Strategies for Value Realization Using AI

After reviewing dozens of enterprise AI initiatives, I've identified a pattern: the gap between transformational success and expensive disappointment often comes down to how CEOs engage with their technology leadership. Here are five essential questions to ask: 𝟭. 𝗪𝗵𝗮𝘁 𝘂𝗻𝗶𝗾𝘂𝗲 𝗱𝗮𝘁𝗮 𝗮𝘀𝘀𝗲𝘁𝘀 𝗴𝗶𝘃𝗲 𝘂𝘀 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀 𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗼𝗿𝘀 𝗰𝗮𝗻'𝘁 𝗲𝗮𝘀𝗶𝗹𝘆 𝗿𝗲𝗽𝗹𝗶𝗰𝗮𝘁𝗲? Strong organizations identify specific proprietary data sets with clear competitive moats. One retail company outperformed competitors 3:1 only because it had systematically captured customer interaction data its competitors couldn't access. 𝟮. 𝗛𝗼𝘄 𝗮𝗿𝗲 𝘄𝗲 𝗿𝗲𝗱𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗼𝘂𝗿 𝗰𝗼𝗿𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 𝗮𝗿𝗼𝘂𝗻𝗱 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 𝗿𝗮𝘁𝗵𝗲𝗿 𝘁𝗵𝗮𝗻 𝗷𝘂𝘀𝘁 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗻𝗴 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀? Look for specific examples of fundamentally reimagined business processes built for algorithmic scale. Be cautious of responses focusing exclusively on efficiency improvements to existing processes. The market leaders in AI-driven healthcare don't just predict patient outcomes faster, they've architected entirely new care delivery models impossible without AI. 𝟯. 𝗪𝗵𝗮𝘁'𝘀 𝗼𝘂𝗿 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗶𝗻𝗴 𝘄𝗵𝗶𝗰𝗵 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗿𝗲𝗺𝗮𝗶𝗻 𝗵𝘂𝗺𝗮𝗻-𝗱𝗿𝗶𝘃𝗲𝗻 𝘃𝗲𝗿𝘀𝘂𝘀 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰𝗮𝗹𝗹𝘆 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗱? Expect a clear decision framework with concrete examples. Be wary of binary "all human" or "all algorithm" approaches, or inability to articulate a coherent model. Organizations with sophisticated human-AI frameworks are achieving 2-3x higher ROI on AI investments compared to those applying technology without this clarity. 𝟰. 𝗛𝗼𝘄 𝗮𝗿𝗲 𝘄𝗲 𝗺𝗲𝗮𝘀𝘂𝗿𝗶𝗻𝗴 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 𝗯𝗲𝘆𝗼𝗻𝗱 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗺𝗲𝘁𝗿𝗶𝗰𝘀? The best responses link AI initiatives to market-facing metrics like share gain, customer LTV, and price realization. Avoid focusing exclusively on cost reduction or internal efficiency. Competitive separation occurs when organizations measure algorithms' impact on defensive moats and market expansion. 𝟱. 𝗪𝗵𝗮𝘁 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗮𝗹 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗵𝗮𝘃𝗲 𝘄𝗲 𝗺𝗮𝗱𝗲 𝘁𝗼 𝗼𝘂𝗿 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 𝘁𝗼 𝗰𝗮𝗽𝘁𝘂𝗿𝗲 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝘃𝗮𝗹𝘂𝗲 𝗼𝗳 𝗔𝗜 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀? Look for specific organizational changes designed to accelerate algorithm-enhanced decisions. Be skeptical of AI contained within traditional technology organizations with standard governance. These questions have helped executive teams identify critical gaps and realign their approach before investing millions in the wrong direction. 𝘋𝘪𝘴𝘤𝘭𝘢𝘪𝘮𝘦𝘳: V𝘪𝘦𝘸𝘴 𝘦𝘹𝘱𝘳𝘦𝘴𝘴𝘦𝘥 𝘢𝘳𝘦 𝘮𝘺 own 𝘢𝘯𝘥 𝘥𝘰𝘯'𝘵 𝘳𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵 𝘵𝘩𝘰𝘴𝘦 𝘰𝘧 𝘮𝘺 𝘤𝘶𝘳𝘳𝘦𝘯𝘵 𝘰𝘳 𝘱𝘢𝘴𝘵 𝘦𝘮𝘱𝘭𝘰𝘺𝘦𝘳𝘴.

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

In this latest Forbes article, I draw a compelling line from Ada Lovelace’s 19th-century foresight to today’s AI-driven enterprise transformations. Lovelace envisioned machines augmenting human creativity—a vision now realized as #generativeAI reshapes industries. Accenture's experience with over 2,000 gen AI projects reveals that only 13% of companies achieve significant enterprise-wide value, while 36% are scaling AI for industry-specific solutions. Success in this new era hinges on more than just technology investment. Companies must also invest in their people, prioritize industry-specific AI applications, and embed responsible AI practices from the outset. Organizations adopting agentic architecture - digital teams comprising orchestrator, super, and utility agents—are 4.5 times more likely to realize enterprise-level value. Here are five key lessons we’ve learned: 1. Lead with value from the top: Executive sponsorship is crucial. Companies with CEO sponsorship achieve 2.5 times higher ROI from their #AI investments.  2. Invest in people, not just technology: Empower your workforce with the skills to harness AI. Organizations excelling in AI transformation invest in broad AI upskilling, adopt dynamic workforce models, and enable human + agent collaboration.  3. Prioritize industry-specific AI solutions: Tailor AI applications to your sector’s unique needs. Companies creating enterprise-level value are 2.9 times more likely to have a comprehensive data strategy to support their AI efforts.  4. Design and embed AI responsibly from the start: Ensure ethical and effective AI integration. Organizations creating enterprise-level value are 2.7 times more likely to have responsible AI principles and governance in place across the AI lifecycle.  5. Reinvent continuously: Stay adaptable in the face of ongoing change. Companies with advanced change capabilities are 2.1 times more likely to achieve successful transformations. These lessons should serve as a practical playbook for navigating the complexities of #AI integration and achieving sustainable growth. Please read the full article to explore how Lovelace’s visionary ideas are shaping the future of business through #generativeAI. https://lnkd.in/gEVzQeRA

🧠 Strategy scales GenAI. Culture sustains it. Leadership ignites it. 🚀 GenAI is no longer just a disruptive force; it’s a defining one. But fundamental transformation doesn’t come from deploying another model. It comes from aligning strategy, culture, and leadership to scale innovation responsibly. Over the past few years, I’ve worked closely with organizations navigating the messy middle of GenAI maturity, where potential is high but direction is often unclear. What distinguishes high-impact adopters from others? Clarity across seven core priorities: 📍 1. Benchmark Maturity Map your current state. Understand the gaps across governance, data, infra, talent, and value realization. You can’t scale what you can’t see. 🏗 2. Build a GenAI Center of Excellence Not just a team, a cultural engine that standardizes experimentation, governance, and reuse across the enterprise. ⚖️ 3. Operationalize Responsible AI From model transparency to ethical deployment frameworks, responsible AI is no longer optional; it’s a reputational imperative. 🎯 4. Prioritize Strategic Use Cases Innovation must be intentional. Focus on use cases that enhance resilience, efficiency, and differentiation, not just novelty. 🔌 5. Invest in Scalable Infrastructure Cloud-native, secure, and observable. A robust AI backbone ensures models don’t just work in notebooks; they perform reliably in production. 📚 6. Foster AI Literacy From execs to frontline teams, shared language fuels adoption. Culture shifts when knowledge becomes a company-wide asset. 📊 7. Measure & Communicate Impact Business value is your north star. Track metrics that matter and tell a compelling story around them. 💡 Here’s my lens: GenAI isn't about chasing the next shiny model; it's about building the organizational muscle to adapt, lead, and scale responsibly. 📢 I’d love to hear from others in the space: What’s been your biggest unlock or challenge on the path to GenAI maturity? Let’s keep this conversation strategic. 🤝 #GenAI #EnterpriseAI #CTOLeadership #AITransformation #TechStrategy #InnovationAtScale #AIinBusiness #ThoughtLeadership #DigitalLeadership

𝗔𝗜 𝗛𝘆𝗽𝗲 𝘃𝘀. 𝗔𝗜 𝗩𝗮𝗹𝘂𝗲: 𝗛𝗼𝘄 𝘁𝗼 𝗰𝘂𝘁 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝘁𝗵𝗲 𝗻𝗼𝗶𝘀𝗲 𝗮𝗻𝗱 𝗳𝗼𝗰𝘂𝘀 𝗼𝗻 𝘄𝗵𝗮𝘁 𝗱𝗿𝗶𝘃𝗲𝘀 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗶𝗺𝗽𝗮𝗰𝘁 We’re living in a time of AI overload.Every day, there’s a new tool, a viral demo, or a promise that AI will transform everything. But for CXOs, the essential question remains: 𝗪𝗵𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘃𝗮𝗹𝘂𝗲? In my work with Fortune 500 clients leading cloud and AI transformations, one thing is clear: Success with AI doesn’t come from chasing trends. It comes from identifying the 𝑟𝑖𝑔ℎ𝑡 𝑝𝑟𝑜𝑏𝑙𝑒𝑚, having 𝑟𝑒𝑙𝑖𝑎𝑏𝑙𝑒 𝑡𝑟𝑎𝑖𝑛𝑖𝑛𝑔 𝑑𝑎𝑡𝑎 𝑠𝑒𝑡𝑠, and 𝑒𝑥𝑒𝑐𝑢𝑡𝑖𝑛𝑔 𝑖𝑛 𝑡ℎ𝑒 𝑟𝑖𝑔ℎ𝑡 𝑏𝑢𝑠𝑖𝑛𝑒𝑠𝑠 𝑐𝑜𝑛𝑡𝑒𝑥𝑡. Here’s a practical lens I use with executive teams to prioritize AI investments: 𝗧𝗵𝗲 𝟯𝗣 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸: 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 – 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 – 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹𝗶𝘁𝘆 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: What specific business issue are we solving? Is it a speed, experience, or insight challenge? 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹: What’s the tangible upside of solving it with AI? Are we talking about revenue growth, New revenue streams, operational efficiency, or improved accuracy? 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹𝗶𝘁𝘆: Do we have the data, skills, and platform to deploy it at scale with the right market timing? Proofs of concept are easy; scalable success is not. 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝗶𝗻𝗴 𝗥𝗲𝗮𝗹 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗩𝗮𝗹𝘂𝗲: 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗳𝗼𝗿 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 – 𝗕𝗼𝗼𝘀𝘁𝗶𝗻𝗴 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 𝗶𝗻 𝗟𝗮𝗿𝗴𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲𝘀 A Fortune 100 healthcare organization deployed generative AI to surface internal documentation and expert insights. Employees now retrieve critical answers in seconds, not hours—accelerating onboarding and reducing duplication of effort. 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗥𝗲𝘁𝗮𝗶𝗹 – 𝗟𝗶𝗳𝘁𝗶𝗻𝗴 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗥𝗮𝘁𝗲𝘀 𝗯𝘆 𝟭𝟴% A global retailer applied machine learning to personalize product recommendations based on browsing behavior and inventory trends. Customers received more relevant suggestions, and e-commerce conversions jumped by nearly 20%. 𝗔𝗜 𝗶𝗻 𝗦𝘂𝗽𝗽𝗹𝘆 𝗖𝗵𝗮𝗶𝗻 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 – 𝗖𝘂𝘁𝘁𝗶𝗻𝗴 𝗘𝘅𝗰𝗲𝘀𝘀 𝗜𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆 𝗯𝘆 𝟮𝟱% A manufacturing firm integrated AI-based demand forecasting into its planning cycle. With more accurate predictions, they reduced excess inventory and saved millions in carrying costs. 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲: If your AI project doesn’t move the needle—on revenue, speed, or experience—it’s probably tech theater. AI is here to stay, but 𝘃𝗮𝗹𝘂𝗲 𝗶𝘀 𝘀𝘁𝗶𝗹𝗹 𝘁𝗵𝗲 𝗡𝗼𝗿𝘁𝗵 𝗦𝘁𝗮𝗿. 𝗪𝗼𝘂𝗹𝗱 𝗹𝗼𝘃𝗲 𝘁𝗼 𝗵𝗲𝗮𝗿 𝗳𝗿𝗼𝗺 𝗼𝘁𝗵𝗲𝗿𝘀: 𝑊ℎ𝑎𝑡’𝑠 𝑎 𝑟𝑒𝑎𝑙-𝑤𝑜𝑟𝑙𝑑 𝐴𝐼 𝑝𝑟𝑜𝑗𝑒𝑐𝑡 𝑡ℎ𝑎𝑡’𝑠 𝑑𝑒𝑙𝑖𝑣𝑒𝑟𝑒𝑑 𝑚𝑒𝑎𝑠𝑢𝑟𝑎𝑏𝑙𝑒 𝑖𝑚𝑝𝑎𝑐𝑡 𝑓𝑜𝑟 𝑦𝑜𝑢𝑟 𝑏𝑢𝑠𝑖𝑛𝑒𝑠𝑠? #AI #Cloud #DigitalTransformation #TheHeartOfProgress

One reason AI initiatives stall? Few execs use AI in their own work. In 3 hours, I take leaders from “I don’t know” to a POV (co-developed with AI!) on how AI can support key strategic initiatives. To crack the code on exec adoption we: >> Focus on Strategic Use Cases that Click with Execs << To get experience with high value use of AI, we dive into cases that directly enhance executive decision-making and strategic thinking. This tends to be a major eye-opener—most leaders don't realize AI can elevate their highest-level work. Once executives experience immediate personal value, they better understand how AI can have immediate impact across the organization. >> Reframe Mental Models << Generative AI operates fundamentally differently from anything we've seen before, so we need to identify why and how digital change playbooks must shift to leverage this moment. I go straight to the heart of the silent organizational barriers that prevent productive adoption, and how to navigate a path forward. >> Start with the Business, Not the Tech << We don’t begin with AI—we begin with your business. We anchor the process with the breakthroughs that will drive real impact—and to get there, we go analog with brainstorming, whiteboards, and post-its, working to envision what advancement could look like. What could be possible if cognitive limits were lifted? What long-standing friction could finally be overcome? This surfaces a library of meaningful, business-driven opportunities. Then, using proven filters and frameworks, we zero in on the highest-impact places to start applying AI. >> Use AI to Develop AI Strategy << We then—on the spot—collaborate with AI to develop executive viewpoints on how AI can accelerate those strategic priorities. This is hands-on work with AI tools to co-create a path forward, often culminating in each group sharing a lightning talk (co-developed with AI) with the broader team. This approach fast tracks execs to: 1️⃣ Build readiness: Gain deep understanding of the new landscape of use cases today’s AI offers, and the organizational structures needed to effectively harness it. 2️⃣ Map use cases: Develop a prioritized library of strategic use cases ready for immediate collaboration with technology and data teams. 3️⃣ Accelerate alignment: Establish common language and jump-start cross-functional alignment on tackling high-impact opportunities. 4️⃣ Hands-on understanding: Acquire hands-on experience with AI tools they can immediately apply to their most challenging strategic work. What do my clients say about this approach? That their teams shift from skepticism to enthusiasm—hungry for more, and from uncertainty to clarity about the next steps. It’s a remarkable change, especially in a few hours. ➡️ Want to learn more? Let’s talk. #AIworkshop

What if technical superiority is not what separates winning AI investments from losing ones? The data is startlingly clear: while 69% of companies have adopted AI in at least one function, only 4% deploy capabilities that consistently produce significant value. This isn't a technology problem: → 64% of AI initiatives stall at the pilot stage (McKinsey) -> 57% of failed implementations cite integration complexity and hidden costs—not model performance—as primary culprits (Deloitte) → 91% of data leaders identify cultural and change management challenges as their main obstacles For VCs, these patterns reveal a critical blind spot in how we evaluate AI companies. We scrutinize technical differentiation while undervaluing implementation capability. When portfolio companies sell AI solutions, they're not just selling technology—they're selling organizational transformation. Yet most vendors leave after implementation, creating: → Dependency relationships that limit expansion revenue → Knowledge gaps that prevent adaptation → Higher total cost of ownership → Barriers to subsequent AI initiatives → Valuation ceilings despite technical excellence The most valuable AI companies aren't those with the best algorithms, but those that bridge capability gaps. The evidence appears in multiple value creation metrics: → Lower customer acquisition costs through improved implementation success rates → Higher expansion revenue from capability-enabled customers → More predictable growth as knowledge transfer accelerates adoption curves → Premium multiples from sustainable competitive advantage beyond the technology itself Consider Colgate-Palmolive's approach: by appointing internal AI champions alongside vendor implementation, they dramatically reduced resistance and accelerated value capture. And resist the temptation to sneer. A legacy company understands legacy problems. When evaluating AI investments, the question isn't just "how good is their technology?" but "how effectively do they bridge capability gaps?" This shifts due diligence priorities considerably. Beyond model accuracy to knowledge transfer methodologies Beyond technical benchmarks to capability-building frameworks.. Beyond initial sales to expansion economics. Beyond features to organizational enablement. Successful AI investing is fundamentally about recognizing that technical differentiation opens doors, but capability transfer builds empires.

Some of the best AI breakthroughs we’ve seen came from small, focused teams working hands-on, with structured inputs and the right prompting. Here’s how we help clients unlock AI value in days, not months: 1. Start with a small, cross-functional team (4–8 people) 1–2 subject matter experts (e.g., supply chain, claims, marketing ops) 1–2 technical leads (e.g., SWE, data scientist, architect) 1 facilitator to guide, capture, and translate ideas Optional: an AI strategist or business sponsor 2. Context before prompting - Capture SME and tech lead deep dives (recorded and transcribed) - Pull in recent internal reports, KPIs, dashboards, and documentation - Enrich with external context using Deep Research tools: Use OpenAI’s Deep Research (ChatGPT Pro) to scan for relevant AI use cases, competitor moves, innovation trends, and regulatory updates. Summarize into structured bullets that can prime your AI. This is context engineering: assembling high-signal input before prompting. 3. Prompt strategically, not just creatively Prompts that work well in this format: - “Based on this context [paste or refer to doc], generate 100 AI use cases tailored to [company/industry/problem].” - “Score each idea by ROI, implementation time, required team size, and impact breadth.” - “Cluster the ideas into strategic themes (e.g., cost savings, customer experience, risk reduction).” - “Give a 5-step execution plan for the top 5. What’s missing from these plans?” - “Now 10x the ambition: what would a moonshot version of each idea look like?” Bonus tip: Prompt like a strategist (not just a user) Start with a scrappy idea, then ask AI to structure it: - “Rewrite the following as a detailed, high-quality prompt with role, inputs, structure, and output format... I want ideas to improve our supplier onboarding process with AI. Prioritize fast wins.” AI returns something like: “You are an enterprise AI strategist. Based on our internal context [insert], generate 50 AI-driven improvements for supplier onboarding. Prioritize for speed to deploy, measurable ROI, and ease of integration. Present as a ranked table with 3-line summaries, scoring by [criteria].” Now tune that prompt; add industry nuances, internal systems, customer data, or constraints. 4. Real examples we’ve seen work: - Logistics: AI predicts port congestion and auto-adjusts shipping routes - Retail: Forecasting model helps merchandisers optimize promo mix by store cluster 5. Use tools built for context-aware prompting - Use Custom GPTs or Claude’s file-upload capability - Store transcripts and research in Notion, Airtable, or similar - Build lightweight RAG pipelines (if technical support is available) - Small teams. Deep context. Structured prompting. Fast outcomes. This layered technique has been tested by some of the best in the field, including a few sharp voices worth following, including Allie K. Miller!

The AI hype cycle is over. Now it’s time for real business value. Organizations spent the last year experimenting with AI tools, often with mixed results. Those who succeeded found that strategic integration is what drives ROI. Here's 11 ways top performers are achieving measurable ROI on their AI investment: 1. Process Automation Integration → Embed AI in existing workflows → 40-60% reduction in manual tasks → Focus on high-volume, repetitive processes Pro tip: Start with processes that have clear metrics and high error rates. 2. Customer Service Enhancement → AI-powered ticket routing and resolution → 30% reduction in response time → Improved customer satisfaction scores Pro tip: Train AI on your top performers' responses to maintain brand voice and solution quality. 3. Data Analytics Acceleration → Automated insight generation → Predictive modeling at scale → 50% faster decision-making cycles Pro tip: Build dashboards that translate AI insights into actionable recommendations for non-technical teams. 4. Revenue Generation → AI-enhanced lead scoring → Personalized customer journeys → 25% increase in conversion rates Pro tip: Use A/B testing to continuously refine AI models against actual sales outcomes. 5. Cost Optimization → Smart resource allocation → Predictive maintenance → 20-30% reduction in operational costs Pro tip: Create an AI savings tracker to document and communicate wins to stakeholders. 6. Product Development → AI-driven feature prioritization → Automated testing and QA → 40% faster time-to-market Pro tip: Implement AI feedback loops between customer support and product teams for continuous improvement. 7. Risk Management → Real-time fraud detection → Compliance monitoring → 65% reduction in false positives Pro tip: Regular model retraining with new fraud patterns keeps detection rates high. 8. Employee Productivity → AI-powered knowledge management → Automated routine tasks → 3-4 hours saved per employee weekly Pro tip: Create AI champions in each department to drive adoption and share best practices. 9. Supply Chain Optimization → Demand forecasting → Inventory management → 30% reduction in stockouts Pro tip: Combine internal data with external factors (weather, events, trends) for better predictions. 10. Content Creation → Automated first drafts → Multichannel optimization → 60% faster content production Pro tip: Build a prompt library of your best-performing content formats and styles. 11. Quality Control → Computer vision inspection → Defect prediction → 45% reduction in quality issues Pro tip: Start with human-in-the-loop systems before moving to full automation. The key? Integration. Success comes from embedding AI into core business processes, not treating it as a standalone solution. What's your organization's biggest AI ROI win? Share below 👇 ♻️ Repost if your network needs this AI implementation blueprint. Follow Carolyn Healey for more content like this.

🎯 The Intelligence Multiplication Game: A Strategic Framework & Hidden Pattern Behind Successful AI Transformation   What many overlook in AI transformation is that it’s not merely about implementing technology; it’s about amplifying organizational intelligence. Here’s a framework I’m developing within Treasure Data that will distinguish leaders from followers:   1️⃣ Intelligence Mapping  The foundational question to consider is:  Where would 10x intelligence create 100x value?    Map your power centers:  • Decision Intelligence: Expert judgment • Process Intelligence: Cognitive workflows • Customer Intelligence: Predictive insights   2️⃣ Signal vs. Noise  This is the winning filter:  ✓ Multiplies existing advantages ✓ Creates compound effects ✓ Scales your best thinking   Everything else is merely addressing tech-seeking problems.   3️⃣ Tech Evolution Path  Your technology stack isn't just changing; it’s gaining intelligence.  SaaS → AI → Autonomous Agents    Critical insight: Some systems require:  • Evolution: AI enhancement • Revolution: AI-native rebuild • Replacement: Integration of autonomous agents   4️⃣ Strategic Execution 90-Day Intelligence Sprint: • Map (30 days): Identify intelligence opportunities  • Test (30 days): Validate multiplication hypotheses  • Scale (30 days): Expand proven patterns    5️⃣ Measure What Matters**  Intelligence Multiplication Factor (IMF) = (Speed × Quality × Scale) ÷ Complexity    🚀 The Strategic Truth:  While others focus on implementing AI features, leaders build intelligence multiplication engines.   💡 The Technical Edge:  • Start with intelligence flows, not technical specifications  • Design for compound advantages  • Incorporate feedback loops into every system  • Measure the effects of multiplication    Eager to hear how you are enhancing your organization’s intelligence?    #AIStrategy #TechLeadership #CIO #Innovation #CISO #AIPlayBook