How to Make Artificial Intelligence a Business Imperative

I asked the smartest people I know about AI... I’ve been reading everything I can get my hands on. Talking to AI founders, skeptics, operators, and dreamers. And having some very real conversations with people who’ve looked me in the eye and said: “This isn’t just a tool shift. It’s a leadership reckoning.” Oh boy. Another one eh? Alright. I get it. My job isn’t just to understand disruption. It’s to humanize it. Translate it. And make sure my teams are ready to grow through it and not get left behind. So I asked one of my most fav CEOs, turned investor - a sharp, no-BS mentor what he would do if he were running a company today. He didn’t flinch. He gave me a crisp, practical, people-centered roadmap. “Here’s how I’d lead AI transformation. Not someday. Now.” I’ve taken his words, built on them, and I’m sharing my approach here, not as a finished product, but as a living, evolving plan I’m adopting and sharing openly to refine with others. This plan I believe builds capability, confidence, and real business value: 1A. Educate the Top. Relentlessly. Every senior leader must go through an intensive AI bootcamp. No one gets to opt out. We can’t lead what we don’t understand. 1B. Catalog the problems worth solving. While leaders are learning, our best thinkers start documenting real challenges across the business. No shiny object chasing, just a working list of problems we need better answers for. 2. Find the right use cases. Map AI tools to real problems. Look for ways to increase efficiency, unlock growth, or reduce cost. And most importantly: communicate with optimism. AI isn’t replacing people, it’s teammate technology. Say that. Show that. 3. Build an AI Helpdesk. Recruit internal power users and curious learners to be your “AI Coaches.” Not just IT support - change agents. Make it peer-led and momentum-driven. 4. Choose projects with intention. We need quick wins to build energy and belief. But you need bigger bets that push the org forward. Balance short-term sprints with long-term missions. 5. Vet your tools like strategic hires. The AI landscape is noisy. Don’t just chase features. Choose partners who will evolve with you. Look for flexibility, reliability, and strong values alignment. 6. Build the ethics framework early. AI must come with governance. Be transparent. Be intentional. Put people at the center of every decision. 7. Reward experimentation. This is the messy middle. People will break things. Celebrate the ones who try. Make failing forward part of your culture DNA. 8. Scale with purpose. Don’t just track usage. Track value. Where are you saving time? Where is productivity up? Where is human potential being unlocked? This is not another one-and-done checklist. Its my AI compass. Because AI transformation isn’t just about tech adoption. It’s about trust, learning, transparency, and bringing your people with you. Help me make this plan better? What else should I be thinking about?

🎯 The CIO's Organizational Playbook for the AI Era... I recently spoke with a CIO friend about how IT teams are changing. Our discussion made me think about what sets apart IT teams that succeed with AI from those that don’t. I looked over my research and reviewed my interviews with other leaders. This information is too valuable not to share: ✓ Build AI-Ready Capabilities 🟢 Establish continuous learning programs focused on practical AI applications 🟢 Implement cross-functional training to bridge technical/business gaps 🟢 Prioritize hands-on AI workshops over theoretical certifications ✓ Master AI Risk Management 🟢 Develop processes to identify and mitigate technical failures early 🟢 Create a strategic AI roadmap with clear risk contingency protocols 🟢 Align all AI initiatives with broader business objectives ✓ Drive Stakeholder Engagement 🟢 Build a cross-functional AI coalition (executives, HR, business units) 🟢 Communicate AI initiatives with transparency to reduce resistance 🟢 Document tangible benefits to secure continued buy-in ✓ Implement with Agility 🟢 Replace waterfall approaches with iterative AI development 🟢 Focus on quick prototyping and real-world testing 🟢 Ensure infrastructure scalability supports AI growth ✓ Lead with AI Ethics 🟢 Train teams on bias identification and mitigation techniques 🟢 Establish clear governance frameworks with accountability 🟢 Make responsible AI deployment non-negotiable ✓ Transform Your Talent Strategy 🟢 Enhance IT roles to integrate AI responsibilities 🟢 Create peer mentoring programs pairing AI experts with domain specialists 🟢 Cultivate an AI-positive culture through early wins ✓ Measure What Matters 🟢 Set specific AI KPIs that link directly to business outcomes 🟢 Implement continuous feedback loops for ongoing refinement 🟢 Track both technical metrics and organizational adoption rates The organizations mastering these elements aren't just surviving the AI transition—they're thriving because of it. #digitaltransformation #changemanagement #leadership #CIO

You don’t need more AI. You need better strategy. Eight steps to drive real impact with AI. 1. Align AI with business goals. AI is only valuable when tied to strategy. Start by asking what you want to achieve. Then link each use case to a real outcome. 2. Engage leadership early. C-suite buy-in drives clarity and speed. Leaders must model adoption and own the “why.” Without this, teams stall or resist the change. 3. Evaluate readiness for change. Fear - not tech - is the biggest blocker. Assess confidence, trust, and communication. Prepare change agents across the business. 4. Assess your tech infrastructure. Legacy tools slow AI to a crawl. Check for speed, scale, and integrations. Strong foundations lead to strong results. 5. Define the right KPIs. What you measure drives what you improve. Set goals around adoption, speed, and impact. Track consistently - and iterate often. 6. Ensure your data is ready. AI is only as good as your data is clean. Fix silos, tag documents, and validate sources. Governance and compliance matter too. 7. Build a phased roadmap. Start with one clear, high-value use case. Test it. Learn fast. Build trust with wins. Then scale thoughtfully with feedback loops. 8. Monitor and adapt constantly. AI strategy is never “one and done.” Review performance, listen to users, adjust. The best teams evolve their playbook often. P.S. Want my free L&D strategy guide? 1. Scroll to the top 2. Click “Visit my website” 3. Download your free guide.