Photo Credit: Igor Omilaev
And it’s probably not the technology.
I’ve spent 20 years sitting across the table from innovation leaders at companies like GE, Corning and Citi and more startups than I can count, watching brilliant people build brilliant things. I’ve also watched those same ideas stall in boardrooms, executive meetings and business units. The technology worked. The business case was solid. The pilot delivered. And still, something wasn’t connecting.
AI is experiencing that problem at an extraordinary scale.
If you’re a CIO or CTO, you don’t need another person explaining what generative AI can do. Your engineers, data scientists and vendors certainly know. You have pilots, dashboards, benchmarks and ROI projections. And fingers crossed, all these AI project might just yield something interesting.
And yet the questions keep coming: Why this? Why now? What problem are we actually solving? What happens if we don’t do it? And perhaps the most frustrating question of all: Why aren’t people using it?
Those questions can sound like resistance to technology. Often, they’re really requests for meaning.
Mind the Gap: On the Roadmap
The AI initiatives I’ve seen move successfully from pilot to enterprise adoption have something important in common. Someone has translated the technical capability into a human and business consequence.
That sounds obvious. It rarely happens.
Worse, every AI initiative feels like it boils down to a productivity discussion. For most teams, that comes with a veiled threat of agents doing their jobs for them. It’s not progress, it’s just scary.
An IT team can explain the model’s accuracy, architecture, latency and security beautifully. What is often missing is the story of what changes for the person using it. What happens to the underwriter who used to spend every Friday afternoon buried in a manual review process? What happens to the customer service representative who worried that AI was going to replace her, then discovered it could take the most tedious part of her job off her plate? What happens to the executive who finally understands a complicated risk model because someone connected the data to one customer, one decision and one real business consequence?
Those stories make the technology comprehensible.
I call stories memory-making devices. They take complexity and give people something they can remember, repeat and use to make decisions. That matters enormously when you’re asking an organization to change how it works. The North Star matters, and more importantly it keeps the team connected in the midst of so much disruption.
What Disconnection Costs IT Leaders
The cost of poor communication shows up in places that rarely appear on an AI implementation roadmap.
Trust: When employees don’t understand how an AI system works, why it was introduced or how decisions are being made, they create their own explanations. Those explanations can become resistance very quickly.
Advocacy: When business leaders can’t explain the value of an AI investment in language their teams understand, they have a hard time defending that investment when priorities shift or budgets tighten.
Speed: AI may accelerate the technology, but adoption still depends on people changing behavior. Confusion, uncertainty and competing interpretations can turn an impressive technical implementation into a very slow organizational one.
What Strong IT Leaders Do Differently
The strongest technology leaders I’ve worked with don’t abandon the data or the technical case. They add another layer to it.
First, they address the uncomfortable questions about jobs, control, risk and accountability rather than hoping those questions disappear. And they give people a role in the future they’re describing.
Second, they paint a picture of what the future looks like for the team. It’s a deep thinking exercise that requires walking through all the potentialities and pitfalls and being real. That last part is particularly important. People don’t adopt technology because they understand the feature set. They adopt it when they can see where they fit.
Finally, this is where storytelling becomes an executive skill rather than a communications exercise. The job isn’t to make the technology sound exciting. The job is to make the business case understandable, memorable and relevant enough that people can act on it.
CIO + CTO + Narrative = Chief AI Storyteller
The technology leader’s job has expanded. You still have to make the technology work, manage risk, protect the enterprise and demonstrate ROI. Increasingly, you also have to make the technology understandable across an organization that may have very different levels of AI fluency, different incentives and very legitimate concerns about what comes next.
That requires more than another presentation explaining the architecture.
It requires a narrative that connects the technology to the business strategy, the people affected by it and the future the organization is trying to create.
The models will continue to improve. The technology will continue to move quickly. Your competitors will continue announcing their next AI initiative.
The harder question is whether your organization understands what all of that means.
That’s where the story matters.
Need help? I’m spending most of my time helping innovative IT leaders get their AI story straight to:
- Drive adoption of new AI tools
- Explain the value, disruption and pure potential
- Foster teamwork through the change
- Inspire teams to not just engage, but to own the transformation
Schedule a call with me, I’d love to talk it through with you.