Topic
Moving AI from experiments to everyday operating practice inside large organisations: use cases, adoption, and the leadership work in between.
The hard part of enterprise AI is rarely the model. It’s the operating model around it: how teams pick use cases, build trust, and turn one-off experiments into durable practice.
The posts below collect that thinking, from setting up a use-case portfolio to why adoption so often stalls in the middle of the organisation.
Most enterprises start their AI journey as scattered, unmanaged experiments. That's not a failure – it's the raw material. The hard part is building a bridge from local chaos to shared capability without killing the learning in the process.
Running Hermes Agent on a VPS gives me one assistant with my tools, files, context, recurring workflows, and review points across Telegram and email.
The agent demos browse the web, write the code, and seemingly do your job. The agents that actually survive inside an enterprise are far less impressive – and that's exactly why they work. A case for starting boring.
Stop building AI prototypes that never make it to production. Discover how shifting from isolated tech demos to real product ownership turns impressive showcases into reliable workflows that actually drive business value.
Moving past basic chat interfaces to custom enterprise AI workflows requires scaling beyond simple intuition. This article explores why manual reviews fail to catch critical regressions when updating prompts, models, or agent behaviors. Learn how to treat your AI setups like core software and build operational confidence through automated testing.