Innovation Strategy Implementation

When a company deploys an AI transformation, everyone fixates on the technology but here’s what is even more important. It's about the people. Over the years, I've developed a simple but powerful tool to evaluate teams for AI readiness. I call it my Will-Skill Matrix for AI! It’s taking a pre-existing model and customizing it for AI deployments based on 13 years of deployment experience. This framework is copyrighted: © 2025 Sol Rashidi. All rights reserved. 𝗛𝗶𝗴𝗵 𝗦𝗸𝗶𝗹𝗹, 𝗛𝗶𝗴𝗵 𝗪𝗶𝗹𝗹: These are your champions - they have the technical capabilities and the hunger to drive AI adoption forward. 𝗛𝗶𝗴𝗵 𝗦𝗸𝗶𝗹𝗹, 𝗟𝗼𝘄 𝗪𝗶𝗹𝗹: Often your most technically brilliant people who resist change. They've mastered existing systems and see AI as either a threat or unnecessary complexity. 𝗟𝗼𝘄 𝗦𝗸𝗶𝗹𝗹, 𝗛𝗶𝗴𝗵 𝗪𝗶𝗹𝗹: Your enthusiastic learners. They may not understand neural networks, but they're eager to embrace AI-driven solutions. 𝗟𝗼𝘄 𝗦𝗸𝗶𝗹𝗹, 𝗟𝗼𝘄 𝗪𝗶𝗹𝗹: These team members neither understand AI nor want to adapt to it. They're comfortable in their current roles and see no reason to change. Here's the counterintuitive insight most leaders miss: The "Low Skill, High Will" group is your hidden treasure in AI transformation. I discovered this at one of my employers during a massive data overhaul. My most valuable contributors weren't always the data scientists with impressive credentials. Often, they were business analysts who couldn't code complex algorithms but brought boundless curiosity and deep business knowledge and a will to figure it out. Why does this matter? Because AI implementation isn't just a technical challenge - it's fundamentally a human change management project. In one particularly tough transformation, I spent months trying to convince resistant technical experts to embrace new methods. Meanwhile, I overlooked enthusiastic business teams eager to learn and adapt. The breakthrough came when I finally shifted focus. By empowering the "High Will" groups and pairing them with technical mentors, our implementation timeline was shortened by nearly 40%. This completely changed my approach to building AI teams. The most successful AI implementations don't just depend on having the best algorithms or the most data engineers. They depend on having people throughout your organization who are willing to reimagine what's possible - and who bring others along with them.

Scaling from 50 to 100 employees almost killed our company. Until we discovered a simple org structure that unlocked $100M+ in annual revenue. In my 10+ years of experience as a founder, one of the biggest challenges I faced in scaling was bridging the organizational gap between startup and enterprise. We hit that wall at around 100~ employees. What worked beautifully with a small team suddenly became our biggest obstacle to growth. The problem was our functional org structure: Engineers reporting to engineering, product to product, business to business. This created a complex dependency web: • Planning took weeks • No clear ownership  • Business threw Jira tickets over the fence and prayed for them to get completed • Engineers didn’t understand priorities and worked on problems that didn’t align with customer needs That was when I studied Amazon's Single-Threaded Owner (STO) model, in which dedicated GMs run independent business units with their own cross-functional teams and manage P&L It looked great for Amazon's scale but felt impossible for growing companies like ours. These 2 critical barriers made it impractical for our scale: 1. Engineering Squad Requirements: True STO demands complete engineering teams (including managers) reporting to a single owner. At our size, we couldn't justify full engineering squads for each business unit. To make it work, we would have to quadruple our engineering headcount. 2. P&L Owner Complexity: STO leaders need unicorn-level skills: deep business acumen and P&L management experience. Not only are these leaders rare and expensive, but requiring all these skills in one person would have limited our talent pool and slowed our ability to launch new initiatives. What we needed was a model that captured STO's focus and accountability but worked for our size and growth needs. That's when we created Mission-Aligned Teams (MATs), a hybrid model that changed our execution (for good) Key principles: • Each team owns a specific mission (e.g., improving customer service, optimizing payment flow) • Teams are cross-functional and self-sufficient,  • Leaders can be anyone (engineer, PM, marketer) who's good at execution • People still report functionally for career development • Leaders focus on execution, not people management The results exceeded our highest expectations: New MAT leads launched new products, each generating $5-10M in revenue within a year with under 10 person teams. Planning became streamlined. Ownership became clear. But it's NOT for everyone (like STO wasn’t for us) If you're under 50 people, the overhead probably isn't worth it. If you're Amazon-scale, pure STO might be better. MAT works best in the messy middle: when you're too big for everyone to be in one room but too small for a full enterprise structure. image courtesy of Manu Cornet ------ If you liked this, follow me Henry Shi as I share insights from my journey of building and scaling a  $1B/year business.

A roadmap is not a strategy! Yet, most strategy docs are roadmaps + frameworks. This isn't because teams are dumb. It's because they lack predictable steps to follow. This is where I refer them to Ed Biden's 7-step process: — 1. Objective → What problem are we solving? Your objective sets the foundation. If you can’t define this clearly, nothing else matters. A real strategy starts with: → What challenge are we responding to? → Why does this problem matter? → What happens if we don’t solve it? — 2. Users → Who are we serving? Not all users are created equal. A strong strategy answers: · What do they need most? · Who exactly are we solving for? · What problems are they already solving on their own? A strategy without sharp user focus leads to feature bloat. — 3. Superpowers → What makes us different? If you’re competing on the same playing field as everyone else, you’ve already lost. Your strategy must define: · What can we do 10x better than anyone else? · Where can we persistently win? · What should we not do? This is where strategy meets competitive advantage. — 4. Vision → Where are we going? A roadmap tells you what’s next. A vision tells you why it matters. Most PMs confuse vision with strategy. But a vision is long-term. It’s a north star. Your strategy answers: How do we get there? — 5. Pillars → What are our focus areas? If everything is a priority, nothing really is. In my 15 years of experience, great strategy always come with a trade-offs: → What are our big bets? → What do we need to execute to move towards our vision? → What are we intentionally not doing? — 6. Impact → How do we measure success? Most teams obsess over vanity metrics. A great strategy tracks what actually drives business success. What outcomes matter? → How will we track progress? → What signals tell us we’re on the right path? — 7. Roadmap → How do we execute? A roadmap should never be a list of everything you could do. It should be a focus list of what truly matters. Problems and outcomes are the currency here. Not dates and timelines. — For personal examples of how I do this, check out my post: https://lnkd.in/e5F2J6pB — Hate to break it to you, but you might be operating without a strategy. You might have a nicely formatted strategy doc in front of you, but it’s just a… A roadmap? a feature list? a wishlist? If it doesn’t connect vision to execution, prioritize trade-offs, and define competitive edge… It’s not strategy. It’s just noise.

"this toolkit shows you how to identify, monitor and mitigate the ‘hidden’ behavioural and organisational risks associated with AI roll-outs. These are the unintended consequences that can arise from how well-intentioned people, teams and organisations interact with AI solutions. Who is this toolkit for? This toolkit is designed for individuals and teams responsible for implementing AI tools and services within organisations and those involved in AI governance. It is intended to be used once you have identified a clear business need for an AI tool and want to ensure that your tool is set up for success. If an AI solution has already been implemented within your organisation, you can use this toolkit to assess risks posed and design a holistic risk management approach. You can use the Mitigating Hidden AI Risks Toolkit to: • Assess the barriers your target users and organisation may experience to using your tool safely and responsibly • Pre-empt the behavioural and organisational risks that could emerge from scaling your AI tools • Develop robust risk management approaches and mitigation strategies to support users, teams and organisations to use your tool safely and responsibly • Design effective AI safety training programmes for your users • Monitor and evaluate the effectiveness of your risk mitigations to ensure you not only minimise risk, but maximise the positive impact of your tool for your organisation" A very practical guide to behavioural considerations in managing risk by Dr Moira Nicolson and others at the UK Cabinet Office, which builds on the MIT AI Risk Repository.

You did some planning around building a feature but didn’t include a set of key business stakeholders. Now, alarms have gone off and questions are being raised about why they weren’t included. More importantly, they are now vetoing your work. Ouch. This happens more often than you think. The way to deal with this is by being proactive and really mapping out the stakeholder tree for key features you are building. What you want to do is get alignment on the When / Who / Why for each major feature you are building. Aligning the "Who" Who are the different teams involved in this Feature? Who are the stakeholders who need to say “Yes”? Who are the stakeholders that have to be kept informed so they don’t veto your work? Aligning the "When" When do you involve stakeholders? How should you involve them? What informal channels should be used to keep stakeholders aligned? Aligning the "Why" (MOST IMPORTANT) What are their motivations? What are their goals? How can you align your strategy with what stakeholders want? There’s no shortcut to this, half of product management is stakeholder management. If you don’t put the time in to think through these questions, you will get your work vetoed.

In today's rapidly evolving business landscape, leveraging AI is no longer a luxury but a strategic imperative. Let's explore the critical components that can empower organizations to thrive in the AI-driven era: ✔️ Identifying AI-Ready Opportunities: Embracing AI begins with identifying areas within your business that can benefit from its transformative potential. By analyzing processes, data, and customer touchpoints, you can pinpoint opportunities where AI can enhance efficiency, accuracy, and customer experience. ✔️ Data-Driven Decision Making: Data is the fuel that powers AI success. The article underscores the significance of cultivating a data-driven culture and investing in robust data infrastructure. A well-curated data repository allows AI algorithms to uncover valuable insights, make informed predictions, and support proactive decision-making. ✔️ AI Talent Acquisition and Development: Attracting and nurturing AI talent is crucial for achieving a competitive edge. Developing a workforce well-versed in AI technologies and methodologies ensures the successful implementation and ongoing optimization of AI initiatives. ✔️ Collaboration between Humans and AI: The article emphasizes that AI isn't about replacing human intelligence but augmenting it. Establishing effective collaboration between AI systems and human teams unlocks new possibilities, enabling organizations to deliver more innovative and personalized solutions. ✔️ Ethics and Responsible AI: As AI adoption grows, so does the importance of ethical considerations. Ensuring that AI applications are designed and used responsibly fosters trust among customers, employees, and stakeholders alike. ✔️ Continuous Learning and Adaptation: The AI landscape is dynamic, and so must be your strategy. Regularly reassessing your AI roadmap, staying abreast of industry trends, and embracing a culture of continuous learning are vital to stay ahead in the AI race. Building a winning AI strategy demands a holistic approach that integrates data, talent, ethics, and adaptability. By embracing AI as a strategic imperative, organizations can revolutionize their operations, deliver unparalleled customer experiences, and secure a sustainable competitive advantage. #AIstrategy #BusinessTransformation #Innovation #DataDrivenDecisionMaking

When will business leaders learn that you can’t go from Excel to AI? Trying to kludge legacy tools into modern infrastructure stacks doesn’t work. Businesses must let go of tools that are older than some of their employees. I got pushback for that take in 2019, but today, my clients don’t have the technical debt that’s preventing their competitors from implementing agents. A core tenet of technical strategy is that decisions made today must amplify the value of future technology waves. Looking at BI tools strategically makes it obvious that they are AI disruptors, not amplifiers. Transitioning away from low maturity BI tools to self-service analytics platforms early set businesses up for AI success today. It freed technical resources to work on contextual data gathering and information architecture rather than spending 80% of their time on reporting and data cleaning. Data literacy and tool maturity have had years to take hold, so the business is filled with semi-technical teams. They’re early adopters of generative AI self-service tools and agent builders. They’re getting more value from AI and avoiding the hype. Products and capabilities have matured iteratively with a cohesive, holistic vision. Transformation is continuous, but a big picture view makes it consistent rather than a series of disconnected pivots and knee-jerk reactions. CIOs must position technology as a pillar of business strategy, so technology decisions must be forward-looking and prescriptive. Technical strategy must be holistic and enterprise-wide. #AI #DataEngineering #AIStrategy #Data

Struggling with stakeholder buy-in? I have a template that can help. The Power-Interest matrix maps key stakeholders into 4 personas: 🔴 The ARCHITECTS (high power, high interest) These are people with a lot of power who are very involved in research (e.g., product managers, design leaders) 🟢 The OBSERVERS (high power, low interest) Someone with a lot of power, but an arms-length distance from your work (e.g., Head of Product., C-suite) 🟡 The EXPLORERS (low power, high interest) They’re super interested in your work, but don’t have a lot of influence in the org (fellow UXRs, designers) 🔵 The CASUAL OBSERVERS (low power, low interest) Someone without a lot of influence or interest in research (think other team members like sales, marketing) To make getting buy-in easier, you need to understand each stakeholder persona, and talk to them accordingly. ARCHITECTS need most attention. They need close management with regular updates + involvement. OBSERVERS only care about business outcomes. They prefer concise reports & summaries that are action-oriented, without jargon. For CASUAL OBSERVERS, you can loop them in on big breakthroughs + findings that matter to their work. EXPLORERS are fans of research. Keep them informed through shared repositories & weekly syncs. For a detailed analysis of each stakeholder and how to engage with them better, go here: https://bit.ly/4b0wGSC If you want to skip the reading, just use my FREE Stakeholder Persona Mapping Figjam template: https://bit.ly/4b4JN5A Which type of stakeholders have you struggled with the most? Please share wisdom in the comments! 👇 #uxresearch #stakeholdermanagement

The rush to implement AI solutions can lead to significant pitfalls. Here's a provocative thought: the greatest risk in AI isn't just inaction. It's implementing without understanding. Let’s unravel why AI implementation demands careful thought and expertise. The promise of AI is undeniable. But when businesses leap without looking, the consequences can be dire. → Mismanaged data leads to flawed predictions. ↳ Garbage in, garbage out—AI doesn't magically fix bad data. → Overreliance can breed complacency. ↳ AI is a tool, not a crutch. → Lack of understanding can result in ethical oversights. ↳ Algorithms must be checked for bias and fairness. → Insufficient expertise can stall projects. ↳ Proper training and a clear strategy are essential. AI implementation isn't just about tech. It's about aligning with business goals and ethics. So, how do we get it right? Prioritize data quality → Clean, accurate data is nonnegotiable. Invest in education → Equip your team with the knowledge to leverage AI effectively. Engage multidisciplinary teams → Combine tech expertise with business acumen. Embed ethical considerations → Regularly audit models for bias and fairness. Iterate and refine → Continuous learning and adaptation are key. Remember, AI isn't a onesizefitsall solution. It's a journey that requires thoughtful planning and execution. Done right, AI can transform businesses, enabling them to act with foresight and agility. Yet, it's the careful, calculated steps that ensure this transformation is both successful and sustainable. What steps have you taken to ensure AI success in your organization? Share your thoughts below.

Your company's growth is a tightrope walk between innovation and complacency. Take too few risks? You'll be forgotten. Take the wrong risks? You'll compromise your brand. Plenty of the world’s most innovative companies we work with at Motto have figured it out, and we’ve seen some patterns. They expand boldly *without* compromising who they are. How’s this possible? By aligning innovation with their core values at the foundational level. Here's what that looks like in practice ↓ ⦿ Value-driven decision making Every new initiative should be measured against your company's fundamental beliefs. If it doesn't align, it's not worth pursuing. ⦿ Create a "failure budget." Allocate resources specifically for experimental projects Reward people for trying, not just succeeding. This tells your team it's okay — wonderful, even — to take calculated risks. ⦿ Implement an innovation framework. Set clear guidelines for new ideas. Leaders should ask themselves… → What will keep our company in the leader position? → What is the impact if we play it safe? → How will this innovation align (or not align) with our values? Make sure innovations contribute positively, inside and out. ⦿ Foster cross-pollination Form diverse "skunk works" teams. Give them a specific goal and deadline. Then, watch as fresh perspectives lead to groundbreaking ideas. ⦿ Embed values through education. Your team should breathe your company's values—When they do, even their boldest ideas will align with your core identity. Innovation isn’t about recklessness— It’s about daring to fly while staying true to your roots. When you master this balance true growth happens. Motto® helps tech companies align vision with bold growth. Let's talk about your next big move. → wearemotto.com