Why Appointing Leads Isn’t Enough (And What to Do Instead)
Are your AI Leads empowered to spread change, or an AI lead in name only?
Most organizations I chat with are making progress with AI. That’s the good news. The bad news is that they are also reporting what I call the ‘bifurcation problem.’ There are a handful of power users…and everyone else. These enthusiasts often get anointed ‘AI leads,’ ‘AI Champions,’ or some other glorified title, and the organization exhales, confident that AI transformation is now underway. Assuming that by appointing AI leads, knowledge will magically spread.
And then... nothing scales.
Six months later, the AI Slack channel has gone quiet, as everyone gets wrapped up in business as usual. The AI Lead is still carrying their full original workload. A few teams are experimenting, but the insights aren’t spreading. Leadership is frustrated. The AI Lead is exhausted. And somewhere in a conference room, someone is asking why the ROI isn’t materializing.
These organizations have confused naming AI leads with enabling AI leads. These are not the same thing.
See if These Symptoms Feel Familiar…
If you’ve been paying attention to how AI adoption is unfolding inside organizations, a few patterns are hard to miss.
AI Leads were appointed, but their job descriptions didn’t change. Their original responsibilities didn’t shrink to make room for their new mandate. They’re being asked to drive transformation in the margins of an already-full role. Enthusiasm is not infinite. It runs out.
There’s no shared language, curriculum, or training program behind the title. Each AI Lead is winging it in their own direction, which means every team gets a different version of AI adoption — and nothing coheres at the organizational level.
AI Leads are operating as islands. They may be evangelizing inside their own function, but there’s no formal mechanism for the marketing AI Lead to share what they learned with finance, or for the operations AI Lead’s discovery to spark an idea in customer success. The organizational design doesn’t support knowledge flow across boundaries.
Leadership assumed that enthusiasm equals capability and walked away. The implicit message sent to AI Leads everywhere: you’re passionate about this, so you’ll figure out how to help others with AI. This is like assuming a talented piano player now knows how to teach someone how to play piano.
If you recognize your organization in any of these patterns, you’re not alone. But you are leaving significant value on the table.
What Enabled AI Leads Accomplish
Before we talk about what’s missing, let’s be clear about what we need from our AI Leads (it’s a lot more than cheerleading).
An effective AI Lead plays three distinct roles simultaneously.
They are a domain-specific implementer: someone who can identify high-value AI opportunities within their functional area, leveraging both their AI fluency and their deep contextual knowledge of how work actually gets done there. No outside consultant has this combination. Your AI Lead does this naturally, but it may stop there without formal guidance.
Which leads us to our second point. They can be enabled to become peer educators. The person others feel comfortable approaching with questions, who can provide hands-on, contextual guidance rather than abstract theory. Research consistently shows that peer learning dramatically improves knowledge retention over formal training alone. Your AI Lead is the person who makes AI feel accessible rather than threatening.
Last, we need them to be cross-functional connectors. A node in a network that allows insights from one part of the organization to spark innovation in another. In the Hyperadaptive™ Model, this happens through communities of practice and AI Activation Hubs. Marketing’s discovery informs sales. Finance’s experiment improves operations. This cross-pollination is how AI knowledge spreads at the speed organizations actually need.
That’s a significant portfolio of responsibility. Now ask yourself, what has your organization done to enable its AI leads?
Most Organizations Fail at Programmatic Support
The biggest mistake I see (and, don’t worry…it’s a common one) is appointing AI Leads and then leaving them to figure it out. This is a structural gap. For some reason, we think that people should be able to ‘figure it out on their own,’ but this lack of support is why AI efforts stall after the pilot phase.
I believe that programmatic support for AI Leads has three key dimensions.
1. Formal Training That Transforms Enthusiasm Into Expertise
Natural curiosity about AI is a great starting point, but it isn’t a destination. AI Leads require formal training that builds three distinct capability sets: AI fluency, process optimization expertise, and the ability to lead change. All three of these matter.
AI fluency means understanding not just how to use AI tools, but how to evaluate them, when to apply them, and where they fall short. (Large language models, for context, still hallucinate. Your AI Leads need to know this, and so do the practitioners they’re guiding.) It also means understanding your organization’s AI governance policies and where the guardrails are.
Process optimization expertise is what separates an AI enthusiast from an AI Lead who can actually create organizational change. It’s the ability to map workflows, identify where AI augmentation adds real value, establish feedback mechanisms, and measure outcomes, not just implement tools.
When Moderna introduced generative AI, they didn’t hand employees access and hope for the best. They prioritized upskilling their workforce and building a foundation of AI literacy that could sustain change over time. That’s the difference between a tool rollout and a transformation.
Last, we need to empower our AI leads to work with their peers; even those who may feel resistance to AI. We can arm them with skills to create ah-ha moments with AI and ways to empathetically relate to those who are struggling with moral or philosophical resistance to AI.
The training also needs to address something that often gets overlooked: what’s in it for the AI Lead personally. Will this role streamline their team’s operations? Create greater visibility with leadership? Open new career pathways? AI Leads who understand both the organizational value and the personal benefit of their role show up with a sense of ownership.
2. A Channel for Knowledge to Actually Flow
Your AI Leads are not just implementers. They are part of the nervous system of your AI adoption effort. Information flows through them to keep the organization updated as AI changes. They become the channel for signals to actually move through them.
This is where another piece of the Hyperadaptive System comes in: AI Activation Hubs. These are dedicated centers where AI expertise resides, AI advancements are tracked, and knowledge gets atomized into digestible, actionable learning. The Hub’s job is to stay current with AI capabilities and then distribute that knowledge through AI Leads to the practitioners who need it.
Think about what that flow looks like in practice.
Imagine it’s a Tuesday morning. Your AI Activation Hub has just identified a new capability in your Microsoft CoPilot that’s directly relevant to contract review in the legal department. They don’t send a company-wide email that gets buried. Instead, they brief the four legal AI Leads, who speak both the language of AI and the language of legal operations, and equip them with the context, talking points, and a simple use case to share with their team. By Thursday, three lawyers are experimenting with it. By the following week, someone has a workflow that cut document review time in half. That story goes back up through the AI Lead, through the Hub, and into the organization’s knowledge base, where it becomes the basis for the next wave of adoption.
This is what an AI learning flywheel looks like in motion. It’s not a training event. It’s a system. And once you get it spinning, it becomes self-sustaining.
3. A Community That Cuts Across the Organization
Motor Oil Group’s AI Garage initiative offers a useful model here. Their AI champions don’t work in isolation. They participate in weekly show-and-tell sessions, share what they’ve learned across functions, and translate AI concepts into their specific business context. The result is what researchers call learning contagion, or the organic spread of effective AI practices across traditional organizational boundaries.
This is what happens when you formalize the community infrastructure around your AI Leads. They stop being isolated enthusiasts and start becoming a strategic network. Regular cross-functional gatherings create forums where the AI Lead from supply chain and the AI Lead from customer experience can discover that they’re solving related problems and accelerate each other’s progress rather than duplicating effort.
Informally, these networks begin to emerge on their own in organizations where AI adoption has gained any traction. Formalizing them by giving them structure, rhythm, and dedicated platforms for knowledge sharing, converts organic momentum into sustained capability.
What This Looks Like When It Works
Unilever didn’t operationalize AI by purchasing licenses and hoping adoption would follow. They trained 23,000 employees in AI usage and simultaneously built the agile capabilities to focus those new skills on the highest-priority organizational challenges. That type of commitment moves AI from a technology rollout to an organizational infrastructure investment.
Wells Fargo went further, developing systematic upskilling programs, establishing academic partnerships with institutions like Stanford, and creating structured career pathways for AI talent. The message to employees was clear: this is a long-term commitment, and your growth inside it is real.
These aren’t one-time events. They’re ongoing systems. And they happen because someone decided that naming AI Leads wasn’t enough.
A Question Worth Sitting With
If you have AI Leads in your organization, take a moment to answer these honestly: Do they have formal training? Not a one-time workshop, but an ongoing program? Do they have protected time to actually do the work, or are they being asked to transform your organization in the spaces between their existing responsibilities? Are they connected to each other and to a central hub that keeps them current? And do they know what’s in this role for them?
If the answer to most of those is not really, you haven’t failed. You’ve just identified the next most important thing to build.
Empower Your AI Leads
At Hyperadaptive Solutions, we built the AI Lead Accelerator specifically to address this gap. It provides a structured program that gives AI Leads the shared language, practical frameworks, peer community, and ongoing support they need to move the needle. If you’re realizing your AI Leads need more than a title, we’d love to talk.
And if you want the full picture of how AI Leads fit into a comprehensive organizational transformation model (including the support structures, stage-by-stage blueprint, and research behind it) my upcoming book Hyperadaptive: Rewiring the Enterprise to Become AI-Native (IT Revolution Press, 2026) lays it all out. Pre-orders are open now at hyperadaptive.solutions/book.
The organizations that scale AI aren’t the ones the ones that built the infrastructure to spread what they know.