Over the past year, we’ve all witnessed an AI gold rush. Companies of every size are racing to “adopt AI” before their competitors do, layering chatbots, content tools, and automation into their workflows. But here’s the uncomfortable question: is all of this actually making us more productive, or is AI quietly slowing us down?
A new term from Harvard Business Review “workslop” captures what many of us are starting to see. It refers to the flood of low-quality, AI-generated work products: memos, reports, slide decks, emails, even code snippets. The kind of content that looks polished at first glance, but ultimately adds little value. Instead of clarity, we’re drowning in noise.
The Illusion of Productivity
AI outputs are fast, but speed doesn’t always equal progress. Generative AI makes it effortless to produce content, but that ease has created a different problem: oversupply. We’re seeing more documents, more proposals, more meeting summaries but much of it lacks originality or critical thought.
When employees start using AI as a crutch instead of a tool, the result is extra layers of text that someone else has to review, fix, or ignore. What feels like efficiency often leads to more time spent filtering through workslop. The productivity gains AI promises on paper are, in practice, canceled out by the overhead of sorting the useful from the useless.
Numbers Don’t Lie
The MIT Media Lab recently published a sobering study on AI adoption. After surveying 350 employees, analyzing 300 public AI deployments, and interviewing 150 executives, the conclusion was blunt:
- Fewer than 1 in 10 AI pilot projects generated meaningful revenue.
- 95% of organizations reported zero return on their AI investments.
The financial markets noticed. AI stocks dipped after the report landed, signaling that investors are beginning to question whether this hype cycle can sustain itself without real business impact.
Why This Happens
The root cause isn’t AI itself it’s how organizations are deploying it. Instead of rethinking workflows and aligning AI with core business goals, many companies are plugging AI in like a patch. “We need to use AI somewhere, anywhere.” The result is shallow implementations that create surface-level outputs without driving real outcomes.
It’s the same mistake businesses made during earlier tech booms. Tools get adopted because of fear of missing out, not because of a well-defined strategy. And when adoption is guided by FOMO, the outcome is predictable: lots of activity, little progress.
Where AI Can Deliver
Despite the noise, I don’t think AI is doomed to be a corporate distraction. The key is focus. AI shines when it’s applied to specific, high-leverage problems:
- Automating repetitive, low-value tasks (think: data entry, scheduling, or document classification).
- Enhancing decision-making with real-time insights from complex data.
- Accelerating specialized workflows in domains like coding, design, or customer support if humans remain in the loop.
The companies that will win with AI aren’t the ones pumping out endless AI-generated documents. They’re the ones rethinking their processes from the ground up and asking: Where can AI free humans to do what they do best?
The Human Factor
We have to remember: AI isn’t a replacement for judgment, creativity, or strategy. It’s a tool one that can amplify our abilities if used thoughtfully. But when used carelessly, it becomes a distraction that actually slows us down.
The real productivity gains won’t come from delegating everything to AI. They’ll come from combining human strengths with AI’s capacity, cutting through the noise, and resisting the temptation to let machines do our thinking for us.
Final thought: Right now, most companies are stuck in the “workslop” phase of AI adoption. They’re generating more content than ever but producing less clarity and value. The next phase will belong to organizations that stop chasing hype and start asking harder questions: What problem are we actually solving? Where does AI fit into that solution?
Until then, we should be honest with ourselves: AI isn’t always speeding us up. Sometimes, it’s slowing everyone down.