By Gleb Tsipursky

Op-Ed Commentary

The East Kootenay is having two useful conversations about artificial intelligence at the same time.

One is about infrastructure. A recent e-KNOW commentary on AI data centres asked readers to look past the technology label and examine who receives the benefits, who carries the costs, and who gets to decide. The other conversation sits closer to the daily work of local businesses: when does an AI tool actually make a business better?

That second question matters now because the KAST TechEdge initiative is giving Kootenay businesses a practical route into digital tools and AI. The program is built around one-on-one consultations, workshops shaped for rural business realities, and connections to local technology providers. The Province of British Columbia has also committed approximately $100,000 to support the initiative and help businesses use technology to improve productivity, reduce costs, and grow.

The opportunity is real. So is the risk of starting in the wrong place.

Too many organizations begin AI adoption by choosing a tool and then searching for a problem it can solve. That sequence encourages experimentation without accountability. Employees try the technology, managers see impressive demonstrations, and nobody agrees on what improvement would count as success. Six months later, the organization may have more software, more AI-generated material, and little evidence that the underlying work improved.

A better approach starts with a productivity test.

Before a business adopts an AI tool for a recurring task, it should name the bottleneck first. What takes too long? Where does work pile up? What gets reworked? Where do employees spend time on routine drafting, searching, summarizing, or transferring information instead of using judgment?

Then establish a baseline. If a business wants AI to speed customer responses, it should know roughly how long those responses take today and how often staff have to correct mistakes. If the goal is faster internal reporting, the business should know how much time the current process consumes and what errors create rework. Without a baseline, almost any new tool can feel productive because novelty is easy to mistake for progress.

Next, assign a human reviewer. Generative AI can produce plausible language even when its reasoning or facts are weak. The person using it needs to know what must be checked, what can be accepted quickly, and what should never be delegated.

This matters especially in small organizations, where one bad output can consume the same limited staff time the tool was supposed to save.

Finally, set a short review period. After 30 or 60 days, compare the result with the baseline. Did the process become faster? Did rework fall? Did customers or employees get a better result? Did the tool simply move work from one person to another? If the evidence is weak, change the workflow or stop paying for the tool.

This discipline also makes AI adoption easier for employees. People resist technology when they believe management is imposing it without understanding their work, or when they fear that speed matters more than quality. A workflow-specific test gives employees a clearer role: help identify the bottleneck, define the failure conditions, and improve the process. That turns adoption from a technology mandate into a practical business experiment.

TechEdge’s local, hands-on approach creates a good setting for this kind of discipline. Rural and small businesses do not need generic advice to “use more AI.” They need help deciding where AI belongs, where human judgment remains essential, and how to tell whether the investment produced value.

The East Kootenay already knows how to ask hard questions about large technology infrastructure. Local businesses can apply the same instinct at a smaller scale. Which task improves? Who checks the result? Who benefits from the time saved? What evidence proves the change was worth making?

AI adoption becomes much less mysterious when those questions come before the software purchase.

– 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

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