How to Leverage AI for Business Strategy

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

In my discussions with boards and CEOs on AI strategy, here are the 6 most common AI questions I hear and how I approach them: 1️⃣🤔 "How do we integrate AI into our existing business model?" Don't start with the technology. Start with your business goals and pain points. Identify areas where AI can enhance efficiency, improve customer experience, or create new value. Develop a roadmap that aligns AI initiatives with your overall strategy. 2️⃣🤔 "What are the risks, and how do we manage them?" Conduct a thorough risk assessment covering data privacy, security, ethical considerations, and potential operational disruptions. Develop a robust governance framework. Consider appointing an AI ethics board. Stay informed about evolving regulations and ensure compliance. 3️⃣🤔 "How do we measure ROI on AI investments?" Define clear, measurable objectives for each AI initiative. Track both quantitative metrics (cost savings, revenue growth) and qualitative outcomes (improved decision-making, customer satisfaction). Be patient – some benefits may take time to materialize. 4️⃣🤔 "Build in-house or partner with vendors?" Be wary of the common trap of overestimating in-house capabilities! Many companies instinctively lean towards building themselves, assuming it'll be "faster" and "cheaper." Reality check: it rarely works out that way. To make an informed decision: 👉Conduct an honest capability assessment. Do you truly have the expertise and bandwidth? 👉Calculate the total cost of ownership, not just initial development. Factor in ongoing maintenance, updates, and opportunity costs. 👉Consider time-to-market. 👉 Is this a core differentiator or a supporting capability? 👉 Assess the pace of innovation in the specific AI domain. Can you keep up with rapid advancements? For most companies, a hybrid approach works best. Build in-house for truly unique, core competencies. Partner for everything else. Remember, the goal is to create value, not to own every piece of technology. 5️⃣🤔 "Which AI use cases should we prioritize?" Focus on high-impact, low-complexity projects to start. Look for areas where you have quality data and clear business objectives. Prioritize use cases that align with your strategic goals and have potential for scalability. 6️⃣🤔 "How do we build an AI-capable workforce?" Don't silo AI in one tech team! Weave it into your entire organization's fabric. Remember, AI isn't just for tech—it's a business-wide transformation tool. Key strategies: 👉Company-wide AI training: From marketing to finance, everyone needs AI literacy. 👉Cross-functional teams: Blend tech experts with domain specialists. 👉Strategic partnerships & M&A: Quickly infuse AI capabilities across functions. 👉Foster an AI-first culture: Encourage all teams to apply AI in their work. 👉Continuous learning: Keep pace with AI advancements company-wide. What other AI-related questions are you grappling with? #AIStrategy #Innovation #DigitalTransformation

90% of CEOs I talked to stall AI initiatives before reaching production Not due to lack of ambition, but because of misalignment between vision and execution. As digital leaders, we’ve all been there Grand AI strategies that falter at the pilot stage Cross-functional teams speaking different languages Stakeholders unclear on AI’s tangible benefits. The challenge isn’t identifying AI’s potential—it’s operationalizing it. Here’s how forward-thinking C suites are bridging that gap: 1) Define Clear Business Outcomes: Start with specific, measurable goals. AI should serve the business, not the other way around. 2) Foster Cross-Functional Collaboration: Break down silos. Ensure data scientists, engineers, and business units work in tandem. 3) Invest in Scalable Infrastructure: Build platforms that can adapt and grow with your AI initiatives. 4) Prioritize Data Governance: Trustworthy AI relies on clean, well-managed data. Establish robust data governance frameworks. 5) Cultivate an AI-Ready Culture: Encourage continuous learning and adaptability. AI transformation is as much about people as it is about technology. The takeaway? Successful AI transformation isn’t a tech project—it’s a business strategy. My name is Moon Yiu, I share insights on aligning AI initiatives with business objectives to drive real value. If you’re navigating the complexities of AI integration and seeking tangible results, let’s connect.