Почему успешный ИИ-пилот не окупается при масштабировании — тема INFOSTART CIO CAMP 2026

How a pilot differs from scale

A successful pilot and a profitable rollout are two different economics, and it's precisely in the transition between them that most AI projects break down. In a pilot, the team works with a clean data sample, a narrow scenario, and a pre-engaged group of users. When scaling up, costs appear that weren't in the original budget: preparing "dirty" data for real-world volume, additional integration work, and employee resistance to the new process.

According to the organizers of INFOSTART CIO CAMP 2026, most rollout stories have a "presentation version": the company defined the task, the team found a solution, the metrics improved, and an economic effect appeared. What stays off-screen is exactly the part that determines the final payback over the long run.

Where the costs hide

The hidden cost items of an AI project aren't the cost of the model itself, but everything around it. These are exactly the points INFOSTART is putting on the agenda, because they most often don't make it into the pilot's business case:

• Additional data preparation and cleaning for real, not test, volume
• Integration that turns out to be harder than assumed at the start
• Employee adoption of the process — a new tool isn't adopted instantly
• Post-launch expenses that weren't in the original budget
• Written-off investments in projects that had to be stopped

"Good pilot metrics don't mean the rollout will pay off after

scaling," the INFOSTART CIO CAMP 2026 announcement states.

Why do projects have to be stopped?

Some AI rollouts are stopped precisely because, at scale, the pilot's economic effect isn't confirmed, and the total cost of ownership exceeds the benefit. Data has to be prepared over and over again, integration requires more resources than budgeted, and employees don't switch to the new process right away — together these factors push the payback point beyond the project's horizon.

Public discussion of such stopped projects is rare: companies usually show only success stories, while failures stay internal. INFOSTART CIO CAMP 2026 is putting them on the open agenda precisely as a source of practical lessons.

What will be discussed at CIO CAMP 2026

At INFOSTART CIO CAMP 2026, CIOs will break down not showcase cases but the real costs, mistakes, and results of AI projects — including those that never made it to production. For a CIO, this is a practical benchmark: how to evaluate an AI initiative not by pilot metrics, but by the total cost of ownership after scaling.

The format is valuable because it shows both sides: the path from problem to effect, and what usually stays off-screen in a presentation — data preparation, integration, and working with the team.

What this means

The gap between a successful pilot and a profitable production rollout is the main risk for corporate AI in 2026. The economics of a project need to be calculated at scale from the start, accounting for the hidden cost items — data, integrations, and human adoption — otherwise a successful demo turns into a money-losing rollout.