By Kelly Monahan and Gabby Burlacu

Executive summary

• Research by The Upwork Research Institute reveals that 71% of full-time employees are burned out and 65% report struggling with employer demands on their productivity. Meanwhile, 81% of global C-suite leaders acknowledge they have increased demands on workers in the past year.¹
• Leaders have high hopes that generative AI will help boost productivity, as 96% of C-suite leaders say they expect the use of AI tools to increase their company’s overall productivity levels. Already, 39% of companies in our study are mandating the use of AI tools, with an additional 46% encouraging their use.
• However, this new technology has not yet fully delivered on this productivity promise: Nearly half (47%) of employees using AI say they have no idea how to achieve the productivity gains their employers expect, and 77% say these tools have actually decreased their productivity and added to their workload.
• By introducing new technology into outdated models and systems, organizations are failing to unlock the full productivity value of generative AI across their workforce. Business leaders need to shift how they organize talent and work by balancing traditional and nontraditional approaches. This includes leveraging alternative talent pools, co-creating measures of productivity with their people, and becoming fluent in the language of skills rather than job descriptions.

A push for productivity amid rising employee strain

As executives are increasing demands and pressures on their employees to boost productivity, workers are feeling the strain. 2023 was declared the "Year of Efficiency" in apprehension of economic headwinds; as organizations entered 2024, they asked their teams to do more with less. In a McKinsey report, one CEO’s response encapsulated the prevailing sentiment: “Act early to lower costs and protect the balance sheet so that you are stronger and leaner when the economy begins to turn more favorably.”​

Amid this backdrop, generative AI emerged as a tool for boosting productivity and enabling people to achieve more with fewer resources. While 85% of leaders are either making the technology mandatory or encouraging its use this year, many workers feel overwhelmed by the added workload and complexity it brings.

The latest Upwork research—surveying 2,500 global C-suite executives, full-time employees, and freelancers in the U.S., UK, Australia, and Canada—uncovers that executives’ expectations of AI’s ability to improve efficiency are high. Ninety-six percent of C-suite leaders say they expect the use of AI tools to increase their company’s overall productivity levels. For many workers, however, the path to value isn’t clear, and some say AI may even make their jobs harder.

In this study, we explore AI’s impact on workforce productivity, the degree to which employers expect their workforces to integrate AI today, and why this integration hasn’t delivered on the promised productivity. While it’s certainly possible to have the best of all worlds—boosted productivity, greater efficiency, and happy employees—this outcome requires a fundamental shift in how we think about, structure, and evaluate work.

Leaders are caught between a rock and a hard place

Eighty-one percent of C-suite executives report that, over the past year, they have expected workers to increase their output with the help of AI tools (37%), expand their skill sets (35%), take on a wider range of responsibilities (30%), return to the office (27%), work with greater efficiency (26%), and work more hours (20%).

Employees are feeling the strain. Seventy-one percent are burned out and nearly two-thirds (65%) report struggling with increasing employer demands. Burnout is high across generations and genders: 83% of Gen Zers are burned out, compared with 73% of Millennials, 71% of Gen Xers, and 58% of Baby Boomers. Women (74%) report feeling more burned out than do men (68%). Alarmingly, 1 in 3 employees say they will likely quit their jobs in the next six months because they are burned out or overworked.

While 69% of C-suite leaders admit they’re aware that employees are struggling to keep up with productivity demands, 84% are adamant their companies value employee well-being over productivity. Most executives feel their companies have moved toward more flexible work models (90%) and that practices are in place to help employees understand how their work connects to higher-level strategic goals (94%).

Considerably fewer full-time employees agree their employer prioritizes well-being (60%), even as most agree they have been able to work more flexibly (73%) and with greater clarity on strategic goals (80%). These perceptions have an impact. Employees who perceive their company to value productivity over well-being are more likely to feel overwhelmed by their workload at least some of the time (73% vs. 56%).  

Impact of AI on productivity

The speed and scale of generative AI adoption have surprised even the most visionary of technologists. The message to leaders has been clear: AI will be their vehicle for driving productivity, creating efficiency, and extracting economic value.

And leaders are bullish about AI’s positive association with productivity.² Ninety-six percent of C-suite executives say they expect the use of AI tools to increase their company’s overall productivity levels. Yet, less than a third of these leaders (26%) have AI training programs in place for their workforce and only 13% report a well-implemented AI strategy (see Upwork Work Innovator Research to learn more).

The majority of AI use appears to be emerging bottoms up, with workers leading the charge. Now, leaders are eager to channel this enthusiasm. Among the increased demands executives have placed on workers in the past year, requesting they use AI tools to increase their output tops the list (37%). Already 39% of companies require employees to use AI tools, with an additional 46% encouraging employees to use the tools without mandating that they do so.

Although 81% of leaders at companies that have deployed AI report an increase in workforce productivity in the past year versus just 42% of leaders at companies that have not, many leaders are dissatisfied with the degree to which AI is being effectively integrated into operations. One in two executives at companies using AI believe their company is falling behind their competitors (51%) and that their workforce’s overall productivity levels are stalled due to lack of employee skills and adoption (50%).

Is AI driving efficiency or fueling burnout?

Employees are just as excited about the potential of AI as their leaders, with 65% believing these technologies can increase productivity. However, this is not necessarily consistent with what they’re experiencing at work.

Nearly half (47%) of workers using AI say they have no idea how to achieve the productivity gains their employers expect. Over three in four (77%) say AI tools have decreased their productivity and added to their workload in at least one way. For example, survey respondents reported that they’re spending more time reviewing or moderating AI-generated content (39%), invest more time learning to use these tools (23%), and are now being asked to do more work (21%). Forty percent of employees feel their company is asking too much of them when it comes to AI.

Executives may be adding fuel to the fire by overestimating their workers’ readiness. Thirty-seven percent of C-suite leaders at companies that use AI said their workforce is “highly” skilled and comfortable with these tools, but only 17% of employees actually reported this level of skill and comfort. Thirty-eight percent of employees, in fact, reported feeling overwhelmed about having to use AI at work.

New technology, old productivity problem

This is not the first time we've seen early-stage technology deliver underwhelming productivity results. The productivity paradox illustrates that throughout modern work history, technological advancements have often outpaced workforce productivity gains. While it's logical to assume that integrating cutting-edge technologies should boost efficiency and output, reality frequently tells a different story.

Economist Robert Gordon documented the slowdown in U.S. productivity since the 1970s, alongside exponential technological growth. Echoing this sentiment, economist Robert Solow famously quipped, "You can see the computer age everywhere but in the productivity statistics."

This productivity paradox arises from several factors: the steep learning curve associated with new technologies, the lack of workforce development investments alongside the technology, and the frequent misalignment between technology and business processes. Consequently, many organizations find that anticipated technology-related gains in productivity are delayed or diminished.

By deploying new technology—no matter how exciting and full of potential—without updating our organizational systems and models, we risk creating productivity strain: employees with yet another thing on their plate who are mentally, practically, and systematically unable to use this technology to achieve the anticipated gains. We risk another productivity paradox with generative AI if we don’t fundamentally rethink the way we work.

Finding balance: AI-enhanced work models for the future

Our research suggests three imperatives for business leaders that will help them shake up the status quo and find balance in traditional and nontraditional approaches to work.

1. Leverage nontraditional talent

When compared with full-time employees, more freelancers claim to be AI-ready. Nearly half (48%) of freelancers say they’re “somewhat” or “highly” skilled at using AI, with over a third (34%) using AI tools at least 1 to 2 days per week. In fact, 48% of C-suite executives report hiring freelance workers to execute delayed AI projects over the past year.

C-suite leaders leveraging freelancers say this approach has at least doubled the following outcomes for their business: organizational agility (45%), the quality of work being produced (40%), innovation (39%), scalability (39%), revenue and bottom line (36%), and efficiency (34%). Over one in three (35%) even report at least doubling the level of well-being and engagement among their full-time workers as a result of bringing freelance talent in.

Perhaps this is why 80% of leaders who leverage freelance talent say this is essential to their business, and 38% of leaders who don’t already leverage this talent pool intend to start in the coming year.

‍

Source: The Upwork Research Institute, 2024
‍

Comparing levels of stress and burnout between freelancers and full-time employees is difficult—their work structures and environments are very different. But we know from our research that 56% of freelancers say they don’t experience struggles to keep pace with client organization productivity demands, as opposed to 35% of their full-time counterparts.

This may be the result of specialized freelancers who come in with the requisite knowledge and have completed relevant training ahead of time. Consequently, this highly skilled talent pool can deliver on leaders' AI-related expectations with greater ease, working alongside full-time workers to more effectively achieve organizational goals.

2. Co-create measures of productivity

Fifty-four percent of employees report that their company doesn’t have an accurate picture of their productivity, and most report that they would be more satisfied (81%) and more productive (76%) if they had more say in how their productivity was measured. In short, 74% of employees believe their organization’s approach to productivity is in need of an overhaul.

Workers who say they’re struggling to keep up with their organization's productivity demands are most frequently evaluated against speed and efficiency outcomes (39%) rather than their contributions to strategy (29%) or their creativity and innovation (24%). When you consider this along with the fact that nearly half (47%) of employees have no idea how to deliver on their company’s AI productivity expectations, a picture emerges of widespread productivity models that require more, faster—and AI strategies that follow suit.

AI, and frankly people, can deliver success measures that go beyond quantity and speed. When workers are more involved in co-creating the measures against which their productivity is evaluated, we see a greater emphasis on creativity and innovation, customer relationship building, and adaptability—attributes that executives believe are important and contribute to the bottom line. By aligning co-created outcomes to AI programs, leaders can clarify the AI productivity expectations and goals of the business, better balancing the needs of both the business and workforce.

3. Build fluency in the language of skills

The implementation of AI has brought about a renewed urgency to move toward skills-based approaches in which the focus of talent strategy is more centered on finding, building, and rewarding skills rather than on job roles. However, many organizations have had trouble putting this into practice. The truth is, executives are fairly clear on what skills they need, with product management (48%), data analytics (46%), generative AI content generation and modeling (40%), and database development (40%) topping the list.

What leaders are less good at is determining the skills they already have in their organization. While they overestimate their workers’ comfort and skill level with AI, many recognize having this blind spot: Only 40% claim a high-level of awareness of the AI skills in their business.

Without this visibility it will be very difficult, if not impossible, to set appropriate objectives and determine where talent like freelancers can best fill gaps. While most executives say that having skills and credentials is more important than having an educational degree for both full-time (60%) and freelance (58%) talent, these skills can only create additive productivity in a larger system when the skills of that system have been adequately cataloged, assessed, and developed.

AI is a groundbreaking, promising technology, but when we introduce it into the same systems and models that have been stymieing productivity for years, we double down on our existing problems. By giving workers access to AI tools within their same jobs and workflows and asking them to conjure productivity from them, we risk escalating the sense of burnout people are already experiencing.

To truly leverage AI's power, we must fundamentally shift the way we organize talent and work. Bringing greater balance to productivity and well-being requires new ways of working. Doing more with less, ignoring alternative talent pools, and sticking with top-down productivity measurement simply won’t work in the era of AI.

Click to view full size

Acknowledgements: The Upwork Research Institute would like to thank Alexandra Levit, Dan Schawbel, Rebecca Scott, and Christine Kim for their contributions to the research report.

About the Workplace Intelligence Survey
Research findings are based on a survey conducted by Walr, on behalf of Upwork and Workplace Intelligence, between April 16 and May 5, 2024. The survey targeted respondents in the U.S., UK, Australia, and Canada. In total, 2,500 global workers completed the survey, including 1,250 C-suite executives, 625 full-time, salaried employees, and 625 freelancers. The survey sampled a mix of male and female respondents, as well as a mix of respondents from different generations (Gen Z, Millennials, Gen X, and Baby Boomers). All respondents were between the ages of 18 – 78, were required to have at least a high school diploma, and were required to use a laptop or computer for their work at least “sometimes.”

‍

¹ The relationship between increased demands and burnout is highly correlated. Our statistical regression model suggests that increased work demands account for at least 30% of a worker’s sense of burnout. Other factors of burnout may be related to those experienced outside of work.

² See Microsoft Work Labs Research for more insights on how workers are adopting gen AI at rates faster than their organization’s ability to keep pace.