What mid-market IT teams wish they knew before deploying AI agents

AI agents are transitioning from an experimental phase to being integral in daily operations, especially for mid-market IT teams looking to enhance productivity without increasing headcount. However, the deployment of these agents brings about unique governance challenges as they can perform actions across multiple systems, necessitating new governance structures beyond traditional human intervention frameworks. Early adopters often underestimate the speed at which AI agents become critical infrastructure, leading to expanded access and blurred accountability, with governance gaps becoming apparent only after deployment. The primary regret among teams is not establishing clear operational boundaries and monitoring protocols from the outset, as retroactive governance can be disruptive and inconsistent. It's crucial to embed governance into workflows early, ensuring that access is explicit, environments are separate, and behaviors are observable. Platforms like Upsun offer foundational support to embed governance in delivery workflows, aiding in the creation of predictable environments and clear boundaries without slowing down innovation. Ultimately, successful AI adoption hinges on deliberate governance integration, ensuring that speed and safety are not seen as trade-offs but as complementary aspects of a well-structured AI deployment strategy.