Are you an engineering leader grappling with how to effectively communicate your AI strategy to your board or executive team? Do the buzzwords and rapid pace of AI development make it feel like an insurmountable challenge to articulate a coherent plan? If so, you’re not alone. Many organizations feel the pressure to “do AI,” but struggle to bridge the gap between aspirational goals and practical execution that resonates with leadership.

The good news is, you’re probably much further along than you realize. A robust AI strategy isn’t always about groundbreaking, custom-built models from day one. Often, it’s about leveraging existing capabilities, solidifying foundational practices, and strategically building momentum. As Squire Bill Widener wisely put it, “Do what you can, with what you’ve got, where you are.” This timeless advice applies remarkably well to establishing a pragmatic AI path.

From my experience, a practical and achievable AI journey for many organizations often looks like this:

1. The Unsung Hero: Data Governance Strategy

Every sound AI strategy, every groundbreaking machine learning initiative, every piece of predictive analytics, ultimately rests on a single, often unglamorous, foundation: data strategy with robust governance and access management. This isn’t a new concept; it’s something many of us have been working on for years, even decades. But its criticality in the age of AI cannot be overstated.

You simply cannot progress meaningfully in AI without a mature data foundation. This means having clear data stewardship, established partnerships across business units to define data ownership, and well-defined access protocols. If you’ve been investing in data warehousing, data lakes, master data management, or even just solid ETL processes, you’ve been building the bedrock for AI without explicitly calling it that. Emphasize these existing strengths to your board; it demonstrates foresight and a solid grounding in what’s required.

2. Leverage Embedded AI: Start Small, Think Big

Once your data house is in order (or at least progressing well), the next step is often the most accessible and immediately impactful: leveraging the embedded AI capabilities within the applications you already use. This is your “do what you can with what you’ve got” moment.

Many enterprise software vendors are aggressively integrating AI into their core products. Companies like Oracle, with their Fusion Cloud Applications, are infusing AI into HR, finance, and supply chain modules to automate tasks, provide insights, and improve user experience. Microsoft is doing the same across its suite, from intelligent suggestions in Office 365 to advanced analytics in Dynamics. By utilizing these features, your teams can gain immediate value, understand the practical applications of AI, and start building AI literacy across the workforce. This isn’t just about using a new feature; it’s about training your workforce to be ready for an AI-first future, demystifying the technology and building internal champions.

3. Build & Track Use Cases with Eager Sponsors

With your data foundation solidifying and your teams gaining familiarity through embedded AI, it’s time to strategically identify and pursue specific, value-driven use cases. This isn’t about throwing spaghetti at the wall; it’s about targeted initiatives with clear business outcomes.

To maximize your chances of success, choose your use cases carefully. Focus on areas where you can achieve tangible, measurable results. More importantly, identify a strong business sponsor; someone eager to adopt and champion the AI solution. This partnership is crucial for overcoming adoption hurdles, securing resources, and ensuring the project aligns with critical business needs. Track the impact of these use cases meticulously. Each success story, no matter how small, builds credibility, demonstrates ROI, and provides valuable lessons for future endeavors. This allows you to close the gap between current operations and the transformative potential of AI through practical, responsible, and value-driven deployments.

By framing your AI strategy around these three pillars, you can present a clear, actionable, and less intimidating roadmap to your board. It highlights existing strengths, leverages readily available tools, and builds momentum through focused, sponsored initiatives. You’re not starting from scratch; you’re evolving, adapting, and building on the solid engineering foundations you’ve already laid.