Innovation Strategy Implementation

Over the weekend, I read Google's paper on how they use AI for internal code migrations—and it’s packed with insights on how to approach legacy system modernization. I’ve attached the paper for those interested, but here’s how I believe some of these strategies can help us tackle complex modernization challenges: 🔎 1. Accelerating Legacy System Modernization Google leverages Large Language Models (LLMs) to automate large-scale code migrations, significantly reducing manual effort and speeding up projects. Applying similar AI-driven approaches can streamline the modernization of legacy systems, cutting through complexity and outdated code. 🔎 2. Combining AI with Proven Engineering Tools By blending LLMs with Abstract Syntax Tree (AST)-based tools, the ensure accuracy and scalability in their code transformations. This hybrid method shows how AI and traditional engineering techniques can work together to deliver safe and reliable modernization. 🔎 3. Reusable Migration Workflows Google created modular, reusable workflows that make onboarding and executing new migration tasks faster and more efficient. Developing similar toolkits for legacy systems could simplify recurring modernization steps and adapt to complex scenarios. 🔎 4. Measuring Success by Business Impact Google focuses on measurable outcomes, like a 50% reduction in project time, rather than just the volume of AI-generated code. This business-aligned metric highlights the importance of demonstrating clear ROI in technology transformation projects. 🔎 5. Safe and Scalable Rollouts Their phased deployment strategy ensures AI-driven changes are rolled out safely, minimizing disruption. Adopting a controlled rollout approach can help manage risks and ensure stability when modernizing critical systems. 🔎 6. Strategic Use of AI Models Google balances using custom fine-tuned models and general-purpose tools depending on the task. This approach offers valuable insight into when to invest in specialized AI solutions versus using adaptable off-the-shelf models. 📌 The Big Picture: Legacy system modernization is about combining AI-driven efficiency with engineering best practices to deliver faster, safer, and more impactful business transformations. 📎 I’ve attached the paper if you’d like to explore it further! #LegacyModernization #GenAI #BusinessInnovation — Enjoyed this post? Like 👍, comment 💭, or repost ♻️ to share with others.

Innovation is only as valuable as the problem it solves. We live in an age where technological advancements move faster than our ability to strategically adopt them. It’s no longer a question of can we implement this? but rather, should we? The real challenge isn’t access to innovation. 𝐈𝐭’𝐬 𝐝𝐢𝐬𝐜𝐢𝐩𝐥𝐢𝐧𝐞. Discipline to pause before we purchase. Discipline to align tools with outcomes. Discipline to measure impact before we declare success. 𝐓𝐡𝐞 𝐃𝐫𝐢𝐯𝐞𝐫𝐬 𝐨𝐟 𝐭𝐡𝐞 𝐓𝐞𝐜𝐡 𝐏𝐚𝐫𝐚𝐝𝐨𝐱: • 𝐒𝐡𝐢𝐧𝐲 𝐍𝐞𝐰 𝐎𝐛𝐣𝐞𝐜𝐭 𝐒𝐲𝐧𝐝𝐫𝐨𝐦𝐞: The irresistible pull towards the ‘new’ and ‘novel’, often at the expense of sustained objectives and an overarching strategic vision. • 𝐅𝐞𝐚𝐫 𝐨𝐟 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐎𝐮𝐭 (𝐅𝐎𝐌𝐎): The anxiety that failing to adopt new technologies or trends could result in missed opportunities for growth or competitive advantage. 𝐓𝐡𝐞 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 𝐂𝐡𝐞𝐜𝐤: • 𝟑𝟎% of App deployments fail • 𝟕𝟎% of Digital Transformation initiatives don’t meet goals • 𝟕𝟎%+ of manufacturers worldwide are stuck in pilot purgatory • 𝟓𝟖% of IoT projects are considered not to be successful • 𝟔𝟏% of manufacturers don’t have specific metrics to measure the effectiveness or impact of AI deployments 𝐀𝐝𝐯𝐢𝐜𝐞 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐓𝐞𝐜𝐡-𝐂𝐮𝐫𝐢𝐨𝐮𝐬 𝐂��𝐦𝐩𝐚𝐧𝐢𝐞𝐬: 1. 𝐀𝐬𝐬𝐞𝐬𝐬, 𝐃𝐨𝐧'𝐭 𝐀𝐬𝐬𝐮𝐦𝐞: Evaluate whether the technology fills a need or optimizes current operations before investing. 2. 𝐀𝐥𝐢𝐠𝐧, 𝐓𝐡𝐞𝐧 𝐀𝐜𝐭: Ensure that any new tech acquisition is in alignment with your strategic business goals. 3. 𝐌𝐞𝐚𝐬𝐮𝐫𝐞 𝐭𝐨 𝐌𝐚𝐧𝐚𝐠𝐞: Develop clear metrics or KPIs to track the success and relevance of your technology investments. 𝐅𝐨𝐫 𝐚 𝐝𝐞𝐞𝐩𝐞𝐫 𝐝𝐢𝐯𝐞 𝐨𝐧 𝐭𝐡𝐢𝐬 𝐭𝐨𝐩𝐢𝐜, 𝐢𝐧𝐜𝐥𝐮𝐝𝐢𝐧𝐠 𝐬𝐨𝐮𝐫𝐜𝐞𝐬:  https://lnkd.in/eX89kQ6n ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

I was once responsible for coordinating the Preliminary Design Review (PDR) for an airplane that, quite literally, wouldn’t get off the ground. At the time, I was working for the largest aerospace engineering company in the world—renowned for creating cutting-edge fighter jets. With such a wealth of experience and reputation, you’d think success in any airplane project would be guaranteed. Think again. This project fell victim to the same pitfalls that can derail any technical development effort. The fundamental forces of flight—lift, weight, thrust, and drag—are concepts most engineering students learn to calculate early on. So how did this project progress so far without an accurate assessment of the design's weight? As is often the case, the problem had as much to do with people and processes as with engineering. The team behind the project was an exceptionally innovative group of idea-makers, deeply trusted by their customer. Their relationship was so close, it seemed they had collectively fallen in love with the concept of the airplane. In their enthusiasm, they overlooked critical systems engineering principles like rigorous requirements validation, stakeholder alignment, and continuous integration of data into decision-making processes. One glaring oversight highlighted this flaw: they forgot to account for the weight of the cables in the initial design calculations. These cables alone were heavy enough to push the design beyond allowable weight limits, rendering the airplane incapable of flight. Physics doesn’t lie, and enthusiasm alone can’t overcome it. This experience underscored key systems engineering lessons that every project should adhere to: 🔍 Thorough Requirements Analysis: Ensure all aspects of the system, including seemingly minor components, are accounted for in design and requirements validation. 🔄 Iterative Design and Review: Conduct continuous, iterative evaluations of the design to catch issues early, rather than allowing them to compound over time. 🤝 Stakeholder Objectivity: Foster open communication and a healthy level of skepticism, even with trusted customers, to avoid "groupthink" or over-attachment to a concept. 📊 Emphasis on Quantitative Data: Balance creativity and innovation with grounded, quantitative assessments to ensure feasibility. Ultimately, this project served as a powerful reminder: no amount of innovation or trust can replace the need for disciplined systems engineering practices. #SystemsEngineering #EngineeringLessons #SystemsThinking #LessonsLearned #PhysicsMatters #LearnFromFailure 

For decades, organizations approached new technology the same way: choose the platform, pilot it, then roll out training. AI requires a different approach. It changes how work is designed, how decisions get made, and how people and technology collaborate.   A recent Forbes article offers a helpful framework for navigating this shift—one that starts with design, not deployment:   → Start with outcomes. What does success really mean—better insights, stronger relationships, improved quality? → Deconstruct the work. Identify what should be automated, what can be augmented, and what must remain human. → Design the collaboration. Determine how humans and AI will share responsibility and decision-making. → Rethink how success is measured. Value metrics that capture trust, learning, and adaptability.   At Microsoft, we're seeing that this shift takes intention. It’s about aligning people, process, and technology—and it means building a playbook that connects business goals to how people work and learn every day.   How are you approaching AI adoption differently than past technology rollouts? https://lnkd.in/gCdnuBMa

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.

If you’re leading AI initiatives, here is a strategic cheat sheet to move from "����𝗼𝗼𝗹 𝗱𝗲𝗺𝗼" to 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘃𝗮𝗹𝘂𝗲. Think Risk, ROI, and Scalability. This strategy moves you from "𝘄𝗲 𝗵𝗮𝘃𝗲 𝗮 𝗺𝗼𝗱𝗲𝗹" to "𝘄𝗲 𝗵𝗮𝘃𝗲 𝗮 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗮𝘀𝘀𝗲𝘁." 𝟭. 𝗧𝗵𝗲 "𝗪𝗵𝘆" 𝗚𝗮𝘁𝗲 (𝗣𝗿𝗲-𝗣𝗼𝗖) • Don’t build just because you can. Define the Business Problem first • Success: Is the potential value > 10x the estimated cost? • Decision: If the problem can be solved with Regex or SQL, kill the AI project now. 𝟮. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗼𝗳 𝗼𝗳 𝗖𝗼𝗻𝗰𝗲𝗽𝘁 (𝗣𝗼𝗖) • Goal: Prove feasibility, not scalability. • Timebox: 4–6 weeks max. • Team: 1-2 AI Engineers + 1 Domain Expert (Data Scientist alone is not enough). • Metric: Technical feasibility (e.g., "Can the model actually predict X with >80% accuracy on historical data?") 𝟯. 𝗧𝗵𝗲 "𝗠𝗩𝗣" 𝗧𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻 (𝗧𝗵𝗲 𝗩𝗮𝗹𝗹𝗲𝘆 𝗼𝗳 𝗗𝗲𝗮𝘁𝗵) • Shift from "Notebook" to "System." • Infrastructure: Move off local GPUs to a dev cloud environment. Containerize. • Data Pipeline: Replace manual CSV dumps with automated data ingestion. • Decision: Does the model work on new, unseen data? If accuracy drops >10%, halt and investigate "Data Drift." 𝟰. 𝗥𝗶𝘀𝗸 & 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 (𝗧𝗵𝗲 "𝗟𝗮𝘄𝘆𝗲𝗿" 𝗣𝗵𝗮𝘀𝗲) • Compliance is not an afterthought. • Guardrails: Implement checks to prevent hallucination or toxic output (e.g., NeMo Guardrails, Guidance). • Risk Decision: What is the cost of a wrong answer? If high (e.g., medical advice), keep a "Human-in-the-Loop." 𝟱. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 • Scalability & Latency: Users won’t wait 10 seconds for a token. • Serving: Use optimized inference engines (vLLM, TGI, Triton) • Cost Control: Implement token limits and caching. "Pay-as-you-go" can bankrupt you overnight if an API loop goes rogue. 𝟲. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 • Automated Eval: Use "LLM-as-a-Judge" to score outputs against a golden dataset. • Feedback Loops: Build a mechanism for users to Thumbs Up/Down outcomes. Gold for fine-tuning later. 𝟳. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 (𝗟𝗟𝗠𝗢𝗽𝘀) • Day 2 is harder than Day 1. • Observability: Trace chains and monitor latency/cost per request (LangSmith, Arize). • Retraining: Models rot. Define when to retrain (e.g., "When accuracy drops below 85%" or "Monthly"). 𝗧𝗲𝗮𝗺 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 • PoC Phase: AI Engineer + Subject Matter Expert. • MVP Phase: + Data Engineer + Backend Engineer. • Production Phase: + MLOps Engineer + Product Manager + Legal/Compliance. 𝗛𝗼𝘄 𝘁𝗼 𝗺𝗮𝗻𝗮𝗴𝗲 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 (𝗺𝘆 𝗮𝗱𝘃𝗶𝗰𝗲): → Treat AI as a Product, not a Research Project. → Fail fast: A failed PoC cost $10k; a failed Production rollout costs $1M+. → Cost Modeling: Estimate inference costs at peak scale before you write a line of production code. What decision gates do you use in your AI roadmap? Follow Priyanka for more cloud and AI tips and tools #ai #aiforbusiness #aileadership

Most companies are still experimenting with AI. A few are already 𝐫𝐞𝐰𝐫𝐢𝐭𝐢𝐧𝐠 𝐭𝐡𝐞𝐢𝐫 𝐏&𝐋𝐬. Our new 𝟐𝟎𝟐𝟔 𝐆𝐥𝐨𝐛𝐚𝐥 𝐀𝐈 𝐑𝐞𝐩𝐨𝐫𝐭 reveals a striking pattern: Only 15% of organizations qualify as true AI leaders — and they’re 2.5× more likely to post double-digit revenue growth and 3× more likely to achieve 15%+ profit gains from AI deployments. Here’s what the top performers do differently: 🔹 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 * They treat AI as a 𝐜𝐨𝐫𝐞 𝐠𝐫𝐨𝐰𝐭𝐡 𝐞𝐧𝐠𝐢𝐧𝐞, tightly aligning AI strategy with business strategy. * They pick 𝐡𝐢𝐠𝐡-𝐯𝐚𝐥𝐮𝐞 𝐝𝐨𝐦𝐚𝐢𝐧𝐬 that unlock disproportionate economic impact and redesign domains/workflows end to end. * They rebuild 𝐜𝐨𝐫𝐞 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐰𝐢𝐭𝐡 𝐀𝐈 𝐞𝐦𝐛𝐞𝐝𝐝𝐞𝐝, not bolted on. 🔹 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 * They build 𝐬𝐜𝐚𝐥𝐚𝐛����𝐞 𝐚𝐧𝐝 𝐬𝐞𝐜𝐮𝐫𝐞 𝐬𝐭𝐚𝐜𝐤𝐬, localize or relocate AI infrastructure for 𝐩𝐫𝐢𝐯𝐚𝐭𝐞/𝐬𝐨𝐯𝐞𝐫𝐞𝐢𝐠𝐧 𝐀𝐈. * They use AI to amplify the impact of 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞𝐝, 𝐡𝐢𝐠𝐡𝐥𝐲 𝐬𝐤𝐢𝐥𝐥𝐞𝐝 𝐞𝐦𝐩𝐥𝐨𝐲𝐞𝐞𝐬 rather than replace them. * They make adoption stick with 𝐡𝐚𝐫𝐝𝐰𝐢𝐫𝐞𝐝 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐂𝐀𝐈𝐎-𝐥𝐞𝐝 oversight. * And they move faster by 𝐩𝐚𝐫𝐭𝐧𝐞𝐫𝐢𝐧𝐠 more -- not less. 𝐅𝐨𝐜𝐮𝐬. 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞. 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧. That's how today's AI leaders turn 𝐩𝐢𝐥𝐨𝐭𝐬 𝐢𝐧𝐭𝐨 𝐩𝐫𝐨𝐟𝐢𝐭𝐬. If you want a glimpse into how the next generation of AI-native enterprises will operate, read our full 𝐏𝐥𝐚𝐲𝐛𝐨𝐨𝐤 𝐟𝐨𝐫 𝐀𝐈 𝐋𝐞𝐚𝐝𝐞𝐫𝐬: https://lnkd.in/epwy6g_4 #AI #Leadership #GenAI #AgenticAI #Strategy #Execution #NTTDATA

I was Wrong about Influence. Early in my career, I believed influence in a decision-making meeting was the direct outcome of a strong artifact presented and the ensuing discussion. However, with more leadership experience, I have come to realize that while these are important, there is something far more important at play. Influence, for a given decision, largely happens outside of and before decision-making meetings. Here's my 3 step approach you can follow to maximize your influence: (#3 is often missed yet most important) 1. Obsess over Knowing your Audience Why: Understanding your audience in-depth allows you to tailor your communication, approach and positioning. How: ↳ Research their backgrounds, how they think, what their goals are etc. ↳ Attend other meetings where they are present to learn about their priorities, how they think and what questions they ask. Take note of the topics that energize them or cause concern. ↳ Engage with others who frequently interact with them to gain additional insights. Ask about their preferences, hot buttons, and any subtle cues that could be useful in understanding their perspective. 2. Tailor your Communication Why: This ensures that your message is not just heard but also understood and valued. How: ↳ Seek inspiration from existing artifacts and pickup queues on terminologies, context and background on the give topic. ↳ Reflect on their goals and priorities, and integrate these elements into your communication. For instance, if they prioritize efficiency, highlight how your proposal enhances productivity. ↳Ask yourself "So what?" or "Why should they care" as a litmus test for relatability of your proposal. 3. Pre-socialize for support Why: It allows you to refine your approach, address potential objections, and build a coalition of support (ahead of and during the meeting). How: ↳ Schedule informal discussions or small group meetings with key stakeholders or their team members to discuss your idea(s). A casual coffee or a brief virtual call can be effective. Lead with curiosity vs. an intent to respond. ↳ Ask targeted questions to gather feedback and gauge reactions to your ideas. Examples: What are your initial thoughts on this draft proposal? What challenges do you foresee with this approach? How does this align with our current priorities? ↳ Acknowledge, incorporate and highlight the insights from these pre-meetings into the main meeting, treating them as an integral part of the decision-making process. What would you add? PS: BONUS - Following these steps also expands your understanding of the business and your internal network - both of which make you more effective. --- Follow me, tap the (🔔) Omar Halabieh for daily Leadership and Career posts.

Is your Cloud and Digital strategy ready for the next evolution? As business leaders strive to maximise ROI from their cloud and digital transformation efforts, a pivotal challenge has emerged: integrating Gen AI into existing strategies. But this is more than a challenge, it’s a unique opportunity to elevate your organisation. Yet, many businesses are hitting roadblocks in their cloud journey, including: 1. Data Management Challenges: As the volume of data grows, organisations struggle to manage and analyse it effectively, limiting their ability to extract actionable insights. 2. Regulatory Complexities: Banking and financial services face regulations such as DORA (Digital Operational Resilience Act), which emphasise the need for robust risk management and resilience planning. 3. Cloud Concentration Risk: Over reliance on a single cloud provider can create vulnerabilities such as potential compliance challenges or increased exposure to systemic risks across providers. 4. High Investment Costs: Initial cloud adoption demands significant financial and time commitments. However, the stakes are high, with cloud computing projected to generate a staggering $3 trillion in EBITDA by 2030. In a digital landscape where Gen AI is a game changer, the cost of inaction is steep. Organisations slow to adapt risk being outpaced by more agile competitors. How can businesses stay ahead of the curve? 1. Integrate Gen AI into Cloud Strategies: Assess current cloud initiatives to identify how Gen AI can add value. Focus on both immediate and future use cases for a sustainable strategy. Studies show that businesses that effectively integrate AI see higher productivity gains and enhanced decision making. 2. Prioritise High Value Applications: Target use cases where Gen AI can deliver the highest ROI. The scalable nature of cloud technology allows businesses to continuously adopt new features and innovations, driving better outcomes in customer support, predictive analytics and personalised services. 3. Enhance Data Governance: Establish robust data governance frameworks to ensure data quality, security and compliance. This enables organisations to leverage AI driven insights while adhering to evolving regulatory requirements like DORA, which emphasises operational resilience. 4. Adopt a Multi-Cloud Strategy: Mitigate cloud concentration risk by diversifying cloud providers, reducing dependency on a single provider and optimising performance. A multi-cloud approach ensures greater flexibility and resilience, especially for meeting regulatory expectations and handling data sovereignty requirements. By aligning cloud and digital transformation efforts with Gen AI, businesses can not only avoid falling behind but also unlock new avenues for growth and innovation. In this era of digital acceleration, embracing change isn’t optional, it’s essential. Thoughts? #Banking #AssetManagement #DigitalTransformation #GenerativeAI

🗺️ 𝐓𝐡𝐞 𝐒𝐭𝐚𝐤𝐞𝐡𝐨𝐥𝐝𝐞𝐫 𝐈𝐧𝐟𝐥𝐮𝐞𝐧𝐜𝐞 𝐌𝐚𝐩: 𝐘𝐨𝐮𝐫 𝐁𝐥𝐮𝐞𝐩𝐫𝐢𝐧𝐭 𝐟𝐨𝐫 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐚𝐥 𝐈𝐦𝐩𝐚𝐜𝐭 Most architects treat stakeholder management like a checklist: • “Get buy-in from these 5 people.” That’s not really how influence works. Influence flows through 𝐧𝐞𝐭𝐰𝐨𝐫𝐤𝐬. Understanding these networks multiplies your impact. 𝐓𝐇𝐄 𝐅𝐑𝐀𝐌𝐄𝐖𝐎𝐑𝐊 1️⃣ 𝐏𝐨𝐰𝐞𝐫 𝐆𝐫𝐢𝐝 (𝐖𝐡𝐨 𝐃𝐞𝐜𝐢𝐝𝐞𝐬?) • High Power / High Interest: Your champions, invest heavily here • High Power / Low Interest: Sleeping giants, wake them up with relevant value • Low Power / High Interest: Your advocates, turn them into evangelists • Low Power / Low Interest: Monitor, don’t over-invest 2️⃣ 𝐈𝐧𝐟𝐥𝐮𝐞𝐧𝐜𝐞 𝐏𝐚𝐭𝐡𝐬 (𝐇𝐨𝐰 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 𝐑𝐞𝐚𝐥𝐥𝐲 𝐆𝐞𝐭 𝐌𝐚𝐝𝐞) Who does the CEO trust for technology advice? Which middle managers shape CFO’s budget decisions? Who influences the influencers...? 3️⃣ 𝐕𝐚𝐥𝐮𝐞 𝐃𝐫𝐢𝐯𝐞𝐫𝐬 (𝐖𝐡𝐚𝐭 𝐌𝐨𝐭𝐢𝐯𝐚𝐭𝐞𝐬 𝐄𝐚𝐜𝐡 𝐒𝐭𝐚𝐤𝐞𝐡𝐨𝐥𝐝𝐞𝐫?) - Career advancement opportunities - Problem-solving and efficiency gains - Risk mitigation and control - Innovation and competitive advantage - Budget optimization and cost control 𝐓𝐇𝐄 𝐌𝐀𝐏𝐏𝐈𝐍𝐆 𝐏𝐑𝐎𝐂𝐄𝐒𝐒 ✅ List stakeholders who impact your architectural decisions ✅ Plot them on the power/interest grid ✅ Tease out any influence paths between them ✅ Map each person’s primary value drivers ✅ Assemble targeted engagement strategies for each quadrant 𝐏𝐑𝐎 𝐓𝐈𝐏𝐒 → The real decision makers aren’t always on the org chart → Sometimes influencing the influencer is more effective than going direct → Different stakeholders need different messages about the same solution → Your influence strategy needs to evolve as people and priorities change Don't think of this as just politics. It’s 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐡𝐢𝐩 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠. The best architects don’t only design systems. 𝐓𝐡𝐞𝐲 𝐛𝐮𝐢𝐥𝐝 𝐭𝐡𝐞 𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐡𝐢𝐩𝐬 𝐭𝐡𝐚𝐭 𝐞𝐧𝐬𝐮𝐫𝐞 𝐭𝐡𝐨𝐬𝐞 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐠𝐞𝐭 𝐟𝐮𝐧𝐝𝐞𝐝, 𝐛𝐮𝐢𝐥𝐭, 𝐝𝐞𝐩𝐥𝐨𝐲𝐞𝐝 𝐚𝐧𝐝 𝐚𝐝𝐨𝐩𝐭𝐞𝐝. 💡 Save this framework. Your next major architecture initiative will thank you. 👇 What’s been your experience with stakeholder influence? Any surprises about who really drives decisions? --- ➕ 𝐅𝐨𝐥𝐥𝐨𝐰 Kevin Donovan 🔔 ♻️ 𝐑𝐞𝐩𝐨𝐬𝐭 | 💬 ���𝐨𝐦𝐦𝐞𝐧𝐭 | 👍 𝐋𝐢𝐤𝐞 🚀 𝐉𝐨𝐢𝐧 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐬’ 𝐇𝐮𝐛 – Join our newsletter and connect with a community that understands. Enhance your skills, meet peers, and advance your career! 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 👉 https://lnkd.in/dgmQqfu2