Context

AI systems were moving from pilots and early deployments into live, production use across the organisation. These systems were beginning to influence operational workflows and business decisions, often on an ongoing basis. While delivery teams had successfully built and launched AI capabilities, the question of who was responsible for running them day to day had not been fully resolved. AI did not fit neatly into existing application support models, yet it was increasingly treated as part of the production estate.

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The Challenge

The organisation faced a gap between deployment and sustainment. Delivery teams were focused on building and improving AI systems, not on providing long term operational support. Traditional support functions were cautious about owning systems that behaved differently from conventional software and continued to evolve after release. When issues occurred, responsibility was unclear: some problems were treated as defects, others as model behaviour, and others as data issues, often involving multiple teams. Without clear ownership, support relied on informal escalation and personal relationships, which did not scale.

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The Decision

The organisation decided to separate the act of building AI from the responsibility for running it in production. Instead of assuming that delivery teams would remain de facto owners, they defined explicit ownership and support models for AI systems once they went live. This included deciding which roles were accountable for behaviour in production, how issues would be triaged, and when involvement from specialist teams was required. They deliberately chose not to create a permanent shadow support function around AI, and equally avoided leaving responsibility diffused across multiple teams.

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What Changed

AI systems entering production were no longer considered complete until ownership and support responsibilities were agreed. Delivery teams began planning transitions earlier, knowing they would not remain indefinite owners. Support teams gained clearer authority and boundaries, making it easier to manage incidents without escalating every issue back to specialists. Some systems progressed more slowly as these decisions were worked through, but fewer entered production without a clear operating home. Responsibility shifted from individuals to defined roles.

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Why This Matters

Many AI initiatives falter after go live, not because the technology fails, but because no one is clearly accountable once the system becomes part of normal operations. Treating ownership and support as explicit operating decisions prevents AI systems from becoming orphaned or overly dependent on specialist teams. Enterprises that address this early are better able to scale AI without accumulating fragile, hard to run systems.