Generative AI talent: Your future turnover concern

Frequent users of generation AI are highly sought after and valuable, often at risk of leaving their current positions. Recent studies offer insights on how to entice them, maintain their involvement, and encourage their retention.

Workers who frequently utilize generation AI tend to exhibit higher levels of productivity and efficiency, yet they also face a heightened risk of being drawn to rival companies. In this installment of The McKinsey Podcast, McKinsey’s talent experts Aaron De Smet and Brooke Weddle discuss with global editorial director Lucia Rahilly the primary desires of these employees and offer actionable strategies leaders can implement to ensure their satisfaction and commitment.

Generation AI is undoubtedly a powerful force with the capacity, arguably, to revolutionize the landscape of work. Despite being in its early stages, skills related to generation AI are already highly sought after across various industries. However, your recent study indicates that individuals proficient in generation AI are more prone to leaving their positions compared to those less adept with technology. Brooke, could you elaborate on this key discovery and explain why employers should take heed of it?

Individuals who possess a high level of proficiency in the skills necessary for interacting with and developing use cases for generation AI are currently more prone to leaving their positions. This discovery holds significance, especially considering that many companies are prioritizing internal upskilling and reskilling initiatives over acquiring new talent, particularly in the field of generation AI and other technological capabilities.

Just to provide context, what defines «gen AI talent»?

It’s crucial to recognize the various categories of generation AI workers, which we’ve classified into four types. Among these groups, 12 percent identified themselves as technical employees, actively involved in developing generation AI and next-generation artificial intelligence platforms, programs, and software. However, the majority of gen AI users don’t view themselves as technical workers. In fact, 88 percent of respondents in the largest group stated that they do not consider themselves technical employees.

Therefore, the majority of individuals who identify as generation AI talent utilize generation AI to enhance efficiency.

How would this type of non-technical usage manifest?

Consider this scenario: A corporate communications professional at a major corporation frequently drafts press releases and emails on behalf of senior executives. They often conduct interviews with CEOs scheduled to speak at town hall meetings or disseminate crucial messages. Crafting an initial speech from the leader’s ideas can be time-consuming, often spanning several hours. However, with the aid of generation AI, they can streamline this process, freeing up time to focus on other tasks such as refining messaging and adopting a more strategic approach to their work.

The Deloitte UK AI Institute conducted research on the prevailing applications of generation AI at present. Interestingly, the most frequently cited use cases for generation AI across various functions are in marketing and sales. This entails generating personalized marketing content, crafting technical documents, and deploying chatbots in service operations to address customer inquiries. While many assume that generation AI is predominantly utilized in highly technical scenarios, an examination of the frequency of these use cases reveals that it is often associated with, at times, mundane tasks.

Over fifty percent of generation AI workers, particularly those who identify themselves as creators and intensive users, pose a potential risk of leaving their current positions, posing a challenge for employers. Leaders aim not only to retain these individuals but also to ensure their ongoing productivity and engagement. Could you elaborate on the ramifications of disengagement for companies?

The implications are significant in two aspects. Firstly, employees possess immense potential to enhance productivity through the utilization of generation AI. However, when disengaged, their productivity notably diminishes, often halving. This decline stems from a combination of reduced work output and fewer working hours, as well as a decrease in the quality of their work correlating with decreased engagement.

Secondly, there’s the risk of these employees simply departing from the company. If they were proficient in their roles and highly skilled in utilizing generation AI, losing them means forfeiting valuable talent. In addition to the challenge of replacing them, if their departure is symptomatic of systemic issues within the organization, it could lead to further attrition among newly hired talent.

Where should leaders focus their efforts? What insights did the research provide regarding the priorities of generation AI talent in their daily work lives?

The research findings were unequivocal in this regard. Compensation isn’t the primary motivator. Instead, it’s about offering flexibility, providing meaningful work, fostering a supportive environment with dependable colleagues and teammates, and demonstrating a clear commitment to the health and well-being of employees.

I believe it’s misguided to assume that companies can shift away from prioritizing meaningful work and employee well-being. Prominent companies I collaborate with acknowledge the necessity of achieving greater productivity and outcomes while also comprehensively understanding what employees desire. They recognize that it’s not solely about financial incentives, but rather about addressing the holistic needs of their workforce.

«Compensation is not the driving factor here. It is about having flexibility, meaningful work, reliable and supportive colleagues and teammates, and a clear sense that the employer is focused on health and well-being.»

Brooke Weddle

How is it conceivable that compensation has declined in significance as a determining factor?

While compensation remains crucial, its impact is more straightforward. Many individuals wouldn’t consider a job unless it meets their minimum compensation expectations. However, once those requirements are met, offering higher pay doesn’t yield significant benefits. While compensation is arguably still the most critical factor in some regards, it operates in a binary manner compared to other factors. As previously discussed, fostering a sense of belonging within a supportive and collaborative community is paramount for talent retention and satisfaction.

As you hinted at, Brooke, flexibility has been a recurring theme in previous iterations of this research, especially during the pandemic. As we transition out of it, what has changed regarding the significance of flexibility and how it may be perceived now?

It’s more about having a heightened sense of autonomy and not solely focused on whether one is physically present in the office or not. Simplifying it to that extent hasn’t been entirely beneficial. Instead, it involves rethinking work schedules, considering collaborative versus independent work setups, and exploring new ways of working together.

I’ve found it fascinating to observe the shifts occurring within large traditional manufacturing companies as they strive to accommodate the needs of older talent and facilitate the entry of mothers into the workforce by addressing childcare concerns. This expansion of flexibility encompasses both tech and non-tech workers, signaling a broader shift in our approach to work arrangements.

It’s not solely about hybrid or remote work arrangements; it also encompasses work schedules and the degree of autonomy individuals have over them. During the pandemic, many individuals transitioned from having two peak periods in their day to three. This highlights the desire for setting boundaries and having control and autonomy over one’s schedule.

As generation AI assumes responsibility for mundane tasks, it shifts the focus towards tasks that require a more human touch. Individuals crave acknowledgment of their humanity. Thus, apart from flexibility, autonomy, and well-being, one of the significant gaps between employers and generation AI talent – and talent in general – in terms of priorities is the need to feel appreciated and valued by leaders and the organization.

«One of the other biggest disconnects between employers and gen AI talent – and, frankly, talent broadly – in terms of what matters, is feeling valued by leaders and by the organization.»

Aaron De Smet

How receptive do you find your clients to implementing this level of autonomy and flexibility?

It varies. Companies that have successfully navigated this challenge typically have a clear alignment between their business objectives and the work environment they create to support them. Challenges arise when companies insist on a return to office without clearly demonstrating how it contributes to driving performance outcomes. The majority of employers are actively exploring ways to make work environments more accommodating to employee needs, while also considering emerging concerns about potential cultural decline, particularly among younger talent.

For instance, they are assessing whether newcomers are receiving the necessary mentorship. Some initiatives to bring employees back to the office are well-intentioned efforts to address these concerns while acknowledging the importance of providing independence and autonomy in work practices.

Let’s say I’m an employer seeking to recruit generation AI workers who are considering leaving their current roles. What factors tend to appeal most to individuals when considering a new employer?

The factors that significantly influence individuals include having empathetic leaders, engaging in meaningful work, offering flexible arrangements, and fostering a sense of inclusivity and community. These elements play a crucial role in attracting and retaining this type of talent.

What’s your interpretation of the shift where flexibility, once the top reason cited by employees for staying in a job, has now dropped to around the fifth or sixth position in this ranking of factors?

Individuals seek environments where they feel valued, encouraged, motivated, and connected. Without these elements, the significance of flexibility diminishes. Flexibility is increasingly seen as a baseline expectation.

There’s a considerable amount of coordination and motivation needed to inspire teams of human employees to achieve exceptional results. However, when tasks transition from being handled by junior-level employees to AI, this dynamic changes significantly.

For a considerable duration, there has been a consistent understanding of what could be termed as «hard work,» typically involving tasks such as paying close attention, taking notes, summarizing information, and distributing it among colleagues or executives. This allowed managers to gauge whether individuals were fulfilling their responsibilities. It served as evidence that employees were dedicating time to tasks such as research, summarization, synthesis, and ultimately leveraging the information productively.

With the advent of generation AI, the concept of hard work undergoes reevaluation. If a colleague can efficiently accomplish tasks that would have previously taken hours using gen AI, does that still qualify as hard work? When contemplating performance management, it’s crucial to redefine hard work, devise alternative methods for measuring and managing it, and consider whether the focus should shift towards the approach (the «how») rather than the specific outcomes (the «what»).

The key shift lies in how we evaluate productivity. In the past, I relied on output metrics, but now that AI can swiftly handle routine tasks, traditional measures like hours worked or lines of code written have become less meaningful. Instead, I need to prioritize assessing the quality and impact of the work.

Consider the work of a songwriter. If they craft an exceptional song in just an hour, would you critique them by saying, «You only produced one song, and it took only an hour?» However, if that song becomes a hit, you’d likely view it more favorably than someone who spent countless hours creating 100 songs that don’t resonate with anyone. We struggle with managing performance when it comes to tasks that are inherently ambiguous. Conversely, AI is adept at taking over tasks that are straightforward to measure, observe, and quantify. It’s an entirely different dynamic, and we’re still grappling with finding definitive answers to these questions.

Do you have any insights into how generation AI might influence our perceptions of spans and layers within organizational hierarchies?

In many instances, I believe it won’t bring about significant changes. However, for those with extensive spans primarily due to routine tasks, there could be shifts, as these routines are now being handled by AI, analytics, robots, and machine learning.

Leaders are facing a situation where the employees most crucial to retain are also the most prone to departure. If employers could implement one or two strategies now to improve the chances of retaining their top workers and keeping them productive, what would those strategies entail?

To start, establish a nurturing work culture that prioritizes productivity, performance, sustainable work habits, and employee well-being. Secondly, allocate resources towards developing leadership skills across all levels, viewing every employee as a potential leader. This approach fosters a cohesive and supportive community, enabling individuals to thrive and sustain their work effectively. Through collaborative efforts and mutual support, employees can enhance both individual and collective productivity.

I’d like to add two points. Firstly, in our research, I was somewhat surprised by the significant number of non-users of gen AI. Companies should consider upskilling, reskilling, and expanding access to gen AI-based tools and applications wherever feasible. With the rapid advancements in this field, it doesn’t seem sustainable to have such a small proportion of heavy users and creators.

Secondly, it’s essential to approach change management holistically. Transitioning to using gen AI often results in time savings. Companies should consider how to guide employees in utilizing this newfound time effectively. It’s unlikely that individuals will naturally generate more productive outcomes without intentional guidance from managers and leaders. Therefore, it’s crucial to thoughtfully consider how work processes are structured and ensure that saved time translates into productive use.

As I delved into this research, I was taken aback by the overwhelmingly positive response to gen AI from the respondents, despite the fact that over two-thirds of them haven’t incorporated it into their work yet. I found it remarkable that only 4 percent of respondents expressed concerns about job displacement.

Respondents are discovering that gen AI is liberating them to engage in work they genuinely enjoy. As they immerse themselves in these tasks, they’re realizing the significant value they can contribute, leading them to conclude, «My job isn’t at risk; in fact, I’m enhancing its value.»

I believe there will indeed be instances of displacement, although many of these cases won’t involve AI users directly. Consider a call center that replaces human agents with AI systems capable of addressing 90 percent of callers’ inquiries. The displaced humans who previously handled these calls aren’t the users of AI. This type of displacement remains a valid concern.

However, we’re leveraging AI to assist users in performing tasks they’re already engaged in. What these individuals are discovering is that AI isn’t a threat to their roles; rather, it enables them to utilize their skills more effectively. By automating simpler tasks that weren’t particularly value-added, AI allows them to focus on tasks that make the most significant contribution.

There are broader issues that require attention. This is why I emphasize the importance of change management and taking a comprehensive approach to understanding the human aspect of integrating gen AI into the workplace. Work dynamics are evolving, and it’s crucial for us to adapt and learn as we progress.

In conclusion, the conversations have shed light on the transformative impact of generation AI on the workforce and organizational dynamics. From redefining productivity metrics to reimagining leadership and change management strategies, the advent of gen AI presents both opportunities and challenges for employers and employees alike. As workplaces evolve, it’s essential for leaders to prioritize factors such as flexibility, meaningful work, and employee well-being to attract and retain top talent. Moreover, fostering a culture of adaptability and continuous learning is crucial for navigating the changing landscape of work. By embracing these principles and remaining open to innovation, organizations can effectively harness the potential of generation AI while ensuring a positive and sustainable future for their workforce.