Innovation Roadmapping For Emerging Technologies

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

A Roadmap for Deploying and Capitalizing on Generative AI* While Generative AI holds enormous promise, leaders in many organizations are struggling with implementation. Some are unsure where to start and concerned about potential risks, while others are wrestling with data, technical and process challenges. To help leaders overcome these challenges and realize the potential, we've developed a GenAI Roadmap with 10 Guideposts, each supported with experts' perspectives, recommendations, and links to additional resources. A pdf of the document, with clickable links, is at https://bit.ly/42lEsms. The thought leaders cited are listed in the first comment, with links to each on p. 19 of the Roadmap. A Generative AI Roadmap - 10 Guideposts 1️⃣ Align Leadership, Double Down on Opportunities 2️⃣ Job 1 – Boost Productivity with Automation 3️⃣ Augment Work of Creatives and Decision-makers 4️⃣ Create Autonomous Agents to Execute Workflows 5️⃣ Level the Playing Field for Users to Explore & Experiment 6️⃣ Keep Humans in the Loop 7️⃣ Build Competitive Moats with Proprietary Data 8️⃣ Stay On or Near the Leading Edge of Tech 9️⃣ Establish, Ingrain and Enforce Guardrails and last but not least, 👉 Align with and Measure OKRs, Recognize and Reward Contributors

Some Highlights: 1. Flip the traditional design-thinking script: Focus first on deeply understanding the core capabilities of the technology, not a specific problem to solve. Let natural cognition make connections later, using flash narrative to explore potential futures. 2. Use the AI expansively at first without directing it, to unlock its full potential. Avoid tight guardrails/constraints initially. 3. Define the current value proposition in terms of customer goals, context, and target users. Then assess how AI can expand each element. 4. For new value propositions, use analogies and metaphors to establish a coherent vision and avoid scope creep. 5. Assemble collaborative, multidisciplinary teams to navigate uncertainty and integrate diverse perspectives through rapid prototyping and feedback loops with actual or potential customer segments..... "Two of us (Johnathan and Jennifer) recently conducted research showing that the main thought process for this style of innovation is to start by understanding the core functions of a technology, then explore how it can be used to solve problems across different domains. Other hallmarks of emergent thinking include evaluating ideas without understanding the criteria for success, improvising ideas with little preparation or planning, and changing a project’s target outcomes. These activities tend to run counter to good business practices promoting efficiency and reliability, and they may even violate some of the core tenets of design thinking — namely the need to identify a clear user problem to address before generating ideas for a solution. Yet, they’re also critical when trying to leverage ChatGPT (or any other emerging technology, for that matter) for innovation." Discovering Where ChatGPT Can Create Value for Your Company https://buff.ly/46X2aqT #ProductStrategy #Innovation

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

🔸 Is Your AI Strategy a Roadmap or a Guess? 🔸 Why the gap between vision and execution is widening, and what smart leaders are doing about it. Over the past year, I’ve spoken with many executives about their ambitions for generative AI. Most say they have a strategy. Few can point to a roadmap. That distinction matters. A strategy signals intent. A roadmap defines the path. Right now, too many AI strategies are broad aspirations with vague promises: ➖ "We'll enhance productivity." ➖ "We'll use GenAI in customer service." ➖ "We'll explore agent-based automation." These are not strategies. They are guesses. Some are informed by research, others by internal pressure to “do something” with AI. But in either case, a guess without a plan rarely produces results. 🔸Why this matters more than it used to🔸 Generative AI is not just another tool. It changes how decisions are made, how services are delivered, and how value is created across the enterprise. In that kind of environment, the cost of an unclear approach is high. Organizations without a real roadmap tend to experience: ❌ Pilots that never scale ❌ Teams running in different directions ❌ Investments with no measurable return ❌ Resistance from employees who were never brought along The danger isn't moving slowly. It's moving without direction, which leads to wasted energy and lost momentum. 🔸 What a real roadmap looks like 🔸 The most successful companies treat GenAI as a transformation initiative, not just a technical experiment. Their roadmaps often include: ✔ A clear business vision for how GenAI and agentic AI will support growth, efficiency, or innovation ✔ A short list of prioritized use cases that balance ambition with feasibility ✔ An honest assessment of organizational readiness across data, talent, governance, and culture ✔ A plan for driving adoption, not just implementation ✔ Metrics that connect AI investments to business outcomes Your roadmap does not need to be perfect. It needs to be specific. In the absence of clarity, organizations tend to default to disconnected pilots and internal debates. 🔸 A question worth asking this week 🔸 If your board asked for a 12-month view of how GenAI would create measurable business value in your organization, could you answer with confidence? If not, it might be time to move from strategy to roadmap. #GenerativeAI #AIStrategy