Security and IT teams are under pressure to adopt AI, but many are seeing the opposite of what was promised.
Tools that demo well don’t hold up in real workflows. Complexity increases. Trust breaks down. And instead of reducing workload, AI can introduce new risks and oversight burdens.
This guide breaks down why AI adoption fails in practice and gives teams a clearer path forward, from evaluation to implementation, with humans in the loop.
What’s inside:
A practical framework for evaluating tools beyond the demo
A step-by-step approach to selecting tools that hold up in production
Key questions to pressure-test vendors before committing
Human-in-the-loop best practices for safe, scalable AI
Real examples of how teams at Udemy, Canva, Jamf, and Vimeo are using AI to reduce workload and improve consistency