The Adoption Spread

The adoption spread is that range of outcomes across people and tasks, including work passed to someone else. An average saving per user can miss it if the calculation stops at the handoff.

In a 2023 experiment involving 758 BCG consultants, GPT-4 users worked more than 25% faster on product-innovation tasks designed to be within the tool’s capabilities. Human-rated quality was over 40% higher than among participants working without it.

A separate group tackled a business problem designed to be difficult for GPT-4: recommending where a fictitious company should invest, using financial data and executive interviews. Here, participants using GPT-4 were less likely to reach the correct answer than those working without it. The tool helped with product ideas and persuasive writing, then misled people on a task that required reconciling evidence.

The same research complicates the idea that AI simply widens existing skill gaps. On the creative task, participants with lower baseline scores made larger gains, nearly catching those who had scored higher. The baseline measured proficiency on a particular task, rather than ranking employees’ general competence.

That experiment measured individual assignments among junior consultants; it didn’t measure colleagues’ cleanup costs. Its results describe GPT-4 in 2023, not the tools on your desks today. Our inference for a rollout is to evaluate the task and the checks a user can demonstrate, instead of treating seniority as permission. A capable employee can trust the tool in the wrong place; someone less proficient can gain substantially from the right assistance.

You bought an amplifier

An amplifier can give a sound approach more reach. It can also turn an unchecked assumption into a complete script with a plausible explanation. Judgment matters at the point where somebody compares that output with the job that needed doing.

Among respondents to the AI-frustrations question in Stack Overflow’s 2025 developer survey, 66% selected solutions that were almost right but not quite; about 45% selected debugging AI-generated code being more time-consuming. These are reports of frustration. They don’t measure net time lost or establish who created the problem and who absorbed the work.

Suppose the script at that handoff matches customer records by name, although the system requires a unique account number. It may run without errors and still update the wrong account. A reviewer has to understand the data rules and test the change against them. If the author supplies code but leaves that investigation to a specialist, the supervisor needs to count the specialist’s effort too.

Human-written code needs review as well. The question is how much work this way of producing it adds or removes. If additional checking and repair outweigh the drafting gain, you pay for the software twice: on the vendor invoice and in extra demands on the people who can repair its output.

Match the response to the failure

Take a small sample of comparable assignments, including work done without AI, and follow them through to acceptance. For the customer-record script, acceptance means the intended account changes and unrelated accounts remain untouched. Record preparation, drafting, review and correction across everyone involved. An abandoned AI attempt followed by a manual redo belongs in the assisted total.

For the same amount of accepted work, the labor saving is the unassisted total minus the assisted total. Keep the breakdown by person: a smaller combined total can still demand more of your only available reviewer. Record waiting for review separately from time spent reviewing. That separates effort saved from delays caused by a queue.

More review hours can be a reasonable price for more usable work. Coaching and reusable checks can also cost effort now that later handoffs may recover. Record that setup separately from recurring work, but include it in the trial’s cost; a hoped-for learning gain still needs to appear in subsequent handoffs. Compare tasks of similar difficulty under the same quality standard, so easier assignments or looser acceptance don’t get credited to AI.

A review gate fits work where a qualified person can check the result before it causes harm. For the customer-record script, run it on test records that share a name but have different account numbers, and inspect which accounts it changes. Define the expected account numbers from the business rule before inspecting the generated code. Otherwise, a reviewer can approve a script that faithfully implements the wrong rule.

Give the reviewer capacity to do that job. Requiring approval without allowing time for inspection just adds a signature. A review that catches the error can be worth doing and still consume the entire drafting gain.

Coaching fits a specific gap the author can learn to close. In the BCG experiment, an overview of prompting and the tool’s limitations did not remove the negative effect on the business problem. The researchers cautioned that this did not make all training ineffective.

Have an experienced colleague work through the actual verification with the author. Then look at the next handoff: can the author explain the matching rule and show a test that would catch the wrong account? That gives coaching an observable purpose. It doesn’t require an outside adviser.

Permission to run that script can depend on demonstrating the check. A learner can still use AI to draft and test without being able to change live records. If nobody can establish that a result is safe, keep that operation on a known, checkable path. Where the assisted workflow already saves total effort at the required quality, let it keep working.

The supervisor can be entirely sincere about the gain. From that desk, the draft arrived sooner. Ask the person receiving it when the work was actually finished.