The Slopranos: The productivity crime drama
When the printing press arrived in Europe, a Venetian friar, one Filippo de Strata, denounced it immediately as a corrupter of knowledge because it allowed anyone to publish anything (I won’t say exactly what he called it, but it was certainly not becoming of a man of the cloth!)
By the seventeenth century, England was gripped by pamphlet wars, as the public sphere was flooded with cheap print. The anxiety then was not scarcity but excess. The worry was not scarcity but excess. Today, five centuries later, the arrival of generative artificial intelligence has revived the same anxiety. The internet (and my inbox) is filling up with what Harvard Business Review has labelled ‘workslop’. That is the dollops of low-quality AI-generated output that clogs inboxes, intranets and Slack feeds.
Workslop is a symptom of how a powerful technology is being mishandled. The early promise of AI was seductive. Faster work, lower costs, heightened productivity. What’s not to love!? Yet, in practice, much of what has been delivered so far is a surplus of undifferentiated text and imagery. We’re producing more than ever before, but not necessarily better. And in a strange inversion, the very tools meant to save time are beginning to waste it. Employees must sift through AI-written emails of dubious clarity, managers wade through reports padded with auto-generated filler and customers encounter a wash of synthetic content that feels both impersonal and endless.
The mistake is not the technology but the intent. Too many organisations have rushed to deploy AI as a factory for content rather than as a central tent pole of intelligence. Leaders equate adoption with output and measure success in terms of volume, not in terms of value. This is how we end up with the workslop problem. An industrialisation of mediocrity that looks like progress until one notices the drag on actual productivity.
Abandon hope, all ye who enter here!
Critics might be tempted to conclude that this proves AI is overblown. Better to abandon it now, they argue, than sink further into the swamp. But this would be to misunderstand history. The dot-com crash did not mean the internet was a failure. It meant we had confused novelty with substance. The same distinction applies here. The proliferation of workslop is not proof that AI cannot work, it’s evidence that we have been using it poorly.
If we are to stay the course with AI, we must use it differently. The real promise lies not in automating more output but in refining input, in sifting through data to highlight what matters, in guiding decisions rather than multiplying documents. AI can serve as a filter, a prioritiser, an analyst, even a second pair of eyes. In these roles, it reduces noise rather than adding to it. The challenge is one of leadership. To realise this promise, executives must set boundaries and objectives. They must insist that every AI tool is judged by its effect on clarity and efficiency, not by the quantity of material it produces.
The workslop debate reveals something deeper. It is about trust. People do not mind technology that makes their work more meaningful. They rebel when it cheapens their labour or adds pointless friction. Trust is earned when leaders are transparent about the limits of AI, when they show restraint in its deployment, and when they hold projects to account against measurable outcomes.
Stay the course
The companies that will thrive in this environment are those that pivot away from AI as a machine for more and see it instead as an instrument for better. That is where platforms like Decidr come into view. We provide an organisational layer in which AI is not just spitting out words but orchestrating decisions, aligning intelligence with business need. In such a model, workslop becomes less likely, not more.
Every revolution produces its fair share of rubbish. The steam engine produced smog, the early internet produced spam and now, generative AI has given us workslop. But just as smog was eventually tackled by cleaner energy and spam by filters cleaned up the emails from Nigerian Princes, so too can workslop be curbed by design and discipline.
The danger is not that AI will ruin productivity forever. The danger is that we let its early misuse distract us from its longer horizon. Properly harnessed, AI can still elevate human judgement, sharpen organisational focus, and free workers from the very drudgery that makes work feel like a grind.
The flood is real, but so too is the opportunity to build a bigger boat.
