There's a moment that repeats itself in engineering orgs of every size. A page that felt instant in development crawls in production. Someone opens the profiler, finds a slow query or a janky scroll, and ships a fix. A few weeks later, a different page does the same thing. Then another. The team starts to talk about performance the way you'd talk about weather — something that happens to you, that you can never quite get ahead of.

A new model dropped this morning. By the time you read this, there is probably another one. Somewhere in your engineering org, a smart, well-intentioned developer has already swapped it into their workflow because the launch thread looked impressive and the demo was undeniable.

That is the problem.

AI workflow automation is the use of artificial intelligence — including large language models and autonomous agents — to run multi-step business processes that adapt to context, handle unstructured inputs, and correct their own errors. Unlike rules-based automation, which executes fixed scripts, it reasons toward a goal and adjusts when conditions change.

Most small and mid-sized businesses move to the cloud expecting one thing above all else: someone else will handle the security. You sign up for Microsoft 365, migrate a few servers to Azure or AWS, adopt a handful of SaaS tools, and the mental model quietly becomes the provider is a giant tech company, so my data must be safe. It is the single most expensive assumption we see at Facet MSP, and it is wrong in a specific, correctable way.