{"id":11123,"date":"2026-06-16T07:23:32","date_gmt":"2026-06-16T13:23:32","guid":{"rendered":"https:\/\/attentionmedia.io\/?p=11123"},"modified":"2026-06-16T07:23:32","modified_gmt":"2026-06-16T13:23:32","slug":"better-prompts-wont-fix-your-workslop-problem","status":"publish","type":"post","link":"https:\/\/attentionmedia.io\/?p=11123","title":{"rendered":"Better prompts won\u2019t fix your workslop problem"},"content":{"rendered":"
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On a recent call, the head of marketing at a mid-sized B2B SaaS company walked me through everything her team did to fix their workslop problem. <\/p>\n

They\u2019d built a shared prompt library on Notion. They\u2019d published a brand voice guide. They\u2019d run AI literacy training twice. They held monthly office hours where the team\u2019s most prolific AI users took questions. The CMO personally wrote a memo modeling thoughtful AI use and gently reminded everyone that the goal was substance over volume.<\/p>\n

Still, the workslop kept coming. Half-finished briefs that read like first drafts of something better. Slide decks that felt fine on the surface and fell apart by the third bullet. Newsletter copy that hit the brief and missed the audience.<\/p>\n

Workslop is the easy symptom to name. Diagnosing where it comes from is harder and sits in a different layer of the organization.<\/p>\n

Where most of the workslop conversation stops<\/h2>\n

BetterUp Labs and Stanford\u2019s research \u2014 the original September 2025 HBR study<\/a> and a follow-up in January 2026<\/a> \u2014 put the numbers in plain view. Forty percent of employees received workslop in the last month. Each instance cost just under two hours to clean up. At a 10,000-person company, that math comes out to roughly $9 million dollars a year<\/a>, gone, to fixing AI-generated work that was supposed to save time.<\/p>\n

The number that lands hardest for me comes from Asana\u2019s State of AI at Work research. Only 19%<\/a> of knowledge workers say they have clarity on what types of work AI should do in their role. That figure explains the rest of the data.<\/p>\n

The dominant fix in the conversation right now is the one my call-mate has been running for six months. Leaders should model purposeful AI use. Teams should set clear guardrails. Individuals should develop what BetterUp calls a pilot mindset. <\/p>\n

Human-AI output should meet the same standard as human-only output. Greg Kihlstrom\u2019s recent MarTech piece<\/a> extends the argument into marketing-specific territory, telling marketing leaders to step up and define handoff lines with IT, legal, and procurement.<\/p>\n

All of this is correct. None of it is wrong. And every bit of it puts the burden on the same place: the individual prompter, the individual leader, the individual mindset. That\u2019s the layer most teams pulled at for the last 18 months, and the actual fix lives somewhere else.<\/p>\n

<\/a><\/p>\n

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Where the system is broken<\/h2>\n

Workslop is what happens when individual people produce AI-generated work and there\u2019s no connective tissue between them.<\/p>\n

In a working marketing team, learning has to move fast. The content specialist figures out in week one that this model needs a longer brief and a tighter persona to generate anything usable. The designer figures out in week two that this image tool wants the brand colors in hex, not in plain English. The email marketer figures out in week three that the AI\u2019s subject lines sound generic unless you feed it the last three subject lines that performed.<\/p>\n

Each of those learnings is real. Each one was earned. In most marketing teams I see, none of that knowledge travels very far. The content specialist doesn\u2019t know what the designer figured out. The email marketer doesn\u2019t know what the content specialist learned. There\u2019s no shared place where someone says, \u201cThis is what worked, here\u2019s how I got there, try it your way and tell me what improves.\u201d<\/p>\n

What you end up with is a team of skilled individuals, each running their own little R&D project in parallel. Each one gets better in their slice. The team\u2019s combined output still produces workslop because no individual\u2019s learning ever reaches the next person. When someone moves teams, that learning walks out the door with them.<\/p>\n

This is the part of the workslop problem nobody is naming clearly. It\u2019s a coordination-of-learning problem, and it doesn\u2019t get solved by another training session or a sharper brand voice guide. It gets solved by building infrastructure that carries learning between people.<\/p>\n

What the AI coordination fix looks like<\/h2>\n

In my book \u201cHyperadaptive,\u201d I call this connective layer the AI activation hub. A hub is a small group of people inside the organization, virtual, physical, or both, whose job is to keep AI capability flowing through the rest of the team in both directions.\u00a0<\/p>\n

It\u2019s a different thing from a help desk, a prompt library, or an AI ticketing inbox. Those are static repositories you go to when you need to look something up. A hub is people whose job is to actively move learning around the team.<\/p>\n

The practical view, in a marketing context. A working hub does a handful of specific things.<\/p>\n