Companies are increasingly adopting AI tools, which often leads to higher internal productivity but fails to translate into improved customer satisfaction or business value. Research indicates that while AI-active users may be up to 5 times more productive than their peers, this individual efficiency does not guarantee a return on investment.
The phenomenon, sometimes called the 'AI Productivity Paradox,' often results in teams working faster without achieving meaningful progress. Excessive AI-generated content, or 'workslop,' can even increase costs for recipients who must verify the output, creating an illusion of activity rather than tangible results.
To address this, a framework using a two-dimensional matrix can help evaluate AI initiatives. The axes measure internal operational efficiency and the value created for customers. Initiatives are categorized into four quadrants: Transformation, Operational Efficiency, Customer Value, and Low Impact.
For example, automating customer feedback analysis scores high on both axes, while AI-based backlog scoring often provides little value. The author has published a template for this matrix on GitHub to assist companies in focusing their AI efforts on goals that drive actual growth rather than superficial metrics.
