Combat AI cognitive fatigue with smarter, not faster, work habits

Staff using artificial intelligence can end up buried in more work rather than less, a reality that clashes with the common belief that AI simplifies workloads. Research from Harvard Business Review reveals that AI doesn’t ease tasks; it amplifies them, driving cognitive fatigue and stretching work hours unsustainably. While many assume AI frees employees for higher-value activities, HBR found that staff actually work faster and end up with expanded to-do lists, not relief.

Ankur Anand, group CIO at tech recruiter Harvey Nash, emphasizes that professionals must grasp how to use AI effectively and recognize its risks to avoid burnout. “That focus will help to reduce the noise around the workload that AI creates,” he told ZDNET, noting that many people hold unrealistic expectations about AI’s productivity boost. He added that organizations often push employees to demonstrate AI’s impact without proper support, creating pressure to prove themselves individually.

To strike a balance between speed and quality, experts recommend focusing on three core areas: tools, guidelines, and outputs.

First, limit your toolset. Alex Read, senior enterprise product manager for data at EDF UK, advises professionals to zero in on tools that directly produce value in their roles. With countless AI services available, sensible users narrow their focus. In his own work, Read concentrates on how AI helps build data platforms and update information accurately. “Anything outside of that scope is noise for me,” he said. Nick Pearson, CIO at Ricoh Europe, agrees: think carefully about how an AI tool helps you create value. He warns that AI can generate endless outputs, but quantity doesn’t equate to quality. “AI can’t inspire people, per se; it can’t naturally create something new, because it’s actually quite recursive,” Pearson explained. Human judgment remains crucial for ethical and capability decisions, he added.

Second, work to the guidelines. HBR’s research found that an initial productivity surge from AI can lead to lower-quality work and turnover if people work harder instead of smarter. Companies should adopt an “AI practice” with norms and standards that constrain AI use productively. At EDF UK, Read participates in an internal AI Center of Excellence that sets policy for safe, scalable adoption. This group evaluates tools for scalability, reusability, and security, ensuring no duplication. “All new tools and services related to AI will go through that hopper and funnel to understand scope and ensure the security, regulatory, and ethical side of things are understood,” Read said. He urges professionals to leverage existing organizational guidelines to foster appropriate tech use.

Third, refine your outputs. Even with vetted tools, overreliance on AI can lead to information overload and stress. Louise Newbury-Smith, head of UK&I at Zoom, recommends focusing on prompting: “Use simple amendments to be specific, such as ‘Give me the top three things with the biggest impact.'” This targeted approach beats asking for everything. She says success hinges on being smart about AI exploitation, with effectiveness coming from enablement and engagement. If a prompt yields too much, refine it until you get what you need. Bernhard Seiser, vice president of digital, data, and IT at AOP Health, stresses staying in the loop: “It doesn’t help the business if you get AI-generated emails that are many pages long, and then you need ChatGPT to summarize the text.” He concludes that while generative AI excels at certain tasks, “you need to use your brain.”