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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.

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Encourage broad AI experimentation and learning by creating multiple channels for sharing. Wrike uses dedicated Slack channels for quick updates, carves out time in monthly all-hands meetings for teams to showcase their AI wins, and maintains a reference library of successful AI-enabled workflows for others to learn from and replicate.

To change the minds of AI-skeptical employees, formal training is less effective than peer-to-peer influence. Empower internal, non-technical AI champions to mentor their colleagues. Seeing a peer with a similar skillset succeed demystifies the technology and provides relatable motivation for adoption.

A key driver for AI prototyping adoption at Atlassian was design leadership actively using the new tools to build and share their own prototypes in reviews. Seeing leaders, including skip-level managers, demonstrate the tools' value created powerful top-down social proof that encouraged individual contributors to engage.

To get teams to embrace AI, leaders should ditch generic mandates like "use more AI." Instead, focus on specific business transformations and highlight the customer value they create. Using company-wide forums for "show and tell" sessions where teams demonstrate unarguable successes makes adoption organic and outcome-driven, not a top-down chore.

By building internal AI agents directly into Slack, their usage becomes public and visible. This visibility is key for driving adoption; seeing a bot turn a message into a PR creates a "holy shit" moment that sparks curiosity and makes others want to use the tool, creating a natural viral effect.

To combat the isolating nature of AI work and share learnings, have AI agents operate in public Slack channels. This allows team members to passively observe how others prompt the AI, revealing new use cases and techniques in a natural, collaborative environment.

Iron Horse replaced typical business updates at the start of leadership meetings with a mandatory "show-and-tell" where each leader demonstrates what they've built with AI. This peer pressure fosters cross-functional inspiration, proving more effective than top-down mandates for driving company-wide adoption.

To drive adoption of AI agents, don't force users into a new application. Instead, integrate the agent directly into their existing collaboration tools like Slack. This approach reduces friction and makes the agent feel like a natural part of the team, leading to higher engagement and user satisfaction.

To overcome skepticism in a large engineering organization, a leader must have deep conviction and actively use AI tools themselves. They must demonstrate practical value by solving real problems and automating tedious work, rather than just mandating usage from on high.

An employee is 5.6 times more likely to adopt AI if a cross-functional teammate uses it—a far greater influence than leaders (2.4x) or direct teammates (3.2x). This is because cross-functional users build tools that solve the messy, real-world coordination problems that plague organizations.