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How Gen AI Can Make Work More Fulfilling


Enjoying work matters — a lot. Our earlier research has shown that employees who enjoy their work are about 50% less likely to look for a new job. But increasing this emotion is about more than free lunch and other perks. People work at work — and it is therefore critical for any effort to improve joy to be grounded in the day-to-day rhythms, routines, and tasks that employees spend their time on.

This is particularly important today, as companies look to integrate generative AI and other technologies into workflows. Instead of just pursuing productivity gains, organizations must also consider the impact these technologies have on employees’ enjoyment of work. Yes, if your rollout is successful, people will be able to work faster and better — but it should also enable them to spend more time on the parts of their jobs that make them happy and reduce the parts that feel like endless toil or cause overwhelming stress.

Our research shows that employees can only tolerate so much toil (defined as work they don’t enjoy) in their day-to-day roles. Beyond four hours a week, people start thinking about leaving their job. This doesn’t mean employees need to be in a state of ecstasy every moment they are working; we found that employees are unlikely to look for a new job if they spend at least 10 hours per week on tasks they really enjoy.

With those thresholds in mind, how can leaders use gen AI to move the needle?

To answer this question, we recently conducted a study of 522 administrative and HR professionals at BCG, in eight countries. As part of this study, participants were offered a portfolio of gen AI tools to help with their day-to-day work, as well as access to training materials to help them use these tools. (The technology and training were delivered differently for our control and experiment groups, which we’ll discuss below.) Our early findings suggest practical ways that companies can encourage adoption, enhance enjoyment, and decrease toil.

Recognize that Managers Matter

The true catalyst for driving impactful gen AI adoption lies in the leadership of managers. Managers are essential in navigating change and enabling teams to shift their ways of working to effectively embed new technologies into daily workflows.

At the start of the experiment, we did a baseline survey to understand current adoption rates, and the impact the technology was having on employees’ effectiveness and joy at work. When we looked at adoption at the team level (we defined a team as individuals working directly for the same manager), we found a significant difference in gen AI adoption: The top quartile of teams had adoption rates that were 350% higher than teams in the bottom quartile. We also found that those teams with higher adoption rates also reported higher levels of effectiveness and joy.

What explained these differences? Because the teams were similar in makeup — individuals had similar roles with similar backgrounds — we took a closer look at manager attitudes and behaviors.

Managers use the technology themselves.

Managers in the top quartile had spent time experimenting with and using the technology 229% more in one month than managers in the bottom quartile.

As one manager told us, “I champion, cheerlead, and conduct regular pulse checks to understand how their experience with the tool is going and start meetings showing what I personally used the tool for.” This behavior creates a virtuous circle: High-using managers have higher-using teams, and high-using teams have higher-using managers.

Managers care about their team.

On average, employees who reported that their managers care about them had 14% higher gen AI usage than employees who do not share this belief. Using a new technology can be intimidating, and managers need to create a psychologically safe — and ideally fun — environment to test out new tools.

For instance, one leader told us: “We directed our teams to use the tool to compose a Shakespearean sonnet or a brief poem about their week at work to share with their colleagues.” The teams that completed this exercise saw 47% higher adoption than other groups. This light-hearted activity created a low-risk environment for teams to experiment that can cascade to larger and more permanent change.

Managers believe in and explain the “why.”

The best managers help employees understand the impact and purpose of the activities they do, especially when any change is required. From the start of our experiment, we saw a 66% difference in reported gen AI usage between participants who believed they needed it for their work and those who did not.

Top-quartile managers had the highest personal belief in the power of gen AI to make work better. As one of these managers told us: “There are always going to be those who are afraid of change. I believe the best thing to do is highlight the associated benefits locally within the team.”

Significantly, we also found that managers whose teams have the lowest adoption are often overwhelmed, tend to be reactive, and don’t themselves understand the role of the tech.

Co-create Gen AI-powered Work with Your Employees

The primary difference between the experimental and control groups was co-creation. The control group was given the tools and access to training materials. The experimental group went through a co-creation process: not having a new work tool, process, or requirement launched from “up above,” but rather having them introduced by the employees for the employees.

Among this group, a subset of managers and peers were selected to work with each gen AI tool before deploying it to their teams. We provided them with data to understand their teams’ effectiveness and joy by task. They discussed how to adapt and apply the tool to daily work in a way that maximized joy and effectiveness, and then directly shared their learnings with their peers. Members of the experimental group also had bi-weekly pulse checks to measure impact and discuss ways to improve the tools and ways of working with them.

At the end of the study period, the experimental teams had twice as many active gen AI tool users who spent twice as long using the gen AI tools compared to the control group. And our initial results show that experimental group also reported 13% greater overall joy over the course of the experiment compared with those who did not.

These results make clear that simply rolling tools out with some light communication and training is not an effective strategy for technology adoption. In our work with organizations that have tried this approach, we’ve seen many enterprise licenses with relatively flat usage. Instead, managers need to work together with their teams to create, adapt, and integrate gen AI tools that work for them in their own day-to-day work to enhance joy and minimize toil.

When you co-create with employees, you find the important organizational nuances that impact adoption, like the underlying attitudes and needs that drive behavior. At another organization, our surveys revealed a perfection-oriented culture: a workforce used to academic, peer-reviewed processes. Reliability concerns (reported by 48% of participants) were highlighted as the major reason employees were not embracing gen AI. Employees were afraid that gen AI would make mistakes, and therefore resisted using it due to fear of reputational damage. The company had to address these reliability perceptions head on to drive experimentation and adoption.

Identify Specific Sources of Joy and Toil

On average, our administrative professional participants spend one-third of their time coordinating the availability of multiple stakeholders for meetings, often across many time zones. They report that the biggest obstacle to their effectiveness is the time they spend waiting for others to respond to their availability requests, which creates time pressure resulting in unnecessary toil.

To address this, we introduced new gen AI/AI-driven calendar tools that would allow administrative professionals to easily share availability and to automate these laborious aspects of scheduling. Participants in our pilots reported not only saving one to two hours a week by using this tool, but 79% also reported that they enjoyed the task more, 86% reported higher effectiveness, and 92% said they would continue to use the tool.

We also surveyed participants’ stakeholders — the leaders whose calendars they manage. These results showed that nearly a quarter reported a noticeable improvement in the administrative support they received and the remainder were equally satisfied.

That said, it’s important when introducing these tools to proceed cautiously. Many of our participants told us that they enjoy helping their stakeholders prioritize and make the most from their schedules. As one participant noted: “Making calendars align is like playing a big game of Tetris. I really enjoy it when I solve the puzzle and make all the pieces fit.” In this experiment we were careful to deploy the gen AI tool against the toil (waiting for others to respond) but not the joyful parts (creatively landing the meeting). Leaders will need to work with employees in this same way to deploy gen AI in ways that deliver productivity without diminishing sources of employee pride and satisfaction.

We also offered participants gen AI tools to assist with other types of tasks, for example, personal and professional development. We found that those who used gen AI at least once a week for professional development tasks, such as brainstorming personal goals and drafting personal development plans, reported 18% higher joy and 13% higher effectiveness than their colleagues who did not use gen AI for these types of tasks.

Other teams that used gen AI to help with their focus work also reported a positive impact: participants in our experiment from HR functions using gen AI for tasks such as preparing emails, analyses, presentations, or reports found that they could spend 29% less time on such tasks (freeing up about two hours per week that they could reallocate to more enjoyable and value-adding work), leading to 17% higher enjoyment and 12% higher effectiveness reported than those who were not using gen AI for similar focus work.

Finally, we observed how employees used the time saved with gen AI. Many chose to re-invest it into doing more enjoyable tasks, such as those that involve problem-solving, interpersonal interactions, and learning and development which further enhanced their overall enjoyment at work.

. . .

As with all technological revolutions, gen AI promises efficiency and effectiveness gains. But we can’t expect employees to earnestly adopt tools they fear will lead to employers capturing all the benefits. For leaders, this is a self-defeating strategy that can ultimately lead to dissatisfaction and increased attrition. By adding joy to the equation, co-creating with employees, and ensuring managers serve as role models and support their people, you can flip the odds of success.

Deboral Lovich is a managing director and senior partner in the Boston office of Boston Consulting Group. She leads the firm’s Future of Work topic globally and is a fellow of the BCG Henderson Institute.

Rosie Sargeant is a project leader at Boston Consulting Group, based in London. She is also an ambassador of the BCG Henderson Institute.

Jacob Smith is a principal at Boston Consulting Group, based in Sweden, and an ambassador of the BCG Henderson Institute.

This article is adapted from Harvard Business Review with permission. ©2024. All rights reserved.

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​An organization run by AI is not a futuristic concept. Such technology is already a part of many workplaces and will continue to shape the labor market and HR. Here's how employers and employees can successfully manage generative AI and other AI-powered systems.

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