Here’s what we covered in Sunday’s edition…
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There’s no question, AI is the elephant in just about every room today. We’ve been pretty quiet about it so far. But not today. Today, we’ve been inspired to discuss the impact of AI on the world of innovation.
Dig in to learn what you should be focusing in and how to integrate AI in a manner than can accelerate your innovation efforts. Just be aware, the machine won’t innovate for you.
Here’s what you’ll find:
This Week’s Article: Fine. Let's talk AI and Innovation.
Share This: How is AI changing innovation?
Case Study: How Mondelez Uses AI to Accelerate Product Innovation
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Why you should read it ☝️
Greg Isenberg’s post captures a shift every innovation leader needs to understand. As AI drives down the cost of execution, the real advantage moves to how fast you can test, learn, and iterate. For corporate teams still optimizing delivery cycles, this post is a clear signal: building is no longer the bottleneck — discovery is. Read it as a prompt to reevaluate where your innovation process creates actual value.
Why you should read it ☝️
This piece shows exactly how a global incumbent is using AI to speed up experimentation and discovery without waiting for a massive transformation effort. Mondelez isn’t just using AI for efficiency; they’re applying it to the front end of innovation to compress product development cycles and reduce the cost of learning.
It’s a clear example of how legacy organizations can adopt AI as an enabler for faster iteration and smarter decision-making. If you’re trying to modernize innovation inside a large company, this is a blueprint worth studying.
Why you should read it ☝️
This article offers a clear look at how leading consumer brands are embedding AI across their product development pipelines, not as a side project, but as a core capability. It’s a valuable reference for how AI can reduce time-to-market, accelerate experimentation, and shift innovation from planning to doing.
Companies like L’Oréal and PepsiCo are using AI to compress cycles from concept to shelf, making the case for why innovation leaders must rethink traditional development timelines. If you’re exploring how to make experimentation faster and more scalable inside your org, this is worth your time.
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