There’s a growing problem that AI creates, and you’ve likely already seen it in your inbox: low-quality, AI-generated e-mails, reports, presentations, and sales pitches. Although it’s already happening, it’s not inevitable. We’ll cover what “work slop” is, why it happens when we rush with AI, and how to replace sloppy outputs with accountable, bias-aware, high-quality results. Together with fellow LinkedIn Learning instructor Angela Wick, I discussed how you can augment your skills with AI and agents and stop AI workslop.
Focus on Outcomes in Addition to Speed
We’re all feeling the pressure: move faster, ship sooner, send the draft. But as leaders, we don’t want speed alone. Instead, we need relevant, reliable outcomes—faster. That’s where “workslop” creeps in: half-baked drafts, unverified citations, bland, formulaic phrasing, and deliverables that add work for someone else.
The solution to this problem lies in changing how we use AI. For example, use AI to help with a very specific intent, then another, rather than doing it all at once. Pair that with human-in-the-loop checks like observability, traceability, and evaluation to ensure accuracy in the generated output and context.
Let’s face it: Your customer doesn’t care whether a model wrote it and rather if the information is right and useful. Treat AI as an amplifier for your thinking, not a replacement for it. When stakes rise in situations like policy, legal, financial, customer-facing content, your accountability rises with them. Verify sources, push for clarity, and never outsource judgment.
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Adding an agent to yesterday’s process only accelerates yesterday’s waste. The smarter move is to decompose work into tasks, then decide which tasks are fit for AI, which must be done by people, and where the handoffs happen. Some tasks disappear. Some combine. Some need new guardrails. This is where organizations struggle: they race to use agents without the analysis to pick the right steps, the right prompts, and the right evaluation loops.
Start by mapping the real “as-is” process while you simultaneously design the “to-be.” Run small, instrumented pilots that measure quality, latency, and risk. Keep humans in the loop where outputs are probabilistic and consequences are high. Build evaluation criteria directly into the workflow: what “good enough” means for a daily status note is not “good enough” for a business report.
Finally, leaders should set a simple rule: it doesn’t matter whether you used AI, worked with a teammate, or your own keyboard, you’re responsible for the quality and the truth. Clear standards stop slop before it spreads to customers, regulators, and brand reputation.
Raise Skills and Culture to Build Literacy, Reduce Bias, and Instill Ownership
Teams need a basic level of AI literacy to use these systems with judgment: what generative models are (pattern predictors), what they’re not (truth engines), where hallucinations occur, and how bias shows up in outputs—text and images alike. Ask a model for “a doctor and a nurse,” and you’ll often see stereotypes surface.
That’s not a reason to avoid AI and rather design with awareness. Build norms around disclosure, verification, and tone. Encourage open conversation as many professionals already use AI but hide it, fearing they’ll look lazy or incompetent. Normalize smart use, not mindless use.
Teach the difference between creative, human voice (speeches, sensitive messages, opinions) and commodity tasks (summaries, status, formatting) where “good enough” can be automated with checks.
Stress communication skills: audience, tone, narrative, and intent are still human responsibilities.
Practical habits help: ask for sources and click them; niche down your prompts; constrain the task; review with fresh eyes after a walk; define “good enough” thresholds by context; and when it really matters, write it yourself and let AI help with structure, examples, or counterarguments you can verify.
The goal is to augment and elevate your craft while avoiding the trap of polished, empty language.
Conclusion
Speed without relevance creates workslop. Aim for outcomes. Use AI as an amplifier instead of an autopilot. Decompose processes so agents do the right tasks with the right guardrails. And invest in literacy, bias awareness, and communication so your team owns the story and the standards.
The practical next steps are simple:
Define and write down what “good enough” means for your common deliverables.
Pick one process, break it into tasks, and choose where AI fits.
Train your team on verification, bias, and micro-prompting.
Pilot, measure, refine.
Talk about it weekly so good practice becomes culture.
That’s how you stop AI slop and actually get the outcomes leaders want—faster, and better.
Need help teaching your teams how to use AI the right way? Get in touch!
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