How to Close the AI Adoption Gap

Winning AI Adoption—How Smart Leaders Make It Stick In my last post, I called out the biggest roadblocks to AI adoption: fear, the status quo stranglehold, and lack of quick wins. Now, let’s talk about what actually works—how the best leaders are getting AI adoption right. Here’s what I’ve seen move the needle: 1. Make AI Familiar Before You Make It Big One exec I worked with introduced AI without calling it AI. Instead, he embedded AI-powered tools into existing workflows—automating scheduling, summarizing reports—before making a major push. By the time AI became a formal strategy, employees were already using it. 🔹 Key takeaway: Small, seamless introductions reduce resistance. Make AI invisible before making it strategic. 2. Use a “Coalition of the Willing” AI adoption isn’t a one-leader show. You need a groundswell. Another leader I coached built a cross-functional AI task force—hand-picking open-minded employees from various teams. These early adopters became internal influencers, pulling skeptics along and proving AI’s value in real time. 🔹 Key takeaway: AI champions make AI contagious. Build a coalition, not just a case. 3. Tie AI to Personal Wins, Not Just Business Goals People don’t embrace change because it’s good for the company. They embrace it when it makes their own work easier. One leader I advised stopped pitching AI in broad business terms. Instead, he tailored the narrative: ✅ For sales? AI means faster deal insights. ✅ For finance? AI means cleaner forecasting. ✅ For HR? AI means better hiring matches. When employees saw how AI could make their specific job easier, adoption skyrocketed. 🔹 Key takeaway: Show how AI works for them—not just for the bottom line. The Leaders Who Win With AI Don’t Just Roll It Out—They Make It Irresistible. AI adoption isn’t about tech implementation. It’s about human behavior. The smartest leaders don’t just introduce AI—they shape the conditions for people to run with it. So, the real question isn’t “Is AI ready for your company?” It’s: Is your company ready for AI? Would love to hear from those leading AI adoption—what’s working for you?

From Toys to Tools: Making Generative AI a True Asset in Healthcare Despite big opportunities for genAI in healthcare, there’s a huge adoption gap at the moment…hard to know exactly how big but there are hundreds of approved applications and only a handful in use in most health systems today. There are lots of very good reasons for this: safety, security, privacy among the many. Right now, many genAI applications in healthcare get great traction for a limited period and then fall into disuse…to me that’s a clear sign that these tools are not yet enabling productivity. It’s a nice to have, not a must have. So how do we move from “toys” to real efficiency-optimizing “tools"? First, why isn’t AI driving real productivity in healthcare yet? 3 primary reasons (there are more!): 1. Accuracy & Hallucination Risks – A single incorrect recommendation can have life-or-death consequences. HC is appropriately cautious here and doesn’t have the monitoring in place to guard against this. Because of these risks, AI today still needs a lot of human oversight and correction. 2. Lack of Workflow Integration – Most AI tools operate outside of clinicians’ natural workflows, forcing extra steps instead of removing them. 3. Trust & Adoption Barriers – Clinicians are understandably skeptical. If an AI tool slows them down or introduces errors, they will abandon it. How Can We Make AI a True Tool for Healthcare? 3 main moves we need to make: 1. Embed Trust & Explainability AI can’t just generate outputs—it has to show its reasoning (cite sources, flag uncertainty, allow inspection). And, it needs to check itself using other gen & non-genAI tools to double & triple check the outcomes in areas of high sensitivity. 2. Seamless Workflow Integration For AI to become truly useful, it must integrate with existing workflows, Auto-populating existing tools (like the EHR) and completing "last mile" steps like communicating with patients. 3. Reducing the Burden on our Workforce, Not Adding to It The tech is not enough…at-the-elbow change management will be needed to ensure human adoption and workflow adaptation and we will need to track the impact of these tools on the workforce and our patient communities. The Future: AI That Feels Invisible, Yet Indispensable Right now, genAI in healthcare is still early—full of potential but struggling to deliver consistent, real-world value. The best AI solutions of the future will be those that:  ✅ Enhance—not replace—clinicians’ expertise ✅ Are trusted because they are explainable and reliable ✅ Reduce administrative burden, giving providers more time for patients ✅ Integrate seamlessly into existing healthcare workflows Ultimately, if we build a successful person-tech interaction, the best AI won't be a novelty but an essential tool to enable us to see where our workflows are inefficient and allow us to change them effectively. What do you think? What’s the biggest barrier to making AI truly useful in healthcare?

These 3 gaps stop AI initiatives in their tracks. Here’s how to break through. We're too focused on tech challenges, and not devoting enough focus + energy to work through the human challenges blocking us from AI value. Here are 3 gaps worth digging into (I see these in most orgs right now). >>>> Leaders who don’t use AI <<<< It's nearly impossible to lead teams toward a bold AI vision if you haven't experienced meaningful value from the technology yourself. Unfortunately, I see this in all kinds of organizations (including some you would not expect). The good news is that with a shift in mindset it doesn’t take long to not only get leaders hands-on, but to do it in a way that leads them to immediate value in their own work. I know because I have a workshop that guides them right there, and it’s magical to see this unlock. The secret is: don’t start by talking about AI. Start by asking business questions that really matter. Prioritize an area to tackle and partner closely with execs to demonstrate how AI can deliver answers that move the business forward. >>>> Your tools vs. their tabs <<<< Employees bypass internal tools for more powerful public ones. Enterprise tools often lag in capability, so people turn to shadow AI use.  It’s about perceived usefulness vs. actual availability. To unblock it, develop a holistic, nuanced, and shared understanding of how your organization defines risk, considering different kinds of risk: 1. Operational risk: People will keep using unapproved AI tools in the shadows if approved ones don’t meet their needs. 2. Competitiveness risk: Falling behind peers or rivals who adopt AI more effectively, faster, and with greater real-world impact. 3. Compliance risk: Sensitive data and workflows may leak outside safe channels, creating exposure for privacy, IP, or regulatory breaches. From THIS lens, open dialogue: build feedback channels, create safe spaces to surface gaps, and prioritize where “better AI” drives “better business”. >>>> Using AI does not = AI value <<<< Most teams are experimenting but struggle to unlock meaningful value. Too often, AI learning programs focus on mechanics over helping people practice applying AI to real problems or incorporate AI into their day to day work. How to unblock it? Stop teaching tools in isolation — reshape learning programs to tackle real problems side-by-side with employees, showing how to connect new AI capabilities to the work that matters most to them. ______ We always tend to underestimate what it takes to make change happen. With AI moving so fast (and feeling so chaotic in many orgs), this is especially dangerous. _____ What do you think??? What other human barriers to AI success should we be talking about here? What other tactics have you found help to break through these gaps? ____ If this is helpful, ♻️ repost to help someone in your network! ____ 👋 Hi, I'm Alison McCauley. Follow me for more on using AI to advance human performance.