Which of your business models finance the rest? Which are growing into your next core? Which moonshots are you actually testing? Reply with your answer or 🤔 if you're not sure. Full Amazon breakdown: 🇬🇧 https://t.co/jSEMRDzpIu 🇳🇱

You don't need Amazon's scale to use this approach. You just need to know: → What you have → Where it sits in the lifecycle → How to manage it consciously Three questions for you 👇

What most organizations miss: You can't innovate while your core is broken. Amazon fixes that first, then experiments with discipline. They organize 30+ models across retail, cloud, media, health, and moonshots. Not by hoping. By mapping.

The breakdown: → Core business (AWS, https://t.co/67DpeIOc3g, @PrimeVideo ) generates 30%+ margins → Those margins fund $10B+ moonshots like Kuiper satellites → 5 value spaces prevent chaos → Different lifecycle phases = different rules, different metrics

They don't treat @awscloud like they treat @AmazonFresh. They don't measure satellite experiments with the same KPIs as their retail cash cow. They manage phases, not projects.

Want to see this process in action? Our AI Opportunity Scan uses the same design thinking approach: → We ask questions about your actual business challenge → Help you figure out if AI is the right tool More info here: 🇬🇧 https://t.co/UMtmymHfFv 🇳🇱

This is what design thinking does: It forces you to understand the problem before jumping to solutions. Same methodology behind the Business Model Canvas. Same process we've used for 15 years. Works just as well for AI.

A chatbot wouldn't fix that. It would make it worse. Faster wrong answers. What they actually needed: → Better data integration → A knowledge base agents could use → Then, maybe, some automation We told them: "Don't build the chatbot yet."

Week 1: Interview 10 customer service agents and 15 customers. Discovery: Customers weren't frustrated by response time. They were frustrated because agents didn't have access to the right information. The real issue? Internal data silos.

Last month, a company wanted AI for customer service automation. Clear brief: "We need a chatbot that can handle 80% of inquiries." Specific. Measurable. Straightforward. But we started with our usual process..

Someone asked us last week: "Why do you keep talking about design thinking? Isn't this about AI?" Fair question. Here's why it matters 🧵

Can't answer these? Then you probably don't have a portfolio strategy. You have a bunch of initiatives and some hope. Full analysis of Amazon's portfolio strategy: 🇬🇧 https://t.co/jSEMRDzpIu 🇳🇱

The questions worth asking: → Which business models actually make money? → Which show potential but need investment? → Which could change your business in 5 years? → Are you balanced between today and tomorrow?

Value spaces keep things organized. Amazon clusters initiatives into 5 themes: - Retail & Commerce - Cloud & Platform - Digital Media - Health & Wellness - Moonshots Everything fits somewhere. Nothing is random.

Different phases get different rules. Experimental projects don't get measured like mature businesses. They have different KPIs, different timelines, different expectations. Yet most organizations apply the same metrics to everything.

The core finances the future. @awscloud generates 30%+ margins. Those profits fund billion-dollar experiments like @ProjectKuiper. Without a profitable core, there's no money for moonshots.

From selling books in 1994 to operating satellite networks, quantum computers, and cloud infrastructure powering half the internet. The secret? They don't manage projects. They manage phases.

Voted already? Whatever challenge you picked, we've probably seen it before. Check out our services here: 🇬🇧 https://t.co/UMtmymHfFv 🇳🇱

"Where to start" = Unclear business priorities "Pilots didn't stick" = Built without validation "Proving business value" = Started with tech instead of outcomes The technology is rarely the blocker. It's the clarity. Which one resonates with you?

We've had 20+ conversations about AI in the past few weeks. Here's what's interesting: Most AI challenges aren't really about AI. They're strategy problems disguised as technology problems.