Expert Insights

James LePage advocates for a distributed approach to AI for software teams, opposed to a concentrated, top-down decree of AI tool use. The focus shifts away from absolute central control to allowing team members to test, develop, and share what tools work best for their processes.

He shares lessons from his hands-on experience with the adoption of distributed A. Instead of forcing a centralized platform, he shows how enabling individuals to experiment freely sparks better outcomes and shared progress.

Hear James explain:

  • Why decentralizing AI tools creates a natural selection of best practices.
  • How a culture of communication and knowledge sharing accelerates adoption.
  • Why empowering individuals to build their own systems beats internal top-down solutions.
  • How ongoing experimentation—not one grand solution—drives meaningful progress.
  • Why treating AI as an enabler, not a central authority, changes how teams unlock value.
  • How embedding AI into existing workflows leads to real, lasting impact.

Quote

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Organically that happens at automatic because everybody chooses their own tools. They already have their own processes... they can choose to infuse AI into those processes should it make sense. When you come to automatic, you're not given kind of this pre-collection of tools that you must use. Everybody here can expense whatever tool they want, so they choose what works for them... everybody kind of builds their own systems that they truly are effective with, and then they share it. If you look at a company that's existed for 20 years remote, you're going to have systems where you share things... When I came into automatic, I was shocked at how much written communication existed.quotation-marks icon
James LePage ,
Director of Engineering AI, Automattic

THE NEW DEFAULT angle

Here's how to infuse AI into your processes effectively:

    • Foster Distributed AI Adoption: Avoid imposing a strict, centralized AI platform upon the team. Instead, allow members to run their own parallel experiments with AI tools that integrate with their workflows.

    • Leverage Your Communication Infrastructure: Share the results of AI experiments and the workflows that worked best across the team. Ensure that written communication channels are active and frequently used.

    • Focus on Enablement: Instead of directing massive resources towards building an all-purpose AI system, allow team members to leverage existing technologies and find ways to incorporate AI tools that solve specific problems in their workflows.

    • Maximize Productive Chaos: Allow team members to choose their AI tools, leading to a variety of AI stacks within the team. This diversity in tool choice can lead to a more organic and robust selection, where worthwhile practices bubble up and failures fall away.

    • Ensure Training and Guidelines: While autonomy is important, provide team members with essential guidelines for using AI, recommended tools, and potential stacks.

    • Integrate AI into Existing Workflows: The appeal of AI comes not from overhauling systems, but from integrating it into tried and tested workflows. Identify processes where AI tools can offer tangible improvements.

    • Foster a Culture of AI Experimentation: Keep the focus on continuous exploration of how AI can be used. Rather than looking at it as a one-time problem solver, regard AI as an evolving tool that can be infinitely tested and trialed to find the best solutions.