Holistic Data and AI Strategy Frameworks

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.

AI strategy that wins: build outcomes, not just models. Most AI plans are shopping lists. Winning strategy is a connected system miss one link and results stall. Common breakdowns (diagnose in seconds) Direction w/o Demand → elegant solution, quiet pipeline Demand w/o Economics → top line up, runway down Advantage w/o Direction → margin today, misallocated effort Economics w/o Advantage → value created, race to the bottom The four pillars (Breakthroughs happen at the overlap, not in a silo) 🧭 Direction — Where AI plays. How it’s governed. How wins are measured. 🎯 Demand — Problem felt weekly. Named owner/sponsor. 💰 Economics — Unit cost & payback. Capacity redeployed or revenue. 🔑 Advantage — Proprietary data. Domain expertise. Reusable components. Build only when these 4 are true (the overlap): 1. Strategic fit: Only we should build it (our data/mission) 2. Relevance: Felt problem this quarter 3. Viability: Profitable at scale (payback ≤ 12 months) 4. Efficiency: Low run cost; reusable components Board metric stack North star: one outcome people feel Pick one metric: lead time • error rate • time to feedback • cost per run • capacity redeployed Decision gates (go only if) ☑️ Workflow + sponsor named ☑️ Baseline + target set ☑️ Data access + governance cleared ☑️ Payback ≤ 6–12 months ☑️ ≥50% components reusable for next 2 use cases 90-day runbook Days 1–15: select workflow, baseline, risk check, sign charter Days 16–45: ship a thin slice with real users, instrument metrics Days 46–90: prove lift, document reuse, decide: scale / pause / kill Quick heat check Direction ☐ Red ☐ Yellow ☐ Green Demand ☐ Red ☐ Yellow ☐ Green Economics ☐ Red ☐ Yellow ☐ Green Advantage ☐ Red ☐ Yellow ☐ Green Repost to help someone in your network make better AI bets. Follow Gabriel Millien for pragmatic AI and ops insights. Save for your next portfolio or board review. Infographic style inspiration: Justin Wright

I’m in board rooms and executive sessions witnessing AI strategies fall into 3 traps: 1. Too vague (“We need to be more innovative.”) 2. Too detailed (30 page deck with 50 slides in the appendix that no one reads) 3. Too disconnected (Misaligned with actual capabilities) If your AI strategy has more slides than decisions, you might be confusing activity with alignment. The result? ✔️An AI strategy that costs $1M and 75% of the use cases aren’t even executable . ✔️A transformation roadmap that spans 5 years, but no one knows what to do next quarter. AI is not just a tool. It’s a force that can reshape your workflows, redefine roles, and reallocate talent. Without a clear strategy, you’ll fall into two traps: 🤯FOMO-driven chaos: Buying licenses ≠ transformation. 🤯Pilot purgatory: Endless experimentation without scale. But here’s the truth: You don’t need a fancier strategy. You need a functional one. What a Good AI Strategy Actually Needs: 🧭 Clarity – What problem are you solving? – Why AI, not automation or process reengineering? ⚙️ Capability Mapping – Do you have the data? – Do you have the people? – Do you have the infrastructure? 📆 Time-Boxed Roadmap – What’s your “Crawl → Walk → Run” plan over the next 3, 6, 12 months? – How are you measuring success at each step? If your AI strategy doesn’t clearly answer those questions… it’s not a strategy. It’s a slide deck! Sol’s Recommendations: 1️⃣ Think Big. Start Small. Scale Smart. A good strategy should fit on one slide. It should move people to act, not stall them in analysis. 2️⃣ Build Feedback Loops INTO the Strategy Strategy isn’t a map—it’s a GPS. It must update as the terrain shifts. That means monthly retros, live dashboards, and real business input—not just consulting jargon. 3️⃣ Don’t confuse motion with momentum. Start small, but make sure it moves the needle. 4️⃣ Map readiness before roadmap. Strategy isn’t just about what you want to do, it’s about what you’re equipped to do now and how fast you can scale. Great AI strategy isn’t built on use cases but also use-case readiness! What’s the worst strategy deck you’ve ever seen? Drop your horror stories (or recovery stories) below. I’m all ears. #Strategy #Execution #FutureOfWork #AILeadership #DigitalTransformation #SolRashidi #RealTalkStrategy #AI #Automation #Agents #AIstrategy #humanresources

80% of AI initiatives FAIL!!!! They fail due to poor planning, unclear value, and misalignment with business goals. History repeats itself. We made the same mistakes with cloud adoption, and we're making them again with AI. In my latest blog "Navigating the Triad: How RISE and CARE Frameworks Transform AI Strategy and Governance," I reveal how the collision of AI, cybersecurity, and business enablement creates a new kind of triple constraint most companies have no clue how to handle. I break down my battle-tested RISE and CARE frameworks that transform this abstract concept into tangible business action:  • RISE (Research, Implement, Sustain, Evaluate) builds your strategic AI roadmap  • CARE (Create, Adapt, Run, Evolve) establishes governance that enables rather than restricts The organizations winning with AI aren't those with the biggest budgets. They're the ones with strategic discipline and governance maturity. What will you choose? Fragmented AI initiatives creating vulnerabilities and risk, or frameworks enabling safe, sustainable innovation? Link to the full blog is in the first comment #AIStrategy #Cybersecurity #AIGovernance #BusinessEnablement

The Craft of AI Product Building #3: AI Product Roadmapping Maria Santos stared at the conference room whiteboard, covered in sticky notes, timeline arrows, and more question marks than she'd ever seen in a strategic planning session. As VP of Product Strategy at TransGlobal Logistics, a $2.3 billion supply chain management company, she faced a challenge that traditional planning frameworks couldn't address: developing a three-year AI roadmap that could transform their business while navigating unprecedented uncertainty. The executive directives was both inspiring and daunting: leverage AI to automate 60% of route optimization decisions, predict supply chain disruptions with 85% accuracy, and reduce operational costs by $50 million annually. The timeline was aggressive—full deployment within 36 months to stay competitive with AI-powered logistics startups that were rapidly gaining market share. But Maria realized that conventional roadmapping approaches were fundamentally inadequate for AI initiatives. AI development introduced variables that defied precise scheduling: Would computer vision technology advance enough to reliably identify damaged packages in their warehouses? Could they acquire sufficient high-quality data to train predictive models for global supply chain disruptions? How would emerging regulations around algorithmic decision-making in logistics impact their deployment timeline? Maria Santos's challenge at TransGlobal Logistics captures a fundamental problem facing enterprise leaders today. Tasked with delivering a transformational AI roadmap - she faced a planning nightmare that traditional frameworks couldn't solve. Maria leveraged an AI Product Strategy Framework that leveraged planning for uncertainty as a strategic asset. This Framework addresses the core challenge of maintaining strategic coherence while accommodating AI development's inherent uncertainty. The Multi-Horizon Planning Structure recognizes that AI requires different planning approaches across time scales: detailed 6-month execution plans with validated technical approaches, tactical 6-18 month capability development with flexible implementation paths, and strategic 18+ month vision frameworks that preserve directional clarity while accommodating breakthroughs. The framework treats uncertainty as a strategic variable rather than a planning obstacle, building optionality through multiple pathways to value creation. Dynamic Influence Layers continuously shape strategy evolution through technology trend monitoring, competitive landscape assessment, regulatory environment tracking, and organizational capability development. This creates responsive planning systems that adapt to rapid AI advancement while maintaining stakeholder confidence and strategic accountability. Read more about Adaptive and Dynamic Roadmapping in Chapter 5 of my upcoming book - The Craft of AI Product Building.

what separates flashy AI pilots from real business transformation?   one word: strategy   this week, we're featuring real-world insights from Sarah Shaiq.   she's a Chief Product and AI Officer with 14+ years transforming emerging tech into culture-shaping businesses.   what you'll discover in Sarah's thoughtful piece:   → strategic framework for selecting AI solutions that deliver measurable ROI → enterprise AI interaction methods proven to enhance decision-making → implementation scope strategies (broad platforms vs. focused departmental tools) → next-generation AI-native business systems transforming operations → key principles for building transparent, flexible AI partnerships   AI succeeds when it addresses specific operational pain points, not when it showcases cutting-edge capabilities.   Sarah's comprehensive guide includes real-world examples from GE Aerospace, Maersk, and Nextracker.   these organizations show how industry leaders achieve 15-25% cost reductions through strategic AI deployment.   want to start transforming your AI strategy from experimental to essential?   check out the piece below. 👇

Seven years ago, I published a book titled The Data Advantage: How to Create a Competitive Advantage with Data and Artificial Intelligence (https://lnkd.in/d2bMXFbG). The core recommendation of the book was to place data and AI at the heart of your company to enhance competitiveness. A lot has changed since 2018. Data, AI, and Generative AI have become mainstream. In three weeks, I will publish my second book, The Intelligence Advantage: Orchestrating Intelligence in the Age of AI. I use an analogy comparing nuclear energy to carbon-based energy: one kilogram of uranium produces three million times more power than one kilogram of carbon. I believe this 3-million-fold scale represents how AI and Generative AI outperform traditional and even digital companies. Companies that do not "blend" with AI will likely disappear, and professionals who do not adapt will become uncompetitive. In this new book, I introduce a framework for adopting AI called the "4 As of AI Adoption," designed for both individuals and organizations: Automate The process of using AI to perform routine, repetitive tasks without human intervention. Automation replaces manual workflows with intelligent systems that can execute defined processes consistently and efficiently, freeing human workers from mundane tasks. This represents the most basic level of AI adoption, focused on efficiency and cost reduction. Augment The integration of AI capabilities to enhance human performance and decision-making. Augmentation combines human expertise with AI's computational power, allowing people to work more effectively by providing them with better insights, recommendations, and tools. Here, AI serves as a partner that extends human capabilities rather than replacing them. Amplify The strategic deployment of AI to multiply the impact and reach of existing processes and capabilities. Amplification takes successful processes or intellectual assets and scales them beyond what would be possible with human resources alone. This allows organizations to expand their impact exponentially, applying expertise and capabilities across more contexts and at greater scale. Awaken The transformative potential of AI to reveal entirely new possibilities, insights, and business models that weren't previously conceivable. Awakening represents the highest level of AI adoption, where systems don't just improve existing processes but fundamentally redefine what's possible. This level drives innovation by uncovering hidden patterns, generating novel approaches, and enabling capabilities that create entirely new sources of value. These four stages create a logical progression from basic process improvement (Automate) to transformative innovation (Awaken), capturing the evolution of how organizations can leverage AI with increasing sophistication and impact. Stay tuned for May 28th..