Generative AI: Productivity at the Cost of Motivation?

The research

In a landmark series of studies conducted by Yukun Liu, Suqing Wu, Mengqi Ruan, Siyu Chen and Xiao-Yun Xie published in Harvard Business Review, more than 3,500 participants completed real-world professional tasks with or without the aid of GenAI. The results were unambiguous. Tasks completed with GenAI — such as writing emails, Facebook posts, and performance reviews — were more polished, empathetic, and analytical than those done alone. Participants reported higher quality and efficiency when using GenAI, with language that was more engaging and personable [1].

However, once participants switched to a new task without AI support, a pattern emerged. Their intrinsic motivation dropped by an average of 11%, and boredom spiked by 20% [2]. This wasn’t just a fluke. Across multiple studies, the psychological dip was consistent: GenAI made tasks easier, but that very ease came at the expense of engagement when the technology was removed.

Why does this happen? According to the researchers, it’s about control. Tasks involving GenAI reduce the cognitive challenge that often makes work fulfilling — things like critical thinking, problem solving, and creative synthesis. When these are outsourced to an algorithm, humans can begin to feel disconnected from the output. They’re no longer the primary agents of their own work, and that loss of autonomy chips away at intrinsic motivation.

Boredom as a warning sign

This pattern is not just a curiosity — it’s a red flag for the future of work. Boredom isn’t benign. It’s often a precursor to disengagement, dissatisfaction, and burnout. A marketing professional, for example, who regularly uses GenAI to generate campaign ideas may find those ideas faster and more refined. But over time, they may also find themselves disengaged from the creative process itself, missing out on the personal satisfaction of seeing an idea through from conception to completion [3].

As GenAI becomes a fixture of knowledge work, this trend may become more widespread. If AI takes on the “best bits” of cognitively demanding tasks, what is left for humans? For some workers, this may mean less growth and fewer opportunities for meaningful development. The work becomes quicker, but also flatter — functionally efficient, but emotionally hollow.

The illusion of mastery

A less obvious, but equally significant risk lies in the illusion of competence that GenAI can create. Because these tools generate smooth, confident outputs, users may mistake fluency for genuine mastery. The long-term result? A workforce that appears productive but lacks the resilience, adaptability, and critical faculties needed to thrive in more complex, high-stakes situations.

These challenges are especially pronounced among younger employees. Gen Z, now entering the workforce, has grown up with AI technologies. While this digital fluency gives them a head start in using GenAI tools, it may also be undermining foundational skills like critical thinking, deep focus, and independent problem-solving.

A study by Microsoft and Carnegie Mellon found that the more Gen Z workers relied on AI for support, the less critical thinking they demonstrated [4]. Maria Gafforio of Canvas8 noted that AI is “weakening the attention spans of Gen Z workers even more [and their] ability to do ‘deep work’” [5]. Because many of them haven’t yet learned how to perform certain tasks independently, they may be ill-equipped to evaluate or improve upon AI-generated work.

This creates a dangerous loop. As AI fills in cognitive gaps, younger workers may miss the opportunity to master core competencies. Over time, this could result in a generation of employees who can use AI effectively, but struggle to build deep expertise, troubleshoot problems, or understand systems holistically.

Productivity platitudes fall short

Many business leaders respond to the rise of GenAI with a familiar refrain: “Now we can all focus on higher value-added tasks.” But as Chris Koch of SAP points out, this is often an empty platitude. Most companies don’t have a stockpile of “higher value” tasks sitting on a shelf, waiting to be done. If they did, someone would already be doing them [6].

What’s more, the time saved by GenAI is often minimal on an individual level. A Microsoft study found that GenAI tools save users an average of 14 minutes per day. That’s helpful — but not transformative. Turning those incremental savings into meaningful work requires conscious effort. Otherwise, the time simply vanishes into unproductive pockets of the workday [7].

Koch argues that leaders must rethink how they structure work around AI. They should ask where GenAI can offer the biggest time savings — not just faster email writing, but tasks like processing complex spreadsheets or reconciling invoices. These savings, if pooled effectively, can contribute to broader organisational goals, like reducing external contractor costs or accelerating project timelines [8].

Reclaiming motivation

To preserve both performance and engagement, organisations need to design AI integration thoughtfully. GenAI shouldn’t be treated as a shortcut that replaces human input wholesale. Rather, it should be a tool that supports and amplifies it.

One approach is to blend AI and human contributions more deliberately. For example, GenAI might draft the skeleton of a performance review, but it is up to the manager to refine the language with personalised insights. Similarly, while AI can be useful for generating ideas or structure, human teams should be encouraged to challenge, reshape and deepen the output — thereby reclaiming their agency in the creative process.

Another helpful strategy is to deliberately structure workflows that alternate between AI-assisted and autonomous tasks. Starting the day with solo, cognitively rich activities and moving to AI-enabled tasks later can help balance stimulation and efficiency. Moreover, making AI’s role in the work process transparent — explaining clearly how it complements human input — can preserve employees’ sense of ownership and contribution.

Finally, organisations must invest in AI literacy. This means more than knowing how to prompt a chatbot. It involves developing the judgement to critique, improve, and selectively override AI output. Workshops, coaching, and scenario-based training can equip employees to use GenAI not as a crutch, but as a creative partner.

The human advantage

AI-enabled employees are not just more efficient, they can also be more engaged, innovative, and fulfilled, provided the right conditions are met. William Arruda, writing in Forbes, notes that “AI doesn’t replace human employees; it enhances their capabilities” [9]. With AI handling routine processes, employees can focus on strategic initiatives and creative problem-solving, areas where human judgement and insight remain indispensable.

Companies that recognise this opportunity will gain a strategic advantage. According to McKinsey, organisations using AI have seen productivity boosts of up to 40% in certain functions [10]. But the gains aren’t only in output. AI can improve work-life balance, job satisfaction, and even employees’ sense of belonging [11].

As PwC’s Chief People Officer Ewan Clarkson put it: “Companies that successfully integrate AI into their workforce will unlock unprecedented levels of productivity, innovation, and employee satisfaction…The secret to success requires looking at the interconnection of three areas: business, technology, and people. To innovate and thrive in a rapidly evolving environment, organizations can drive productivity, growth, and transformation by harnessing the power of disruptive technologies, while ensuring that the human element remains at the forefront.” [12].

Generative AI: Productivity at the cost of motivation?

AI is not just a tool — it’s a force reshaping the meaning of work. It promises speed, scale, and precision. But it also threatens to deskill, demotivate, and disengage, especially if used without thought for the human experience. To build a sustainable future of work, companies must adopt a human-led, technology-powered model. That means preserving opportunities for deep thinking, rewarding originality over replication, and ensuring workers feel a sense of ownership over their tasks. GenAI can elevate human performance. But only if we remember what it means to be human in the first place.

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