In the mid‑1990s, “Business Process Re‑engineering” (BPR) swept through boardrooms. The promise was simple but radical: tear up legacy workflows and rebuild them around what information technology could suddenly make possible. ERP suites, networked PCs, and early workflow tools weren’t just automating tasks - they were reshaping how companies thought about work.

Fast‑forward thirty years. Generative AI has arrived, and history is rhyming loudly. Once again, incremental automation won’t cut it. Chatbots that answer faster or spreadsheets that fill themselves are useful, but they don’t unlock the step‑change in value that today’s technology can deliver. To capture that upside, leaders need the same audacity that BPR demanded: a willingness to question every assumption about how value is created.

But there’s a crucial difference: AI systems learn after deployment. Where BPR was often a one‑time surgical strike, AI demands a living, breathing process that continually refines itself through feedback. The organisations that will succeed with AI are those prepared to rethink value streams end‑to‑end - and to treat that redesign as an ongoing, learning‑driven discipline.

Over the past year, I’ve been developing AIchemy™ - a structured yet adaptive approach that positions AI as a ‘supernatural ally,’ empowering transformative leadership. AIchemy™ synthesises insights from neuroscience, developmental psychology, and systems thinking to guide leaders through the mindset shifts required for AI‑centred redesign.

Instead of prescribing another four‑box framework or a one‑off workshop, AIchemy™ provides a practical journey that helps teams:

  • see where AI truly changes the game (and where it doesn’t)
  • co‑create new workflows that balance human judgement with machine intelligence
  • embed continuous feedback so the process, the people, and the models evolve together

If you, like me, lived through the BPR era, dust off that muscle memory - but update it. Swap “radical, one‑time surgery” for radical, continuous learning. Measure not just time‑to‑market but learning velocity. And recognise that the hardest work isn’t writing code; it’s choreographing people, processes, and values so they can flourish alongside AI.

The good news? We’ve done something like this before. The better news? We now have tools - and mindsets - capable of taking us even further.