Last updated on August 7th, 2026 at 09:01 pm

By Gleb Tsipursky, PhD

Canadian businesses are adopting artificial intelligence quickly, but many are still measuring the wrong thing. They count licences, prompts and hours apparently saved. They rarely count the work that colleagues must redo after an AI-assisted task reaches them incomplete, misleading or difficult to verify.

That omission matters because rework can erase a productivity gain while leaving the original team convinced that automation succeeded. A sales proposal may take half as long to draft, yet require an account manager to correct claims and restore customer context. A support summary may arrive faster, yet force an analyst to reopen the source record. A marketing asset may look finished until legal, brand or product teams discover that its confident language rests on weak evidence.

B2B News Network recently highlighted the problem of AI “work slop” for Canadian small and medium-sized businesses and separately examined why executives disagree so sharply about AI return on investment. These are two sides of the same issue. Firms cannot evaluate AI honestly when the cost of repairing its output disappears into another person’s calendar.

Canadian B2B companies should create an AI rework ledger. This would be a lightweight operational record of important AI-assisted work that required correction, verification or recovery after it left the original user’s hands. The aim is not to punish experimentation. It is to reveal where apparent speed shifts effort downstream.

The ledger should capture five facts.

First, what task was AI used for, and what outcome was expected? “Used Copilot” or “generated content” is not enough. The record should identify the business process: preparing a proposal, qualifying a lead, summarizing a contract, drafting a customer response or updating a product record.

Second, what defect created rework? Common categories include invented facts, missing context, outdated data, incorrect formatting, an unsupported recommendation or language that sounded plausible but did not fit the customer. A short category is more useful than a long narrative because patterns become visible across teams.

Third, who absorbed the correction cost? The person who generated the output may save 30 minutes while someone in sales operations, customer success, compliance or finance spends an hour repairing it. Naming the receiving function exposes whether a local efficiency is creating an organizational loss.

Fourth, how much effort did recovery require? Precision to the minute is unnecessary. A simple scale—under 15 minutes, 15 to 60 minutes, one to four hours, or more—will usually reveal whether a workflow is improving.

Fifth, what changed afterward? The answer might be a better source document, a required verification step, a narrower use case, clearer customer data or a decision to keep a human in control. Without a recorded change, the same defect becomes recurring overhead.

This approach fits Canada’s current policy debate. The federal government’s consultation on AI transparency asks how people and organizations should receive clear information about AI systems and outputs. Transparency should not stop at disclosure. Business leaders also need internal visibility into whether automated work remains dependable after it moves between departments.

The ledger would also sharpen investment decisions. Statistics Canada reported that 19.2 per cent of businesses used AI to produce goods or deliver services in the second quarter of 2026, three times the share two years earlier. Adoption is no longer the interesting question. The more useful question is where AI produces durable value after verification, correction and handoffs are included.

A rework ledger should remain small enough to use. Companies can begin with consequential workflows and review the results monthly. Leaders should look for repeated defects, departments that absorb hidden repair work and tools whose benefits depend on unusually skilled employees checking every output. They should also identify successful cases where rework declines because teams improve data, prompts, training or process design.

The practice can support employees rather than turning into surveillance. Records should focus on workflow failures, not individual blame. Workers are often the first to recognize that an AI tool is creating polished but unreliable output. Treating their corrections as operational intelligence gives them a reason to report problems instead of quietly fixing them.

B2B buyers increasingly demand evidence that a technology will improve a real process, not merely produce an impressive demonstration. Vendors that help customers measure rework can make a stronger case than those that report only usage. They can show where the product reduces total effort, where it needs guardrails and which tasks remain poor candidates for automation.

AI productivity is not the amount of work a machine appears to complete. It is the improvement that remains after people verify the result, repair mistakes and deliver something a customer or colleague can safely use. Canadian B2B firms will understand that difference only when they start counting the work that comes back.

Gleb Tsipursky, PhD, is 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). His work and commentary have appeared regularly in newspapers including The New York Times, the Toronto Star, the New York Daily News, and The Plain Dealer in Cleveland https://disasteravoidanceexperts.com/aibook