While “use AI or get left behind” is true, it’s not a technical strategy.
We’ve all heard the messaging: AI will transform everything, early adopters will dominate, and organizations that don’t embrace it will become irrelevant. This rhetoric, while compelling, creates the same pattern we’ve seen with every emerging technology—urgency without direction.
The result? Organizations rush to adopt AI tools without systematic approaches, leading to inconsistent usage, unmeasurable outcomes, and the classic “shiny object syndrome” that leaves teams with scattered implementations but no real transformation.
As leaders, we know that simply using AI ourselves isn’t enough to drive organizational change. Leading from the front—my general style—doesn’t scale when it comes to technical strategy. We need to pave the way and lower the barrier to entry for using AI in valuable and consistent ways across our teams.
Start with a Simple Question
As Geoff Woods suggested, we should start by asking “How can AI help me do this?” I’d reframe this as: “How can AI help me with this task?”
This single question becomes the lowest barrier to entry for AI adoption. It’s specific, task-focused, and immediately actionable. Rather than thinking about AI in abstract terms, this question grounds usage in real work that needs to get done.
Once you start asking this question regularly, you need a framework to evaluate what AI gives you back.
The Core Framework: Understand → Validate → Decide
Every interaction with AI should follow this simple loop:
Understand: Do I understand what the AI is saying? Never use AI responses you don’t fully comprehend. If the output is unclear, ask follow-up questions or request explanations.
Validate: Can I verify this information or approach? This might mean checking against existing knowledge, testing code, or cross-referencing with reliable sources. For areas outside your expertise, validation becomes even more critical.
Decide: Should I use this output? Just because AI provides an answer doesn’t mean it’s the right solution for your specific context.
This validation process works best when combined with proven practices that prevent common AI adoption pitfalls.
Best Practices for Strategic AI Use
A critical challenge I see when organizations begin adopting AI is the abdication of critical thinking. Here’s how to avoid this trap:
Do These Things:
- Start with a question: Always begin with “How can AI help me with this task?” to ground your usage in real work.
- Think out loud: Use AI to explore ideas, generate options, or get unstuck. It’s a powerful brainstorming partner.
- Review and understand outputs: Never use AI responses you don’t fully understand. Ask for clarification when needed.
- Refine prompts iteratively: Good AI use is a back-and-forth process. Don’t expect perfect results on the first try.
- Document wins: Share successful use cases with your team to build shared organizational knowledge.
- Keep sensitive data secure: Only include sensitive information in prompts when explicitly permitted by your organization.
Critical Challenges to Avoid:
- Copy-paste without thought: You remain accountable for the quality of all outputs, regardless of their source.
- Blaming the AI: “It told me to” is never an acceptable explanation for poor decisions or outcomes.
- Bypassing critical thinking: Use AI to challenge and test your thinking, not replace it entirely.
- Assuming it’s always right: AI can sound confident while being completely wrong or misleading.
- Over-engineering prompts: Keep interactions natural – ask like you’d ask a knowledgeable colleague.
Strategic AI Means Measurable Results
When organizations implement this systematic approach, the acceleration in delivery becomes measurable and significant.
Code Flow Analysis: What normally took days was completed in hours. For a recent migration project, we converted 1,301 objects in 4 months with a 66% reduction in blocker resolution time, completing a full stack migration that would have taken significantly longer using traditional approaches.
Quality Improvements: AI enables work in areas outside current expertise. I regularly use AI for pre-PR code reviews to ensure code meets industry best practices, especially valuable when working with technologies I haven’t touched in years.
Demo Development: Teams are building functional demos over their weekends that previously required weeks of development time, accelerating proof-of-concept cycles and client feedback loops.
These aren’t isolated successes – they represent what’s possible when AI usage follows a systematic approach rather than ad-hoc experimentation.
Next Steps for Your Organization
Don’t let your organization blindly adopt AI. Establish a strategy starting with a simple question: “How can AI help me with my current task today?”
Begin with individual adoption of the question-and-framework approach, then scale successful patterns across teams. Document wins, share learnings, and build organizational knowledge systematically.
The organizations that will truly benefit from AI aren’t necessarily the first to adopt every new tool – they’re the ones that develop sustainable, measurable approaches to AI integration that amplify human capabilities rather than replace human judgment.
Start today. Ask the question. Follow the framework. Measure the results.
