Guest Opinion | Orcas businesses need a human handoff rule before AI becomes routine

||| FROM GLEB TSIPURSKY for DISASTER AVOIDANCE EXPERTS ||| 

Orcas businesses do not need a grand AI strategy before they start experimenting. They need a clear rule for when automation stops and a person takes over.

San Juan County itself recently adopted generative AI use policies, with the recognition that the guidelines will have to evolve as the technology changes. At the same time, the Economic Development Council’s 2026 Economic Survey is asking business owners, managers, and workers to identify the challenges and opportunities shaping the local economy. Those two developments point toward the same practical question: how do we use new tools without losing judgment about what works here?

For a small business or nonprofit, AI can help draft customer messages, summarize documents, prepare marketing copy, organize research, or generate a first version of a proposal. The central risk appears when responsibility becomes fuzzy, especially when the system is wrong.

Every recurring AI-assisted workflow should therefore have a human handoff rule. Define the conditions that automatically send the task back to a person. Those conditions might include uncertain facts, unusual customer circumstances, a price or refund, a safety issue, a financial commitment, or any public statement that could damage trust if it is wrong.

Then keep a simple exception log for 30 days. Record where the automated output needed correction, what context it missed, how much time the fix took, and who made the final call. At the end of the month, review the pattern.

That exercise often reveals what software dashboards miss. A tool may make the first step faster while quietly creating more checking, correction, and follow-up. Or it may prove genuinely useful on routine work while repeatedly failing on the cases that require local knowledge.

The point is to make experimentation accountable. Clear handoff rules let employees and owners know where the boundaries are. They also make it easier to expand a workflow once the evidence shows it deserves to scale.

On an island where businesses and community organizations depend heavily on trust, the most useful AI policy may fit on one page: what the system may do, when a person must step in, and how exceptions get recorded. That is enough to turn experimentation into learning rather than guesswork.

Gleb Tsipursky, PhD, a behavioral scientist, CEO of Disaster Avoidance Experts, and author of The Psychology of AI Adoption at Work: From Resistance to Results (Georgetown University Press, 2026). https://disasteravoidanceexperts.com/aibook.  gleb@disasteravoidanceexperts.com