Kevin Indig gathers five studies on where AI costs marketing teams time

Kevin Indig published a column in Search Engine Land on 9 September titled "The AI hours nobody on your marketing team is counting." It gathers findings from five studies that separately measure where AI in a working team ends up costing hours: building custom tools, fixing model output, and checking whether a draft is right. The point of the piece is that these hours are real and mostly invisible on a team's dashboard.

What the five studies actually say

The clearest number comes from a METR study Indig cites, published in July 2025. METR paired 16 experienced open-source developers with AI tools on 246 real tasks in their own repositories. The developers expected AI would make them 24 percent faster. Measured, they were 19 percent slower.

HubSpot's State of AI report tells Indig where the workload has moved: 91 percent of marketing leaders say their teams use AI, and 66 percent say their company builds its own AI tools for marketing.

BetterUp Labs and Stanford surveyed 1,150 US knowledge workers on "workslop", a term for AI-generated work that a colleague then has to redo. 41 percent said they received workslop in the previous month. Sorting each one out took a mean of one hour and 56 minutes. BetterUp estimates the annual cost at 9 million dollars for a 10,000-person firm.

A Workday study, cited via IT Brief, puts a matching figure on the other side of the trade: for every 10 hours AI saves, teams hand back roughly 4 hours fixing and rewriting.

Upwork surveyed 2,500 leaders and workers. 39 percent named time spent checking and fixing AI output as their top new task; 23 percent named time spent learning tools. Both are pure overhead.

The pattern behind the numbers

Indig's argument is that AI moves work rather than removing it. The hours move from doing the task to building, learning, and maintaining a system that helps someone else do it. On a marketing team, that shows up as engineers who once worked to get other websites to link to theirs now building an internal keyword tool, a writer who once released four posts now editing eight drafts, and a director whose weekly review has become a review of the review.

Two kinds of hidden cost show up across the five studies. One is the rebuild cycle: teams building custom AI workflows that cover a smaller and smaller part of the job as public tools catch up, then taking them apart. The other is the cost of watching over the AI: people checking, correcting, and redirecting AI output, work that appears as staff time in a Slack channel and nowhere in the plan.

Indig also names a market shift underneath the productivity question. Google's AI Overviews, ChatGPT, Perplexity and Claude answer many of the queries that used to send readers to a website. So the same team, spending more time on AI-adjacent work, delivers a smaller organic result. The AI hours become expensive because they compete with the hours that used to build brand, links, and audience.

None of the five studies contains a fix. The one habit that emerges is measurement. If a team is going to hand a task to a model, it needs to count the setup time, the review time, and the redo time next to the raw hours saved, or the savings claim is doing the same work as the "20 percent faster" claim the METR developers made about themselves before they were timed.

Source

Search Engine Land: The AI hours nobody on your marketing team is counting by Kevin Indig, 9 September 2026.