Most GCC AI pilots stall at the same place. Here's why.

We mapped the GenAI rollouts across the GCCs we work with, 22 of them, and the pattern was almost boring in its consistency. The pilot works. The demo lands. The technology does what was promised. And then it stops, at roughly the same point every time: the moment it would have to change how the work is actually done.

That’s an ownership problem. A pilot needs an engineer. Getting past a pilot needs someone senior enough to redesign a workflow, absorb the disruption, and hold the line while the function re-learns its job. Most centres staffed the first and skipped the second.

The tell is where the energy goes. During the pilot phase, attention concentrates on the model: the prompt, the retrieval, the eval scores. Those are engineering questions, and GCCs are good at engineering questions. The moment the pilot succeeds, the open questions turn organisational. Who signs off that this output is good enough to send to a customer? Whose headcount changes? Which team’s existing metric gets worse before the new workflow makes it better? Nobody was hired to answer those, so they sit unanswered, and the pilot sits with them.

When we look at where a stalled pilot actually stopped, it’s almost always one of 4 failures.

The first is no owner. The pilot was run by an engineering team or an innovation function, and when it succeeded there was no line leader whose job it became. It went looking for a home and didn’t find one.

The second is no path to production. The pilot ran in a sandbox with mock data and a friendly reviewer. Nobody had mapped what it would take to run it against live systems: the security review, the data-access approvals, the integration into the tools people already use. The model was production-ready long before the environment around it was.

The third is unclear decision rights. Everyone agreed the pilot was good. No one had the authority to decide it now runs the workflow, changes who does what, and overrides the team that owned the old process. Consensus isn’t a decision, and a pilot needs a decision.

The fourth is an unowned workflow. The model does its part well, but the human steps around it, the checking, the exception handling, the escalation when the model is unsure, were never designed. So the workflow is faster in the demo and slower in practice, because the people using it are improvising the parts the pilot never specified.

The centres that get to production do something the stalled ones don’t: they name an owner before they name a use case. That owner is a line leader whose own number moves when the workflow changes, the head of the function the AI is supposed to help, or someone senior placed directly over it.

That owner starts from the workflow. They map where the work actually gets stuck today, decide which decisions stay human, which get assisted, and which are handed over, and design the steps around the model with the same care the engineers gave the model itself. The pilot succeeds against a business metric they already answer for, so when it works there’s no argument about whether to scale it. It’s already theirs.

They also plan the path to production before the pilot proves anything. Security review, data access, the integration into the systems people use every day, those get scoped in parallel with the build. The result is unglamorous and it ships. The demo is less impressive than the ones that stall, because a real production workflow has to handle the cases a demo skips.

The centres that move from pilot to production share one trait: they put a senior leader on the operating-model question before they scaled the tool. Someone whose job wasn’t to build the model but to decide which decisions stayed human, which got assisted, and which were handed over, and who had the standing to make that stick across a function.

This is where most GCCs misread the problem. When pilots stall, the instinct is to add model talent, more ML engineers, another applied-scientist req, a stronger platform team. That rarely helps, because the thing that stalled was unowned. The gap is a leadership seat.

The profile is specific and it’s scarce. You need someone who can hold a technical conversation with the model team and a change-management conversation with the function, and who has the authority to decide between them. That person sits closer to a transformation lead or a product owner with real operating experience than to a principal engineer. The market doesn’t label the role cleanly, which is part of why it goes unfilled: centres write a requisition for the skill they understand, and the seat they need is never named.

If your pilots keep clearing the technical bar and dying at rollout, the missing piece is almost certainly a seat. It’s the difference between speed and speed that survives contact with the parent’s quality bar.

Before the next pilot, answer three questions on paper. Who owns the workflow this touches, a named line leader, not a committee? What business metric does success move, and does that owner already report on it? And who has the authority to change the process and override the team that runs it today?

If any answer is missing, the pilot will stall in the place the previous ones did, no matter how good the model is. Fix the owner question first. The technology is the part that already works; the organisation around it is the part you have to build, and it’s a hiring decision as much as an engineering one.

For most GCCs, the fastest route past the wall is to fill the owner seat before the next use case is chosen, then let that person pick the workflow they can actually carry to production. That sequence looks slower on the roadmap and finishes faster in reality, because it never hits the stall.