Insights
Songbird's notes on choosing useful AI projects, evaluating tools, setting sensible guardrails, and building systems your team can actually run.
The notes
Not every task should be automated. Some work should run on its own, some is better with a person in the loop, and some should stay fully human. A simple way to sort them.
For teams asking: When should AI do the work, help with the work, or stay out of it?
AI pilots stall when they start with a tool instead of a real problem. The fix is to start from the work, pick one stuck spot, and make the first project small enough to finish.
For teams asking: Why do AI pilots fizzle?
A good AI demo is not the same as a good fit. Judge vendors on fit, data, risk, cost, integration, training, review, and what happens after the pilot, not on how the demo looks.
For teams asking: How should a small or growing business judge AI tools and vendors?
A small-team AI policy does not need to be a binder. It needs to answer a few practical questions: approved tools, data boundaries, review, customer-facing use, sensitive work, and who owns it.
For teams asking: What should an AI policy include if we are not a large enterprise?
Bring it to a short discovery call.