Most Small Businesses Automate The Wrong Work With AI

Forbes has published a report with a blunt thesis: most small businesses are automating the wrong work with AI.

The claim lands at a moment when adoption of artificial intelligence among small and mid-sized companies has shifted from experiment to routine. Tools are cheap, access is easy, and the pressure to show AI activity — to customers, to investors, to peers — is real. According to the Forbes analysis, that pressure is producing a predictable failure pattern: businesses deploy automation against tasks that were never expensive to begin with, while leaving their actual cost and revenue bottlenecks untouched.

The report's framing puts the burden on task selection, not technology choice. The question a small-business owner should ask is not which AI tool to buy, but which piece of work, if automated, would actually change the economics of the business. Answer it honestly, and the shortlist of worthwhile automation targets is usually short. Answer it lazily, and AI becomes a line item that generates activity reports rather than savings.

Why does this pattern repeat? Because the wrong work is the easy work. Drafting social posts, summarizing meetings, generating boilerplate email — these tasks are visible, low-risk, and simple to hand to a model. Automating them produces a satisfying demo and a story to tell. It rarely produces margin. The harder candidates — lead qualification, pricing decisions, inventory forecasting, customer retention — demand that owners first understand and document how the work actually gets done. That preliminary step is where most automation efforts stall.

The distinction matters financially. Automation applied to a task that consumes thirty minutes a week caps its return at thirty minutes a week, regardless of how elegant the implementation is. Automation applied to a bottleneck — a process that delays orders, loses leads, or forces senior staff into repetitive manual review — compounds across every transaction the business runs. As Forbes frames it, the value of AI for a small business is a function of what sits underneath the automation, not the automation itself.

There is also an opportunity-cost dimension to the report's argument. Time spent configuring an AI assistant for cosmetic tasks is time not spent mapping the workflows where the business actually bleeds money. For a ten-person company, that misallocation is not a rounding error; it can be the difference between an AI initiative that pays for itself and one that quietly becomes shelfware after the trial subscription lapses.

The corrective is unglamorous. Before automating anything, an owner should audit where hours and errors concentrate. Rank tasks by cost, frequency, and pain. Automate the top of that ranked list, not the tasks that demo well. Then measure whether the automation changed a number that appears in the accounts — labor hours, turnaround time, conversion rate — rather than a number that appears in a productivity dashboard.

The Forbes report does not argue that small businesses should slow their AI adoption. Its argument is sharper: adoption aimed at the wrong work delivers the appearance of transformation without the substance, and the businesses that benefit will be the ones that did the diagnostic work their competitors skipped.