The Role Of Leadership In AI Scaling

𝗕𝗲𝘆𝗼𝗻𝗱 𝗠𝗟 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: 𝗛𝗼𝘄 𝗧𝗿𝘂𝗲 𝗔𝗜 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗖𝗿𝗲𝗮𝘁𝗲𝘀 𝗠𝗮𝗿𝗸𝗲𝘁 𝗗𝗼𝗺𝗶𝗻𝗮𝗻𝗰𝗲 Two years ago, I witnessed a pivotal moment. Two competitors in the same industry launched AI initiatives with nearly identical budgets. Today, one has transformed its market position while the other quietly disbanded its AI team. The difference wasn't talent, technology, or timing. It was the presence of true AI leadership. After guiding AI transformations across multiple sectors, I've observed a clear pattern: organizations conflate technical implementation with strategic leadership — a costly misconception in the algorithmic age. 𝗧𝗵𝗲 𝗔𝗜 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗗𝗶𝘃𝗲𝗿𝗴𝗲𝗻𝗰𝗲 Most executives approach AI through a traditional technology lens: selecting vendors, implementing solutions, and measuring ROI. However, organizations creating asymmetric returns operate from a fundamentally different framework. When I joined a life sciences company's transformation, they had invested $15M in ML capabilities with minimal impact. Within 18 months of shifting to an AI leadership approach, those same technical assets drove a 28% market share increase in their core business line. 𝗧𝗵𝗲 𝗧𝗵𝗿𝗲𝗲 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝘀 𝗼𝗳 𝗔𝗜 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 True AI dominance emerges at the intersection of three capabilities most organizations develop in isolation: 𝟭. 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Redesigning core business processes around algorithmic decision-making, not just augmenting existing workflows. One healthcare organization restructured its entire patient journey based on predictive insights, creating a competitive moat its technology-focused competitors couldn't replicate. 𝟮. 𝗗𝗮𝘁𝗮 𝗦𝗼𝗽𝗵𝗶𝘀𝘁𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Moving beyond data volume to data uniqueness. The market leaders I've worked with systematically identify and capture proprietary data assets that create algorithmic advantages that are impossible for competitors to match, regardless of their AI investment. 𝟯. 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗩𝗲𝗹𝗼𝗰𝗶𝘁𝘆: Implementing governance models built for algorithmic speed. One financial services firm reduced model deployment from months to days, allowing them to capture temporary market inefficiencies before competitors could respond. The organizations achieving market dominance are those with leadership capable of orchestrating these dimensions simultaneously. Have you observed this leadership gap in your industry? 𝘋𝘪𝘴𝘤𝘭𝘢𝘪𝘮𝘦𝘳: 𝘛𝘩𝘦 𝘷𝘪𝘦𝘸𝘴 𝘦𝘹𝘱𝘳𝘦𝘴𝘴𝘦𝘥 𝘢𝘳𝘦 𝘮𝘺 𝘱𝘦𝘳𝘴𝘰𝘯𝘢𝘭 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘴 𝘢𝘯𝘥 𝘥𝘰𝘯'𝘵 𝘳𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵 𝘵𝘩𝘰𝘴𝘦 𝘰𝘧 𝘮𝘺 𝘤𝘶𝘳𝘳𝘦𝘯𝘵 𝘰𝘳 𝘱𝘢𝘴𝘵 𝘦𝘮𝘱𝘭𝘰𝘺𝘦𝘳𝘴 𝘰𝘳 𝘳𝘦𝘭𝘢𝘵𝘦𝘥 𝘦𝘯𝘵𝘪𝘵𝘪𝘦𝘴. 𝘌𝘹𝘢𝘮𝘱𝘭𝘦𝘴 𝘥𝘳𝘢𝘸𝘯 𝘧𝘳𝘰𝘮 𝘮𝘺 𝘦𝘹𝘱𝘦𝘳𝘪𝘦𝘯𝘤𝘦 𝘩𝘢𝘷𝘦 𝘣𝘦𝘦𝘯 𝘢𝘯𝘰𝘯𝘺𝘮𝘪𝘻𝘦𝘥 𝘢𝘯𝘥 𝘨𝘦𝘯𝘦𝘳𝘢𝘭𝘪𝘻𝘦𝘥 𝘵𝘰 𝘱𝘳𝘰𝘵𝘦𝘤𝘵 𝘤𝘰𝘯𝘧𝘪𝘥𝘦𝘯𝘵𝘪𝘢𝘭 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯.

As I hope we all know by now, AI isn’t the future anymore. It’s here. Yet if you're counting on AI to bail you out of a weak strategy or misalignment with key priorities and resources... it won't. Here’s the catch: how you approach AI matters more than whether you adopt it. I’ve seen and heard of too many companies and teams that either go all-in with lofty AI visions 💡 (without ground-level execution) or allow fragmented team experiments 🧩 (without leadership alignment). Both approaches fall short. The real unlock comes when we connect top-down vision with bottom-up innovation. ✨ From the top down: Leaders must set the direction. Where should AI create impact? How do we define success? What resources and guardrails will help teams thrive? Without this, AI becomes noise. ⚡ From the bottom up: Teams need the freedom to test and play. The best AI use cases often come from people closest to the customer: CSMs, insights managers, analysts, and support reps. They see problems daily that leadership may never spot. 💥 Where the two meet: That’s where transformation happens. Vision meets execution. Strategy meets curiosity. AI becomes a multiplier of insights, of efficiency, and of customer connection. 🔑 Action Steps for Leaders: 1️⃣ Set a clear AI vision – define where it will drive the most value. 2️⃣ Invest in enablement – give teams the tools and training to explore. 3️⃣ Celebrate small wins – encourage experimentation and scale what works. 4️⃣ Create feedback loops – connect leadership strategy with team discoveries. As CCO, my lens is always the customer. And AI, when approached top-down and bottom-up, isn’t just a tool. It’s connective tissue. It brings leaders, teams, and customers closer together. And that’s how we create the future. 🚀 Together. #CreateTheFuture #AI #ArtificialIntelligence #CustomerExperience #Leadership #Innovation #FutureOfWork #CustomerSuccess

These days I’m sure grateful for the Change Management work I did as a student at Harvard. The data is sobering. 👉 MIT’s NANDA study: 95% of generative AI pilots fail to move into production. 👉 McKinsey: 70% of initiatives remain stuck in development or expansion after a year. 👉 Abandonment: 17% of projects in 2024 → 42% in 2025. 👉 Scaling success: only 5–10% of companies ever get there. The technology is not the problem. The people, processes, and organizational structures are. That’s where John Kotter’s 8 Steps for Leading Change still feel urgent today. AI isn’t just a tool you stack on top of existing workflows. It requires rewiring how companies operate. Yet most organizations continue to treat AI adoption like a software upgrade rather than a deep transformation. ↳ Create Urgency → Leaders assume urgency is obvious. It’s not. AI must be framed with data and stories that make stakes clear: competitors will use efficiency to outscale you. ↳ Build a Guiding Coalition → Pilots run by IT alone fail. Cross-functional coalitions with visible champions succeed. ↳ Form a Strategic Vision → Saying “we’re investing in AI” is not a vision. Linking it to growth, efficiency, and innovation is. ↳ Remove Barriers → Resistance is natural. Job fears are real. Change management has to dismantle these barriers directly. ↳ Generate Short-Term Wins → Early ROI in back-office functions builds trust and momentum. Without visible wins, resistance hardens. ↳ Institute Change → AI sticks when embedded in hiring, training, incentives, and culture. Startups don’t wrestle with this. They scale with AI by avoiding new hires and redesigning work as they go. Large companies face the harder task: unlearning, rewiring, and rebuilding. The lesson from Kotter and from the data is the same: Transformation is not about the technology. It’s about change leadership. If we want AI to succeed inside large companies, we have to stop asking: ❌ “How do we scale the model?” ✅ “How do we scale trust, adoption, and organizational learning?” Three actions to drive forward now: ✅ Use data and stories to prove urgency at every level. ✅ Create early ROI wins and broadcast them widely. ✅ Embed AI into culture, not just IT, through hiring, training, and incentives. Do. Fail. Learn. Grow. Win. Repeat. Forever. ♻️Repost & follow John Brewton for content that helps. 📬 Subscribe to Operating by John Brewton for deep dives on the history and future of operating companies (🔗 in profile).

In every AI consulting engagement, I ask leaders 3 questions that reveal whether they’ll scale—or stall. Most have never considered them. After helping 70+ organizations move beyond the AI hype cycle into real-world implementation, I've found that asking the right questions early creates clarity where most teams get stuck. Whenever I consult with leadership teams, these 3 questions consistently benchmark how effectively they're scaling AI across the workforce. Here they are: 𝗤𝗨𝗘𝗦𝗧𝗜𝗢𝗡 1: When it comes to AI and people working together - where should this new structure report into? Who owns what in this division of labor? Who has accountability for what? Which operating models need to adjust? 𝗤𝗨𝗘𝗦𝗧𝗜𝗢𝗡 2: What does good AI+Human judgment look like? How do we maintain, if not strengthen human capital through critical thinking and good judgment, while accelerating tasks when working alongside AI? 𝗤𝗨𝗘𝗦𝗧𝗜𝗢𝗡 3: The AI proof of concepts (POCs) are done, we're all in - how do we scale and drive adoption across the workforce? They surface hidden barriers to adoption and identify where leadership gaps might exist before implementation begins. Most importantly, they create alignment on what "good" looks like for your specific organization. So, the real challenge isn't launching a POC, developing a prototype, or even pushing it into production (i.e. you now have DevOps and CI/CD turned on) It's what happens next! AI - POST LAUNCH! That’s when the real work begins, which is UNKNOWN to many unless you’ve had as many deployment cycles (and mistakes) as I’ve had. Scale does not come from pushing something into production. It comes from all the work and preparation for post-launch. While many are focused on deployment, the real differentiator lies in how effectively organizations adapt to post-AI launch—and what that demands of us as leaders. Technology deployment might get you headlines, but workforce transformation delivers the ROI. What questions are you asking as you scale AI in your organization?

𝐀𝐈 𝐑𝐞𝐚𝐝𝐢𝐧𝐞𝐬𝐬 𝐈𝐬 𝐚 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐓𝐞𝐬𝐭—𝐍𝐨𝐭 𝐚 𝐓𝐞𝐜𝐡 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 Nearly every enterprise is using #AI. But most workforces aren’t ready for it. Axios calls this a hinge moment. Kyndryl reports 71% of leaders say their workforce isn’t prepared. And Scott Snyder & Hreha point to the knowing–doing gap: while employees feel pressure to adopt AI, many hesitate to use it—often silently. From the boardroom, this isn’t just a tech adoption issue. It’s a strategic misalignment—between ambition and accountability, efficiency and empowerment. 𝐈𝐟 𝐀𝐈 𝐝𝐨𝐞𝐬𝐧’𝐭 𝐭𝐫𝐚𝐧𝐬𝐥𝐚𝐭𝐞 𝐢𝐧𝐭𝐨 𝐡𝐢𝐠𝐡𝐞𝐫 𝐦𝐚𝐫𝐠𝐢𝐧𝐬, 𝐟𝐚𝐬𝐭𝐞𝐫 𝐭𝐢𝐦𝐞 𝐭𝐨 𝐯𝐚𝐥𝐮𝐞, 𝐨𝐫 𝐢𝐦𝐩𝐫𝐨𝐯𝐞𝐝 𝐭𝐚𝐥𝐞𝐧𝐭 𝐥𝐞𝐯𝐞𝐫𝐚𝐠𝐞, 𝐰𝐞’𝐫𝐞 𝐧𝐨𝐭 𝐬𝐜𝐚𝐥𝐢𝐧𝐠 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 — 𝐰𝐞’𝐫𝐞 𝐬𝐜𝐚𝐥𝐢𝐧𝐠 𝐜𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲. In my experience across global markets, adoption succeeds when leadership connects the dots between tools, talent, and trust. Here’s what that looks like: ✅ 𝐑𝐞𝐟𝐫𝐚𝐦𝐞 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐚𝐬 𝐜𝐚𝐩𝐚𝐜𝐢𝐭𝐲 Treat saved time like capital—reinvest it into innovation, AI fluency, or customer impact. ✅ 𝐌𝐚𝐤𝐞 𝐛𝐞𝐡𝐚𝐯𝐢𝐨𝐫 𝐜𝐡𝐚𝐧𝐠𝐞 𝐞𝐚𝐬𝐢𝐞𝐫 Embed AI into workflows. Use templates, reduce friction. Help employees adapt, not improvise. ✅ 𝐑𝐞𝐝𝐞𝐬𝐢𝐠𝐧 𝐫𝐨𝐥𝐞𝐬 𝐰𝐢𝐭𝐡 𝐞𝐦𝐩𝐥𝐨𝐲𝐞𝐞𝐬 Co-write AI-first job descriptions. When people shape their future role, they own it. ✅ 𝐌𝐚𝐤𝐞 𝐭𝐫𝐮𝐬𝐭 𝐦𝐞𝐚𝐬𝐮𝐫𝐚𝐛𝐥𝐞 Normalize experimentation. Use peer mentors. Reward usage. Don’t confuse training with adoption. Boards should ask not just if AI is deployed—but how leadership is measuring usage, accountability, and performance lift. That’s governance in action. 𝐖𝐡𝐚𝐭’𝐬 𝐨𝐧𝐞 𝐬𝐭𝐞𝐩 𝐲𝐨𝐮𝐫 𝐛𝐨𝐚𝐫𝐝 𝐨𝐫 𝐥𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐭𝐞𝐚𝐦 𝐢𝐬 𝐭𝐚𝐤𝐢𝐧𝐠 𝐭𝐨 𝐚𝐥𝐢𝐠𝐧 𝐀𝐈 𝐚𝐦𝐛𝐢𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐚𝐜𝐭𝐮𝐚𝐥 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧? #BoardLeadership #AIAdoption #DigitalTransformation #ExecutiveLeadership

The secret to 10x impact from AI is changing *what* work you do, not only how your team does that work. See AI as more than a “productivity tool.” To succeed and become executives, leaders must think of AI differently than coders, designers, PMs, and other ICs. Here is how to *lead* with AI: It can be used to do things faster or more easily, but that isn’t where the real opportunity is. The real opportunity for leaders to grow their careers using AI is by using it to create net new value for the company: new products, better margins, or systems that fundamentally reduce cost or complexity. Creating new value is what will win you new opportunities, responsibilities, and eventually, a promotion. Using AI to do this requires knowledge and experience with AI tools and applications, a clear strategy, and the leadership skill to guide the process. Here’s how I would go about gaining that knowledge, creating the strategy, and leading the change in my organization: First, I’d deeply engage with AI. I would set aside time to personally test tools, follow AI experts, attend workshops, and build a mental model of where AI can create real leverage in my organization. I would also ask my team where they are currently using AI and what sort of results they are seeing. Second, I’d craft experiments. The leaders who will stand out will ask: what can we do now that we couldn’t do before? What cost structures can we eliminate? What customer problems can we solve in a new way? I would ask these questions and create hypotheses based on what I learned playing with tools and from others. I would then test these hypotheses with funded experiments that have meaningful but manageable impact. Third, I’d lead AI adoption by shaping culture. I'd ensure clarity on the “why” behind our AI efforts and I’d create a culture where experimentation is encouraged and failure is safe. I’d set expectations that we “use AI,” identify champions, and work with those who are resistant so that they feel supported in the change but also understand that it is a new expectation and not a request. The challenge with leading AI today is that it is already in your organization. Some are using it, others are opposing it and fearing it, everyone is aware of it. If you don’t lead your team through its use, you’ll lose control of it. Teams will adopt it unevenly, causing friction and confusion. On the flip side, if you lead well, it has the ability to 10x your impact and skyrocket your career. AI is not a tech problem for most leaders. It’s a change management problem. If you are a strategic, curious, and thoughtful leader you will be able to manage this change for the benefit of your team, your business, and your career. I write more about this in today’s newsletter for paid subscribers. I designed a 30-day AI Leadership Sprint and a number of other resources you can use to lead AI adoption in your org. Read the newsletter here: https://buff.ly/QMlF266 What's missing?