Innovation Roadmapping Process

In 2021, I proposed an initiative I thought was brilliant—it would help my team make faster progress and better leverage each member's unique skills. Brilliant, right? Yet, it didn’t take off. Many ideas or initiatives fail because we struggle to gain buy-in. The reasons for resistance are many, but Rick Maurer simplifies them into three core categories: (1) "I don’t get it" Resistance here is about lack of understanding or information. People may not fully grasp the reasons behind the change, its benefits, or the implementation plan. This often leaves them feeling confused or unsure about the impact. (2) "I don’t like it" This is rooted in a dislike for the change itself. People might feel it disrupts their comfort zones, poses a negative impact, or clashes with personal values or interests. (3) "I don’t like YOU." This is about the messenger, not the message. Distrust or lack of respect for the person initiating the change can create a barrier. It might stem from past experiences, perceived incompetence, or lack of credibility. When I work with leaders to identify which category resistance falls into, the clarity that follows helps us take targeted, practical steps to overcome it. - To address the "I don't get it" challenge, focus on clear, accessible communication. Share the vision, benefits, and roadmap in a way that resonates. Use stories, real-life examples, or data to make the case relatable and tangible. Give people space to ask questions and clarify concerns—often, understanding alone can build alignment. - To address the "I don't like it" challenge, emphasize empathy. Acknowledge potential impacts on routines, comfort zones, or values, and seek input on adjustments that could reduce disruption. If possible, give people a sense of control over aspects of the change; this builds buy-in by involving them directly in shaping the solution. - And to address the "I don't like you" challenge, solving for the other two challenges will help. You can also openly address past issues, if relevant, and demonstrate genuine commitment to transparency and collaboration Effective change isn’t just about the idea—it’s about knowing how to bring people along with you. #change #ideas #initiatives #collaboration #innovation #movingForward #progress #humanBehavior

This slide gets copied and stolen from me more than any other. It’s the blueprint for saving 4+ years and $4+ million on failed AI initiatives. Start with an iterative PMPV framework to avoid 4 expensive mistakes. Propose – Top-down and bottom-up opportunity discovery workshops. The business articulates its needs vs. being told what should be built. The opportunity is assessed. Does it require AI, or can a less expensive technology work? Measure – AI Product Managers work with stakeholders/customers to define the problem space and assess the opportunity size. They work with the data/AI team to assess feasibility and estimate costs. Prioritize – The 3 assessments allow the business to reach a consensus on a value-based prioritization without being dragged into technical solution complexity. The roadmap is updated. Validate – Did the initiative deliver the expected impact, revenue, margins, etc.? If not, why, and is it salvageable? If it did, can more value be delivered quickly? How much? The roadmap is updated/reprioritized. The roadmap can’t be static. New opportunities emerge, and some opportunities don’t pan out. Businesses need to take a pipeline approach with multiple opportunities on the roadmap. It can’t be opinion-driven or abandoned for every fire drill. Opportunity size estimation is critical, or the loss from constant reprioritization cannot be quantified. Loss allows AI Product Managers to push back. That’s it. Iterative PMPV is a lightweight product strategy framework that supports the unique needs of AI features and products. Remember, frameworks are only as good as the people who manage them. No AI Product Manager == No AI products, revenue, or cost savings…just a giant cost center. #ProductManagement #AIStrategy

Over the years, I've discovered the truth: Game-changing products won't succeed unless they have a unified vision across sales, marketing, and product teams. When these key functions pull in different directions, it's a death knell for go-to-market execution. Without alignment on positioning and buyer messaging, we fail to communicate value and create disjointed experiences. So, how do I foster collaboration across these functions? 1) Set shared goals and incentivize unity towards that North Star metric, be it revenue, activations, or retention. 2) Encourage team members to work closely together, building empathy rather than skepticism of other groups' intentions and contributions. 3) Regularly conduct cross-functional roadmapping sessions to cascade priorities across departments and highlight dependencies. 4) Create an environment where teams can constructively debate assumptions and strategies without politics or blame. 5) Provide clarity for sales on target personas and value propositions to equip them for deal conversations. 6) Involve all functions early in establishing positioning and messaging frameworks. Co-create when possible. By rallying together around customers’ needs, we block and tackle as one team towards product-market fit. The magic truly happens when teams unite towards a shared mission to delight users!

I pitched a LOT of internal data infrastructure projects during my time leading data teams, and I was (almost) never turned down. Here is my playbook for getting executive buy-in for complex technology initiatives: 1. Research top-level initiatives: Find something an executive cares about that is impacted by the project you have in mind. Example: We need to increase sales by 20% from Q2-Q4 2. Identify the problem to be overcome: What are the roadblocks that can be torn down through better infrastructure? Example: We do not respond fast enough to shifting customer demand, causing us to miss out on significant selling opportunities. 3. Find examples of the problem: Show leadership this is not theoretical. Provide use cases where the problem has manifested, how it impacted teams, and quotes from ICs on how the solution would have greatly improved business outcomes. Example: In Q1 of 2023 multiple stores ran out of stock for Jebb Baker’s BBQ sauce. We knew the demand for the sauce spiked at the beginning of the week, and upon retroactive review could have backfilled enough of the sauce. We lost an expected $3M in opportunities. (The more of these you can provide the better) 4. Explain the problem: Demonstrate how a failure of infrastructure and data caused the issue. Clearly illustrate how existing gaps led to the use case in question. Example: We currently process n terabytes of data per day in batches from 50 different data sources. At these volumes, it is challenging to manually identify ‘needle in the haystack’ opportunities, such as one product line running low on inventory. 5. Illustrate a better world: What could the future world look like? How would this new world have prevented the problem? Example: In the ideal world, the data science team is alerted in real-time when inventory is unexpectedly low. This would allow them to rapidly scope the problem and respond to change. 6. Create requirements: Define what would need to be true both technologically and workflow-wise to solve the problem. Validate with other engineers that your solution is feasible. 7. Frame broadly and write the proposal: Condense steps 1-5 into a summarized 2-page document. While it is essential to focus on a few use cases, be sure not to downplay the magnitude of the impact when rolled out more broadly. 8. Get sign-off: Socialize your ideal world with potential evangelists (ideally the negatively impacted parties). Refine, refine, refine until everyone is satisfied and the outcomes are realistic and achievable in the desired period. 9. Build a roadmap: Lay out the timeline of your project, from initial required discovery sessions to a POC/MVP, to an initial use case, to a broader rollout. Ensure you add the target resourcing! 10. Present to leadership alongside stakeholders: Make sure your biggest supporters are in the room with you. Be a team player, not a hero. Good luck! #dataengineering

I used to believe Customer Success should drive the product roadmap. Here’s what I know now. The roadmap should be a collaborative design, built by Sales, CS, Support, Product, Marketing, and Leadership together. No one team sees the full picture. ▶️ Marketing sees market shifts. ▶️ Sales hears why deals are lost. ▶️ Leadership ties it all to strategy. ▶️ Product builds scalable solutions. ▶️ Support sees recurring pain points. ▶️ CS sees where customers struggle. When we isolate roadmap ownership, we build for one team. When we collaborate, we build for the entire business. Want true collaboration? Set it up intentionally: 1️⃣ Monthly cross-functional planning meetings: Bring leaders together to align on customer feedback, market signals, and business priorities. 2️⃣ Voice of Customer (VoC) programs: Collect real user feedback consistently — surveys, interviews, success metrics. 3️⃣ Closed-lost analysis with Sales: Review why deals are lost and what patterns could inform the roadmap. 4️⃣ Support ticket and escalation reviews: Identify top friction points that need attention. 5️⃣ Market research and trend studies: Analyze competitor moves and emerging trends quarterly. 6️⃣ Executive alignment sessions: Validate that roadmap priorities map directly to company strategy. The roadmap shouldn’t be a surprise. It should be a shared vision. One that every team feels connected to — and proud of. How does your company approach roadmap collaboration today? Because if you're only building with one team's input, you're only solving one piece of the puzzle. ____________________ 📣 If you liked my post, you’ll love my newsletter. Every week I share learnings, advice and strategies from my experience going from CSM to CCO. Join 12k+ subscribers of The Journey and turn insights into action. Sign up on my profile.

Roadmaps are not one-size-fits-all. They should be tailored to each team. Why? Because roadmaps aren’t just timelines, they’re communication tools. And what you communicate depends on your audience. Consider these examples: - Product Development Teams need detailed, execution-focused roadmaps. Think engineering commitments by quarter, discovery vs. delivery status, and alignment on what’s coming next. - Sales Teams are looking for big-picture stories. They need to know which features will excite customers and when they might expect them. These roadmaps focus on value propositions rather than granular details. - Leadership needs a strategic view. Roadmaps for them focus on initiatives and capacity planning, linking back to the company's broader vision and goals. To create all these roadmap versions effectively, we need collaboration between product operations and product teams. That way, each roadmap serves its specific purpose and audience. Take Rebecca’s example from my Product Operations book with Denise Tilles. By keeping these roadmaps aligned with business rationale, she was able to bridge the gap between sales expectations and product realities, building trust and transparency across the organization. She also introduced a clear framework for sharing feature status across teams. This included stages like Discovery, Alpha, Beta, and GA. Understanding these phases ensures that everyone, from sales to engineering, knows the real status of a product feature and can communicate that clearly to customers. The magic happens when product operations steps up to support these efforts. By providing tools and frameworks, ProductOps help teams to align their roadmaps with strategic intents and prevent the kind of overselling that happens when teams aren’t on the same page. In short, roadmaps aren't just plans, they’re how you build alignment. How are you tailoring roadmaps for different departments in your organization? Let me know in the comments!

Your organization says it wants innovation. Your processes suggest otherwise. I watched a brilliant executive spend 6 weeks getting approval to test a $50/month software tool. By the time she got the green light, two competitors had already deployed similar solutions and moved ahead. This isn’t stupidity. It’s institutional logic taken to its absurd conclusion. Here’s what’s really happening: Every approval layer was added for good reasons. Every committee was formed to prevent real disasters. Every process was implemented to solve actual problems. But collectively, they’ve created something no one intended: organizations so protected from making bad decisions that they can’t make ANY decisions. The “We Already Have a Process” Problem Walk into any corporate meeting about innovation and listen to the language: • “How does this align with our existing governance framework?” • “What’s the ROI justification for deviating from proven methodologies?” • “We need to ensure this integrates with our current compliance requirements.” These aren’t questions. They’re defensive mantras. The underlying message is clear: If your innovation doesn’t fit our existing framework, the problem isn’t with the framework—it’s with your innovation. The Expert Authority Trap The CIO who built their career preventing security breaches. The CFO who optimized cost structures. The Legal counsel who knows every compliance pitfall. These aren’t obstructionist bureaucrats. They’re experts whose professional identity depends on understanding why things might go wrong. But expertise optimized for preventing known problems becomes a barrier to discovering unknown opportunities. What Actually Works The organizations winning this game don’t eliminate their governance systems—they create parallel tracks. → High-risk decisions get rigorous 6-week evaluations → Low-risk experiments get 6-day pilot approvals → Different types of innovation get different types of oversight The Real Challenge This isn’t about process—it’s about identity. When systems change, people must grapple with fundamental questions: What’s my role? What’s my value? Who am I in this new world? The IT professional trained to prevent problems must learn to enable possibilities. The finance analyst who eliminated costs must develop intuition about when spending money to learn is the most economical choice. The Bottom Line Organizations that thrive in the next decade won’t choose between innovation and control. They’ll master both simultaneously. They’ll develop what I call “institutional ambidexterity”—the ability to be stable AND adaptive, careful AND experimental, systematic AND creative. The question isn’t whether your organization can change. It’s whether your organization can learn to change intelligently. What’s the biggest innovation killer in your organization? Share your story in the comments—I read every one.

Getting back to what I know and love best... the application of disruptive innovation (DI) theory to the vertical lift domain. So why is this important? If you are a disruptor, incumbent, investor or other stakeholder it can help you to better predict and forecast the potential impact of DI as well as form a strategic gameplan or competitive response based on empirical evidence spanning several decades. There are three forms of DI driven by the eVTOL as enumerated in the illustration: 1️⃣ Low-end - disruptor enters near the bottom of the established market with a lower tier offering in terms of price and performance 2️⃣ New market - disruptor seeds an adjacent market with a new, economical use case based on a mix of novel and traditional performance attributes 3️⃣ High-end - disruptor enters near the top of the established market with an aspirational offering that carries a very high list price The blue charts on the right are based on a generic model of DI and tweaked to reflect the particular go-to-market approach. Closer inspection shows differences in the y-axis, the DI trigger (the squiggly in the bottom left), and the labels on the bell-shaped distribution of needs in the upper right. Performance under low-end disruption is measured against speed, range, payload and endurance which are traditional attributes that define the mainstream VTOL requirement. This bottom up entry carries a very low list price reflective of anemic performance. The DI trigger that opens the door to over-served customers here is the need for a low cost offering with less raw performance and fewer bells and whistles. Disruption occurs when the upstart improves the performance of their offering enough to meet the mainstream requirement (gold dot) while preserving their economic advantage moving upmarket. New market disruption targets a niche opportunity serving non-customers far removed from the established market, with a new set of performance attributes that mix some traditional ones with new and novel features. The DI trigger is the need for new, economical capabilities that encourage usage by buyers not swayed by the established lineup. Disruption occurs if the upstart is able to improve on enough traditional measures of performance to meet the mainstream requirement. The high-end disruptor markets a premium offering with a high price to match. The DI trigger is the need for aspirational features attractive to demanding customers in the very top tier of the market, where pricing far exceeds the mainstream requirement. Disruption occurs when the upstart is able to drive their costs down to offer more affordable pricing that siphons customers from the mainstream. Each form of DI follows a pattern that can be used to predict uptake. At a minimum, the trajectory or rate of improvement in the green line is indicative of whether and how fast disruption can take hold. The strategic ramifications are textbook and the subject for a follow-on. #eVTOL #AAM #DI

There are many traps we can fall into when it comes to innovation. Being aware of them is critical to avoiding them. We have helped customers literally create billion dollar verticals in just a few years - but a key factor in their success was avoiding a number of common traps. Below are some of the most common traps we have seen organizational leaders (or committees) fall into while evaluating early-stage ideas and projects. *** Putting a strong project on delay. “Great project, but not the right time. We will kickstart this in a few months when XYZ has happened.” BUT: The project will lose its momentum and likely its champions. *** Folding it under, or combining it with, an ongoing project. “This fits really well with ongoing project X, so let's make it part of that.” BUT: You will lose the speed, uniqueness and autonomy of the team and their idea. *** Handing it over to another team, with no representation from the original team. “We have the perfect team with the right competence, they are ideal for this.” BUT: The new team is not invested and has no context of the decisions made. The project will fade into obscurity. *** Giving the project a big budget, too soon, in order to execute it quickly. “LOVE this idea. Let’s just clear a ton of money for this team right away.” BUT: The project will take off in a direction that seems reasonable at the time but is likely still wrong. *** Someone - to whom the idea does not make sense - takes ownership (and chokes it). “Honestly, we do not have a right to play in this space. It’s a waste of time. Let’s slo-mo kill this one.” BUT: Catastrophic for innovation culture. *** Squeeze the new idea into an existing business or operating model. “We know exactly how to do this - we have the templates, the channels, the processes - it's GO time.” BUT: The product will lose its uniqueness and what made it innovative in the first place. There are many others, of course, any that come to mind for you? #innovation, #execution, #medicieffect Ivan Tornos, JehanZeb Noor, Orsa Britton, Joe White, Chris Yeh, Minerva Tantoco, Anders Gustafsson, Regina Curry, Bruce Stephenson, Rita McGrath, Scott D. Anthony, Hondo Geurts, Dimitris Bountolos, Marie-Claire Barker, Marc Allen, Jonathan Beane, Zhen Su, MD MBA, Paulash Mohsen,

AI doesn't wait for your yearly review. Neither should your strategy. Static roadmaps are being replaced by living, evolving systems. The shift isn't about more meetings or bigger decks. It's about embedding agility into the core of how strategy is created, tested, and refined in the age of AI. Here are 13 ways leaders are leveraging AI to shape their strategic planning: 1/ Real-Time Monitoring Systems ↳ AI-powered dashboard integration ↳ Automated trend detection 💡Pro tip: Set up 15-minute daily stand-ups focused solely on emerging AI trends. 2/ Rolling Quarter Framework ↳ 90-day action sprints ↳ Monthly strategy refinements 💡Pro tip: Keep 70% of resources committed, 30% flexible. 3/ Scenario Planning Networks ↳ Multiple future state mapping ↳ Risk-opportunity matrices 💡Pro tip: Create 3 scenarios for every major decision: baseline, accelerated AI adoption, and disruption. 4/ Digital Twin Strategies ↳ Virtual strategy modeling ↳ Quick iteration cycles 💡Pro tip: Test strategic changes in digital environments before real-world implementation. 5/ Adaptive Team Structures ↳ Fluid role assignments ↳ Skills-based reorganization 💡Pro tip: Rotate 20% of team members quarterly across departments for fresh perspectives. 6/ AI Intelligence Streams ↳ Automated competitor analysis ↳ Market sentiment tracking 💡Pro tip: Set up AI alerts for both direct competitors and adjacent industry innovations. 7/ Micro-Learning Systems ↳ Just-in-time training ↳ Adaptive learning paths 💡Pro tip: Schedule 20-minute weekly team sessions on new AI tools. 8/ Decision Velocity Framework ↳ Rapid testing protocols ↳ Fast-fail mechanisms 💡Pro tip: Define your "reversal cost threshold" - the point at which a decision needs more review. 9/ Stakeholder Feedback Loops ↳ Continuous alignment checks ↳ Dynamic priority adjustment 💡Pro tip: Create a weekly survey that takes less than 30 seconds to complete. 10/ Resource Fluidity Models ↳ Dynamic budget allocation ↳ Skill-based resourcing 💡Pro tip: Keep 25% of your innovation budget unallocated for emerging AI opportunities. 11/ Crisis-Ready Culture ↳ Rapid response protocols ↳ Distributed decision rights 💡Pro tip: Run monthly "AI disruption simulations" with different teams leading each time. 12/ Data-Driven Pivots ↳ Automated trend analysis ↳ Predictive modeling 💡Pro tip: Define specific metrics that automatically initiate strategy reviews. 13/ Continuous Communication ↳ Strategy visualization tools ↳ Real-time progress tracking 💡Pro tip: Use AI tools to create strategy briefings under 2 minutes. The most resilient teams aren’t the ones with the perfect plan. They’re the ones built to adapt in real time. Continuous strategy isn’t a trend; it’s the new baseline for staying competitive in an AI-driven market. Which of these shifts are you implementing? Share below 👇 _____ Follow Carolyn Healey for more AI and leadership content. Repost to your network if they will find this valuable.