Context

The organisation had developed a steady pipeline of AI pilots across different business areas. These initiatives were delivering promising results in controlled settings and were increasingly being used beyond their original scope. However, many of these systems sat in an ambiguous state: no longer experimental, but not fully operational either. Innovation teams continued to refine models, while operational teams were unsure when-or whether-they were expected to take ownership. AI was proving its value, but its place within the organisation’s operating model remained unclear.

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The Challenge

The difficulty was not technical performance, but accountability. Pilots were often labelled “successful” based on accuracy or usefulness alone, even though no agreement existed on support, oversight, or long term responsibility. Existing governance processes were designed for traditional software and did not neatly fit AI systems that continued to evolve after deployment. As a result, responsibility for running these systems was informal, dependent on individuals rather than roles. The organisation faced the risk of accumulating operational debt in the form of AI systems that were relied upon but not properly owned.

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The Decision

The organisation chose to draw a firm boundary between experimental work and operational AI. Rather than allowing pilots to drift into production by default, leadership defined explicit conditions that had to be met before a system could be treated as live. This included agreeing who would own the system after handover, what level of governance applied, and what ongoing involvement was expected from the teams that built it. Importantly, the organisation accepted that some pilots would not progress if these conditions could not be met, instead of forcing them into production prematurely.

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What Changed

The transition from pilot to production became a deliberate decision rather than an implicit one. Teams began designing pilots with eventual ownership in mind, or clearly positioning them as time bound experiments with no expectation of longevity. Operational teams were more willing to accept AI systems because responsibilities and escalation paths were explicit. Fewer systems made it through to long term use, but those that did were treated as part of normal operations rather than exceptions that required special handling.

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Why This Matters

Many organisations struggle not with proving AI’s potential, but with sustaining it responsibly. Allowing pilots to linger in an undefined state creates hidden risk and erodes trust in AI over time. Stability comes from making clear decisions about when experimentation ends and accountability begins, even if that means saying no to systems that appear valuable in isolation.