Strategies for Implementing Innovation in Product Design

I'm not usually one to share my product design 'hacks.' Hope this helps more folks tap into the 🪄 of better product thinking. 1. Steal workflows from industries outside of tech. Architects, game designers, even chefs—everyone solves complex problems differently. Borrow their frameworks. It’s wild how much it improves your design logic and product flows. 2. Every new feature should subtract something old. If adding a feature doesn’t naturally replace or improve something else, you’re layering complexity. The best products stay sharp because they evolve—not accumulate. 💥 3. Use constraints to force better solutions. Limit the width. Limit the colors. Limit the interaction patterns. Constraints make you think deeper, and users will never feel the difference—except that everything just works. 4. Kill unnecessary settings. If a setting exists to “fix” something that could have been designed better by default, you’ve taken the lazy route. The best products have fewer decisions for users to make, not more. 5. Build interactive prototypes, even for simple ideas. Static designs don’t reveal problems—movement does. Sketch out transitions, hover states, and micro-interactions early. It sharpens your design instinct fast. 6. Start with mobile. Not because “mobile-first” is trendy—but because smaller screens force brutal prioritization. If the design works on mobile, scaling it up feels like a reward. 7. Test for boredom, not just usability. “Does this work?” is step one. Step two is asking, “Would I use this every day without hating it?” Usable products get abandoned. Engaging ones stick. 8. Design without data at your own risk. Placeholder content lies. Inject real (or semi-real) data early. Long names, weird edge cases, and incomplete info will blow up pixel-perfect layouts faster than anything else. 9. Never trust the first solution. The first design is often the most obvious. The second one starts to explore. The third version? That’s usually the winner. Keep pushing until it surprises you. --- PS - There are somehow 125,000 of y'all following along. Appreciate your support 🙏 🎁 For regular product design/product building insights, don’t miss ADPList’s Newsletter — my free weekly newsletter: https://lnkd.in/guJJsBaT

Product managers & designers working with AI face a unique challenge: designing a delightful product experience that cannot fully be predicted. Traditionally, product development followed a linear path. A PM defines the problem, a designer draws the solution, and the software teams code the product. The outcome was largely predictable, and the user experience was consistent. However, with AI, the rules have changed. Non-deterministic ML models introduce uncertainty & chaotic behavior. The same question asked four times produces different outputs. Asking the same question in different ways - even just an extra space in the question - elicits different results. How does one design a product experience in the fog of AI? The answer lies in embracing the unpredictable nature of AI and adapting your design approach. Here are a few strategies to consider: 1. Fast feedback loops : Great machine learning products elicit user feedback passively. Just click on the first result of a Google search and come back to the second one. That’s a great signal for Google to know that the first result is not optimal - without tying a word. 2. Evaluation : before products launch, it’s critical to run the machine learning systems through a battery of tests to understand in the most likely use cases, how the LLM will respond. 3. Over-measurement : It’s unclear what will matter in product experiences today, so measuring as much as possible in the user experience, whether it’s session times, conversation topic analysis, sentiment scores, or other numbers. 4. Couple with deterministic systems : Some startups are using large language models to suggest ideas that are evaluated with deterministic or classic machine learning systems. This design pattern can quash some of the chaotic and non-deterministic nature of LLMs. 5. Smaller models : smaller models that are tuned or optimized for use cases will produce narrower output, controlling the experience. The goal is not to eliminate unpredictability altogether but to design a product that can adapt and learn alongside its users. Just as much as the technology has changed products, our design processes must evolve as well.

How Do You Avoid POC Purgatory and Prioritize Innovations with Big Returns? This is a question that comes up in every industry panel—and probably from your finance leaders too. But it’s not a new dilemma. Every time there’s a leap in technology—whether it was Big Data or now with Agentic AI—this question resurfaces. While true innovation can’t be fully predicted, I believe it can be programmed for a higher chance of commercial success. Here are three strategies that have worked for me: 1. Run Purposeful Hackathons Hackathons are everywhere, but too often, the energy fizzles out post-event. Instead, create theme-based tracks that align with your business strategy. Build cross-functional teams (data, domain, design) to ensure ideas are robust and actionable. 2. Commercial Innovation Process As an outcome of hackathon, review all ideas and advance the best ones into a structured innovation process. Allocate seed resources to test these ideas in the market—including willingness to pay. Only after real-world validation should you build business cases and incubate ideas with clear stage gates 3. Celebrate and Scale Success Recognize and celebrate wins across the organization—commercial successes, but also the creation of reusable capabilities and Intellectual Property. This inspires future innovation and encourages ongoing investment. Bottom line: Don’t treat innovation as a one-off event. Make it an ongoing, integrated process. When innovation is embedded into your company’s DNA, you’ll see bigger returns and avoid getting stuck in POC purgatory. How do you keep innovation moving in your organization? Share your thoughts below! 👇

GTM Systems teams CANNOT be order-takers in the AI era. Innovation won’t come from requirements - it will come from experiments. And these teams must evolve into true Product orgs. I see this as the most overlooked challenge with adopting AI in GTM Systems. A successful strategy means you need to move FAST. Experiment. Prototype. Iterate. This is the default standard in Product culture. The Problem: this approach runs counter to Biz Tech culture. Salesforce & Internal Tools experts will hear this and say I’m crazy. “You need strict governance & careful planning to scale systems infrastructure.” And previously, I would completely agree. But the AI era is a different beast for a few reasons. 1) Teams don’t know what’s possible or what they want from AI. • Success is judged by behavior change, not completion of a backlog item. • The value of AI will emerges through usage and iteration • New features will not result from traditional requirements gathering. 2) AI has completely shifted the delivery timetable. • Historically, the goal is to craft a long-term GTM Systems roadmap. • Then, you break key initiatives into months long implementation cycles. • But AI innovation is moving too fast to only ship 1x in 3 months. • Companies need to adopt a rapid experimentation mindset. 3) You CAN move fast by investing in composability. • An API-first approach allows you to ship outside core infrastructure. • Previously, all new feature build happened in tools like Salesforce. • You’re constrained by technical debt, dependencies, and more. • Now, you can deploy AI solutions in isolation. • An app that communicates to other systems via API is relatively low risk. Realistically, this approach will make most Biz Tech teams uncomfortable. Rapid experimentation historically led directly to scalability issues. But this is the default way of operating for core Product teams. A few ways they get it right without leaving a wake of technical debt: 1) Use MVPs with clear scope • Ship measurable slices of value to learn, not solve a whole problem up front. 2) Invest in composability • Every test is built with future modularity in mind - winning ideas can scale. 3) Leverage Users for Research • Stakeholders & Users are a source of insights, not requests. • It’s the old Henry Ford quote: “If I asked people what they wanted, they would have said faster horses.” 4) Document Assumptions • Experiments have clear hypotheses - learn from every test, even if it fails. GTM Systems teams have the opportunity to lead innovation like never before. AI is delivering the much-needed attention and investment in this function. And for the first time, they are less constrained by stakeholder requests. These teams can finally DRIVE strategy, not just support it. But success will depend on their ability to embrace this new approach. __ #AI #GTM #CRM