Only 25% of enterprise AI pilots reach production, Deloitte finds

Ever seen an AI demo that looked flawless, then watched the actual rollout fall apart within weeks? That gap between a convincing pilot and a working production system is exactly where most enterprise AI investment is currently getting stuck.

However, according to the Deloitte 2026 "State of AI in the Enterprise" report, which was conducted based on a poll of over 3,000 executives, less than one-fourth of the survey participants have advanced 40% or more of their AI pilots to production. This means that the majority of the investment in AI is stuck in a permanent pilot hell.

The proof of concept usually works in optimal conditions with carefully managed data, a limited number of users, and specialists who will help to identify errors. The production environment lacks everything mentioned above.

PoCs are often developed to check whether the model can fulfil a particular purpose. The problem is that management should understand how exactly it will affect the business. Otherwise, "improved efficiency" will not become a measurable business case at all.

Even a technically sound pilot can drift indefinitely without one accountable business owner deciding whether results justify further investment.

There may be one team responsible for the engineering part, one team responsible for the data, and yet another team responsible for the compliance of the system, but there must be somebody responsible for the result of the process. This clean and managed pilot data has nothing to do with enterprise data that is fragmented, out-of-date and permission-limited.

There is no way for a better model to make up for lack of context and understand which records should be available for a particular user.

McKinsey's 2025 State of AI survey found that top-performing organisations were nearly three times more likely to have redesigned their workflows around AI and to have strong senior-leadership buy-in from the start.

Small pilots conveniently hide the combined cost of model calls, retrieval, monitoring, and human review. IBM and Oxford Economics surveyed 2,000 technology executives in early 2026 and found 84% hadn't fully operationalised AI financial management, and 85% lacked real-time visibility into their own AI spending.