To overcome resistance from non-developers (in finance, HR, etc.), frame AI agents not as a replacement but as a tool that grants immediate access to new capabilities. For example, providing an agent that can instantly pull data reports that previously required a formal request to another team demonstrates clear, undeniable value.
To scale AI adoption in a large engineering org, bypass widespread resistance by applying the '1-9-90' community rule. Focus on empowering the top 1% of 'creators' to build AI knowledge into the systems. Their work will enable the 9% of 'tinkerers' and ultimately serve the 90% of 'consumers' without requiring everyone to become an expert.
A more effective way to increase developer velocity with AI is to have champion engineers embed knowledge directly into the systems. This includes creating context engineering techniques, `agents.md` files, and agent skills within the repo itself. This way, any agent pointed at the repo benefits, rather than relying on every individual developer's expertise.
Companies are hesitant to build products on a new protocol if it's controlled by a single competitor, fearing the roadmap will only serve that company's goals. Placing critical standards like the Model Context Protocol (MCP) in a neutral foundation gives the entire ecosystem the confidence to build upon it, accelerating adoption.
In foundations like the Agentic AI Foundation, direct competitors like Stripe and PayPal collaborate effectively on standards for agentic commerce. They work together to define the foundational 'rules of the game' and create the 'game board.' Once that neutral groundwork is established, they can then compete fairly on top of it.
Different global regions have unique technology usage patterns that must inform standards. For instance, China's mobile-first environment creates a much stronger immediate need for agent-to-agent (A2A) protocols that allow different apps to interact. This highlights why diverse global perspectives are required for robust, universally applicable standards.
To keep pace with AI's rapid development, a new tech foundation cannot operate on a traditional, slow timeline. The Agentic AI Foundation model works because its working group members are the industry's key innovators, and participation is part of their core job. This shared business incentive ensures they move quickly to establish standards they can build on.