There is a pattern in failed AI projects that is so common it should have its own name.
An organisation invests in building something genuinely useful. A knowledge assistant. An automation workflow. A custom GPT that could save hours of work every week. The technology works. The demo is impressive.
And then nobody uses it.
Not because it is bad. Because the people who were supposed to use it were never part of building it. They do not trust it. They do not understand it. They have workarounds that feel safer. The change was done to them, not with them.
This is what happens when technology runs ahead of adoption.
The other failure mode
The opposite is equally common. An organisation runs workshops. Prompt training. Awareness sessions. Everyone is excited about AI. They understand the potential.
But there is nothing to actually use. No tools built for their specific workflows. No architecture that connects to their data. The training was generic, and six weeks later the enthusiasm has faded because there was nothing concrete to apply it to.
This is what happens when adoption runs ahead of technology.
Two tracks, one programme
The solution is not to do technology first and then bolt on change management. It is not to do change first and hope the technology catches up.
It is to advance both as equal partners from day one.
Technology enablement delivers the tools, architecture, and infrastructure. Custom GPTs. Knowledge assistants. Retrieval systems. Voice agents. Whatever the use case demands.
Change adoption delivers the capability, confidence, and governance. Prompt training. Workflow standards. Shared libraries. Team ownership. Decision frameworks.
Every sprint advances both tracks. Every deliverable has a technology component and a people component. They are not separate workstreams running in parallel. They are two aspects of the same work.
What this looks like in practice
In a recent engagement with a travel company, the dual-track approach meant:
- When we built a custom GPT for Guest Operations, we simultaneously trained the team on prompting techniques specific to their workflows.
- When we created a knowledge assistant with retrieval architecture, we also established shared prompt libraries and workflow standards the team could extend.
- When we designed the architecture for customer-facing AI, we ensured the team understood it well enough to maintain and evolve it independently.
The result: the team does not just use the AI. They own it. They are extending it themselves because they were part of building it.
Why this matters for your organisation
If you are planning an AI initiative, ask yourself:
- Who will use this after it is built?
- Were they involved in designing it?
- Can they explain how it works to a new team member?
- Can they extend it without calling the people who built it?
If the answer to any of these is "no," you have a technology project, not a transformation. And technology projects have a shelf life.
Dual-track is not more expensive or slower. It is actually faster to value, because the capability is ready to use the moment the technology is ready to deploy. There is no adoption gap. No training lag. No period where the tool sits unused while people figure out how to incorporate it.
The organisations getting real, lasting value from AI are the ones where technology and people move together. Everything else is just software with an expiry date.