As the use of artificial intelligence in the workplace advances, some fear it may have a negative impact on meaningful work, leading to the layoff of knowledge workers. Bert Verhoeven and Vishal Rana write that it doesn’t have to be like that. They call on leaders and managers to embrace innovation while reimagining tasks and roles, dividing work into components in which AI bolsters efficiency and quality, with elements that amplify innovative value through genuine, purposeful human interactions.
Artificial intelligence (AI) is steadily transforming the landscape of knowledge work, from rudimentary to intellectual tasks, stirring a medley of reactions regarding its impact on deeply personal, meaningful work. We perceive meaningful work as a complex and subjective concept, often embodying tasks that align with our core values, interests, and ethical considerations. This type of work heightens our sense of purpose and self-worth and fosters a connection with a greater cause. The COVID-19 pandemic’s devastating impact has further intensified our questioning of the meaning and purpose of life and, by extension, the significance of our work. The pandemic has given rise to phenomena such as ‘the great resignation‘ and ‘quiet quitting‘, illustrating a shift in our engagement with work.
Even before the pandemic, research was pointing towards a widespread lack of engagement in the workplace. According to Gallup, this disengagement was costing the world $7.8 trillion in lost productivity. As AI forges ahead, an intriguing question arises: may AI cause a crisis in many organisations by replacing meaningful work, leading to devastating lay-offs of knowledge workers? Or is it inviting leaders and managers to embrace innovation while reimagining tasks and roles? This could be achieved by dividing work into components in which AI bolsters efficiency and quality, and elements that amplify innovative value through genuine, purposeful human interactions. In sum, can AI help us discover meaningful work?
The incorporation of AI into writing tasks, as evidenced by the impending integration of large language models like Bard and ChatGPT with platforms like Google Docs and MS Office, brings profound implications. This technology is drastically altering our engagement with written communication in myriad sectors, including education and business. The allure of instantaneous efficiency through AI-assisted drafts is compelling, yet the perils of unchecked AI outcomes are equally significant. As AI’s prowess in rule-based capabilities grows, embracing areas like grammar, spelling, syntax, and flow, there is expectancy that future versions will master even more nuanced, soft factors such as context, tone, and emotion.
This rapid development illuminates the blurring boundary between AI and the domain of meaningful work. AI is not only capable of writing essays, policies and curriculum, but can also do data analysis and provide feedback, although not without flaws (yet). Nevertheless it may soon be good enough to assist with tasks like reflection, providing feedback and performance reviews. Although it may not fully grasp and convey the complex human experience, it’s noteworthy that many human-led performance reviews similarly falter in the empathy department. Is this the end of meaningful human writing and reflection? Reframing our perspective could prove beneficial.
As AI integrates into more tools and services, we can choose to perceive it as a collaborative ally rather than a threat. AI has already showcased its effectiveness as a research aid and a partner in creative and critical thinking, challenging us to scrutinise and question our cognitive patterns. This synergetic relationship with AI might fuel an evolution in our skills, particularly in writing, fostering growth and creativity. Nonetheless, we must confront the prospect of AI encroaching upon work that was meaningful when conducted by humans.
Given AI’s potential, which activities are at risk of becoming meaningless? This question calls for discerning exploration, bold leadership, and informed decision-making. To allow employees time for meaningful tasks, researchers have suggested that organisations identify and automate discrete tasks within work roles that employees prefer not to perform. However, with the advent of Generative AI, even preferred tasks that could be more efficiently accomplished by AI may need to be reassessed.
AI-enhanced vs human-centred
Are we on the brink of AI replacing personal work, or are we simply realising that much of what we deemed personal wasn’t truly personal to begin with? This conundrum leads to a new exploration in the workplace; an exploration focusing on reshaping roles to balance AI enhancements with inherently human elements. In this process of reshaping roles, the first critical step is to rethink our understanding of meaningful work. As AI becomes an integral part of our professional landscape, we need to identify roles within the organisation that could be enhanced with AI. Routine, repetitive, and data-intensive tasks, which were traditionally considered ‘human’, are often the best candidates for automation. But does that mean we are ceding our work to AI? Not necessarily.
Understanding the capabilities and limitations of AI is an important first step. Various AI technologies such as machine learning, natural language processing, and robotics process automation offer different possibilities. Then, the key lies in distinguishing between tasks and roles that can be enhanced by AI and those that intrinsically require human touch, emotion, and understanding. Visualising future roles involves creating new roles or reshaping existing ones to better fit an AI-assisted workflow. The design of human-AI interaction in these reshaped roles is critical. It seems to us that this design should aim at AI (1) automating routine tasks, (2) providing actionable insights, (3) supporting the creative process, (4) aiding in data analysis and decision-making. Concurrently, human tasks should be redesigned around (1) providing contextual understanding, (2) editing the AI output, (3) facilitating creativity and innovation to achieve impact, and (3) deploying interpersonal skills leading to collaboration, synergy, and serendipity – components that, despite significant advancements, AI has not yet been able to fully replicate.
Knowledge worker/AI relationship
Investment in the appropriate AI technology aligns with the vision of future roles. This might involve purchasing AI tools, developing custom AI solutions or even collaborating with AI companies. Following this, higher education must rethink how to add value to the new knowledge worker-AI relationship. Organisations must support training programs for managers and employees to understand how AI can enhance their roles, the benefits and limitations of AI and its ethical considerations. Once this new structure is in place, a pilot program for the AI-enhanced roles can be rolled out. Gathering feedback, monitoring the program’s effectiveness, and iterating based on these findings would further optimise this system. The impact of the AI-enhanced roles on the organisation and the employees must then be evaluated. This assessment includes analysing the effects on work meaning, employee satisfaction, AI efficiency/productivity and any unforeseen issues or benefits. If the pilot program proves successful, the AI-enhanced roles can be scaled up across the organisation. However, this journey does not end here. An ongoing dialogue about AI, meaningful work, and their role in the organisation should be maintained. This includes regular discussions about AI developments, ethical considerations, and the evolving nature of AI-enhanced roles.
Ongoing exploration of human-AI success
The ultimate objective should be to reshape roles and tasks to gain AI efficiencies while preserving and discovering more meaningful, personal human interaction. This is not about choosing between AI and human effort potentially ending up in millions of layoffs, but about learning to leverage both for more significant, fulfilling outcomes, creating value for organisations and society. This transition will require a potential transformation of work culture, organisational structure, and leadership model, but the promise of more harmonious, meaningful work combined with productivity gains is well worth the effort.