Workslop, ROI, and the true value of AI in business.
- Gabriel Casara
- 5 days ago
- 4 min read
AI projects only generate results when they are well-structured. The rest is just a workaround.
Gabriel Casara, CGO of BlueMetrics
AI-generated summary:
The article discusses the concept of the workslop, presented by the Harvard Business Review, and shows how it threatens not only productivity but also trust and collaboration in companies. The central criticism is clear: without strategy, solid data, and governance, AI tends to generate more problems than solutions. Based on the experience of over 200 delivered projects, BlueMetrics argues that the path to extracting real value from AI lies in the combination of robust data engineering, business acumen, and a proprietary method (blue4AI) that guarantees ROI, predictability, and measurable results.
In recent days, the term "workslop" has gained traction following an article in the Harvard Business Review. The word describes an increasingly common phenomenon: work produced by AI tools that appears sophisticated but is fundamentally generic, empty, and of little use. Lengthy reports, elegant presentations, or well-formatted analyses that, in practice, offer no substance and do not aid in decision-making.
The impacts of workslops go beyond rework. The HBR article shows that this type of output directly affects trust and collaboration among team members: those who receive empty content tend to see the sender as less creative, less trustworthy, and even less competent. Furthermore, there are significant financial losses, since each instance of a workslop can consume hours of rework and cost millions in wasted productivity when considered on an organizational scale. The problem, therefore, is not just one of operational efficiency, but also of culture and strategy.
A controversial study by the MIT Media Lab reinforces this concern: 95% of companies do not report a measurable return on their AI investments. It is important to note that this report has methodological limitations and is being debated in academia. Even so, it echoes a reality we have been observing: many AI initiatives fail not because of the technology itself, but because of how they are implemented.
The HBR article also points to ways to reduce the workslop problem. Among them are the role of leaders in modeling the intentional use of AI, defining clear quality standards, promoting a "pilot mindset" that combines initiative with optimism about AI's potential, and reinforcing the idea of creative collaboration. In short, AI should be treated as a tool to enhance results and not as a shortcut to skip reasoning or execution steps.
Our CTO, Fabiano Saffi, comments:
“Often, analyses generated by GenAI are beautiful but generic. The problem is that they don't answer critical business questions or indicate what should be done. Even worse is when those who generate this content can't review it and think it's good enough.”
This is the crux of the problem. AI doesn't replace strategy, it doesn't replace business knowledge, and it can't do all the work on its own. When used without a method, AI may seem productive, but it ends up creating rework, frustration, and hidden costs.
Another significant risk is relying on generic AI platforms that haven't been trained with language models tailored to an organization's context. In this scenario, responses can be decontextualized and, in even more serious cases, the AI can hallucinate, inventing facts or data that compromise strategic decisions. Common examples include market recommendations based on outdated information, analyses that ignore critical industry variables, or even the creation of unrealistic metrics that sound plausible but have no basis.
At BlueMetrics, our commitment is precisely this: to deliver AI solutions that generate concrete, measurable results in the short term. To achieve this, we combine structured data with advanced technologies such as GenAI and Machine Learning, always paying attention to the context, business strategy, and ROI of each client.
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What really works
At BlueMetrics, we've already delivered over 200 AI and data projects to more than 90 clients. What we've learned along this journey is simple: for AI to generate real value, each project needs to be very well structured.
One of our biggest differentiators is our expertise in data engineering: it's not enough to simply connect an AI platform to any database. We build pipelines that guarantee contextualized, reliable data, ready to feed intelligent solutions. This is what separates generic analyses from truly actionable recommendations.
Furthermore, strategy comes before technology. Thanks to our portfolio of solutions already delivered, we clearly understand the objectives of each project, the expected ROI, and the success indicators. This understanding prevents AI from becoming just an easy shortcut for generating empty content.
And we do all this through a proprietary method: blue4AI, a framework that ensures agile deliveries, with clear steps and consistent objectives, aligned with each client's strategy. This method is not a theoretical concept: it was created from our practical experience and today is one of the pillars that guarantees the quality and predictability of our projects.
Before the hype, look at the ROI.
Workslop is not just a conceptual fad. It's a warning that rushing to adopt AI without governance, without well-structured data, and without clear objectives can be costly.
Just as we saw in the past with cloud computing, many today view AI with suspicion. But we know that, when applied correctly, it transforms businesses and opens new avenues for growth.
The difference between hype and results lies in how the technology is implemented. Our experience shows that it's entirely possible to capture real value with AI, provided the project is thought out from start to finish: from data to strategy, from execution to ROI.
At BlueMetrics, we follow a simple principle: AI isn't about doing less work, it's about generating more value.
Gabriel Casara is CGO at BlueMetrics and believes in the value of intelligence, whether artificial or not.
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