Innovation Prototyping Methods

Over the last year, I’ve seen many people fall into the same trap: They launch an AI-powered agent (chatbot, assistant, support tool, etc.)… But only track surface-level KPIs — like response time or number of users. That’s not enough. To create AI systems that actually deliver value, we need 𝗵𝗼𝗹𝗶𝘀𝘁𝗶𝗰, 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 that reflect: • User trust • Task success • Business impact • Experience quality    This infographic highlights 15 𝘦𝘴𝘴𝘦𝘯𝘵𝘪𝘢𝘭 dimensions to consider: ↳ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 — Are your AI answers actually useful and correct? ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 — Can the agent complete full workflows, not just answer trivia? ↳ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 — Response speed still matters, especially in production. ↳ 𝗨𝘀𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 — How often are users returning or interacting meaningfully? ↳ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗥𝗮𝘁𝗲 — Did the user achieve their goal? This is your north star. ↳ 𝗘𝗿𝗿𝗼𝗿 𝗥𝗮𝘁𝗲 — Irrelevant or wrong responses? That’s friction. ↳ 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻 — Longer isn’t always better — it depends on the goal. ↳ 𝗨𝘀𝗲𝗿 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 — Are users coming back 𝘢𝘧𝘵𝘦𝘳 the first experience? ↳ 𝗖𝗼𝘀𝘁 𝗽𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 — Especially critical at scale. Budget-wise agents win. ↳ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝗽𝘁𝗵 — Can the agent handle follow-ups and multi-turn dialogue? ↳ 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗲 — Feedback from actual users is gold. ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — Can your AI 𝘳𝘦𝘮𝘦𝘮𝘣𝘦𝘳 𝘢𝘯𝘥 𝘳𝘦𝘧𝘦𝘳 to earlier inputs? ↳ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 — Can it handle volume 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 degrading performance? ↳ 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 — This is key for RAG-based agents. ↳ 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗦𝗰𝗼𝗿𝗲 — Is your AI learning and improving over time? If you're building or managing AI agents — bookmark this. Whether it's a support bot, GenAI assistant, or a multi-agent system — these are the metrics that will shape real-world success. 𝗗𝗶𝗱 𝗜 𝗺𝗶𝘀𝘀 𝗮𝗻𝘆 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗼𝗻𝗲𝘀 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? Let’s make this list even stronger — drop your thoughts 👇

Was asked what my "Sprint planning secret" was. My secret is to do something effective instead of a fake-Agile waterfall-planning session complete with SWAG story-point estimates and tactical planning—something that makes no room for learning as we work. Instead, pick a single story. Keep asking "Can we make this smaller?" until the answer is no. (Most teams don't know what "small" actually is, so they'll have to learn how to do this.) Throw out any of those small stories that aren't worth doing (the best way to get faster is to not build stuff nobody wants), and put all but the most valuable of the stories back on the backlog. Build that most valuable thing. Given that a story represents a customer's problem, not a solution (another thing fake-Agile shops get wrong), sit down with your product people and, ideally, a representative customer and collect enough information to START (not finish) the work. One customer is enough (you've got to start somewhere)—release to more customers and adjust once you've got something concrete in your hands. Continuously collect additional information and feedback as you work with very small incremental releases to skin-in-the-game customers. Better yet, get rid of Sprints altogether. There's some value in doing some things on a regular cadence, but doing everything on the same cadence seems ineffective to me.

“We are already using recycled plastic” If this is your answer when asked “How do you select circular materials?”, you should reconsider your approach to material strategy❌ Using recycled materials is an excellent step forward, but it’s only part of the story. True circularity means diving deeper and asking the right questions: 💡What are the main criteria of this project? Pick your 3 core pillars (high-priority) from the material selection principles wheel and 3 sub-pillars (low-priority). These will guide your decision-making and ensure alignment with your project's most critical goals. 💡How do you balance your criteria? Weight your trade-offs: no material is perfect, but if you know your priorities, you can make informed decisions that balance performance, circularity, and cost. Understanding where you can compromise and where you must hold firm allows for a more strategic approach to material selection. 💡How do you make data-driven decisions? Apply metrics to your decision to be objective and transparent. Use specific, quantifiable metrics to evaluate the performance, sustainability, and cost-effectiveness of each material. This approach ensures that your choices are based on solid data rather than assumptions, leading to more robust and defensible decisions. By addressing these questions, you can move beyond just using recycled materials and embrace a holistic strategy for circular material selection. Let’s push the boundaries of sustainability and make choices that truly drive positive environmental and economic impact. Ready to design with purpose? 📌Save it. Repost it. Start using it today ➡Do you wanna know more? Get in touch and be part of the change! #circularmaterials

Every PM wants to measure the success of their product. But most struggle to do it correctly. As a product management hiring manager, leader, and coach, I've seen that many product managers struggle with defining the right success metrics They focus on generic metrics like acquisition, engagement,  retention These are insufficient. My recommendation is to ask concrete questions when thinking of metrics Here's a list of questions I ask: 𝗧𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝘂𝘀𝗲𝗿 𝗳𝗶𝗿𝘀𝘁 1. What is the user’s goal? 2. What human need do they want to fulfill? 3. What action signifies that their need is met? 4. Is that action enough to know user’s job is done? 5. How can I measure that action? 𝗧𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝘂𝘀𝗮𝗴𝗲 𝗮𝗻𝗱 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 1. How many users are using the product? 2. How many users should be using it? 3. Which users aren't using it but should be using it? 𝗧𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝗵𝗼𝘄 𝗺𝘂𝗰𝗵 𝘂𝘀𝗲𝗿𝘀 𝗲𝗻𝗷𝗼𝘆 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 1. How many users like the product? 2. How much do they like it? 3. What action(s) show they “like” it? 4. How can I measure those actions 5. Do they like it enough to keep coming back? 6. If yes, how often should they come back? 𝗧𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗼𝗳 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝘁𝗵𝗲𝘆 𝗮𝗿𝗲 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝘄𝗵𝗶𝗹𝗲 𝘂𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 1. Are users finding it hard to complete certain actions? 2. Are there things that users dislike? 3. Are there enough options for users to choose from? 4. Are there things that users want to do, but the product doesn’t allow them to? 5. Can we measure all the above? 𝗧𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗼𝗳 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 1. Can I cheat on any of the above metrics? 2. Do above metrics give the most accurate answer? 3. Are all metrics simple enough for everyone to understand? 𝗧𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗻𝗲𝘁 𝗶𝗺𝗽𝗮𝗰𝘁 𝗼𝗻 𝘁𝗵𝗲 𝗼𝘃𝗲𝗿𝗮𝗹𝗹 𝗽𝗿𝗼𝗱𝘂𝗰𝘁/𝗰𝗼𝗺𝗽𝗮𝗻𝘆 1. Are  above metrics a true representation of success? 2. Any other parts of user journey I should measure? 3. Will a positive impact on above metrics lead to a negative impact on other critical metrics? 4. Is the tradeoff acceptable? -- How easy or tough do you find creating success metrics? What is your process?

AI Prototyping Tools Masterclass: If you've been bouncing between v0, Bolt, Replit, and Lovable wondering, "Which one should I actually be using?" You're not alone. They all look impressive. But if you don’t understand what each one actually does best, you're just spinning your wheels. So, let’s break it all down: — ONE - The 4 Major Players (and What They’re Built For) Let me remind you, these aren’t just "tools" anymore. They’re fast-evolving cloud development environments And each one has a clear edge. 1. v0 by Vercel This one’s all about beautiful front-end design - out of the box. Clean UIs, polished interactions, and a $3.25B valuation behind it. Perfect if you’re spinning up a demo for stakeholders... And want something that looks amazing fast. Just don’t expect deep backend stuff without plugging in extras like Supabase. 2. Bolt Built for speed. The CEO told us the whole thing runs in the browser, no VMs & no lag. That's the reason it went from $0 to $40M ARR in just 6 months. If you’re testing ideas fast (think 10-minute prototypes), this is your tool. It’s flexible, but you'll need to connect things like a database yourself. 3. Replit This one goes deep. Founded by Amjad Masad and now valued at $1.16B, Replit gives you full-stack power. Built-in auth, built-in database, built-in deployment. If your prototype needs to function like a real product, this is the play. It’s not as slick as v0 or as lightning-fast as Bolt... But when it comes to handling real logic, Replit is in a league of its own. 4. Lovable Lovable is becoming the most loved "vibe coding" tool. Founded by Antonin Osika, and it hit $17M ARR in just 3 months. Honestly? It’s the easiest tool in the game, especially if you don’t code. Drag, drop, sync with Supabase. That’s it. No setup headaches. No complex environment. Perfect for non-technical PMs or anyone who wants to go... From idea to live prototype without touching a line of code. — TWO - ADJACENT TOOLS But wait, there’s a twist. These tools aren’t where AI prototyping stops. There are adjacent tools you’ll want to layer in depending on your skill level: If you’re just looking to generate quick code or play around with ideas: → ChatGPT and Claude work great. But if you want to build something real (and you can code): → Tools like Cursor, Windsurf, Zed, and GitHub Copilot are insanely powerful. A great flow in my experience so far? Start in Bolt or Lovable → Sync to GitHub → Then build deeper in Cursor. — I broke all this down in my latest newsletter drop: "Ultimate Guide to AI Prototyping Tools (Lovable, Bolt, Replit, v0)" If you want to understand how to actually use these tools and which one fits your workflow best, go here: https://lnkd.in/eRypMZQ8 It’ll save you weeks of trial and error.

"The Role of Digital Twin Technology in Bridge Engineering." With the rapid advancement of digital technologies, the construction and maintenance of bridges are evolving beyond traditional engineering methods. One of the most transformative innovations in recent years is Digital Twin Technology, which is reshaping how we design, monitor, and maintain bridges by integrating real-time data, predictive analytics, and AI-driven insights. What is a Digital Twin? A digital twin is a virtual replica of a physical bridge that continuously receives real-time data from IoT sensors embedded in the structure. These sensors monitor structural conditions, load distribution, environmental impacts, and material fatigue, creating a dynamic and interactive model that mirrors the actual performance of the bridge. This virtual model allows engineers to simulate different scenarios, detect anomalies early, and optimize maintenance strategies before actual failures occur. How Digital Twins Are Revolutionizing Bridge Engineering 1. Real-Time Structural Health Monitoring (SHM) IoT sensors collect continuous data on factors such as temperature, stress, vibration, and corrosion. AI-powered analytics process this data to identify patterns of deterioration and potential structural weaknesses. Engineers can access real-time insights from remote locations, reducing the need for frequent on-site inspections. 2. Predictive Maintenance & Cost Efficiency Traditional maintenance relies on scheduled inspections, often leading to unnecessary costs or delayed repairs. With digital twins, predictive analytics help forecast which parts of a bridge will require maintenance and when, optimizing repair schedules. This proactive approach extends the lifespan of the bridge and reduces long-term maintenance expenses. 3. Simulation & Risk Assessment Engineers can simulate extreme weather conditions, earthquakes, and heavy traffic loads to assess a bridge’s resilience. This allows for better disaster preparedness and risk mitigation, ensuring public safety. In construction projects, digital twins can be used to test different design alternatives before actual implementation. 4. Sustainability & Smart City Integration By optimizing material usage and maintenance, digital twins help reduce environmental impact. They also enable better traffic flow analysis, contributing to the development of smarter and more efficient transportation networks. Integrated with Building Information Modeling (BIM) and Machine Learning, digital twins are a key component of smart infrastructure development. Video source: https://lnkd.in/dkwrxGDE #DigitalTwin #BridgeEngineering #SmartInfrastructure #CivilEngineering #StructuralHealthMonitoring #Innovation #IoT #BIM #AIinConstruction #civil #design #bridge

How to Choose the Right Metrics for Any Product Feature? 📌 Here is a simple approach with an example. This works equally well for cracking interview and on the job. Start by Asking yourself three questions: 1. Why are we creating this feature? Is it for Revenue, Retention, Growth, or Engagement? 2. How would we know if people know about this feature? Mapping user journeys will help you find this. 3. How would we know if people are using this feature? Awareness and Activation. By answering these questions and mapping the user journey you will come across: 1. Success Metrics 2. Awareness Metrics 3. Adoption & Retention Metrics But why are we considering the other two when only success metrics can do that job? See, there could be multiple scenarios once you launch the product: Scenario 1: People are aware of the product, and people are using the product, but success metrics aren't moving. Reason: Your assumption that this feature will help you move your success metric was wrong. Scenario 2: People are aware of the product but aren't using it. Reason: Your feature needs to be more useful and usable. Scenario 3: People aren't aware of the feature/product. Reason: You haven't done a good job at GTM & and awareness. You can also see your success metrics moving without any change in adoption or awareness metrics, however, this would mean that success cannot be attributed to your feature. Additionally, it's also recommended to have a "do not disturb" or "guardrail" metrics list, to track any 2nd order effects from the feature. eg: More downloads (success metric), should not lead to lower ratings (guardrail metrics). Let's take the example of WhatsApp's 24-hour Statuses/Story feature: 1. Success metrics: Why have we created this feature? To increase daily active users or the Number of app opens per user per day. 2. Adoption metrics: How would you know if people are using it? Measure status posted per day/week, no. of status views per user. 3. Awareness metrics: Are people aware of this feature? How many have created their 1st status, and how many have visited the status tab? This method not only helps you find the right metrics but also helps you do a first-level root-cause analysis. Get the list of the top product metrics from HelloPM here: https://lnkd.in/gzTE7Q7v P.S. Want to deep dive into other powerful PM concepts and cases? Immerse yourself here 👉🏽 https://lnkd.in/dgXfiaSg What other PM concepts do you want me to simplify next?

What if the best solutions for your process started with cardboard? When testing new ideas or improvements, jumping straight to high-cost, permanent solutions can be risky—and expensive. That’s where cardboard engineering comes in. Cardboard is one of the simplest, most cost-effective tools for rapid prototyping and testing ideas. It’s lightweight, easy to shape, and lets you visualize, test, and refine your concepts before committing to more expensive materials. Why Cardboard Is Perfect for Prototyping: 1️⃣ Low-Cost Experimentation Testing with cardboard lets you try multiple iterations of a design without worrying about material costs. 2️⃣ Fast Feedback Loops You can build and modify a prototype in minutes, gathering instant feedback from your team or operators. 3️⃣ Hands-On Collaboration Cardboard prototypes allow teams to actively engage with ideas, making it easier to identify issues or opportunities for improvement. 4️⃣ Visual Validation Sometimes, seeing a physical model highlights challenges that wouldn’t be obvious in a drawing or plan. How to Use Cardboard for Lean Improvements: 🔍 Test Workstation Layouts Use cardboard cutouts to mock up layouts and placement of tools, parts, and equipment. Adjust until everything flows smoothly. 📦 Simulate Material Flow Prototype racks, bins, or carts to ensure materials are stored and moved efficiently before building them with more durable materials. 🛠️ Design Fixtures or Jigs Create cardboard versions of fixtures or jigs to test their functionality in the process. Refine the design before investing in the final version. 📐 Test Ergonomics Mock up equipment or workstation designs with cardboard to test ease of use, reach, and operator comfort. Example of Cardboard in Action: A manufacturing team wanted to redesign a workstation to reduce operator motion. Instead of committing to expensive reconfigurations, they used cardboard to prototype the layout. After several iterations, they found the optimal setup, reducing motion by 25% and saving hours of work. Cardboard isn’t just for packaging—it’s a powerful tool for testing and refining your ideas. By prototyping with low-cost materials, you can experiment, learn, and improve quickly without breaking the bank.

💡 Stop Starving Your Venture — But Don’t Feed It a Buffet. One of the biggest myths in corporate venture building is that you either: A) Throw chump change at a new idea (and watch it crawl), or B) Burn mountains of cash and hope for a miracle. Both miss the mark. The real play? Metered, milestone-based funding. 🔑 How it works: Fund the next riskiest assumption, not the whole roadmap. Release cash only when evidence proves traction (LOIs, paid pilots, usage metrics). If proof stalls, pause or pivot. If proof pops, double down. This isn’t “spend big.” It’s “spend right to learn fast.” Think of it like fuel stops in a race: too little and you sputter out, too much and you carry dead weight. The art is topping up just in time to stay in front. 👀 Questions to ask before writing the next cheque: - What’s the single learning we’ll unlock with this tranche? - How will we know (within weeks, not years) if it worked? - What’s the kill-switch if it doesn’t? Fund with intention, validate in sprints, scale what wins. That’s not reckless spending — that’s disciplined growth. #CorporateVenturing #Innovation #MilestoneFunding #GrowthStrategy

GenCAD - Turning Images into Editable 3D Designs Creating CAD models is still slow, manual, and often frustrating - especially when dealing with complex geometries. That’s why a team at MIT developed GenCAD, a new AI-powered system that generates parametric, editable CAD models directly from images. 👉 Instead of working with meshes or point clouds (which are hard to edit), GenCAD focuses on real-world engineering needs: - Modifiability - Manufacturability - Cross-modal generation (image → CAD) 🔍 How it works: GenCAD combines: - Autoregressive transformers (to model CAD command sequences) - Contrastive learning (to align images with CAD representations) - Latent diffusion (for high-quality generation) 📄 Paper: https://lnkd.in/eahBwEfC 🔗 Website: https://gencad.github.io/ 💻 Code: https://lnkd.in/eJgrNBqs