Developing Prototypes for Tech Innovations

Prototyping is how ideas turn into evidence. It surface hidden assumptions, generate better stakeholder conversations, test specific hypotheses, reveal unforeseen interactions, and give you a concrete artifact to evaluate before code or tooling locks you in. Use low fidelity sketches and storyboards when you need speed and divergent thinking. They help teams externalize ideas, reason about user goals, and map flows before pixels appear. They are deliberately rough to avoid premature polish. Move to click through wireframes in Figma when the question is structure and navigation. Validate information architecture, menu depth, labeling, and path efficiency while changes are still cheap. When the feel of interaction matters, use interactive digital prototypes to evaluate micro interactions, timing, and visual polish. Treat them as validation instruments, not trophies. Plan change criteria up front so attachment to a pretty artifact does not silence real feedback. Some questions require real performance and materials. Coded prototypes and functional hardware mockups tell you about latency, reliability, durability, ergonomics, and safety. In medical devices and other regulated domains, high fidelity functional and contextual testing is expected for Human Factors validation. Not every question lives on screens. Experience prototyping and bodystorming put bodies in space to surface constraints that lab tasks miss. Acting out a shared autonomous ride with props reveals comfort, cue timing, and social norms. Wearing a telehealth mockup for a week exposes stigma, routine friction, and alert patterns that actually fit domestic life. Before building intelligence, simulate it. Wizard of Oz studies let a hidden human drive system responses while participants believe the system is autonomous. You learn vocabulary, trust dynamics, acceptable latency, and recovery strategies without heavy engineering. AI of Oz replaces the human with a large language model so you can study conversational realism early. Manage risks like model bias, hallucinations, and outages with guardrails and logging so findings remain trustworthy. Strategic prototypes also matter. Provotypes and research through design artifacts challenge assumptions, surface values, and force early conversations about privacy, power, and trade offs that slides tend to dodge.

This is how Anthropic decides what to build next—and it's brilliant. Instead of endless spec documents and roadmap debates, the Claude Code team has cracked the code on feature prioritization: prototype first, decide later. Here's their process (shared by Catherine Wu, Product Lead at Anthropic): Step 1: Idea → Prototype Got a feature idea? Skip the spec. Build a working prototype using Claude Code instead. Step 2: Internal Launch Ship that prototype to all Anthropic engineers immediately. No polish required—just functionality. Step 3: Watch & Listen Track usage religiously. Collect feedback actively. Let real behavior, not opinions, guide decisions. Step 4: Data-Driven Prioritization - High usage + positive feedback → roadmap priority - Low engagement or complaints → back to iteration This "prototype-first product shaping" flips traditional product development on its head. Instead of guessing what users want, they're measuring what users actually use. The beauty? They're dogfooding their own tool to build their own tool. The feedback loop is immediate, honest, and impossible to ignore. The takeaway: Your best product decisions come from real user behavior, not theoretical frameworks. Sometimes the fastest way to validate an idea isn't a survey or interview—it's a working prototype.

Most PMs are still writing 15-page PRDs that developers skim and designers ignore. Meanwhile Nadav Abrahami spent 20 years building Wix into a $4B company, then left with 30 of his best engineers to solve the problem he watched PMs struggle with the entire time: you can describe a feature in a thousand words, or you can just build it in 10 minutes. The stat that should wake people up: MIT found 95% of enterprise AI projects fail to reach production. The prototypes break down before they ship. The gap between "cool demo" and "something that works" is where most teams die. What Nadav explains in this episode is the workflow that closes that gap (https://lnkd.in/gW8AXKXG). His team at Wix used to assign three developers for weeks to build functional prototypes for major features. Now every single feature goes through AI prototyping before a line of production code gets written. The time cost went from weeks to minutes. The real insight though is his framing of where PMs go wrong. They treat AI prototyping like vibe coding, dump a massive prompt, and hope. His approach: discuss with the AI first. Ask it "how do you understand this?" the same way you'd sanity-check with a developer. Because anything that can be misinterpreted will statistically be misinterpreted, and unlike a developer, the AI won't tell you your spec makes no sense. One line from the conversation that stuck: "PMs just got a huge get out of no developers jail card." The prototype becomes the spec. The PRD covers edge cases. Together they should leave zero questions for the engineering team. Three years from now, PMs who can't prototype are going to be like designers who can't use Figma in 2015. Technically still employable. Practically falling behind every sprint.

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  

Good news: AI is killing the project manager product manager. If you've built your career as a PM around tracking tasks, sending reminders, and monitoring status updates, I have bad news: your job is being automated. But for creative product leaders? The future has never been brighter. At Siena, we're seeing a fundamental shift in how product gets built: Our source of truth is no longer PRDs—it's prototypes. What's fascinating is how this dissolves the traditional PM role as a "translation layer" between business requirements and engineering implementation. Think about the old world: - PRDs in one corner - Design specs in another - Technical documentation somewhere else - All needing to merge perfectly (which they rarely did) You'd only catch misalignments after weeks of development when stakeholders would say "that's not what I meant." Now, our PMs start with prompts, not documents. We can create prompts, database schemas, and build working prototypes of entire features without connecting to the backend. Engineers then transform these into enterprise-ready implementations. This is transformative because: 1. It disambiguates expectations between stakeholders 2. It catches issues before a single line of production code is written 3. It lets PMs directly create rather than just describe While PRDs still matter for cross-functional communication, they're no longer the central artifact. The prototype is. The best PMs today aren't just managing processes—they're building experiences, testing directly with users, and iterating in real-time. The question isn't "Who will manage this project?" but "Who has the vision to define and show what we should create?" The future of product management isn't about managing—it's about making. At Siena, we're betting that the most valuable PMs will be those who combine creative vision, customer empathy with technical fluency to bring that vision to life. Some will struggle with this transition. Others will discover capabilities they never knew they had. What's your take on the future of product management?

Product development in 2024 - the old way: • Design low-fi wireframes to align on structure • Create pixel-perfect Figma mockups • Socialize designs with stakeholders • Wait weeks for engineering capacity to build • Build core functionality first • Push "nice-to-have" animations to v2 • Ship v1 without thoughtful interactions • Iterate based on limited feedback • Repeat the cycle for 3-6 months Product development in 2025: • Quickly prototype in code with AI tools like Bolt • Generate functional prototypes in hours, not days • Deploy to real URLs for immediate testing • Add analytics to track actual usage patterns • Test with users while still in development • Designers directly create interaction details • Engineers implement interaction details by copying working code • Ship v1 with thoughtful animations and transitions • Iterate rapidly based on both qualitative and quantitative data • Implement improvements within days Last week, we hosted William Newton from Amplitude to share how this shift is fundamentally changing their product development approach. "I made those interaction details myself. I made those components myself, and I sent them to my engineer and he copied and pasted them in." Features that would have been pushed to "future versions" are now included in initial releases. Loading animations, transition states, and micro-interactions that improve user confidence—all shipped in v1. This approach doesn't eliminate the need for thoughtful design and engineering. Instead, it changes the order of operations: - Traditional process: Perfect the design → Build the code → Ship → Learn - Emerging process: Prototype in code → Learn while building → Ship with polish → Continue learning The limiting factor is shifting from technical implementation to your taste and judgment about what makes a great experience. When designers and PMs can participate directly in the creation process using the actual medium (code), they make different—often better—decisions about what truly matters.

For years, building software was a reality defined by necessity: you either had to code it yourself or assemble a team with the technical skills—and that typically required a significant budget. But recently, that has changed, and it’s never going to be the same again. Imagine this: you have a deep understanding of a problem that’s been nagging at you—a challenge you know better than anyone else. Perhaps you’ve long believed there’s a better way to solve it, but you never had the means to test your theory. Today, with tools like bolt.new and lovable.dev, you can get your idea out of your head and onto the screen in the form of a clickable prototype. And when you’re ready to take things further, platforms like cursor and windsurf offer you the flexibility to build with your own tech stack. This isn’t about building a product just for the sake of building—it’s about rapidly prototyping to learn what parts of your theory might be off, and what parts hold real promise. With these tools, you can build just enough to validate your assumptions, gather market feedback, and iterate quickly. It’s a new opportunity for founders to test ideas faster than ever before, shifting the focus to learning and problem-solving rather than just chasing a product launch. However, there’s a trade-off. In a world where anyone can build, the true differentiator becomes understanding the problem itself. The founders who succeed aren’t simply those who can whip up a prototype—they’re the ones who know what to build and, more importantly, why they’re building it. A deep understanding of the problem and a clear vision for solving it is what sets apart products that merely exist from those that truly make an impact. So… What problem do you understand better than anyone else—a challenge with both urgency and a clear, unsatisfied need—that current solutions simply aren’t addressing? Drop your thoughts in the comments or share your ideas. Let’s spark a conversation about how we can all step into roles that once felt out of reach, and build solutions that are highly valuable.

As a product leader, I’ve spent years refining product development cycles — from ideation to launch. But AI is forcing all of us to rethink the how. Recently, I’ve been diving into how AI can enhance prototyping, and tools like blot.new or V0.dev have genuinely impressed me. What have I learned? 🔹 Instead of static designs in Figma → we’re using blot.new to turn those into working UIs It accepts plain-text prompts and instantly scaffolds React components styled with Tailwind CSS. The UI output is clean, componentized, and ready to plug into a real product. 🔹 Product managers can write functional prompts directly No need to wait for handoffs. A PM can now write something like: “A form with email/password input and a login button, responsive for mobile” …and blot.new returns the actual code and live UI preview within seconds. 🔹 A/B tests without code deployments We can test variations of user flows or UI layouts directly in blot.new, collect early feedback, and refine before it ever hits the dev backlog. What this changes: ✅ PMs and designers are now more hands-on with execution ✅ Engineers spend less time on throwaway prototypes ✅ Idea-to-feedback loops are dramatically shorter This shift has been energizing. And we’re just scratching the surface. Curious if others are doing the same. How are you integrating AI into your product workflow? #ProductLeadership #AIinProduct #PromptDrivenDevelopment #PrototypingWithAI #blotnew #TailwindCSS #React #RapidIteration #LeanProduct

Prototypes are not only great for learning concepts better, but they are also great for doing highly reliable project estimations, here's how ... Prototyping is about building fast and low-quality stuff that is never meant to be shipped to production nor is expected to follow any best practices. They are meant to gather the information, build an understanding, and then be thrown away. Project estimations are hard as we all are guilty of making random guesses while estimating efforts. Instead, it is better to build a quick prototype and make an informed decision. Prototypes help us answer some critical questions - what will be a tentative task breakdown - how long each task would take - what are the unknown unknowns - which parts of the system are hard, and where the risks lie - more importantly, can this even work Beyond technical feasibility, prototypes also help in gauging whether people even care about the solution. Sometimes, showing a quick demo is enough to validate interest or reveal disinterest before you go all-in. It's often better to spend a few hours testing an idea than to invest weeks in something doomed from the start. If you are learning new things every day, Prototyping is the easiest way to build a practical understanding, be it around system design, advanced algorithms, or even an idea that you find interesting. You often learn faster by doing than by just reading or planning. Remember, it's okay to discard prototypes. Their value is in what they teach you, not in their longevity. Make prototyping a habit. Most prototypes don't take more than 200 lines. Treat them as experiments and not polished products. So, when in doubt, code it out. #AsliEngineering #CareerGrowth

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