Have you ever read a piece of work, suspected generative AI was involved, and thought the result felt… sloppy? The term “workslop”, used to describe low-quality, AI-generated content that lacks substance, has quickly entered the lexicon. Many teams are now navigating unnecessarily convoluted emails, low-context reports, inaccurate summaries and surface-level content, to name a few. But a bigger challenge emerges when this same low-quality AI-generated content is published externally. Over time, it erodes brand voice and creates a sea of sameness.
But the issue is that core content doesn’t necessarily mean better content. Generative AI has made production effortless, but without discipline and human creativity guiding it, scale can come at the expense of clarity and differentiation.
And this is more than just a quality-control problem. It’s often a signal that organisations are adopting AI tools faster than they are adapting the culture, governance and working practices around them. It’s not the technology itself, but how it is being operationalised. What’s often missing is a shared understanding of how AI should fit into everyday workflows and expectations.
A closer look at marketing
Marketing teams are on the front line of this shift. They’re under constant pressure to increase output, with more channels, more formats and more personalisation, so it’s no surprise AI adoption is accelerating here.
But without disciplined content architecture and governance, AI risks creating what some are calling a ‘workslop economy’; more output, but less value. Organisations also need shared norms around how AI-generated work should be questioned, refined and improved before it reaches customers or colleagues.
Teams want to move fast (40% of marketers say ‘fast and efficient execution’ defines success) while also delivering quality content (42% say it is ‘high content quality and consistency’). These stats from a global report conducted by Contentful and Atlantic Insights, also found that nearly half (48%) of marketers are looking to AI-powered content tools to help them find the middle ground.
The tension is understandable. Marketing has always been a function under pressure to do more with less and AI appears to offer a release valve, promising speed and scale.
As AI tools become faster and more capable, the value of human judgement, context and editorial discipline only increases. The ability to combine human creativity with AI-driven insights is becoming essential to producing and scaling ideas with measurable impact. The marketing skills that matter most today are data analysis and interpretation (46%) and digital experience design (40%), followed by personalisation strategy (37%) and writing for AI tools (37%).
This shift requires a reframing of what good work looks like. It is no longer about who can generate the most content the fastest, but who can apply judgement, context and relevance to ensure that every piece of output serves a clear strategic purpose. Otherwise, the risk is that teams become curators of AI-generated drafts rather than creators of differentiated ideas.
The organisational cost of ‘workslop’
Beyond marketing, the ‘workslop economy’ carries wider organisational implications. Leaders may assume productivity is increasing because there is more visible output. In reality, this is often a cultural signal. When organisations reward visible activity rather than thoughtful outcomes, AI simply amplifies the behaviour already present. Meanwhile, employees quietly absorb the hidden tax of reviewing, editing, clarifying and sense-checking AI-generated material, in addition to maintaining a new set of tools that require continuous inputs. If not managed thoughtfully, this can erode both efficiency and morale.
There is also a reputational risk. Sloppy AI-assisted content can introduce factual inaccuracies, generic phrasing, or inconsistent messaging that chips away at brand credibility. Customers and stakeholders may not always detect the use of AI, but they can detect when something feels off, such as language that’s overly verbose yet oddly vague, polished yet impersonal, confident yet lacking depth.
Crucially, the proliferation of ‘workslop’ can mask deeper strategic gaps. If teams rely on AI to fill in thinking that has not yet been done, the technology amplifies ambiguity rather than resolving it. In this sense, AI becomes a mirror, reflecting the clarity (or lack thereof) within an organisation’s strategy and decision-making processes.
Key steps to pivot away from the “workslop” trap
AI’s real enterprise advantage lies in both acceleration and augmentation. Structured content, clear operating models and strong data foundations are what separate meaningful transformation from short-term experimentation. To avoid the ‘workslop’ trap, organisations need to move beyond ad hoc usage and towards intentional operations.
First, establish clear content architecture. AI performs best when it operates within well-defined frameworks, which includes brand guidelines, tone-of-voice principles, audience personas and approved messaging pillars. Without this scaffolding, outputs will default to generic patterns drawn from the broadest possible training data. With it, AI can become a powerful assistant that reinforces, rather than dilutes, brand distinctiveness.
Second, embed human-in-the-loop workflows. Rather than relying on individuals to apply judgement inconsistently, organisations should design automated workflows that define exactly when and where human review is required. This ensures that AI-generated outputs are systematically validated before reaching customers or stakeholders, particularly for high-impact or high-risk content. Making human oversight a built-in step helps maintain quality at scale.
Third, implement auditability by default. Organisations need visibility into how AI is being used across the content lifecycle: who generated content, where AI was applied, what changes were made and who approved the final output. This level of transparency is critical for accountability, compliance and continuous improvement. It also enables senior leaders to move beyond assumptions and understand how AI is actually being operationalised across teams.
Fourth, invest in AI literacy, as well as AI tools. Employees need to understand how generative models work, where they are strong and where they are prone to hallucination. Training should focus on prompt design, critical evaluation of outputs and ethical considerations. Building literacy also builds confidence, allowing teams to challenge AI outputs rather than accept them at face value. When people feel confident challenging AI-generated suggestions, they are more likely to use the technology as a collaborator rather than a crutch.
Fifth, align incentives with impact. Incentives shape behaviour and behaviour ultimately shapes culture. If performance metrics reward speed and volume alone, ‘workslop’ will inevitably proliferate. Instead, organisations should measure success through outcomes such as engagement quality, customer relevance, strategic clarity and measurable business impact. This encourages teams to use AI selectively and thoughtfully, rather than indiscriminately.
Finally, organisations must be explicit about how AI is expected to reshape each role. More senior leaders, for instance, may need to spend more time critically evaluating AI-assisted outputs, while junior team members may take greater responsibility for drafting and iterating with these tools. Clearly defined, AI-related responsibilities help distribute accountability across the team and ensure that low-quality outputs do not slip through the cracks.
Reclaiming substance in an AI-boosted workplace
The rise of the ‘workslop economy’ is not inevitable, but it is a real risk if organisations conflate technological adoption with meaningful transformation. Ultimately, the organisations that benefit most from AI will be those that double down on the uniquely human strengths of good judgement, curiosity, creativity and contextual understanding.
They will treat AI as a partner that enhances thinking. And they will recognise that the true competitive advantage lies not in producing more content, but in producing better, clearer and more purposeful work.
By Sara Sullivan, SVP of Solution Engineering
- People & Culture