Stop Buying AI Agents and Start Building Change Champions
The Hidden Cost of Lone AI Leaders
The dirty secret behind AI deployment failures isn't technical—it's cultural.
While you're debugging algorithms, your competitors are building change champions. Organisations spend millions on algorithms while ignoring the $50 billion question - who's actually going to use this?
Gartner predicts over 40% of agentic AI projects will be cancelled by 2027, not because the technology doesn't work, but because organisations can't bridge the gap between executive vision and frontline adoption.
The AI industry sells a lie. Better algorithms equal better adoption.
In reality, the best AI sits unused while inferior solutions with change champions scale enterprise-wide.
The problem isn't your AI agents. It's the missing change champions who should be driving adoption from every level of your organisation.
Let’s get into why this is happening and what to do instead.
Why Most AI Transformations Fail
Most AI agent deployments are expensive automation theatre in disguise. Your vendor demos look brilliant, your pilots show promise, then 74% of projects stall because nobody thought to build the human infrastructure for adoption.
Here's how most organisations tackle AI agent deployment—and why it's a proven route to failure.
They appoint a Chief AI Officer to drive transformation from the top down, expecting one person to be technical genius, change management expert, and cultural transformer simultaneously—a recipe for burnout and failure.
Then we see the launch of flashy pilots with impressive demos that showcase AI capabilities but ignore the operational reality of getting hundreds of employees to change their daily workflows. These pilots create excitement in boardrooms while leaving frontline teams completely unprepared.
The focus remains on the technology stack, spending 90% of effort on algorithms and data while treating change management as an afterthought, despite 70% of failures stemming from people and process issues. Your Q4 AI budget review will expose whether you have adoption infrastructure or just expensive demos.
Everyone expects immediate adoption, assuming that if the AI works technically, people will naturally embrace it without considering resistance, training needs, or workflow disruption. This ignores the fundamental reality that adoption beats algorithms every time.
This approach fails because it treats AI deployment as a technology problem when it's fundamentally a human behaviour problem.
The CAIO arrives with fanfare, creates promising prototypes, then watches projects stall as business units haven't been brought along, training never happens, and revolutionary AI tools sit unused while competitors race ahead.
The Change Champion Networks Solution
Stop betting on brilliant CAIOs. Start building champion networks.
What you should be doing is this: Build a three-tier network of change champions before you deploy a single AI agent.
Executive Champions
CEO and senior leadership who model AI adoption and provide air cover for transformation. Harvard Business Review research shows the most effective CEOs lead with "direct involvement" in AI initiatives, showing up as peers in critical working sessions rather than distant sponsors.
Operational Champions
Department heads and middle managers who redesign workflows and manage the people side of change. These leaders serve as the critical bridge between executive vision and frontline reality, translating AI strategy into practical implementation steps.
Frontline Champions
Respected employees who serve as peer advocates and first touchpoints for AI adoption. Research shows these champions bring "peer credibility" and act as trusted voices who normalise AI discussions within their teams.
The Proof: The Holmes Murphy Model
Holmes Murphy, a leading commercial insurance brokerage, avoided the lone-leader trap by creating an AI leadership ecosystem instead of betting everything on a single AI officer.
Their structure includes an AI Leadership Team of deeply engaged senior executives (CEO, CIO, COO, Chief Legal Officer) plus a cross-functional AI Centre of Excellence with five individual contributors who translate AI capabilities into business applications.
Before
Traditional approach would have appointed one CAIO to drive enterprise-wide transformation
After
Distributed leadership model with champions at every level actively managing adoption
Result
Sustainable AI capability that ramps up quickly for each new initiative, plus an inadvertent "leadership incubator" effect.
ITAGroup's CEO Brent Vander Waal took this further, spending hours in cross-functional sessions with managers and frontline staff, mapping operations and pinpointing AI opportunities. The initiative culminated in a company-wide hackathon chaired by the executive team.
The signal was clear: this transformation belongs to everyone, not just the IT department.
The Takeaway: Adoption Reality vs. AI Theatre
"Your AI strategy isn't a technology problem—it's a people problem."
The mathematics are clear: it takes only 10-20% adoption of an innovation for rapid acceptance by the majority to follow. But that critical mass needs to come from trusted peers and immediate supervisors, not distant executives or vendor presentations.
The companies in the successful 26% who achieve AI value aren't winning because of superior algorithms—they're winning because they built the human infrastructure for adoption first. Adoption beats algorithms every time.
Action This Week
If you’re attempting any of this, here’s what you can do.
First, identify three operational managers who could bridge technology and adoption. People who understand both the business and have credibility with teams. These are your potential champion candidates, not your technical experts.
Second, run an influence mapping session asking a simple question:
“Who would teams actually trust with AI changes?“
The answers might surprise you and will reveal your real change network.
The window for building this capability is closing as AI becomes table stakes for competitive advantage. While you're reading this, your competitors are deploying their third AI agent because they solved the adoption problem first. Companies that can deploy AI agents quickly and effectively will pull ahead while others struggle with adoption challenges that were entirely preventable.
Until next time,
Chris
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