The promise of a single, all-knowing AI assistant is seductive, but the reality is proving much messier.
Many organisations are discovering that general-purpose AI tools like Copilot, while sophisticated, often fall short in the real world. Why? Because they’re designed to do too much for too many, and that’s a recipe for inconsistency and frustration.
Teams report that these “AI jacks of all trades” are patchy at best. Sometimes helpful, often inaccurate, and rarely trusted for critical decisions. That’s because these tools are wired to be everyone’s assistant, rather than solving a clear, focused problem.
People don’t crave more features. They crave reliability, clarity, and trust. We’re hardwired to favour the tool that does one thing brilliantly over the one that promises everything and delivers mediocrity.
The alternative? Purpose-built Retrieval-Augmented Generation (RAG) AI tools.
These are designed for a specific domain, connected to your trusted internal data, and optimised for accuracy and transparency.
They don’t try to do everything. They just do one thing well, radically improving decision-making and freeing your people to focus on what matters.
The lesson: Don’t fall for the myth of the one-size-fits-all AI. Your teams need the right tool for the right job. Simple, focused, and reliable.
Sometimes, the best innovation not a full English breakfast robot, it’s a better toaster that never burns your toast.
