Prototyping in Corporate Innovation

AI ENABLES PERMISSIONLESS INNOVATION The review gauntlet that product orgs use to "ensure quality" often kills breakthrough ideas before they see the light of day. Strategy reviews, product committees, design approvals—each layer of gatekeepers favors safe, consensus-driven concepts over the risky, opinionated bets that create real innovation. AI prototyping is changing this dynamic entirely. Smart PMs are now bypassing traditional approval processes by building functional AI prototypes themselves. Instead of pitching abstract concepts to committees, they're: - Creating working prototypes in hours or days - Testing directly with real customers - Gathering concrete feedback and usage data - Iterating based on actual user behavior - Walking into review meetings with proof, not just PowerPoints The result? They're presenting stakeholders with tangible experiences and customer validation rather than hypothetical arguments. It's much harder to kill an idea when users are already loving the prototype. The new playbook: Build first, get permission later. When you have a bold product idea, don't let it die in committee. Use AI to prototype your vision, validate it with real users, then use that momentum to navigate the approval process from a position of strength. What innovative ideas are you sitting on that could benefit from this approach?

Day 80: AI Prototyping for Businesses This is a timely topic, as Chris Wolf posted about VMware Private Cloud for AI RAG Starter using NVIDIA software. The ability to build an AI pipeline is essential for prototyping projects and building what Dell Technologies calls an AI Factory. More Insights from ChatGPT AI prototyping is a critical step in the development process, allowing businesses to test AI models and validate their potential before full-scale deployment. Prototyping helps organizations mitigate risks, refine AI models, and ensure that AI projects deliver measurable value. Here’s an overview of how to approach AI prototyping in a business context. Key Steps in AI Prototyping 1. Define the Problem and Objectives Definition: Clearly outline the business problem you want to solve with AI and define the goals of the prototype. Application: Establish measurable objectives, such as improving operational efficiency, enhancing customer experience, or reducing costs. This ensures that the prototype is aligned with the organization’s strategic goals. 2. Select the Right Data: Definition: Choose the relevant data sources needed for the prototype and ensure that the data is clean and well-organized. Application: Data is the foundation of any AI model, so it’s essential to use high-quality, representative data for the prototype. Data governance and privacy considerations should be part of this step. 3. Choose the Right AI Model: Definition: Select an AI model that is appropriate for the problem at hand. This could be a machine learning model, natural language processing (NLP) algorithm, or another AI approach. Application: Depending on the business problem, different AI models will be more or less suited to the task. Use tools like AutoML to experiment with different models and select the most effective one. 4. Build and Train the Model: Definition: Develop a working AI model and train it using historical data. Application: Training is a crucial phase where the AI model learns patterns from the data to make predictions or decisions. The accuracy and effectiveness of the model will depend on the quality of data and the robustness of the algorithm. 5. Test and Validate: Definition: Test the prototype using real-world data to assess its performance and validate its outcomes. Application: Measure the model’s performance against predefined metrics, such as accuracy, precision, recall, or ROI. This helps ensure the model meets business expectations before moving to full deployment. 6. Iterate and Refine: Definition: Use feedback from testing to refine and improve the prototype. Application: Prototyping is an iterative process. Analyze the results, identify areas for improvement, and adjust the model to improve performance. Iterate until the model is ready for deployment.

Prototyping has always been where things get real. It’s that moment when your idea leaves the whiteboard and starts taking shape in the hands of a team. It’s messy. It’s iterative. It’s essential. And it’s where I’ve seen AI unlock something truly powerful. Not in theory. In practice. The prototyping phase is full of pressure — timelines, resources, testing, approvals. And yet, so much of it is still held back by manual, slow, fragmented processes. That’s where AI comes in. AI can generate design options in minutes. It can simulate user behavior before a product even exists. It can help you test, fail, and refine without burning months of budget. But here’s what I keep telling leadership teams: AI isn’t a shortcut. It’s an accelerator. And if your team isn’t ready to move faster and think differently, it won’t stick. This is why change management matters. When you bring AI into something as high-impact as product development, it affects everything — how teams collaborate, how decisions are made, how success is measured. It shifts the dynamics. That shift needs structure. Trust. Communication. Leadership. At AI-First Mindset™, our organizational transformation consulting helps you do exactly that. We work across five core areas — from leadership engagement to skill development and cultural change — to make sure your team is aligned, equipped and confident. Because adopting AI in product development shouldn’t feel risky. It should feel like progress. Learn more about using AI to accelerate prototyping and drive innovation by following me on LinkedIn. #AIProductDevelopment #InnovationThroughAI #AIInProductDesign

🎨One of the most critical skills to build 0 to 1 products both at a startup or inside a large company is the art of prototyping.🎨 I call it art and less of science because it involves a lot of creativity to get to a working model with the least amount of time and cost and there is quite literally no standard repeatable process. Great product managers and product companies however have managed to create the right prototypes repeatedly rather scrappily by truly understanding what really matters the most. One of my favourite examples is of how Google Books was born. In 2002, Larry Page began wondering if it was possible to make every published book ever published searchable online. As the cofounder, Larry could have assigned a team of engineers to the problem and given them a nice budget. Instead he got a digital camera, rigged it to a tripod and set the contraption up on a table in his office. He pointed the camera at the table, turned on a metronome to pace his movement and starting snapping pictures whilst Marissa Mayer turned the page. Based on this crude prototype, they were able to estimate what it took to digitise a book. Google Books was born. There are numerous such examples. ✴️Airbnb was born over a design conference weekend when the founders put up their single airbed on a static website and got some guests validating their hypothesis. ✴️Netflix knew it was possible to rent dvds over mail because they could mail themselves a dvd and it came back to them intact. ✴️Ubers prototype was users would text their location and behind the scenes they used phone calls to despatch black town cars. ✴️The iPhone’s very first prototype was a phone module rigged to an iPod. If one were to abstract the key guiding philosophies that go into making great prototypes, it would be 👉 Not thinking of scale first. This is what kills most new products. Instead of validating value with a few people and then think of gradually scaling, big companies especially think scale first and inevitably spend a monstrous amount of time to build an unproven hypothesis at scale to immediately deliver business value. Inevitably when it fails, management has no further motivation to pursue given the huge cost already incurred. 👉Use existing infrastructure to quickly put together something to prove the concept works and users love it. Almost often no custom tech solutions are built but rather reusing existing solutions to make a scrappy contraption. 👉 A sense of urgency. Almost often these scrappy contraptions were put together in a very short time frame to quickly test the hypothesis. A word of caution here is that these guiding principles may not work for all industries say such as health care where human life is at stake. So be mindful of absolute non negotiables when defining your prototypes. #productmanagement #prototyping #productcraft #zerotoone

Most AI ideas die before they even get off the ground. Why? Because teams get stuck in endless debates instead of building something tangible. The best way to get leadership buy-in, align teams, and validate your AI concept? Prototyping. But here’s the secret—you don’t need to code to prototype AI effectively. Instead of diving into AI coding tools like Cursor or Replit, you can use no-code AI prototyping tools like Notion AI, UX Pilot, CustomGPTs, and Voiceflow to move even faster. In our latest AI Community Learning Series, Polly M Allen (Ex-Principal PM, Alexa AI) and Rupa Chaturvedi (AI UX Leader, ex-Amazon, Google, Uber) shared how to: ✅ Align teams faster with interactive AI prototypes (instead of lengthy PRDs) ✅ Use no-code tools to build AI-powered experiences—without writing a single line of code ✅ Pick the right AI use cases and avoid overcomplicating solutions Plus, they demoed how to build a Shopping AI Assistant live—showing exactly how to structure, test, and refine AI interactions in minutes. Curious how they did it? Full recap + session replay 👇 Have you built an AI prototype before? What worked (or didn’t)? Share your thoughts below! #ProductManagement #AI #Design #Prototyping

As CIO I’ve seen months building time and millions in budget wasted on ideas never reaching prod. There were many things not working as they could, but if I had to change ONE thing, it would be this one: When evaluating use cases the process was something like 1 - brainstorm use cases 2 - put them into Excel 3 - Discuss “potential value” 4 - Pick on value + gut feel 5 - Measure delivery, not impact Leading to many dead ideas and wasted budget, because the use case was too complex, too big, it took too long to develop…. There are MANY things worth improving in the way enterprises realize use cases. But if I could go back in time and only change ONE thing, it would be the core methodology. Here's the framework we use at Calibo today, to evaluate opportunities: Phase 1: Use Case Discovery  → Start with business problems, not AI capabilities → Define measurable KPIs upfront (revenue impact, cost reduction, time saved) → Check if someone already solved this (avoid reinventing the wheel) Phase 2: Strategic Scoring  → Rate each use case on Impact, Feasibility, and Strategic Fit → Size the effort (Small = 4 weeks, Medium = 8 weeks, Large = 12+ weeks) → Kill bad ideas early, double down on winners Phase 3: Rapid Prototyping  → Build in 8-10 week sprints maximum → Test with real users and real data → Measure actual performance vs. projected KPIs Phase 4: Business Validation  → Track results for 90 days post-deployment → Document what worked, what didn't, and why → Scale winners, sunset failures This methodology helps enterprises avoid the POC graveyard that I saw fill up as CIO. We've documented this in our full Digital Business Innovation methodology. But even these four phases would transform how most organizations approach AI. Repost this to your network if this was helpful! P.S. If you want our full 66 page innovation methodology document, there is a link in the comments.

I had a fascinating conversation with Steve Quinlan of NatWest Group recently, and it really highlighted a fundamental issue in how many product teams approach experimentation. Too often, "experimentation" is seen as something that happens after a feature is built. This is the cart-before-the-horse. You've already invested significant time and resources, and now you're hoping to validate if it was worth it. True experimentation should be about validating and developing ideas before they enter serious development and as they go through design. Steve sits with a 'prototyping' function at Natwest created with this purpose in mind. They focus on de-risking development by rigorously testing and iterating on ideas early in the process. This approach not only saves valuable resources but also ensures that the final product truly meets customer needs. Moreover, Steve's team's work disambiguates from the narrow view that experimentation is just about A/B testing. It's about a broader, more strategic approach to product research, discovery and validation. It begs the question: how many product teams are missing out on this critical early-stage validation? How often are we building features based on assumptions rather than solid evidence, even if they are 'tested' before release? Shifting our mindset to prioritize prototyping and early-stage experimentation can revolutionize how we build products and drive innovation. How does your team ensure that experimentation is integrated into the entire product development lifecycle, not just tacked on at the end? #experimentation #cro #productmanagement #growth #digitalexperience #experimentationledgrowth #elg  

Having built over 65+ prototypes ranging from quick experiments to comprehensive experiences, using tools like Replit, V0, Lovable, and Firebase Studio, I've seen firsthand: You may not be able to simply vibe-code your way directly into production, especially not at enterprise scale. But the real challenge is: How quickly and effectively can we bridge the gap from prototype to production? If turning a prototype into reality still means creating hundreds of Jira tickets, dozens of PRDs, and spending countless hours in grooming sessions, across product, UX and engineering, then we haven't really evolved. Prototyping tools and vibe-coding aren't the bottleneck. The bottleneck is our outdated workflow. The future isn't just faster prototyping; it's fundamentally reshaping how product, UX, and engineering teams collaborate. Less documentation, more direct integration. Less friction, more innovation. That's the conversation we should be having. #waysofworking #ai #genAI #prototyping #vibecoding #aienablement #transformation #changemanagement

𝐓𝐡𝐞 𝐑𝐢𝐬𝐞 𝐨𝐟 𝐭𝐡𝐞 "𝐁𝐮𝐢𝐥𝐝𝐞𝐫" 𝐏𝐌 𝐢𝐧 𝐭𝐡𝐞 𝐀𝐈 𝐄𝐫𝐚 This was sparked by an insightful discussion from Madhu Gurumurthy (Head of Product, Gemini) on the changing landscape of product development with Suhas Motwani and Aditya Mohanty of The Product Folks. The core idea is a shift away from the traditional, archaic, document-heavy process to a more dynamic, builder-centric model. For years, the gold standard was a linear flow: Idea -> lengthy PRD -> Design -> Debate -> Build. The Product Manager was the master of the PRD, the central document that defined everything. This process was methodical and structured, but often slow. But the world is rapidly changing. The rise of AI and low-code/no-code platforms is democratizing creation/building. The cost to build and iterate has plummeted. In this new reality, speed of learning/iterating is the ultimate competitive moat. This is where the "Builder" archetype emerges. The "Builder" is a new breed of product person, someone who doesn't just write about the product but actively participates in its creation. Instead of a lengthy PRD, their primary tool is the prototype. They are hands-on, working side-by-side with engineering and design to bring valuable, validated ideas to life quickly. The focus shifts from "describing" to "showing." This new flow looks more like: Idea -> Prototype -> Debate -> Refine -> Build. It’s a more iterative, collaborative, and ultimately faster way to build products that customers love. It's less about being a "manager" of a process and more about being a "builder" of a product. This isn't to say that strategy, vision, and customer empathy are no longer important. They are more crucial than ever. But the way we translate that vision into reality is undergoing a fundamental transformation. What are your thoughts on this evolution? Are you seeing the rise of the "Builder" in your organization?

𝐀𝐈 𝐏𝐫𝐨𝐭𝐨𝐭𝐲𝐩𝐢𝐧𝐠: 𝐓𝐡𝐞 𝐆𝐚𝐦𝐞-𝐂𝐡𝐚𝐧𝐠𝐞𝐫 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞𝐬 𝐃𝐢𝐝𝐧’𝐭 𝐒𝐞𝐞 𝐂𝐨𝐦𝐢𝐧𝐠 This past week, I led a two-day session on AI prototyping at University of California, Berkeley. One insight stood out: you don’t need coding skills to develop a working prototype of an app. Many executives walked in thinking prototyping required a dedicated employee with specialized skills. But when they saw #AI coding agents update a prototype in real time right before their eyes the MINDSET SHIFT was immediate. We role-played a typical product development process: - An executive provided feedback -The AI made changes instantly -The prototype actually worked, no bugs, no delays The reaction? A mix of excitement and hesitation. The excitement? AI can help leaders test more ideas in less time, whether improving core business processes or exploring new opportunities. The hesitation? “This is a great tool… but is my organization ready to use it?” The challenge here isn’t learning the technology. It’s building an organization that can adapt one that’s nimble enough to respond to rapid change before management even has time to review and approve it. So, where do you start? -Play with the tools. Build something simple, even a to-do list. -Focus on iterations: test, refine, and test again. -Think beyond AI as a technology, see it as a strategy for innovation. How is your company thinking about AI-driven prototyping?  What barriers do you see? Drop a comment below! 🔔 Follow Jeff Eyet 🔑✨ for more insights. ♻️ Found this valuable? Repost and tag a colleague who should see this. #AIPrototyping #Innovation #BIGInsights #AIForExecutives #TheBerkeleyInnovationGroup