A parks department is weighing an AI tool that triages resident-submitted maintenance requests, such as broken playground equipment or downed tree limbs, for faster routing to crews. Adding a human-review step before each AI-triaged request is dispatched would slow response times but reduce misrouting errors. How should department leadership balance this tradeoff?

Balancing a responsible-AI tradeoff for a lower-stakes, still public-facing process means applying oversight proportionate to actual risk, such as spot-checking a sample of AI-triaged requests or reserving full review for requests flagged as higher-risk, like anything involving a potential safety hazard, rather than swinging to either extreme. Eliminating review entirely treats a public-facing municipal process as risk-free, ignoring that misrouted requests, like a downed limb blocking an accessible path, can still create real safety consequences even in a generally low-stakes system. Requiring full human review of every request imports a level of scrutiny appropriate for genuinely high-stakes, life-safety decisions into a context where it isn't justified, and the added review burden would erase the efficiency gain the AI tool was meant to provide. Waiting for a guarantee of zero misrouting errors sets an unattainable bar that no operational system, AI-assisted or fully manual, can realistically meet, and would indefinitely block a tool that could still meaningfully improve response times with reasonable safeguards in place. A scope caveat: what counts as proportionate oversight should be revisited periodically as request volume and error patterns change, not fixed permanently at launch. A concrete check: track the misrouting rate on the sampled or flagged requests and adjust the oversight threshold if errors climb above an acceptable level.