Innovation Roadmapping Process

Wheel for Sustainable Business Innovation 🌎 The sustainability landscape is evolving rapidly, and businesses are increasingly expected to integrate environmental and social considerations into their innovation processes. However, traditional innovation frameworks often fall short by focusing solely on customer needs, financial returns, and technical feasibility, leaving critical planetary challenges unaddressed. A more comprehensive approach is needed—one that embeds sustainability at the core of value creation. The 130+ Value Proposition Types Wheel is a practical tool that helps organizations frame innovation efforts across four key dimensions: People, Planet, Profit, and Progress. It provides over 130 value types that businesses can leverage to ensure their projects contribute meaningful solutions to global challenges such as climate action, resource efficiency, social inclusion, and technological advancement. This approach shifts the focus beyond immediate customer needs to include long-term sustainability impacts across entire ecosystems. By using structured frameworks like this, companies can link their innovation projects directly to UN Sustainable Development Goals (SDGs), addressing critical issues such as climate resilience, biodiversity, and social equity. The tool also encourages the use of metrics to track progress, making sustainability-driven innovation more actionable and measurable across industries. It helps businesses unlock new forms of value while addressing both environmental risks and opportunities. The tool is adaptable to different phases of the innovation process, from identifying unmet needs to scaling solutions in the market. It guides organizations in understanding how their innovations create value in areas such as climate action, circularity, supply chain management, and stakeholder engagement. This makes it relevant for both B2B and B2C companies aiming to enhance their impact while future-proofing their operations. Originally developed by Explorer Labs, this tool has been referenced in the past and continues to remain highly useful as businesses advance their sustainability journeys. As 2025 begins, leveraging tools like this can help organizations move from incremental improvements to transformative solutions, embedding sustainability into innovation processes that deliver lasting value. #sustainability #sustainable #business #esg #climatechange #innovation #SDGs

🏗 How To Tackle Large, Complex Projects. With practical techniques to meet the desired outcome, without being disrupted or derailed along the way ↓ 🤔 99% of large projects don’t finish on budget and on time. 🤔 Projects rarely fail because of poor skills or execution. ✅ They fail because of optimism and insufficient planning. ✅ Also because of poor risk assessment, discovery, politics. 🎯 Best strategy: Think Slow (detailed planning) + Act Fast. ✅ Allocate 20–45% of total project effort for planning. ✅ Riskier and larger projects always require more planning. ✅ Think Right → Left: start from end goal, work backwards. ✅ For each goal, consider immediate previous steps/events. ✅ Set up milestones, prioritize key components for each. ✅ Consider stakeholders, users, risks, constraints, metrics. 🚫 Don’t underestimate unknown domain, blockers, deps. ✅ Compare vs. similar projects (reference class forecasting). ✅ Set up an “execution mode” to defer/minimize disruptions. 🚫 Nothing hurts productivity more than unplanned work. Over the last few years, I've been using the technique called “Event Storming” suggested by Matteo Cavucci to capture user’s experience moments through the lens of business needs. With it, we focus on the desired business outcome, and then use research insights to project events that users will be going through towards that outcome. On that journey, we identify key milestones and break user’s events into 2 main buckets: user’s success moments (which we want to dial up) and user’s pain points or frustrations (which we want to dial down). We then break out into groups of 3–4 people to separately prioritize these events and estimate their impact and effort on Effort vs. Value curves (https://lnkd.in/evrKJUEy). The next step is identifying key stakeholders to engage with, risks to consider (e.g. legacy systems, 3rd-party dependency etc.), resources and tooling. We reserve special timing to identify key blockers and constraints that endanger successful outcome or slow us down. If possible, we also set up UX metrics to track how successful we actually are in improving the current state of UX. When speaking to business, usually I speak about better discovery and scoping as the best way to mitigate risk. We can of course throw ideas into the market and run endless experiments. But not for critical projects that get a lot of visibility — e.g. replacing legacy systems or launching a new product. They require thorough planning to prevent big disasters and urgent rollbacks. If you’d like to learn more, I can only highly recommend "How Big Things Get Done" (https://lnkd.in/erhcBuxE), a wonderful book by Prof. Bent Flyvbjerg and Dan Gardner who have conducted a vast amount of research on when big projects fail and succeed. A wonderful book worth reading! Happy planning, everyone! 🎉🥳

AI needs a lot less conversation and more action. Most businesses don’t have an AI Action Plan, so they’re stuck in endless planning cycles and proof of concept purgatory. An AI Action Plan details: Opportunities the business is working on. These are use cases that align with the business and operating models. I recommend a 1:1 ratio of productivity/efficiency to revenue growth initiatives. Most internal efficiency AI should be bought vs. built. AI costs are dropping, and vendors serve internal use cases for less than the business can. Customer-facing products generate much higher returns. Monetization strategy. Define the critical pieces of AI go-to-market: Pricing, customer adoption, design, scaling, and optimization. Set the expectation that costs and returns must be estimated upfront. Break the “we won’t know until we build it” cycle that leads to proof of concept purgatory. Data and basic analytics make accurate, upfront opportunity size, cost, and return estimation feasible. Product roadmap. Break big initiatives into features that can be delivered quarterly. The one good thing about PoCs is rapid delivery and feedback cycles. Build products with that approach, and returns show up rapidly, too. Align feature delivery to develop cohesive products that support a use case, workflow, process of work, or customer need. I wrote a how-to guide for building AI Action Plans with a template you can use here: https://lnkd.in/gmJZ63Cf

Product leaders, stop hiding behind docs! If your team is still spending all their time in PRDs and product strategy docs, they're not operating in 2025. AI prototyping has literally changed the game. Here's how teams should do it: — THE OLD WAY (STILL HAUNTS MOST ORGS) 1. Ideation (~5% actually prototyped) “We should build X.” Cool idea. But no prototype. Just a Notion doc and crossed fingers. 2. Planning (~15% use real prototypes) Sketches in Figma. Maybe a flowchart. But nothing a user could actually click. 3. Discovery (~50% try protos) Sometimes skipped. Sometimes just a survey. Rarely ever tested with something interactive. 4. PM Handoff (~5%) PM: “Here’s the PRD.” Design: “Uhh… where’s the prototype?” PRDs get passed around like homework. 5. Design Design scrambles to build something semi-clickable, just so people stop asking “what’s the plan?” 6. Eng Start Engineering starts cold. No head start. They’re building from scratch because nothing usable exists. — WHAT HAPPENS - Loop after loop. Everyone frustrated. - Slow launches. Lots of guesswork. - And no one truly understands the user until it’s too late. — THE NEW WAY (THIS IS HOW WINNERS SHIP) 1. Ideation PMs don’t just write ideas. They prototype them. Want to solve a user problem? Click, drag, test. There. No waiting. No “someday.” You build it, even if it’s ugly. 2. Planning Prototypes are the roadmap. You walk into planning with a live flow, not a list of features. And everyone’s like: “Oh. THAT’S what you meant.” 3. Discovery Real users. Real prototypes. You send them a flow and you watch them break it. You’re not guessing anymore. You’re observing. 4. PM Handoff PMs don’t just hand off docs. They ship working demos alongside the PRD. No more “interpret this paragraph.” Just click and see it work. 5. Design Designers don’t start from scratch. They take what’s already tested, validated, and tweak it. Suddenly, “design time” is “refinement time.” 6. Eng Start Engineers don’t wait around. They start with something usable. If not, they prompt an AI tool to build it. And we’re off to the races. — If you want to see how AI prototyping actually works (and learn from expert Colin Matthews), check out the deep dive: https://lnkd.in/eJujDhBV

Estimating timelines and workloads is a challenging task for data analysts. Here's a structured approach to bring clarity to the unknown: 1. 𝗕𝗿𝗲𝗮𝗸 𝗜𝘁 𝗗𝗼𝘄𝗻: Start by breaking the project into smaller, manageable tasks. Think about data collection, cleaning, analysis and visualization. It's easier to estimate pieces than the whole puzzle.     2. 𝗣𝗮𝘀𝘁 𝗮𝘀 𝗮 𝗚𝘂𝗶𝗱𝗲: Look back at similar projects you've tackled. Use these as benchmarks. No exact matches available? Break down the differences and adjust your estimates accordingly.     3. 𝗕𝘂𝗳𝗳𝗲𝗿 𝗳𝗼𝗿 𝗨𝗻𝗰𝗲𝗿𝘁𝗮𝗶𝗻𝘁𝘆: Always include a buffer time for unforeseen challenges (because they will come). A good rule of thumb? Add 20% more time to your initial estimate.     4. 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸: Present your initial structure and timeline to the team or stakeholders early on. Their insights might highlight areas you've overlooked or suggest shortcuts you hadn’t considered.     5. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝗮𝗻𝗱 𝗔𝗱𝗷𝘂𝘀𝘁: As the project progresses, keep an eye on timelines versus actual progress. Be ready to adjust your estimates and communicate changes proactively.     6. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝘀: After project completion, reflect on the accuracy of your estimates. What went as planned? What didn’t? Documenting these learnings will refine your future estimates. Estimating is as much an art as it is a science. It requires understanding the scope, drawing on experience and anticipating the unexpected. Embrace this process and with each project, your forecasting will get better. How do you forecast the timelines for your data projects? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #businessanalytics #projectmanagement #projecttimeline #estimation

How can a maturity model inform leadership development? Read on to find out. Using a Maturity Model has helped me (and organizations I work with) to align leadership development with the company's growth stages. I created my own model below to help organizations understand the level their leaders are at, and where they need to be. Leaders often use this in our coaching sessions to self-identify their strengths and leadership development needs. Here's a quick guide to understanding each stage: Stage 1: Reactive Here, leadership is crisis-driven, focusing on immediate challenges. Leaders and teams may be experiencing firefighting, rework and poor results. Leaders at this level are typically not leading themselves or others effectively. They are likely to be in need of training, mentoring and coaching supports to move out of this stage and into stage 2. Stage 2: Stabilizing At Stage 2, there is a slight shift from reactive to proactive working. Leaders are taking steps to implement standard procedures, involve people in improvement work, improve communication, and take feedback more seriously. At this stage, leaders are still in the early stages of learning however their efforts are obvious to others. Regular support remains necessary for leaders at this stage. Stage 3: Organizing Stage 3 involves formalizing systems and planning for the future. Here. leaders build frameworks for effective communication and collaboration so that their teams can become more self-managing. They coach and mentor their teams, providing them with the skills and autonomy to deal with the day to day. Teams can mostly meet their expectations. Stage 4: Integrating In Stage 4, leaders operate with a systems mindset. They see and share a clear vision and shape cross-functional collaboration to work towards that vision. They are emotionally intelligent, transparent in their own communication, and skilled in giving and receiving feedback, fostering trust among their teams. As a result, teams often exceed expectations. Stage 5: Optimizing Finally, Stage 5 is called Optimizing. This stage emphasizes continuous improvement and innovation. Leaders value diverse perspectives, and promote collaboration over competition so that teams can solve problems creatively. Leaders at this stage are the best in business. Under their leadership, people and teams not only exceed expectations but continuously develop themselves and engage in continuous improvement to sustain their place above their competitors. 💡 Leaders are not born with the skills to lead at stage 5. They develop these skills over time, with support. Use this maturity model (or choose one that suits you) to determine where your organization and your leaders are at, and identify the specific behaviors and capabilities needed to improve. #leadershipdevelopment #maturitymodel #leadershipskills #organizationalbehaviour

What if we reimagined the Double Diamond through the lens of Jobs-to-be-Done? 🤔 Product Management is about mastering various methodologies and knowing when to apply them. No single framework fits all scenarios - the key is understanding how different approaches can complement each other to drive better outcomes. I have been learning and practicing the art and science of Innovation through the concepts of JTBD, Human Centered Design, Design Thinking, Customer Driven Innovation, Continuous Discovery, Product Discovery, Lean, etc., I've found these methodologies aren't just related, they're deeply interconnected pieces of the same puzzle. I took the classic double diamond design thinking framework and applied JTBD to it and here is how it looks in my view. While the double diamond model divides the journey into Problem → Solution spaces, the evolved version speaks the language of jobs and outcomes 💎Left Diamond: Transformed from problem-finding to "Jobs & Outcomes" - focusing on understanding what customers are trying to achieve in their contexts. 🌉The Bridge: "Opportunity Statements" replace "Problem Definition" - shifting from fixing issues to unlocking potential. Opportunity Statements are what Tony Ulwick calls "Hidden Growth Opportunities". These statements guide our innovation direction. 💎Right Diamond: Maintains the Design/Develop and Iterate/Deliver phases, but shifts validation focus to measuring how effectively we enable customers to achieve their desired outcomes. This framework moves beyond problem-solution thinking to create value through deep understanding of customer progress and success metrics in the form of jobs and outcomes. Have you integrated different innovation frameworks in your work? What have you learned? Would love to hear your experiences! #innovation #JTBD #designthinking #productdiscovery

90% 𝗣𝗠𝘀 𝘀𝘁𝗿𝘂𝗴𝗴𝗹𝗲 𝘁𝗼 𝗱𝗼 𝘁𝗵𝗶𝘀 𝗶𝗻 𝘁𝗵𝗲𝗶𝗿 𝗲𝗮𝗿𝗹𝘆 𝗰𝗮𝗿𝗲𝗲𝗿 👇🏻 When we have so much to do, connecting dots between them becomes a real challenge. One of the hardest things is 𝗹𝗶𝗻𝗸𝗶𝗻𝗴 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝘁𝗼 𝗿𝗲𝗮𝗹 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀. Most of the PMs don’t get it right initially and it’s confusing to figure out how to relate all the strategic data points. So how do we do it the right way? Opportunity Solution Tree (OSTs) by Teresa Torres are one of the most effective frameworks for product discovery and decision-making. 👉🏻 𝗢𝗦𝗧𝘀 𝗵𝗲𝗹𝗽 𝘆𝗼𝘂 𝘃𝗶𝘀𝘂𝗮𝗹𝗹𝘆 𝗺𝗮𝗽: 1️⃣ Business Objectives: The measurable outcomes your team is working toward. 2️⃣ Opportunities: Key user needs or pain points that can drive those objectives. 3️⃣ Solutions: Potential features or ideas to address those opportunities. 4️⃣ Assumption Tests: Experiments to validate whether your solutions will work. 👉🏻 𝗢𝗦𝗧𝘀 𝗼𝗳𝗳𝗲𝗿 𝘀𝗲𝘃𝗲𝗿𝗮𝗹 𝗺𝗮𝗷𝗼𝗿 𝗯𝗲𝗻𝗲𝗳𝗶𝘁𝘀: Aligning work with outcomes: They draw a direct line between your team’s efforts and the business impact, helping everyone see the "why" behind the work. Prioritizing alternatives: They let you evaluate which opportunities or solutions are most promising, reducing the noise and focusing the team. Avoiding logical flaws: OSTs expose common pitfalls like pursuing solutions not tied to outcomes or skipping discovery. 👉🏻 𝗛𝗼𝘄 𝘁𝗼 𝗴𝗲𝘁 𝘀𝘁𝗮𝗿𝘁𝗲𝗱: 𝗦𝘁𝗲𝗽 1: Define a measurable outcome. Tie it to business impact (e.g., “Increase onboarding completion by 15%”). 𝗦𝘁𝗲𝗽 2: Map real opportunities. Use user research and analytics to group pain points into actionable categories. 𝗦𝘁𝗲𝗽 3: Brainstorm broadly but realistically. Generate multiple solutions tied to specific opportunities. 𝗦𝘁𝗲𝗽 4: Test assumptions first. Validate risky ideas using prototypes or A/B tests before committing resources. 𝗦𝘁𝗲𝗽 5: Review and refine regularly. Ensure your OST adapts as user needs and priorities evolve. Spot logical gaps. Ask yourself - - Are we solving a real problem or jumping to solutions? - Do we have too many competing outcomes or directions? - Are we overloaded with solutions and need to narrow focus? 👉🏻 Opportunity Solution Trees help you prioritize work that delivers meaningful results. If you’ve used OSTs, what tips or lessons have you learned? PS: Would you want me to create a complete guide on OSTs with practical examples in the simplest way? Let me know in the comments!

Imagine you're planning a simple trip to the grocery store. In the best-case scenario, you arrive, find a parking spot right in front, and there's no line at the checkout. In the most likely scenario, the store is a bit busy—you park a little further away, wait in a short line, but everything goes fairly smoothly. In the worst-case scenario, the store is packed, there are no parking spots, you wait in a long checkout line, and the item you need is out of stock. This everyday scenario illustrates the concept of three-point estimates, a valuable tool for planning tasks with uncertainty, particularly in software testing. In testing, whether you're estimating the effort needed for automation framework development, or regression test execution, considering three different outcomes—Optimistic, Most Likely, and Pessimistic—can provide a more realistic estimate. Let’s break it down with a work-related example. Suppose you're preparing a test strategy. If everything goes perfectly, it might take 8 days (Optimistic). If typical challenges arise, it could take 10 days (Most Likely). But if significant delays occur, it might take 12 days (Pessimistic). Using the formula for three-point estimates: The formula for calculating the estimate using these three scenarios is: E = (Pessimistic + 4 x Most Likely + Optimistic) / 6 Applying this to our example: E = (12 + 40 + 8) / 6 = 10 days This approach provides a balanced estimate, leaning towards the most likely scenario, while still considering the best and worst possibilities. While this method is more time-consuming and requires thorough documentation to avoid misunderstandings, it ultimately leads to more accurate and realistic project timelines. Have you tried using this technique in your projects? Please share your experience in the comments below. #SoftwareTesting #QualityAssurance #TestMetry #Estimation