Experience-Driven Product Development

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.

Prototypes aren't for testing your product. They're for testing your assumptions. Most teams get this backward, and it costs them weeks of wasted effort and a product nobody wants. A prototype isn't a tiny product; it's a medium for learning. It's a tool designed to ask a specific question and test a core assumption with the right audience. An unintentionally designed prototype is a flawed input, and even with advanced teams and tools, flawed inputs only amplify flaws. The true power of a prototype isn't in its polish, but in the intentional "message" it sends. To unlock this power and truly accelerate collective learning across your organization, you must design with intent: ✺ Low-Fidelity Prototypes: These are for asking foundational, "Does this even solve the right problem?" questions. They signal that everything is up for debate. The intentional message is: "Let's explore the idea, not the pixels." ✺ Medium-Fidelity Prototypes: Use these to test core user flows and information architecture. The intentional message is: "Is this journey intuitive?" By keeping them a little rough, you prevent stakeholders from getting fixated on visual design. ✺ High-Fidelity Prototypes: Reserve these for the final stages to test things like micro-interactions, brand consistency, or subtle emotional responses. The intentional message is: "We're almost there. What are we missing?" This is how you turn prototyping from a simple task into a strategic lever for change and Team Learning. It ensures your team isn't just building things, but is learning together and making better decisions about what to build and why. It's how you break down silos and create a "Holding Environment" for generative dialogue. What's a time you intentionally used a low-fidelity prototype to prevent a high-stakes meeting from spiraling? Let’s discuss in the comments below. #ProductDesign #SystemsThinking #StrategicDesign #UXStrategy #DesignLeadership #ComplexSystems #TeamLearning #Prototyping #OrganizationalDesign #Innovation

„PLM is dead. Long live AI-driven Product Innovation.“ Why traditional PLM won’t survive without artificial intelligence Let’s face it: PLM in its classical form – static databases, rigid workflows, complex interfaces – is broken. Enter AI. Not as an add-on, but as the core engine that will redefine how we build, manage, and evolve products. Here’s how AI is turning PLM from a digital archive into a living, learning innovation system: ⸻ 1. From Lifecycle Management to Lifecycle Intelligence PLM used to store product data. AI now interprets it. From CAD files to IoT signals, AI connects the dots to create insights across the entire lifecycle. Example: AI models predict product performance in the field based on design parameters + usage data. No more “design → test → fail” loops. ⸻ 2. Engineering Assistants in Your PLM AI copilots are coming to your PLM interface – think ChatGPT, but trained on your engineering data. Tasks like: • Auto-generating design variants • Summarizing change requests • Recommending parts from past projects Result: Less time searching, more time creating. ⸻ 3. Breaking the Silos – Finally. AI doesn’t care if your data is in Teamcenter, SAP, or buried in Excel. With LLMs and vector search, AI creates a unified knowledge graph – across systems, departments, and formats. Suddenly, R&D, Quality, and Service are speaking the same data language. ⸻ 4. Goodbye Templates. Hello Generative Engineering. Why design from scratch when AI can suggest the best geometry based on constraints, materials, and cost targets? Generative AI tools in PLM are shifting engineers from “modeling” to “modifying”. ⸻ 5. Continuous Learning from the Field Products generate data long after launch. AI feeds this back into the system: • Which components fail most often? • What usage patterns reduce product lifespan? • How do customer needs evolve? This closes the loop: Field → Design → Better Products ⸻ The Bottom Line: PLM will not disappear – but it will evolve. From static to smart. From management to intelligence. From document-based to insight-driven. If your PLM doesn’t learn, predict, or recommend – it’s already outdated. ⸻ #AIinEngineering #PLM #ProductInnovation #DigitalThread #SmartManufacturing #GenerativeDesign #EngineeringCopilot #TechTransformation

Most companies are using AI for efficiency. Some are accelerating value creation. A great case study is how Colgate-Palmolive is driving innovation. Here are specific ways they are embedding GenAI across innovation processes to substantlly improve research and product development. These come from an excellent article in MIT Sloan Management Review by Tom Davenport and Randy Bean (link in comments). 💡 AI-Driven Product Concept Generation Accelerates Ideation By linking one AI system that surfaces consumer needs with another that crafts product concepts, Colgate-Palmolive can swiftly generate creative ideas like novel toothpaste flavors. This AI-augmented workflow produces a broader product funnel and allows rapid iteration, enabling more employees to participate in the innovation process under guided human oversight. 🔍 Retrieval-Augmented Generation Enhances Data Reliability The firm’s use of retrieval-augmented generation (RAG) integrates company-specific research, syndicated data, and real-time trends from sources like Google search data. This approach minimizes the risk of hallucinations and ensures that responses are deeply grounded in verified, internal content—delivering more accurate market analysis and trend detection. 🤖 Digital Consumer Twins Validate and Refine Concepts Moving beyond traditional focus groups, the company has developed “digital consumer twins”—virtual representations of real consumer behavior. These digital twins rapidly test hundreds of AI-generated product ideas. Early evaluations show a high level of agreement between virtual feedback and actual consumer responses. This innovation speeds up early-stage concept validation and reduces reliance on slower, more limited human panels. 🔐 Democratizing AI Through a Secure Internal AI Hub Colgate-Palmolive’s AI Hub provides employees with controlled access to advanced AI tools (including models from OpenAI and Google) behind corporate firewalls. Mandatory training on responsible AI use, including guardrails and prompt engineering best practices, ensures that employees harness these tools safely and effectively. Built-in surveys and KPI tracking further enable the company to measure improvements in creativity, productivity, and overall work quality. 🌐 Bridging Traditional Analytics with Next-Gen AI for Measurable Impact By integrating traditional machine learning with cutting-edge generative AI, Colgate-Palmolive is not only boosting operational efficiencies but also driving strategic growth. This seamless blend supports tasks ranging from market research and innovation to marketing content creation—demonstrating a holistic, value-driven approach to adopting AI that is a model for other organizations.

Game Prototyping Cheat Sheet In collaboration with Mykola, we made a guide to streamline your game prototyping process and find the fun faster. 𝟭. Define Core Mechanics 🎮 • Identify your game's essential interactions. • Test mechanics rapidly and frequently. • Ensure mechanics are fun in isolation. • Don't layer complexity too early. 𝟮. Follow the Process Flow 🔄 • Begin with concept clarity. • Move to rapid prototyping. • Incorporate feedback and iteration. • Finish with concept validation. 𝟯. Ask the Key Questions❓ • Is your core gameplay intuitive? • Can players grasp the primary goal immediately? • Does the prototype show the game's unique appeal? • Can your concept adapt easily after feedback? 𝟰. Avoid Common Mistakes ❌ • Overambitious scope → Focus on core mechanics first. • Neglecting feedback → Use rapid cycles of testing. • Excessive polish too early → Prototype quickly, refine later. • Poor onboarding → Use contextual hints and tutorials. 𝟱. Track the Right Metrics 📊 • Win Rate • Level Churn • D1-D3 retention • CPI • Playtime 𝟲. Remember the Golden Rule 🔥 • Fail fast, learn faster. ---- DM Mykola Veremiev if your studio needs help testing or scaling the next game idea

For most in the product world, the idea of experimentation or testing immediately conjures up the idea of A/B testing. But A/B testing is really only possible under very specific circumstances. You typically need high volumes of traffic, simple UX choices and clear, measurable metrics. For example, it lends itself well to marketing website content driving conversion, but not to intricate product features inside apps that are meant to encourage product usage. So a great many product managers reject the idea as impossible and resort instead to just blindly building and launching whatever gets requested. But A/B testing is merely one very specific tool. It does not equal the concept of experimentation. True product experimentation is very simple: 1> You come up with ideas and theories (hypotheses) about customer problems or needs, ideally based on feedback and observation. 2> You ideate and build a very basic solution concept for that hypothesis. 3> You validate that concept with customers. 4> Based on the validation, you iterate, pivot or reject the concept. 'Validation' does not mean A/B testing. It does not need to be perfect. Furthermore, A/B testing is itself very far from perfect anyway. There are some very significant technical, methodological and maturity issues which mean that the vast majority of people using these tools are not seeing what they think they are seeing. No, the objective is simply to find the best possible way you have to sense check the solution with the market, and for that to be appropriate effort and cost for the stage of development of the idea. An initial rough prototype could be tested by asking some staff members to try it. Slightly more developed ideas could be tested by using surveys. B2B product prototypes can be tested by asking 2-3 customers. These are all perfectly acceptable forms or validation and 'testing'. Any kind of validation that takes you outside of your own opinions and bias is better than none. #product #productmanagement #productexperimentation #productdiscovery #productstrategy #digitalexperience #ecommerce

💡Guerrilla Testing: 5 tips & tricks Guerrilla testing is an informal, low-cost, and rapid method for gathering user feedback on a product. Unlike more formal usability testing, which often takes place in controlled environments with recruited participants, guerrilla testing is typically done in public places with people who are available at the moment, such as in cafes, parks, or shopping malls. 1️⃣ Prepare ✔ Define clear objectives. Before starting, clarify what you want to learn from the testing (and why you want to do it). Focus on specific aspects of your product when defining objectives. ✔ Prepare design materials: Bring sketches, wireframes, or a prototype that can explain product ideas and be easy to interact with. 2️⃣ Choose the right location ✔ High foot traffic areas: Choose places where your target audience is likely to be. ✔ Relaxed atmosphere: Select locations where people feel comfortable and not rushed so that they are more likely willing to participate. ✔ Offer incentives: Offer small incentives like a coffee voucher or a snack to encourage participation. ✔ Be friendly & approachable: A smile and a casual approach go a long way in getting people to participate. ✔ Be ready to improvise: Guerrilla testing environments are unpredictable, so be prepared to adapt your script and approach on the fly. 3️⃣ Keep it simple & engage with participants ✔ Brief introduction: Keep your introduction short and to the point. Explain what you're doing, how long the testing will take, and what participants will get out of it. ✔ Minimal tasks: Focus on 1-3 key tasks during the 10-minute session to keep the testing brief and engaging. 4️⃣ Capture the essentials ✔ Avoid leading questions: Ask open-ended questions to get genuine feedback rather than guiding participants towards a specific response. ✔ Note-taking: Jot down key observations, but don't let it distract you from engaging with the participant. ✔ Record (with permission): If possible, record the session using a phone or a notepad app to capture nuances you might miss during the test. 5️⃣ Analyze and iterate quickly ✔ Immediate review: Go through your notes and recordings as soon as possible to capture fresh insights. ✔ Document and share key findings: Keep a record of all the insights you gathered, and ensure your team has access to this information. 📕 Guides ✔ A guide to guerrilla testing (by Nick Babich)  https://lnkd.in/dhBZbXkW ✔ A Guerrilla Usability Test on Dropbox Photos (by Francine Lee) https://lnkd.in/dNRFUbtd 🖼 Usability testing methods by Maze #usability #ui #uidesign #ux #uxdesign #testing #design

Being data-driven is often viewed as mastering measurement and optimization—but don't leave discovery and innovation on the table! When it comes to data, an organization's first impulse is to chase certainty, relying on dashboards, precision KPIs, and refined datasets. This is an important efficiency boost, but it's important to keep in mind that breakthroughs and new business models rarely result from meticulous planning. They emerge when someone recognizes an unusual pattern or an overlooked anomaly. This accidental brilliance is precisely what modern data-driven organizations must foster in addition to their hunt for efficiency. When it comes to their use of data, most companies aren't structured for serendipity. They operate in cycles of predictability, continuously refining data to meet expectations. While this optimization generates immediate efficiency gains, it often follows the economic principle of diminishing returns—each incremental improvement costs a bit more and delivers a bit less. Genuine data-driven innovation requires spaces for "curated chaos": environments intentionally designed to surface unexpected findings. Perhaps paradoxically, this demands a high level of data maturity—robust capabilities that create a stable foundation from which exploration can safely occur. Innovation and a data-driven mindset build on the same foundation. Both require intellectual bravery, eye-to-eye interaction across hierarchies, and patience to detect subtle signals. Curated chaos isn't a call to abandon rigor; it's creating spaces where overlooked connections can naturally emerge. It means deploying analytics not merely for measurements and predictions, but as exploratory instruments—provoking questions and challenging assumptions. The most innovative data-driven companies embody such structured curiosity. They balance analytical discipline with openness to surprise. They reward thoughtful questioning as vigorously as decisive answers and recognize that breakthroughs often appear quietly within noise. While optimization often provides the comfort of predictability and quantifiable returns, discovery operates on a different economic model where small investments in exploration can yield disproportionate value. While your competitors perfect their dashboards, consider what they might be missing—the next crucial insight might not be hiding in the cleanest dataset, but in the anomalies you've initially aimed to get rid of. Don’t just optimize with your data—explore it!

How do top UX designers find problems before coding?  They don’t start on screens Paper Prototyping:  Test Ideas Before You Build Them What is Paper Prototyping?   Sketch potential concepts, flows, or screens on paper   Test with real users before investing in digital prototypes Why it matters: • Fast • Cheap • Reveals fundamental usability issues Your Paper Prototyping Kit: • Phone & browser cutouts • Loading indicator •  Under construction” page • Blank paper for on-the-fly screens Tips Before Testing: • Make sure the “computer” knows the screens • Keep all screens consistent in fidelity • Avoid mixing hi-fi & lo-fi screens During the Test: • Scrolling: Use long sheets of paper • Dropdowns: Layer selections on top of screens • Overlays: Place overlay sheet on top Iterate Quickly: •  If a problem appears repeatedly, redraw screens immediately • Quick iterations = faster design solutions Paper prototyping = fast, cheap, effective early testing 💡 Question for you: Have you tested your designs on paper before building digital prototypes?Share your experience below!

When it comes to innovation, the key isn’t just the idea, it’s in the data. Take Kraft Heinz with their Crystal Light Vodka Refreshers. The company saw that nearly 20% of their existing Crystal Light consumers were already using it as a mixer in alcoholic drinks. That’s a consumer signal they could act on. With this insight, Kraft launched a product that not only aligns with customer behavior but taps into an established demand. Then there’s PepsiCo with Cheetos Mac n’ Cheese. PepsiCo Mexico identified a growing demand for household cooking staples and a lack of innovation in the mac and cheese category. Once again, this wasn’t a shot in the dark; it was informed by consumer research. The result? 35M+ pesos in sales in the first two years. Understanding consumer preferences gave Pepsi the edge to succeed. Consumer insights data helps you understand your audience to make confident, data-backed decisions to launch innovations that expand your brand’s reach and drive volume growth. Consumer insights data helps you understand your audience to make confident, data-backed decisions to launch innovations that expand your brand’s reach and drive volume growth. How are you using consumer insights for your next innovation? #RetailMedia #ConsumerInsights #MarketingStrategy #ExperientialMarketing #Innovation #CPG