How to Align Teams and Technology During Transformation

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?

My AI lesson of the week: The tech isn't the hard part…it's the people! During my prior work at the Institute for Healthcare Improvement (IHI), we talked a lot about how any technology, whether a new drug or a new vaccine or a new information tool, would face challenges with how to integrate into the complex human systems that alway at play in healthcare. As I get deeper and deeper into AI, I am not surprised to see that those same challenges exist with this cadre of technology as well. It’s not the tech that limits us; the real complexity lies in driving adoption across diverse teams, workflows, and mindsets. And it’s not just implementation alone that will get to real ROI from AI—it’s the changes that will occur to our workflows that will generate the value. That’s why we are thinking differently about how to approach change management. We’re approaching the workflow integration with the same discipline and structure as any core system build. Our framework is designed to reduce friction, build momentum, and align people with outcomes from day one. Here’s the 5-point plan for how we're making that happen with health systems today: 🔹 AI Champion Program: We designate and train department-level champions who lead adoption efforts within their teams. These individuals become trusted internal experts, reducing dependency on central support and accelerating change. 🔹 An AI Academy: We produce concise, role-specific, training modules to deliver just-in-time knowledge to help all users get the most out of the gen AI tools that their systems are provisioning. 5-10 min modules ensures relevance and reduces training fatigue.  🔹 Staged Rollout: We don’t go live everywhere at once. Instead, we're beginning with an initial few locations/teams, refine based on feedback, and expand with proof points in hand. This staged approach minimizes risk and maximizes learning. 🔹 Feedback Loops: Change is not a one-way push. Host regular forums to capture insights from frontline users, close gaps, and refine processes continuously. Listening and modifying is part of the deployment strategy. 🔹 Visible Metrics: Transparent team or dept-based dashboards track progress and highlight wins. When staff can see measurable improvement—and their role in driving it—engagement improves dramatically. This isn’t workflow mapping. This is operational transformation—designed for scale, grounded in human behavior, and built to last. Technology will continue to evolve. But real leverage comes from aligning your people behind the change. We think that’s where competitive advantage is created—and sustained. #ExecutiveLeadership #ChangeManagement #DigitalTransformation #StrategyExecution #HealthTech #OperationalExcellence #ScalableChange

AI isn’t just a technology shift— it’s a people shift. Inside every company there are Catalysts, Converts, and Anchors. Each need different strategies: In the 10 years of Reforge, we’ve seen inside thousands of transformations. Establishing growth teams, from project to product management, from sales-led to product-led, and many more. Check it out here: https://lnkd.in/gAfDBmP3 There is a pattern that always repeats itself in these transformations. But with the shift to AI, the stakes are much higher. There are three different internal audiences when thinking about AI adoption and transformation: 🎇 Catalysts 🔄 Converts ⚓ Anchors Just like a good product and marketing strategy, you need to segment your audience and have different plans. Catalysts ↳ Early adopters, already tinkering on personal accounts. ↳ They know staying current is non-negotiable for their careers ↳ Intrinsically motivated, deeply curious. Your job: remove friction, hand them bigger problems, then get out of the way. If you slow them down, they’ll bail—and take your future with them. Converts ↳Willing, but hesitant. ↳Crave clear permission, structure, training, and visible incentives. Your job: build the structure to convert them. Provide structured training, highlight internal successes, connect AI objectives to existing KPIs, and include in performance reviews/rewards. With the right scaffolding, they’ll shift their day-to-day habits. Reforge Learning can really help w/ Converts: https://lnkd.in/gAfDBmP3 Anchors Every company has employees who view new tools as threats to hard-won expertise or even to job security. Ignoring that tension lets quiet resistance stall the entire program. How to work with them ↳ Set clear expectations and timelines. Ambiguity breeds rumor mills; specificity forces a decision. ↳ Invest in re-skilling where there’s willingness. Some Anchors simply need structured coaching to pivot their deep domain knowledge into AI-augmented roles. ↳ Know when to cut losses. If an Anchor continues to block progress—even after support—it may be kinder to orchestrate a respectful exit than to let drag become your company’s default speed. The two biggest mistakes companies will make: 1. Believing Everyone Is A Catalyst I can guarantee you they aren’t. As a result, the rest of the company won’t make the shift and the real Catalysts will get frustrated and leave. Founders by nature are Catalysts and over-assume everyone else operates like they do. 2. Assuming Anchors will eventually “get on board.” With incremental shifts, you can wait skeptics out; with AI, you’re racing a clock that rewrites markets in months, not years. A small pocket of resistance can freeze data flows, block experimentation, and hand your advantage to faster-moving rivals. Treating every employee the same may sound fair, but it can be fatal. Segment first, craft distinct paths, and move each group with intention.

When a company deploys an AI transformation, everyone fixates on the technology but here’s what is even more important. It's about the people. Over the years, I've developed a simple but powerful tool to evaluate teams for AI readiness. I call it my Will-Skill Matrix for AI! It’s taking a pre-existing model and customizing it for AI deployments based on 13 years of deployment experience. This framework is copyrighted: © 2025 Sol Rashidi. All rights reserved. 𝗛𝗶𝗴𝗵 𝗦𝗸𝗶𝗹𝗹, 𝗛𝗶𝗴𝗵 𝗪𝗶𝗹𝗹: These are your champions - they have the technical capabilities and the hunger to drive AI adoption forward. 𝗛𝗶𝗴𝗵 𝗦𝗸𝗶𝗹𝗹, 𝗟𝗼𝘄 𝗪𝗶𝗹𝗹: Often your most technically brilliant people who resist change. They've mastered existing systems and see AI as either a threat or unnecessary complexity. 𝗟𝗼𝘄 𝗦𝗸𝗶𝗹𝗹, 𝗛𝗶𝗴𝗵 𝗪𝗶𝗹𝗹: Your enthusiastic learners. They may not understand neural networks, but they're eager to embrace AI-driven solutions. 𝗟𝗼𝘄 𝗦𝗸𝗶𝗹𝗹, 𝗟𝗼𝘄 𝗪𝗶𝗹𝗹: These team members neither understand AI nor want to adapt to it. They're comfortable in their current roles and see no reason to change. Here's the counterintuitive insight most leaders miss: The "Low Skill, High Will" group is your hidden treasure in AI transformation. I discovered this at one of my employers during a massive data overhaul. My most valuable contributors weren't always the data scientists with impressive credentials. Often, they were business analysts who couldn't code complex algorithms but brought boundless curiosity and deep business knowledge and a will to figure it out. Why does this matter? Because AI implementation isn't just a technical challenge - it's fundamentally a human change management project. In one particularly tough transformation, I spent months trying to convince resistant technical experts to embrace new methods. Meanwhile, I overlooked enthusiastic business teams eager to learn and adapt. The breakthrough came when I finally shifted focus. By empowering the "High Will" groups and pairing them with technical mentors, our implementation timeline was shortened by nearly 40%. This completely changed my approach to building AI teams. The most successful AI implementations don't just depend on having the best algorithms or the most data engineers. They depend on having people throughout your organization who are willing to reimagine what's possible - and who bring others along with them.

How can leaders transform their teams to be AI-first? It starts with mindset. An AI-first mindset means: Seeing AI as an opportunity, not a threat. Viewing AI as a tool to augment teams, not just automate tasks. Using AI to reimagine work, not just optimize work. As leaders, it’s on us to build this mindset within our teams. Here are 5 ways we do this at HubSpot: Use AI daily: Lead by example—trust grows when teams see leaders embrace AI themselves. I use it everyday and share very specific use cases with our company on how I use it. Now every leader is doing the same with their teams. The result is that we will have almost everyone in the company use AI daily by the end of year. Apply constraints: Give clear, focused challenges. We kept headcount flat in Support while growing the customer base by 20%+. Result - the team innovated with AI and over achieved the target. Smart constraints drive innovation. Establish tiger teams: Empower small, agile groups to experiment, innovate, and teach the organization. We have AI Tiger teams in every function - they share progress in Slack channels and there is so much energy with small groups experimenting and learning. Be a learn-it-all: Foster a culture of continuous learning. Share openly about successes and failures alike. We have dedicated 2 full days to learning and scaling with AI this quarter as a company - we have lined up great speakers, ways to experiment and gamified learning. Measure progress and share it: Measure which teams are completing learning modules, using AI everyday and share that openly. A little healthy competition goes a long way in driving AI-fluency. AI isn’t just a technology shift. It’s fundamentally reshaping how work gets done—and that requires shifting our mindset first. Leaders who embrace AI now will unlock creativity, performance, and impact. Are you building an AI-first mindset with your team? #Leadership #AI #Innovation #Mindset #FutureOfWork

🎯 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

⚙️ AI is transforming the way we work. But leadership? That still starts with people. We’re in the midst of an AI revolution. Tech is moving fast. Automation is accelerating. And leaders are being pushed to integrate these tools—fast. But here’s what’s also happening: Teams are unsure where they fit. Burnout is creeping in Human connection is thinning. Leaders today face a unique dual mandate. Embrace AI, upskill teams, and stay competitive. And lead with empathy, care, and adaptability. Here are 8 steps I use with my executive clients to lead through this kind of change with clarity and confidence: 1. Acknowledge the Disruption: Start by naming the shift. Teams need to know you see the change and are leading through it, not avoiding it. 2. Lead with Empathy: Check in with your team to see how they are coping. Emotional clarity builds trust and resilience. 3. Upskill, Don’t Just Automate: Invest in reskilling. AI isn’t here to replace people—it’s here to enhance them. 4. Model AI Literacy: Be the first to learn and try new tools. Your curiosity sets the tone. 5. Encourage Dialogue: Let teams ask questions, explore new tools, and even fail. Innovation needs room to breathe. 6. Communicate Transparently: Share what you know—and what you’re still figuring out. Clarity over certainty builds credibility. 7. Balance Performance with Well-Being: Don’t just measure output. Pay attention to energy, burnout signals, and team cohesion. 8. Stay Anchored to Purpose: Remind people why the work matters. AI can improve outcomes, but it’s human meaning that drives real engagement. 💡 The tools may be new, but the best leadership is still rooted in trust, communication, and clarity of purpose. If you’re navigating this kind of landscape, I support leaders and teams to adapt with purpose and performance in mind. 📩 To learn more, email me at mc@mccoachingnyc.com. #AIleadership #executivecoaching #changemanagement #futureofwork #wellbeing #digitaltransformation #peoplefirst

“Treating AI like a tool instead of a transformation…” - has been coming to the forefront this week in a number of great conversations with Paul Gonzalez, Ryan Kurt, John Winsor, and Debbie McCarthy. Here are some #symptoms, #consequences, and #interventions for addressing treating #AI like a tool, a fairly common condition these days. 🔍 SYMPTOMS - Signs that an organization is treating AI as just another tool: 1. Isolated Pilots with No Enterprise Integration: Teams experiment in silos without strategic alignment or cross-functional visibility. 2. Lack of Executive Engagement or Ownership: Leadership delegates AI to IT, innovation, or digital teams rather than championing it as a core shift. 3. Training Focused Only on Features, Not Mindsets: Enablement efforts emphasize prompts and mechanics, skipping over mental models, ethics, and role evolution. 4. No Reexamination of Work, Process, or Strategy: AI is slotted into current workflows rather than prompting a redesign of how work gets done. 5. Success Measured by Usage Stats, Not Business Value: Metrics like prompt counts or log-ins dominate while productivity, creativity, and impact remain unmeasured. 🚨 CONSEQUENCES - What happens when AI is treated as a tool, not a transformation: 1. Low and Superficial Adoption: Employees dabble but don’t deeply embed AI into their daily problem-solving or decision-making. 2. Missed Opportunities for Competitive Differentiation: While others rethink their business models, you're just speeding up status quo tasks. 3. Change Fatigue Without Strategic Progress: Energy is spent experimenting with AI, but there's no visible value or momentum to show for it. 4. Workforce Confusion and Misalignment: Without a coherent narrative, people are unsure whether AI is optional, risky, or central to their future. 5. AI Initiatives Get Sunset Before They Scale: Without framing AI as a transformation, initiatives lose funding, attention, and champions. 💡 INTERVENTIONS - How to reframe and re-energize your AI approach: 1. Anchor AI to Strategic Intent: Define how AI enables your core strategy, mission, and market positioning. Make it a business imperative, not a tech experiment. 2. Develop an AI Integration Approach: Develop an approach to help teams and individuals understand when and where to bring AI to the table. Prosci’s AI Integration Framework provides the foundation anyone needs to identify when to partner with a digital collaborator. 3. Elevate Executive Ownership: Position leaders as the narrators of the AI story, modeling usage, creating urgency, and aligning investments. Prosci’s AI Adoption Diagnostic elevates the AI-sponsor role. 4. Invest in Mindset Shifts, Not Just Skillsets: Train for adaptability, ethical reasoning, prompt literacy, and AI teaming—not just tool proficiency. 5. Measure Transformation, Not Just Activity: Track AI’s impact on outcomes: decision speed, innovation velocity, employee empowerment, and customer value. “To what end!”