Your AI Productivity Goals May Be Rewarding Work Nobody Read

Somewhere in your company this week, a person who never pressed generate is fixing what came out. An AI push is easy to score from the other end of that exchange, by counting usage and the hours saved by whoever used the tool. The cost lands on the colleague who opens the file, and that scorecard never looks there.

Tobi Lütke, the Shopify chief executive who told staff last year that using AI is a baseline expectation, has a name for what comes back. In a podcast interview Fortune reported on 17 September, Lütke described employees tossing "slop grenades" at each other: AI-written work the sender did not really read, left for colleagues to review (Fortune).

The best current advice for managing an AI mandate upward is to swap "use more AI" for a defined outcome. That advice is right as far as it goes, but two of the four outcomes it offers, faster delivery and more output, can be met by exactly the unread work Lütke is describing. The goal has to be measured where the work lands, with the person who receives it.

The cost is booked to whoever opens the file

A survey of 962 US full-time desk workers by researchers at Stanford and BetterUp puts numbers on this. It was fielded in September 2025 and posted in February as a preprint, not yet peer reviewed (Liebscher et al.). The authors call the problem workslop: AI-generated work that looks like it completes a task but lacks the substance to move it forward.

Just over half, 52.7 percent, said at least some of the AI-generated work they send colleagues is unhelpful, low effort, or low quality. Those are people rating their own output, not complaining about someone else's. On the receiving side, 37.6 percent said they had been sent work like that in the past month, and estimated that reading, discussing, fixing, or redoing it cost them about 3.4 hours a month. Managers found it more taxing to deal with than individual contributors did, and recipients came away seeing the sender as less trustworthy and less reliable.

None of that shows up on a usage dashboard. The sender banks the time saved, and the researchers describe that saving as redistributed to the coworkers who must interpret, correct, or redo the work (Liebscher et al.).

The strongest predictor was being encouraged to use AI

That cost is not spread evenly, and where it concentrates points back at how mandates get written. Of the factors that held up in the analysis, the one most strongly tied to sending any workslop was agreement with a single statement: "my supervisor or organization encourages us to use AI." People who felt less in control of their own AI use were more likely to send it too, and the heaviest senders tended to work where it was hard to raise problems or talk about capacity. The authors draw the conclusion themselves, warning that encouraging or mandating AI adoption can "run the risk of backfiring by increasing workslop instead of productivity" (Liebscher et al.).

A cross-sectional survey is a single snapshot, so this shows the two traveling together rather than one causing the other. The finding still bears hardest on marketing leaders, because AI is written into the jobs they are hired for. In Promptafire's jobs index, 56 percent of the 2,314 live marketing postings we track across 136 companies name AI as a requirement or responsibility. For senior individual contributors and for leadership roles (director, head of, VP, or managing a team) it is 62 percent; for entry-level roles, 38 percent. The people most likely to set an AI goal for a marketing team are the ones whose own job ads most often ask for AI.

Speed and output goals can be met with unread work

That puts the weight on the goal those leaders write down. Kevin Indig and Amanda Johnson's playbook for managing an AI mandate upward, published in Growth Memo on 21 September, gets most of the way there. It opens with a 30-day tally of the hours your team puts into AI, from checking and fixing its output to learning the tools, collected anonymously if possible. One of its rules is to turn "use more AI" into a defined outcome by asking leadership to pick the primary goal: lower cost, faster delivery, higher quality, or more output (Growth Memo).

The trouble is in that menu. Faster delivery and more output can both be measured at the sender, and measured there, both can be met by sending work nobody read. The playbook's own example of a testable goal shows how. "Cuts brief prep from 3 hours to 1" is properly specific, and an AI-drafted brief sent unread would hit it on day one. The writer or agency working from that brief absorbs the difference in a column nobody is tracking.

The fix is to write every goal at the receiving end. Keep the speed target and add the condition it has to survive: brief prep drops to an hour and the share of briefs sent back with questions does not rise. For content, count the AI-assisted drafts that pass editing on the first round rather than every draft produced. For automations, report agent-only runs separately from human-rescued ones. A commenter proposed that split to a vendor who admitted in r/AI_Agents that a client's agent, flawless on paper for four months, got stuck about twice a week and was quietly fixed by hand (r/AI_Agents).

A rework count only works if reporting it is safe

Goals set at the receiving end need numbers collected there, and how you collect them decides whether they are honest. The survey's authors flag the soft spot in their own hours figure. People estimate time poorly from memory, and accurate numbers need logging. A 30-day tally is a fine opening move for one budget conversation. A goal that runs all year needs events recorded as they happen, such as a draft sent back, a brief returned with questions, or a run someone had to restart. Events also show where the problem sits. One practitioner running agent workflows at an insulation company logged every failure in a plain text file and found, after three months, the same five causes, two of which moved out of the model into ordinary code (r/AI_Agents).

The vendor in that thread fixed things before the client noticed, so the client only ever saw the flawless version. The same thing happens inside a company whenever logging a fix reflects badly on whoever logs it, and the survey found the heaviest senders where problems were hard to raise. Growth Memo's instruction to make clear that nobody is being evaluated applies to the standing count too: attach it to the workflow, never to the person who logged it.

The evidence has limits. It is one cross-sectional survey of self-reports, a playbook, and practitioner posts, and none of it measures a marketing team before and after a mandate. The survey also cuts against the gloomiest reading. On average, people estimated that 43.9 percent of the work they received from colleagues was helpful, high-quality AI-generated work, against 14.4 percent that counted as workslop, and the workslop came from about two colleagues each. A receiver-side count is unlikely to condemn AI across your team. It should produce a short list of workflows, which is what you want on the table before anyone sets next year's AI target.

Sources

• Workslop: Examining the prevalence, antecedents and consequences of low-quality AI-generated content at work, Liebscher, Lee, Rapuano, Kellerman, Niederhoffer and Hancock, PsyArXiv preprint, 5 February 2026
• Shopify CEO says employees' 'slop grenades' are making more work for everyone else, Fortune, 17 September 2026
• Managing the AI mandate upward, Growth Memo, 21 September 2026
• My agent 'works' four months straight. The truth is it's me patching it twice a week., r/AI_Agents, 21 September 2026
• Shopify's CEO calls it "slop grenades." We've been cleaning up the same thing in AI rollouts., r/AI_Agents, 23 September 2026
• Promptafire jobs index, live marketing postings, 29 September 2026