When ChatGPT launched in November 2022, I became curious about how far I could stretch it. I asked myself a specific question: Could this become my innovation copilot?
I spent the following days teaching ChatGPT my innovation methodologies - Jobs To Be Done for understanding customer needs, Systematic Inventive Thinking for generating ideas, TRIZ for technical problem solving, Creative Advertising Templates for go-to-market. Within a few days, I had turned these experiments into a Udemy course that's now helped over a thousand learners build their own "ChatGPT Innovation Machine."
In some tasks, that "old" GPT 3.5 did surprisingly well. In others, it still struggled. But even in the ones it excelled, AI didn't replace the innovation process. It simplified it. Tasks that previously required extensive time during workshops could now happen in focused conversations. The methodology remained the same - but the friction disappeared. And I could also share my best practices with others.
This experience connects directly to Amazon's "Invent and Simplify" leadership principle: "Leaders expect and require innovation and invention from their teams and always find ways to simplify. They are externally aware, look for new ideas from everywhere, and are not limited by 'not invented here.' As we do new things, we accept that we may be misunderstood for long periods of time."
The principle contains a crucial insight that many miss: "Simplify" isn't an afterthought to "Invent" - it's often the harder and more important part. Amazon experts note that true simplification requires questioning whether something should exist at all before optimizing how it works. As Peter Drucker put it: "Nothing is less productive than to make more efficient what should not be done at all."
Ethan Mollick, Wharton professor and author of Co-Intelligence, frames AI's innovation potential this way: "AI ideates better than most people... at least for starting ideas, it does a better job than most of us do." His research showed that in head-to-head comparisons, AI-generated ideas often outperformed human-generated ones on quality and purchase intent.
But here's the nuance Mollick emphasizes: AI excels at recombination - connecting concepts in unexpected ways. It acts as what he calls a "connection machine," finding relationships between ideas that humans might miss. This doesn't replace human innovation - it simplifies the path to good starting points.
For business leaders, the implication is clear. The bottleneck in innovation often isn't generating ideas - it's the friction in the process: scheduling workshops, coordinating experts, iterating through drafts. AI can compress this dramatically. What used to take weeks can happen in hours.
The principle also emphasizes being "externally aware" and not being limited by "not invented here." Mollick's advice aligns perfectly: "Use AI for everything you possibly can... The only way to figure out what it's good for is disciplined experimentation."
Organizations that dismiss AI because it wasn't developed internally, or wait for perfect use cases before experimenting, miss the point. The principle demands that leaders look everywhere for ideas - including from tools that didn't exist two years ago.
The final line of the principle acknowledges that innovation often means being "misunderstood for long periods of time." When I started building AI-powered innovation workflows in late 2022, many colleagues were skeptical. Today, those same approaches are becoming standard practice.
The organizations that embrace AI as a simplification engine for innovation - not as a replacement for human creativity, but as a way to remove friction from the creative process - will find themselves ahead when others finally catch up.
I recently recorded an episode with Dean Guida on the AI & Data Driven Leadership podcast, where we discussed, among other things, the importance of making innovation simple, and the principle of Invent and Simplify. Listen below.