AI and Cognitive Fatigue: A Challenge for Businesses

Artificial Intelligence (AI) is often seen as a means to enhance productivity at work. However, a study from the Harvard Business Review highlights a less glamorous aspect of this technology: AI does not reduce the workload; it intensifies it. Indeed, instead of decreasing tasks, AI seems to multiply them, which can lead to increased cognitive fatigue and unsustainable working hours for employees.

Ankur Anand, Chief Information Officer at Harvey Nash, emphasizes the importance of understanding how to use AI effectively to avoid this cognitive overload. He warns against unrealistic expectations regarding the productivity boost promised by AI. "This focus will help reduce the noise around the workload that AI creates," he stated, suggesting that many people have unrealistic expectations about the productivity increase that AI could offer.

Finding a Balance

To make the most of AI at work, it is essential to find an effective balance between quickly executing tasks and producing high-quality work. Experts recommend focusing on three key areas: tools, guidelines, and outcomes.

Limit Your Toolset

Alex Read, Senior Product Manager for Data at energy provider EDF UK, stated that the best way for professionals to benefit from AI is to concentrate on tools that help them create value in their roles. While there are thousands of potential AI-powered services on the market, Read emphasized that sensible professionals limit their horizons. For example, in his own role, he focuses on how AI can help him build a data platform and update information accurately, efficiently, and productively: "Anything outside of that framework is noise to me."

Nick Pearson, Chief Information Officer at technology specialist Ricoh Europe, also highlighted the importance of carefully considering how an AI tool can help produce value in a role. "If you think about the term 'generative AI,' the technology is very good, by definition, at generating outputs," he said. However, quantity does not necessarily mean quality. Pearson suggested that it is important to focus on AI's blind spots, especially since most models are trained on pre-existing content.

Work According to Guidelines

HBR's research revealed that an initial spike in productivity when adopting AI can lead to a decline in work quality, turnover, and other issues, as people work harder rather than smarter. To address this problem, HBR stated that companies must adopt an "AI practice," or a set of standards and norms around the use of AI that help professionals ensure they are using AI in a constrained yet productive manner.

Within EDF UK, Read is part of an AI Center of Excellence that helps define policies for the effective use of AI across the organization. This group includes other technology representatives, such as a Senior AI Lead and a Principal Software Engineer.

Refine Your Outcomes

Even when tools are evaluated and deemed acceptable, there can still be an over-reliance on AI outputs. Worse still, some professionals may become overwhelmed by the information they receive, leading to increased stress and diminished benefits.

Louise Newbury-Smith, Head of Zoom in the UK and Ireland, stated that one way to ensure your outputs are limited is to focus on prompts. "Use simple modifications to be specific, like 'Give me the three things with the biggest impact.' This approach should guide your prompt, rather than saying, 'Give me everything you know about this topic.'"

The bottom line for professionals is that effective applications of AI depend on your active involvement. Think before using AI, and think again before sharing your results within the organization.