The useful question is not whether AI can perform a task, but how the task should be redesigned when AI becomes part of the operating loop.
Begin with bounded tasks
Summarisation, classification, retrieval and pattern identification are often easier places to introduce AI than decisions with significant consequences. Clear inputs and expected outputs make performance easier to evaluate.
Keep accountability explicit
When an output affects money, eligibility, people or critical operations, organisations should specify who reviews it and who owns the final decision. AI assistance should not make accountability ambiguous.
Give users enough context to review
A reviewer needs access to the source information, assumptions and relevant history behind an AI-assisted output. Interfaces should make verification practical rather than presenting generated content as unquestionable fact.
Watch for operational failure modes
Poor source data, inappropriate access, changing prompts and over-reliance by users can create problems even when the underlying model performs well. Monitoring should therefore cover the whole operating system around AI.
Automate selectively
The best outcome may be partial automation: AI prepares, organises or recommends while a person handles judgement and exceptions. This can reduce routine effort without removing the contextual understanding the organisation still needs.