Technology Strategy Consulting

𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 𝗮𝗻 𝗔𝗜 𝗦𝗧𝗥𝗔𝗧𝗘𝗚𝗬 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆? This is one of the clearest roadmap you’ll ever get to build your own: ⬇️ 1. 𝗔𝗜 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗚𝗼𝗮𝗹 𝗦𝗲𝘁𝘁𝗶𝗻𝗴 (𝗧𝗵𝗲 𝗖𝗼𝗿𝗲): This is your strategic north star — where you define your ambition and guide every downstream decision. • Drivers → Why are you doing this? Clarifies the business/tech forces pushing AI forward.   • Value → What are you aiming to achieve? Links AI directly to measurable outcomes.   • Vision → Where is this going long-term? Provides inspiration and direction across teams.   • Alignment → Is everyone rowing in the same direction? Ensures synergy. • Risks → What could go wrong? Sets the baseline for governance and responsible AI.   • Adoption → Who will actually use it? Anticipates friction and enables change management. 📍 This is the master blueprint — Without this, you’re just building disconnected POCs. No clear target = no impact. 2. 𝗔𝗹𝗶𝗴𝗻𝗲𝗱 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 (𝗠𝗮𝗸𝗲 𝗜𝘁 𝗙𝗶𝘁 𝗬𝗼𝘂𝗿 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀): This is where your AI ambition meets the reality of your broader enterprise. • Business Strategy → AI must serve the core business goals — not exist as a side project.   • IT Strategy → Ensures your infrastructure can support scalable AI.   • R&D Strategy → Aligns innovation with AI capabilities and funding priorities.   • D&A Strategy → Without data strategy, no AI strategy will scale. • (...) Strategy → ... 📍 Connect AI to the real levers of power in your organization — so it doesn’t get siloed or shut down. 3. 𝗔𝗜 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹 (𝗠𝗮𝗸𝗲 𝗜𝘁 𝗥𝗲𝗮𝗹):   Once you know what you want to do, this defines how you’ll deliver it at scale. • Governance → Sets up ethical, legal, and operational oversight from day one.   • Data → Builds the pipelines and quality foundations for smart AI.   • Engineering → Equips you with the technical backbone for deployment.   • Technology → Selects the right tools, platforms, and architecture.   • Organization → Assigns ownership and accountability.   • Literacy → Ensures the workforce can actually work with AI. 📍 This is your AI engine room — without it, strategy stays theoretical. 4. 𝗔𝗜 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 (𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝘁𝗵𝗲 𝗩𝗮𝗹𝘂𝗲):   Now it’s time to build — but with structure and intent. • Ideation/Prioritization** → Surfaces the best use cases, aligned with strategy.   • Use Cases → Translates goals into concrete applications and MVPs.   • Buy-Build → Decides how to deliver: in-house, outsourced, or hybrid.   • Change Management → Drives real adoption beyond pilots.   • Value/Cost Management → Measures success and ensures scalability. 📍 This is where value is realized — where strategy finally touches the customer and the business. 𝗬𝗼𝘂𝗿 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝘀𝗵𝗼𝘂𝗹𝗱 𝘄𝗼𝗿𝗸 𝗹𝗶𝗸𝗲 𝘆𝗼𝘂𝗿 𝘁𝗲𝗰𝗵 𝘀𝘁𝗮𝗰𝗸: 𝗙𝘂𝗹𝗹𝘆 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱, 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗮𝗻𝗱 𝗯𝘂𝗶𝗹𝘁 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲! Graphic source: Gartner

Most IoT projects fail not because of bad tech, but because they skip steps in the lifecycle. I’ve seen so many promising concepts crash and burn simply because the fundamentals were missed. From the first sketch to global scale, here's the typical journey of building a smart, secure, and scalable IoT product. Think of it as a checklist learned the hard way: ➞ 1. Define What You’re Solving Start with clarity. What’s the user’s pain? What will the device do and why should it exist? ➞ 2. Sketch, Test, and Validate Early Visualize the product. Run UX mocks. Talk to users before touching a circuit board. ➞ 3. Pick the Right Components Choose sensors, MCUs, and radios that fit your power, size, and connectivity needs. No overkill. ➞ 4. Build a Working Prototype Fast Use dev boards like ESP32 or Raspberry Pi to get a physical demo running - fast and cheap. ➞ 5. Write the Firmware that Powers It All Sensor reading, power management, wireless comms - firmware is the nervous system of your device. ➞ 6. Design User Interfaces That Fit High-level UI/UX for apps, dashboards, or voice - the IoT experience starts here. ➞ 7. Choose Your Communication Protocols MQTT, HTTP, CoAP? Match protocol to power, speed, and security needs. ➞ 8. Connect to Cloud or Broker Route device data to platforms like AWS, Azure, or Mosquitto. This is where logic lives. ➞ 9. Handle Incoming Data Intelligently Clean, store, and prep the data for analytics. Garbage in = garbage predictions. ➞ 10. Build User Interfaces Users Actually Want Dashboards, apps, voice commands, craft interfaces that simplify decisions, not add friction. ➞ 11. Push Intelligence to the Edge (Optional) Run AI/ML models directly on-device for faster, offline, and more private decisions. ➞ 12. Secure Everything - End to End Encrypt data, manage keys, and ensure OTA updates are safe. Trust starts here. ➞ 13. Certify Before You Scale Pass FCC, CE, or HIPAA if needed. Compliance isn’t fun, but skipping it is worse. ➞ 14. Refine for Mass Manufacturing Optimize your design for cost, quality, and assembly. Prototypes don’t scale, processes do. ➞ 15. Roll Out in Controlled Phases Test in the real world. Start with pilots or beta testers before full deployment. ➞ 16. Monitor, Maintain & Scale It Track logs, run OTA updates, gather user feedback and evolve your platform constantly. The IoT lifecycle isn’t linear - it’s a loop of learning, building, and scaling. ♻️ Repost if this gave you a clearer roadmap ➕ Follow me, Nick Tudor, for real-world lessons on building and shipping connected systems that work

Imagine you’re driving a car with no dashboard — no speedometer, no fuel gauge, not even a warning light. In this scenario, you’re blind to essential information that indicates the car’s performance and health. You wouldn’t know if you’re speeding, running out of fuel, or if your engine is overheating until it’s potentially too late to address the issue without significant inconvenience or danger. Now think about your infrastructure and applications, particularly when you’re dealing with microservices architecture. That's when monitoring comes into play. Monitoring serves as the dashboard for your applications. It helps you keep track of various metrics such as response times, error rates, and system uptime across your microservices. This information is crucial for detecting problems early and ensuring a smooth operation. Monitoring tools can alert you when a service goes down or when performance degrades, much like a warning light or gauge on your car dashboard. Now observability comes into play. Observability allows you to understand why things are happening. If monitoring alerts you to an issue, like a warning light on your dashboard, observability tools help you diagnose the problem. They provide deep insights into your systems through logs (detailed records of events), metrics (quantitative data on the performance), and traces (the path that requests take through your microservices). Just as you wouldn’t drive a car without a dashboard, you shouldn’t deploy and manage applications without monitoring and observability tools. They are essential for ensuring your applications run smoothly, efficiently, and without unexpected downtime. By keeping a close eye on the performance of your microservices, and understanding the root causes of any issues that arise, you can maintain the health and reliability of your services — keeping your “car” on the road and your users happy.

Your stakeholder asks for a report. You deliver it. You have failed. Fulfilling orders makes you a technician, not a strategist. You become a service center, creating reports that generate no real value. The data request is not the problem. It is a symptom. Your most valuable work is finding the disease, not delivering the painkiller. This is what we call "Getting to the 'Ask Behind The Ask'". This is how you stop rejection before it starts. This is how you make your work impossible to ignore. Stop being an order taker. Become a problem definer. The great analyst achieves three things before touching any data: 1. Uncover The 'Why'. Every request is a symptom. Your job is to find the root cause. 2. Translate 'Why' into 'What'. Design the correct analytical solution for the actual business problem. 3. Define Success. Establish what a "home run" outcome looks like before you begin. Order takers deliver reports. Problem solvers uncover the real problem -- the 'Ask Behind The Ask' -- so they can deliver outcomes. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

🚀🚀 Why Load Testing & APM Should Be Non-Negotiable in Your SDLC🚀🚀 In today's digital landscape, delivering high-performing applications isn't just nice to have—it's mission-critical. Yet many teams still treat performance as an afterthought. Here's why integrating Load Testing and Application Performance Management (APM) throughout your SDLC is essential: 1. The Performance Reality Check Studies show that 53% of users abandon a mobile site if it takes longer than 3 seconds to load. Even a 100ms delay can hurt conversion rates by 7%. The cost of poor performance? Amazon calculated that every 100ms of latency costs them 1% in sales. 2. Why Early Integration Matters 2.1 Load Testing in SDLC: ✅ Identifies bottlenecks before production deployment ✅ Validates system capacity under expected user loads ✅ Prevents costly post-release performance fixes ✅ Ensures scalability requirements are met 2.2 APM Throughout Development: ✅ Real-time visibility into application behavior ✅ Proactive issue detection and resolution ✅ Performance baseline establishment ✅ Continuous optimization opportunities 3. Grafana: The Game Changer for Performance Monitoring Grafana has revolutionized how we visualize and monitor application performance with it's ✅ Unified Dashboards - Correlate metrics from multiple data sources ✅ Real-time Alerting - Get notified before users experience issues ✅ Historical Analysis - Track performance trends over time ✅ Custom Visualizations - Tailor views for different stakeholders ✅ Cost-Effective - Open-source with powerful enterprise features 4. Key Metrics to Track: ✅ Response times and throughput ✅ Error rates and success ratios ✅ Resource utilization (CPU, memory, disk) ✅ Database query performance ✅ User experience metrics 5. The Bottom Line Performance isn't just a technical concern—it's a business imperative. Teams that embed load testing and APM into their SDLC deliver more reliable, scalable applications that drive better user experiences and business outcomes. Your SDLC needs to include APM / Load testing for optimal customer satisfaction to cost ratio. What's your experience with performance testing in your SDLC? Share your wins and lessons learned below! 👇 #SoftwareDevelopment #LoadTesting #APM #Grafana #DevOps #PerformanceTesting #SDLC #Monitoring #TechLeadership

Every company has an "AI strategy" now. But 90% suck. Here's step-by-step how to build one that doesn't: AI strategy is different from regular product strategy. This is the battle-tested framework Miqdad Jaffer & I use. We've used at Shopify, OpenAI, & Apollo: — 1. SET CLEAR OBJECTIVES At Shopify, Miqdad killed dozens of technically cool AI projects... And doubled down on inventory management. Why? That’s where merchants were losing money. No business impact = no AI initiative. Simple as that. Look for pain points humans consistently fumble, impact their growth, and first solve that with AI. — 2. UNDERSTAND YOUR AI USERS Users don’t adopt AI the same way they adopt a button or a new flow. They don’t JUST use it. They test it, build trust with it, and only then rely on it. So, build something that empowers them throughout their journey with your product. — 3. IDENTIFY YOUR AI SUPERPOWERS Not everyone has access to the same behavior signals... User context, or proprietary data that make outputs smarter over time. That’s your moat, the data nobody else can use. Not the fancy models. Not the MCPs. Not even revolutionary AI agents. Your goal is to build around your moat, not your product or models. — 4. BUILD YOUR AI CAPABILITY STACK In AI, speed beats pride. Think of it this way: A team spends 9 months building their own LLM. Meanwhile, a smaller competitor ships with OpenAI and captures the market. So, did you make the smartest move by trying to build everything yourself? Great PMs lead when to build and when just to leverage. — 5. VISUALIZE YOUR AI VISION In 2016, Airbnb used Pixar-level storyboards to communicate product moments. Today? Tools like Bolt, v0, and Replit make it possible in hours for a fraction of a cost. Create visiontypes that show: → Before vs. after (and make the “after” impossible to do manually) → Progressive learning and smarter experiences → Human + AI collaboration in real workflows — 6. DEFINE YOUR AI PILLARS At this stage, you’re building a portfolio of some safe and some big bets: → Quick wins (1–3 months) → Strategic differentiators (3–12 months) → Exploratory options (R&D, future leverage) And label each one clearly: Offensive = creates new value Defensive = protects from disruption Foundational = unlocks future bets — 7. QUANTIFY AI IMPACT If your AI strategy assumes flat, linear returns - you’re modeling it wrong. AI compounds with usage. Every interaction trains the system, feeds the flywheel, and lifts the entire product. Even Sam Altman shared that just adding a “thank you” feature increased OpenAI’s operational cost by millions.... — 8. ESTABLISH ETHICAL GUARDRAILS One biased result. One hallucination. One misuse. And the entire product feels unsafe. Set guardrails around every part of the process to make it safe... From all the hallucinations that disrupt your trust! — Making a great strategy is still hard. But these steps can help.

𝗣𝗿𝗼𝗰𝘂𝗿𝗲𝗺𝗲𝗻𝘁 - 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗮𝘃𝗼𝗶𝗱 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝘃𝗲𝗻𝗱𝗼𝗿 𝗹𝗼𝗰𝗸-𝗶𝗻 𝗼𝗿 𝗶𝘀 𝗶𝘁 𝗮 𝗴𝗶𝘃𝗲𝗻? There is a compelling case for off-the-shelf Procurement solutions. But there are potential downsides to consider. 𝗪𝗵𝗮𝘁 𝗶𝗳: ▪️ new features are tied to hefty price hikes ▪️ evolution to changing business needs is not possible ▪️ architecture options are dictated by vendor upgrade plans ▪️ product roadmap do not align with your specific plans and needs ▪️ the flexibility promised through rich functionality does not materialise Yes, 𝘄𝗵𝗮𝘁 𝗶𝗳, 𝘁𝗵𝗲 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 𝗼𝗳 𝗿𝗮𝗽𝗶𝗱𝗹𝘆 𝗱𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝘁𝘂𝗿𝗻𝘀 𝗶𝗻𝘁𝗼 𝗮 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗿𝗶𝘀𝗸? Talking to many companies on their Digital Procurement, this major worry is real. Given the long range of investment payback, it would be an illusion to bet on building own solutions. 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝗮 𝘁𝗼𝗸𝗲𝗻 in this case - 𝗶𝘁'𝘀 𝗰𝗲𝗻𝘁𝗿𝗮𝗹 𝘁𝗼 𝗮 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗮𝗻𝗱 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿𝗰𝗲 of a company. Find here a few points which come to mind to 𝗮𝘃𝗼𝗶𝗱 𝗮 𝗳𝘂𝗹𝗹 𝘃𝗲𝗻𝗱𝗼𝗿 𝗹𝗼𝗰𝗸-𝗶𝗻 but build a stable Digital Procurement architecture, while keeping flexibility: ✅ Build a 𝗺𝗼𝗱𝘂𝗹𝗮𝗿 𝗣𝗿𝗼𝗰𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 choosing solutions with an API-first to easily integrate or replace components over time. Modern solutions can be integrated and orchestrated without hard dependencies! ✅ 𝗛𝘆𝗯𝗿𝗶𝗱𝗶𝘀𝗲 𝘆𝗼𝘂𝗿 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 by buying core capabilities like Source to Contract solutions but build or extend AI plug-ins or custom Automations. Intelligent Automation & Orchestration solutions provide extra flexibility and not just a patch. ✅ 𝗘𝘅𝗶𝘁 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗳𝗿𝗼𝗺 𝗱𝗮𝘆 𝟭, factoring in possible migration paths to prevent costly transitions later. For example ingesting the data of your Spend Analytics provider regularly into your own data lake. ✅ 𝗠𝘂𝗹𝘁𝗶-𝘃𝗲𝗻𝗱𝗼𝗿 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 which does not overcommit to a single provider but uses a mix of best-of-breed tools where flexibility matters most. Rationalising your vendor choice can bite you down the line. Procurement Tech should evolve at pace with your business needs, not lock you into someone else’s roadmap. The best strategy here is: Flexibility as a principle. ❔What's your view on this challenge. Anything missing on the picture? ❔Can vendor lock-in be minimised.

🚀 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐢𝐧𝐠 𝐌𝐋-𝐁𝐚𝐬𝐞𝐝 𝐏𝐃𝐄 𝐒𝐨𝐥𝐯𝐞𝐫𝐬: 𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 Last week, I shared insights from the study 𝘞𝘦𝘢𝘬 𝘉𝘢𝘴𝘦𝘭𝘪𝘯𝘦𝘴 𝘢𝘯𝘥 𝘙𝘦𝘱𝘰𝘳𝘵𝘪𝘯𝘨 𝘉𝘪𝘢𝘴𝘦𝘴 𝘓𝘦𝘢𝘥 𝘵𝘰 𝘖𝘷𝘦𝘳𝘰𝘱𝘵𝘪𝘮𝘪𝘴𝘮 𝘪𝘯 𝘔𝘢𝘤𝘩𝘪𝘯𝘦 𝘓𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘧𝘰𝘳 𝘍𝘭𝘶𝘪𝘥-𝘙𝘦𝘭𝘢𝘵𝘦𝘥 𝘗𝘢𝘳𝘵𝘪𝘢𝘭 𝘋𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵𝘪𝘢𝘭 𝘌𝘲𝘶𝘢𝘵𝘪𝘰𝘯𝘴 by Nick McGreivy and Ammar Hakim (link in comments). The authors highlighted a crucial issue: many #ML -based #solvers aren't benchmarked against appropriate baselines, leading to misleading conclusions. ⚠️ 𝐒𝐨, 𝐰𝐡𝐚𝐭’𝐬 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡? 🤔 The key lies in comparing the #Cost vs. #Accuracy of #algorithms, reflecting the inherent trade-off between efficiency and precision in numerical methods. While quick, low-accuracy approximations are common, highly accurate results typically require more computational time. ⏱️ 📊 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲-𝐒𝐩𝐞𝐞𝐝 𝐏𝐚𝐫𝐞𝐭𝐨 𝐂𝐮𝐫𝐯𝐞𝐬: 𝐀 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐢𝐧𝐠 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝 From my experience, the most effective way to benchmark is by using Pareto curves of accuracy versus computational time (see the figure below). These curves offer a clear, visual comparison, showing how different methods perform under the same hardware conditions. They also mirror real-world engineering decisions, where finding a balance between speed and accuracy is critical. ⚖️ An example of this can be seen in Aditya Phopale’s master thesis, where the performance of a #NeuralNetwork-based solver was compared against the state-of-the-art general purpose #Fenics solver. 🔍 𝐂𝐡𝐨𝐨𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐁𝐚𝐬𝐞𝐥𝐢𝐧𝐞 𝐒𝐨𝐥𝐯𝐞𝐫 Nick McGreivy and Ammar Hakim also emphasize the importance of selecting an appropriate baseline. While Fenics might not be the top-notch choice when it comes to computational efficiency for a specific problem (e.g., vs spectral solvers), it is still highly relevant from an #engineering perspective. Both the investigated solver and Fenics share a similar philosophy: they are general-purpose, Python-based solvers that are based on equation formulations. 🧩 Additionally, unlike #FiniteElement solvers like Fenics, the investigated Neural Network solvers don’t require complex discretization. Thus, Fenics serves as a suitable baseline for practical engineering applications, despite its "limitations" from a more theoretical context. 💡 𝐖𝐡𝐚𝐭 𝐀𝐫𝐞 𝐘𝐨𝐮𝐫 𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬? I’m curious to hear from others: what best practices do you follow when benchmarking ML-based PDE solvers? Let’s discuss! 👇

Business value vs. platform health? It’s not either-or. It’s a portfolio strategy. Over the years, I’ve seen companies take on massive multi-year re-platforming efforts. These usually succeed in launching a shiny new platform...But at what cost? 1️⃣ The business is often set back significantly during the transition. 2️⃣ The new platform rarely delivers the features the business actually needed when the effort was launched - requirements were lost in translation, subject matter experts left and knowledge lost, context was missing. 3️⃣ Worse, the team stopped learning. No customer feedback for years, no iteration, and by the time the platform is done, the market has changed. On the flip side, I’ve also seen what happens when platform health is ignored: Customer satisfaction is impacted with more disruptions and incidents. Tech debt grows while the work slows down. Less productivity from engineering and product teams. Morale dips. Velocity drops. Less experimentation. Growth stalls as competitors move faster and better. That’s why I approach this challenge like a portfolio. 📊 Most of the investment should go toward delivering business value. That should NEVER stop. 🔧 But 20–30% must be consistently allocated to platform modernization, platform health, engineering excellence, and reducing technical debt with consistent incremental delivery Neglecting that part of the portfolio doesn’t just build up future risk — it quietly erodes your ability to move fast and deliver impact. It's not a tug-of-war between tech and business. It’s about investing wisely in both — today and for the long run.

I built the data and AI strategies for some of the world’s most successful businesses. One word helped V Squared beat our Big Consulting competitors to land those clients. Can you guess what it is? Actionable. Strategy must clear the lane for execution and empower decisions. It must serve people who get the job done and deliver results. Most strategies, especially data and AI strategies, create bureaucracy and barriers that slow execution. They paralyze the business, waiting for the perfect conditions and easy opportunities to materialize. CEOs don’t want another slide deck and a confident-sounding presentation about “The AI Opportunity.” They want a pragmatic action plan detailing strategy implementation, execution, delivery, and ROI. They need a framework for budgeting based on multiple versions of the AI product roadmap that quantifies returns at different spending levels. They need frameworks to decide which risks to take. Business units don’t want another lecture about AI literacy. They need a transformation roadmap, a structured learning path, and training resources. They need to know who to bring opportunities to, how to make buying decisions, and when to kick off AI initiatives. Most of all, data and AI strategy must address the messy reality of markets, customers, technical debt, resource constraints, imperfect conditions, and business necessity. Technical strategy is only valuable if it informs decision-making and optimizes actions to achieve the business’s goals.