agentic AI features. AI empowers them to independently perform tasks like data analysis or competitive research that they previously had to delegate, increasing their autonomy and speed.

At Block, the most surprising impact of AI hasn't been on engineers, but on non-technical staff. Teams like enterprise risk management now use AI agents to build their own software tools, compressing weeks of work into hours and bypassing the need to wait for internal engineering teams.

Non-developer teams like support and HR are adopting technical tools because their workflows now involve AI agents. Since building and maintaining these agents requires engineering input, the engineers' preferred tools get pulled into these other departments, blurring organizational lines.

A key driver of internal AI adoption is its visibility. When employees see AI agents being used effectively in public group chats like Slack, it creates a contagious effect, demonstrating use cases and encouraging others to experiment and adopt the tools themselves.

Contrary to the belief that PMs are the earliest tech adopters, go-to-market functions (sales, marketing, support) are leading agent adoption. Their work involves frequently recurring, pattern-based tasks that are a perfect fit for automation, putting them ahead of the curve.