Welcome back to Inspire AI, the podcast where we explore what happens when technology stops being a tool and starts becoming a teammate. I'm your host, Jason McGuinthe. Today we're talking about yes, agentic AI. Autonomous, goal-driven AI agents that can plan, decide, and act with minimal human input. And I want to start with a truth that is uncomfortable but liberating. Most enterprise AI transformations don't stall because the models aren't good enough. They stall because the humans weren't brought along. There's a gap that says it all. In late 2025, research highlighted that 76% of executives believed employees were enthusiastic about AI. But in reality, only 31% of employees said they actually were. It's not a technology problem, it's a change management problem. So in this episode, we're gonna walk through a practical, evidence-based playbook for making agentic AI adoption real. Not just a collection of pilots that never scale. We're gonna cover the key frameworks leaders are using to drive AI transformation. Case studies from McKinsey, Cinq, Bank of America, and others. The leadership moves that reduce fear and build trust and how to prevent burnout that comes when change feels endless. Because the real transformation isn't AI installed, now go get it. It's AI embedded in workflows, in culture, and in people. You see, agenc AI is not just a faster assistant, it's kind of an operational actor. If a chatbot is like an intern waiting for instructions, an agent is more like a coordinator who can take a goal such as reduce customer churn or close the books faster, maybe even resolve IT tickets, and then run the play across systems, steps, and decisions. And that's why it's triggering one of the biggest organizational shifts since the digital revolution. It's changing how how work is structured, how decisions happen, and even what a team looks like. Because now teams can be human plus AI agents operating together. If you listen to my last episode, this is a great segue. Leaders often treat this like a normal software rollout. Agentic AI is not a normal rollout. McKenzie put a fine point on it. Piloting is easy, creating value is hard. And the organizations that win are the ones that reconfigure work itself around AI, not just bolt AI onto old practices. Which brings us to the core lesson of the episode. Agentic AI adoption is 10% algorithms and 90% people. That's not a slogan, it's just reality. Because the barriers are predictable, fear of job loss, lack of skill confidence, distrust in outputs, uncertainty about expectations, and exhaustion from yet another transformation. AI transformations are endless these days. If you don't manage that, you get what many executives privately confess pilot purgatory. A portfolio of proofs of concept, but not much business value. So how do we do this right? I always say start with a framework. People, process, technology built around frameworks. AI change management is not any different. Organizations don't need more hype, they need structure. So let's translate the major frameworks into something usable. McKenzie says reconfiguring work plus a North Star is the way to go. Their approach emphasizes five critical steps. And the first one is the anchor. Craft a North Star based on outcomes, not tools. If your story to employees is, hey, we're rolling out agents, they're gonna get anxious. If your story is we're reducing the busy work that steals your best hours, and we're investing in you to move up the value chain, you create motion. McKinsey also stresses trust building and co-creation because Gen AI adoption isn't passive usage. It asks employees to experiment, to co-create, and continually reskill back to watching out for that exhaustion, the burnout, the endless change. Am I right? And zooming out for a second, McKinsey's AI agentic organization lens reframes the enterprise around humans working alongside virtual and physical AI agents as a new operating model. BCG, Boston Consulting Group, they have five must-haves for AI upskilling. Five, kind of a coincidence, but who knows? Their must-haves include assessing needs, preparing people for change, unlocking willingness to learn, and making it a C-suite priority, and using AI to teach AI. That's an interesting one. Here's the stat that should make any executive pause. BCG reported that only a small fraction of companies felt they'd begun AI upskilling in a meaningful way. Even as leaders personally used AI tools more and more. So the gap isn't interest, but it's execution. Here's another professional services firm, Mercer, bringing together capabilities across risk, capital, people, investments, management, consulting, etc. Mercer frames the problem as continuous reinvention, an AI augmented operating system where agenc AI pushes companies to redesign work and orchestrate the best mix of humans and agents. Their change drivers include integrated infrastructure, leadership for hybrid work, empowerment, new governance, reimagined structures, a culture of collaboration, process redesign, and importantly, the power of skills. I guess I would translate all that and say your HR and your AI strategy can't live in different buildings anymore. And then finally there's Forrester. Because change fatigue is a value problem. Here's a line that every transformational leader should print out. Change fatigue isn't about stamina, it's about value. People don't burn out because change exists. They burn out because change feels like effort without payoff. That's going to matter when we get to momentum. But first, let's make this real with case studies. Back to McKinsey. McKinsey didn't just advise on Gen AI adoption, they lived it. They rolled out an internal Gen AI platform called Lily. And what stands out isn't the tech. It's the change management. Leaders modeled the behavior relentlessly. One leader described starting meetings with questions like, Have you asked Lily? I know that sounds small, but culturally, it's huge. It sends signals reverberating through the cultural spine of the organization. It says AI isn't an optional experiment. It's part of how work gets done here. They trained new hires early. They reinforced usage norms and they supported peer learning. Treating adoption like a workforce shift, not a product launch. And that combination leadership modeling, plus enablement, plus community, well, that's the blueprint. Because people don't copy policy, they copy behavior. Alright, next is Bank of America and their make work easier rule. Bank of America is a master class in simple principle. If AI makes employees' daily work easier, adoption should follow. In April of 2025, Bank of America reported more than 90% of employees were using AI-driven tools, including internal capabilities tied to their AI assistant ecosystem. They also described productivity and service improvements linked to this adoption. And what's notable in the way AI was positioned, it's not a replacement, more of an enabler. When employees experience AI as it uh removes friction, the resistance curve changes. And in regulated industries, that's the goal. Compare usefulness with governance. So trust grows at the same time. Alright, maybe we should close the enthusiasm gap real quick. Let's go back to that opening disconnect. Executives assume employees are excited. Employees, hmm, not so much. How do you close that gap? Tell the truth early. How about that? If you oversell, people stop believing you. If you acknowledge uncertainty, people will lean in. Agentic AI is powerful, but it's also new. So talk openly about what you know, what you don't know, and how you'll govern risk. McKenzie emphasizes trust building through governance and transparency as a central step in change management for Gen AI. BCG's upskilling their guidance explicitly addresses that many employees see AI skills as intimidating or threatening. And the response is psychological safety plus practical pathways. The message isn't learn AI or else. It's learn AI so your work gets more interesting and your options expand. Because I'll say this till I'm blue in the face. Distributed leadership beats AI overlords. You need visible executive sponsorship, but adoption spreads fastest through peer networks, champions, super users, managers who translate AI into day-to-day work. That's how you convert mandate into movement. Know your tools, know how they work, know how to use them, and reward the behavior you want. If experimentation is valued, recognize it. If learning matters, give time for it. Because culture is just incentives, repeated long enough to become instinct. Speaking of the long game, AI transformations don't fail only at the start. They fail in the middle, when novelty fades and the workload remains. Think about that. The workload remains. Forrester's framing is clearest. Fatigue shows up when people don't perceive value. So sustaining momentum becomes a discipline. Keep value visible. Publish wins monthly or quarterly. Time saved, cycle time reduced, fewer errors, better service outcomes, not vanity dashboards, but real felt benefits. Remember that North Star I talked about? Once the first wave lands, define the next horizon. So teams don't ask, is it over? Is that it? Is that it forever? Your North Star approach should be built exactly for this. Orienting around outcomes. So change stays purposeful. And don't forget to maintain support after GoLive. You can't just vanish when tools launch. You gotta keep an AI help desk, champions, communities of practice or excellence, whatever you want to call it. Because agents evolve. Workflows need iteration. It's not a one and done thing. We're gonna continue to evolve. And you gotta adapt. If everything changes at once, adoption becomes survival, not innovation. But don't forget to pace the transformation. Because if everything changes at once, adoption becomes survival, not innovation. Think about staged rollouts plus clear priorities. That is what will protect your morale. Here's a bit of a thought on people first agentic AI playbook. Maybe a practical list. If you're leading agentic AI adoption, here is your Monday morning list. Start with outcomes, not tools. That's where we define the North Star. Then build trust early with governance and transparency and clarity on the accountability. Co-create with employees. Don't just deploy to people. Build with them. Overinvest in upskilling. Make it structured, role-based, practical learning at scale. Model from the top. Normalize usage through leadership behavior. Design for daily usefulness. Adoption accelerates when work gets easier. And finally, fight fatigue with visible wins. Value is the anecdote. Just remember, your advantage isn't competing with AI. Your advantage is learning to orchestrate it, to use it as leverage for better thinking, better output, and better decisions. Agentic AI is going to reshape the industry. Your industry, my industry, every industry. Not as a single product launch, but as a new operating reality. Humans plus AI agents working in a shared system. And whether that becomes a stronger, more human enterprise, or a riddle, one strained by fear and fatigue type enterprise, that depends on how we lead the change. Don't lead it with hype. Lead it with trust. Lead it with learning. Lead it with real investment in people. If you feel this episode sparked something for you, share it with a colleague, someone leading transformation. Or anyone really trying to make sense of what's next. Until next time, stay curious, build boldly, and keep the human in the loop.