By Jim Benton, CEO, Adapt
A study has revealed that more than 40% of Canadian employees have received ‘work slop’ caused by low-quality, AI-generated content. As a result, it is estimated that each instance of work slop takes around two hours to correct, resulting in millions of dollars in lost productivity.1
It has been reported that as of mid-2026, approximately 78% of global businesses are using AI in at least one business function.2 Despite this, studies show that 62% to over 80% of workers lack confidence or training in AI, with many reporting they don’t have the skills to use the tool in their daily tasks, which can lead to errors and work slop.3
What is AI work slop & what is the cost to Canadian SMEs?
Many workers are using AI for daily tasks such as generating reports, drafting product plans, or writing code more efficiently. However, if used incorrectly, workers can produce low-quality, AI-generated content that may look correct at first glance, but after closer inspection, is inaccurate and requires hours of manual quality control.
Lost productivity: When AI is used haphazardly, it can lead to hours of corrections, which costs time and money. AI should be used with employee oversight to support projects, not be used to replace human judgement and taste.
Brand reputation: If an external stakeholder receives work slop that hasn’t been identified by your team, it can lead to fractured relationships. Clients and customers that are given rushed and incorrect work will likely no longer want to continue to partner with your business.
Personal progression: While work slop can affect a business overall, it can also sink personal career growth. Employees feel pressure to use AI tools to advance in their company. However, if they are not trained in avoiding work slop, using an AI agent can have the opposite impact on their career.
How can SMEs prevent work slop?
Make training a key objective: Without appropriate training and guidance using AI tools, errors will occur. Business owners should ensure that employees are trained in understanding AI limitations so they do not use agents in a way that produces work slop.
Encourage team feedback: Team collaboration with AI prevents work slop by allowing feedback at every stage of generating work. Instead of employees producing work with AI in a silo, they can rely on coworkers to help guide what they make with AI, from idea to finished result.
Create manageable workloads: Workers who are unable to manage their workloads will often complete tasks quickly using AI, which can cause work slop. To prevent this, promote quality over quantity throughout the business and ensure that unmanageable workloads don’t hinder this ethos.
Introduce a review process: AI-generated work should never be presented to senior stakeholders or clients unless checked by an experienced team member. Business owners should ensure that all AI-generated tasks are reviewed and fact-checked before being presented as the final version.
Encourage transparency: To reduce AI work slop, business owners should encourage workers to use AI to support and enhance tasks, not to complete them altogether. It’s also important for workers to be clear about how AI has supported the tasks they’re working on to make the reviewing process more efficient.
Create manageable workloads: Workers who are unable to manage their workloads will often complete tasks quickly using AI, which can cause work slop. To prevent this, promote quality over quantity throughout the business and ensure that unmanageable workloads don’t hinder this ethos.
How the ARC framework can help workers understand AI
Before employees can use AI responsibly, they need to understand what it actually does. In “AI for Startup Leaders,” we present a framework called A-R-C that captures the three core capabilities of AI:
- Agency: AI can work with tools, run code, and complete tasks on an employee’s behalf. For example, it can query data from your CRM, draft documents, or update a spreadsheet.
- Reasoning: AI can plan ahead and think through problems step-by-step. The latest models can break down complex challenges, consider multiple approaches, and work through logic chains.
- Context: AI can base its answers on the information you provide in each conversation. It can understand natural language, identify objects in photos, and parse data in spreadsheets.
The A-R-C model is crucial, as many workers use AI as an encyclopedia that produces facts, which isn’t what the software was created for. When employees use AI this way, work slop can occur. To avoid this, workers should be educated on the main capability of AI, which is reasoning over their business context and taking action.
When employees understand A-R-C, they approach AI outputs differently. They recognize that AI is best used to reason from their business context rather than look up answers. Instead, they use AI to speed up how they collaborate with other teams and improve efficiency.
Notes to the editor:
This article was written by Jim Benton, CEO at Adapt, the AI native company for businesses.
Adapt is an AI-powered agentic platform that connects business tools, answers questions with trusted data, and takes action on the user’s behalf. Ask anything about the business, get insights backed by real data, and automate workflows across the business’s entire tool stack.
Sources:
1 – HRReport
2 – McKinsey
Jim Benton is an entrepreneur and proven SaaS executive who led Chorus.ai to a $575M acquisition as CEO. Jim leads strategy and execution as CEO of Adapt.
