Instead of each employee using their own separate AI, the more effective model is a central, multiplayer AI that acts as a shared 'company brain' or teammate. This approach, which Motion is building with its 'Runneth' agent, prevents duplicated efforts and builds a shared company-wide context.

Simply giving every employee access to ChatGPT or Claude backfires. It creates isolated workflows, duplicated effort, and internal FOMO, directly contradicting the goal of increased efficiency. This highlights the need for a shared AI infrastructure.

The overhead of maintaining personal AI agents is too high for most employees. The successful model, seen at Shopify and Ramp, is a centralized, company-wide "super-agent" managed by a dedicated team, ensuring it remains reliable and useful for everyone.

Every initially gave each employee a personal AI agent but found this created a massive maintenance burden and knowledge silos. They shifted to shared agents focused on team functions (e.g., analytics). This centralizes maintenance, improves continuity when employees leave, and scales benefits across the entire team.

Sendoso found that creating a central AI team relieved employees of the pressure to self-learn AI development. This stopped "usage theater," where people felt compelled to constantly use AI to seem productive, and instead allowed them to collaborate as subject matter experts.

To manage the complexity and risk of AI agents, companies should adopt a centralized model. Rather than allowing individuals to build agents freely, a dedicated internal team should build, govern, and distribute a suite of approved agents to departments, ensuring consistency and control.

HubSpot's AI team progressed from individual experimentation to cross-functional pods, and finally to a centralized unit under one leader. This structural change eliminated competing priorities and coordination costs, allowing the team to commit to bigger, bolder goals and execute at a higher pace.

Initial success with AI workflows led to hundreds of unmanageable, invisible automations. Sendoso pivoted to creating role-based AI "digital colleagues" with org charts and managers, making AI implementation transparent and manageable for the human team.

Encouraging unmanaged creation of AI agents—or "agent sprawl"—results in conflicting outputs and fragmented customer messaging. With different agents accessing different data sources, companies get inconsistent answers to simple questions like company ARR, undermining strategic alignment.