The evolution of AI has transformed how enterprises do business, but it has also brought with it an increasing volume of low-quality, inaccurate content and noise, otherwise known as AI “slop”.
For many, this is most visible in social media feeds that are now filled with AI-generated videos and images. While some of this content is harmless entertainment, the conversation becomes more serious when the same technology is applied in business environments where accuracy, accountability and trust are non-negotiables.
In a business setting, AI slop takes on a different form than how it appears on social media feeds. Rather than obviously artificial content, it often appears as polished or well-written, seemingly credible outputs that are built on unverifiable information. As enterprises accelerate AI adoption, the consequences of these unreliable outputs are becoming harder to ignore. AlphaSense data highlights this growing concern, with mentions of AI slop across media coverage increasing by 20% between Q4 2025 and Q1 2026.
One of the key limitations of many general-purpose AI tools is their reliance on publicly available information. Business decisions often require access to proprietary sources that are unavailable to broad consumer models. As AI becomes more deeply embedded in high-stakes decision-making, a distinction is emerging between systems that provide quick, generic responses and those designed to deliver specialised, domain-specific intelligence.
At the same time, the rapid growth of AI-generated content is creating a cycle in which poor-quality information feeds future AI outputs, further reducing overall quality. Organisations that prioritise trusted data sources, transparency and domain expertise will be best positioned to break this pattern.
AI is only as reliable as the data behind it
General AI tools are systems that can understand, learn, and reason across many domains. These tools focus on fundamental capabilities like reasoning and learning, making them helpful assistants in streamlining workflow processes and increasing productivity. However, the generic web data used to train these models can produce equally as generic outputs, resulting in AI slop when used at scale.
Companies are now embedding these models into agentic workflows, where AI systems autonomously interact with enterprise data, APIs, and external applications to complete end-to-end tasks. This restricts enterprises to data that is often difficult or impossible to substantiate, resulting in aesthetic outputs that lack value and trustworthiness.
General AI tool use fosters several challenges that can contribute to AI slop in organisations. AI-generated content can quickly overwhelm internal knowledge bases, making information harder to verify and trust. At the same time, errors can be amplified through feedback loops, as inaccurate or low-quality content is repeatedly recycled and reinforced, cluttering systems with generic and often irrelevant information. While AI can significantly accelerate content creation and improve efficiency, it can also bypass many of the quality-control checks that would traditionally be built into the process, increasing the risk of inaccuracies going unnoticed.
In contrast, domain-specific AI focuses on depth over breadth, designing systems that solve narrowly defined, high-value problems within a specific domain. Rather than relying on broad, publicly available training data, these tools are built on proprietary and curated datasets, which enables them to produce much higher-quality and contextually relevant outputs. By anchoring AI in domain expertise rather than generic data, these specialised tools deliver insights that meet the precision and accountability requirements of enterprise use. Features like transparent sourcing, citation-backed outputs, and deeper research capabilities ensure that information is traceable, validated, and trusted.
This level of precision isn’t optional for enterprises operating at scale and within highly regulated environments such as financial services and healthcare, where the risks of relying on AI slop can result in significant downstream business threats like loss of credibility or regulatory fines.
The danger of inaccuracies in AI feedback loops
Poor inputs and outputs can influence responses over time, further exacerbating the AI slop challenge and creating a negative feedback loop.
For example, in environments using general models, AI-generated content has created a self-reinforcing cycle of declining quality. AI systems often learn from the internal information they have access to, including past outputs, and they often absorb and reproduce existing errors. If these materials include errors, the errors are recycled, and generic patterns reinforce themselves. It’s quite simple: if the underlying training data is low-quality, the model’s outputs will reflect that same standard.
Original thinking also declines when humans are less involved. And as processes continue, the loop repeats, and the quality of outputs continues to decrease exponentially. AI tools often do not prioritise verification for accuracy, nor do they carefully consider word choice when filling gaps in content. Thus, AI slop clouds content with artificial noise at an ever-growing pace. This trend is showing up in a very visible way across major company communications. A recent articleexplored the use of AI’s signature “It’s not this—it’s that” format, which has shown up in a shockingly large number of external corporate communications channels. According to data from AlphaSense, use of this verbiage nearly doubled in both 2024 and 2025.
Beyond communications, the risk of AI slop can impact businesses in significant ways across company functions, particularly as AI becomes an increasingly prevalent workstream in decision-making functions. If crucial decision-making is influenced by inaccurate, or even redundant, information, teams cannot and should not be confident in their outputs.
The difference between insights and outputs
Businesses that depend too heavily on general-purpose tools, without the right data domain-specific context, risk introducing more “slop” into their operations rather than creating value. In practice, this means more time is spent manually double checking outputs that were intended to improve efficiency in the first place.
Domain-specific AI provides a more reliable route forward, but this technology alone is not enough. Enterprises need clear governance, human oversight, robust verification processes and regular evaluation of how AI is being used across workflows. The right approach will vary by organisation, use case and risk profile.
Those that strike the right balance will be better equipped to make faster, more informed decisions. As AI continues to reshape business decision-making, the gap between credible insight and generic output will only become more important.
Chris Ackerson
Chris Ackerson is SVP Product at AlphaSense. An experienced product leader, Chris is focused on applying innovations in information retrieval, natural language processing, deep learning and recommendation systems to enable amazing search and discovery experiences.
