Rethinking productivity in the age of AI 

Young woman presenting speech chart
Young woman presenting speech chart

Can artificial intelligence (AI) quench a years-long productivity drought?

Executives think so: More than half believe that AI and automation will fuel a 10%–30% productivity boost at their companies in the next three years, and two in five expect staggering gains of over 30% — most notably in healthcare, insurance, and transportation and logistics. But despite the leaps that enhanced human-machine teaming can bring, the productivity equation is highly complex.

History shows that solving for a productivity lift takes more than investment in new technology or rolling headcount reductions. As the world of work changes and uncertainty becomes the norm, achieving and sustaining productivity gains requires rethinking how we design work, stimulate new workflows, manage workforce transitions and measure value in all its forms.

For the most part, employees and the C-suite agree on what depletes productivity. We know busy work tops the list; interruptions, poor organizational structure and stress are also in the top five for both groups. But while an unsustainable workload is the fourth-highest item for employees, it’s much further down the list for executives (at #9). Similarly, difficulty finding the right information ranks higher for the C-suite (#2) than for employees (#6). AI-powered tools and solutions can certainly help reset work and our work habits to ease these concerns.

Regardless of what’s stalling it, productivity is becoming more intangible, and the equation for measuring it is no longer fit for purpose. Even as our changing economy brings major shifts in where work is done and what adds value, AI and new work models are providing novel ways to create that value beyond full-time equivalents (FTEs). Yet embracing these opportunities requires people insights beyond job titles, and today’s metrics don’t capture the true impact people have on productivity.

Today’s talent models are guided by linear views of productivity that tend to park one FTE in one role and leave them there. Mercer’s Global Talent Trends 2024 study reveals that three in five executives (63%) want to cut jobs and not people in the age of AI, but they often lack the talent insights to drive the necessary decision-making.

Because the gains promised from technology implementation often fail to materialize, the challenges around measuring productivity become even more acute. Now that AI is disrupting white-collar and blue-collar work, executives face a hard reckoning over the investments they’ve made and for the decisions to come. More than one-third of HR (34%) worries about an insufficient productivity lift from AI and automation, and employees are also concerned — most notably about what rising productivity expectations will mean for their day-to-day workloads. Before we can realize the full potential of generative AI (Gen AI) and other innovations, we need to consider whether our culture, metrics, work design and governance will stall or unlock the quantifiable gains from tech investments.

Evolving the productivity equation

From steam engines to AI, the lag time between technology breakthroughs and productivity gains shows that ROI doesn’t come overnight. In the Solow paradox  of the 20th century, computing power exploded while productivity stalled. Then, in the 1990s, labor output caught up — albeit mostly within a few sectors in the US. This suggests that some economies and policies are better positioned for adapting to change and that the effects of technological innovation aren’t distributed evenly throughout the globe.

Mercer’s Global Talent Trends 2024 study found that executives’ views on productivity vary by industry:

  • Employers in chemicals, professional services, technology, and transportation and logistics are leading the charge in rethinking productivity based on AI and new ways of working. Construction, energy and retail are just getting started on their journeys.
  • In their efforts to boost productivity, media and communications firms are the most likely to consider employee mental health and well-being.
  • The manufacturing and automotive sectors are the most likely to measure productivity by inputs (for example, hours worked) and outputs (such as sales or goods produced), respectively.
  • Executives in healthcare are the most likely to worry that the way they measure productivity does not fully capture the true value workers deliver.


One change that has yet to be reckoned with is the shift toward more knowledge-based and relational work, which doesn’t always align with the traditional hours-in, widgets-out productivity measures. Monitoring tools can help assess these efforts more objectively, but they are less adept at evaluating areas such as internal networking, people development, talent agility, brand-building and innovation — all of which can have an exponential impact on the business. Organizations that cut roles in these vital areas for short-term gains might face a net productivity loss in the long term.

Without more comprehensive and real-time metrics, other factors — such as politics, busyness, presenteeism and a focus on the what but not the how — are often used as proxies for the value individuals bring. Prioritizing these areas without fully assessing their impact can stall or even reverse growth. Too much “busy work” was flagged as the top productivity drain by more than two in five executives (46%) and employees (42%) in 2024.

Given that 82% of the workforce feels at risk of burnout this year, an overemphasis on short-term productivity gains could quickly become a zero-sum game. Financial strain is the top driver of burnout risk among employees, who spend roughly six work hours per month worrying about money. This suggests that educating the workforce on financial stability could deliver a productivity lift in terms of time savings. But with exhaustion and workload also fueling burnout concerns this year, long-term productivity could be further hampered by absenteeism and extended sick leave.

Alongside these concerns, there’s a fear that AI adoption will lead to higher expectations around productivity. And if employees feel pushed too far, they’re more likely to unionize in the hopes of better rewards and working conditions (31% of HR leaders believe this will be a major challenge this year). Employers that hope to see a productivity boost from AI might consider fielding employees’ concerns proactively before unions or collective bargaining can blunt the returns.

The good news is that more executives than ever before are being held accountable for outcome measures such as total worker health and well-being (50%), delivering on the World Economic Forum’s Good Work standards (43%) and employee engagement (40%), as opposed to badge swipes and other inputs. Investing in these areas is essential for driving long-term, sustainable growth.

Some of these people-centric efforts have yet to permeate the organization despite the concerns about burnout. Forty-five percent of executives report investing in tools to monitor employee productivity in the past three years, and more than half (56%) plan to do so in 2024. A word of caution: Nothing dampens creativity and innovation more than feeling micromanaged and tightly monitored. Greater attention is often needed regarding what these tools actually measure and how the data is being used to assess workers’ contributions. 

Resetting habits that are past their sell-by dates

It’s clear that our productivity measures and metrics need an upgrade. HR leaders predict that rising labor costs will be the biggest workforce challenge in 2024, and one in three executives notes that AI is prompting them to rethink how they measure productivity today.

Firms that take a narrow view of productivity could miscalculate the real ROI on their labor spend and respond by trimming the wrong proportion of headcount. Even now, HR believes that reductions in force will affect about 20% of the workforce this year. By shifting the focus away from FTEs and toward future skill needs, employers can start evolving the dialogue from jobs and productivity to skills and potential. This approach has a better chance of safeguarding future productivity.

Amid fluctuating demand, what creates value today isn’t likely to move the needle tomorrow — at least not sufficiently. Workers report that they now spend 34% of their time on mundane or repetitive tasks that are ripe for automation. One way to keep productivity high is by removing the low-value work from FTEs’ plates and reassigning it to a mix of automation and alternative talent pools. This sort of work design exercise is already paying dividends (one in three HR leaders reports productivity gains from these efforts). However, this is not a one-and-done solution. It will likely need constant reviews and adjustments to keep pace with changing demands.

As work gets more dynamic and we face the full brunt of talent scarcity issues (a concern among roughly half of executives), there’s a growing need for talent to become an enterprise resource, not departmental assets or fixed job holders. Those that are leading the charge here are already figuring out which jobs truly need to be fixed or dedicated and which ones can have partial or full flexibility in their activities — effectively allowing more talent (or latent productivity assets) to flow to where the work demand is emerging.

Job redesign, of course, is only half the equation. We also face the need to source a different talent profile and have better insights into workers’ skills and potential. But even with improved talent science in place, it’s painfully obvious that static job descriptions and rigid performance management metrics will likely fail to meet the moment.

As we embark on this transformation, we also need to consider how different workforce segments are adapting and thriving. On average, men today spend more time than women on efforts that broaden their skill sets, such as creative pursuits and internal gigs. If we do not systematically nudge all workers to take up these opportunities, this imbalance will negatively impact future career prospects — especially in organizations that move toward skills-powered talent models or lean more heavily on AI to distribute work.

So, where do we go from here?

Solving the productivity equation

It is clear we need a new productivity equation that’s a better fit for today’s workforce, and whether we get it right will have profound implications for business, people and society. Here are five ways to approach productivity that address the increase in complexity:

Quantifying productivity is more straightforward in some roles than in others. It’s easy to grasp a salesperson’s impact on the bottom line or to count how many units a factory worker produces per hour. Managers can track these metrics today without a huge investment.

Other functions, especially back-office and knowledge roles, such as marketing and HR, have a less tangible impact on productivity. It can be tricky to fully account for the true value these roles bring to the organization. Flexible work arrangements have exacerbated the issue. At the firms encouraging more onsite attendance this year, 28% of HR leaders cite difficulties in managing hybrid and remote teams.

Investing in a holistic, firm-wide understanding of productivity will fuel more effective performance management and more informed workforce planning. This perspective can help pinpoint what increases productivity — and even address what holds workers back from reaching their full potential.

Humans and AI excel at different things — the former in empathy, strategy and sociocultural context, the latter in analytics, automation and bulk content creation. Employers can leverage these strengths to boost productivity through work design: deconstructing jobs into tasks, redeploying those tasks to the optimal blend of talent and automation, and reconstructing work into new functions and workflows accordingly. Modern work design tools can support this process at scale.

Further, as Gen AI democratizes knowledge and creativity, it also cuts expensive skills premiums by making work more accessible to more people. This gives employers an edge in addressing talent and skills shortages. Organizations that embrace upskilling, flexible work arrangements and skills-powered work models will be well-positioned to reap the rewards of AI-fueled productivity.

Keeping talent supply matched with demand can improve productivity by reducing labor costs. Employers with more reactive talent models often resort to costly buy/borrow talent strategies, desperate hiring frenzies and painful reductions to even out the workforce. This approach can throttle company valuations, as eight in 10 investors see routine layoffs as a red flag.

The executive’s role as a chief strategist has never been more important. It takes sophisticated, data-driven and proactive strategies to predict demand and scale capacity accordingly. This requires deeper strategic thought and better integration of AI, analytics, stakeholders and reporting strategies.

Luckily, Gen AI frees up more time to assess and optimize productivity at the enterprise level. Executives and HR can use modern SWP with real-time dashboards and skills overlays to model different scenarios and optimize their people strategies based on the actual skills and capacity needed, not just FTEs.

As we mentioned before, it’s tough to gauge productivity in a way that accounts for individual contributions.

Start by redefining the outcomes and impact a job needs to bring and what “good” versus “great” performance looks like. Couple this with clear insights on what skills are essential and growing in relevance and how employees match up. Not only does this help leaders avoid making poor talent decisions on an individual level, but it also helps employees direct their learning efforts toward those areas that hold future value for the enterprise.

This approach demands effective talent insights on every worker — their soft skills, technical skills (one in three HR leaders assess these for talent marketplaces today) and what motivates them — and a robust skills taxonomy linked to jobs. Productivity multiplies when employees’ jobs match their motivations, and AI-driven assessment practices are already helping deliver these insights at scale.

One opportunity that’s prompting a rethink on productivity is amplified intelligence — the power of Gen AI to facilitate higher-quality outputs and better decision-making. Even if the hours-to-widgets ratio stays the same for a Gen AI user, their heightened expertise and work quality can ultimately drive increased revenue and a competitive edge for the firm. This opportunity allows companies to redefine the experience or tenure needed for certain roles.

Many firms are exploring large language models (LLMs) and Gen AI tools to improve productivity — especially in data analysis (46%), to improve decision-making (43%) and to develop new business offerings (40%). But here’s the catch: nearly everyone else is, too. Something as commoditized and widely available as ChatGPT can help level the playing field for all competitors, but it won’t deliver a sustainable competitive advantage unless combined with diverse human voices and trainings on how to work together.

The other challenge is that real workforce output today is often a matter of collective efforts, AI-enhanced collaboration and sustainable business practices more broadly. In the future, measures of workers’ performance and even employability may include learning future skills and being receptive to new technology as well as their ability to collaborate across time zones, cultures and organizational structures to deliver value. This trend demands a shift in how we develop leaders’ skills and more effective assessments of employees’ digital readiness. It also requires incentivizing the shift toward more digital-first and inclusive work practices.

As AI gains momentum, expect companies to differentiate themselves by using it to build and empower diverse teams. Some opportunities here include:

  • Building diverse, well-rounded teams — AI can scan employee data and identify people who might work well together based on complementary skills, experiences and other agreed-upon indicators.
  • Fostering firmwide digital literacy — Digital skills and comfort levels vary across the workforce. Educate your people on new technologies, good data habits and developing a risk-based mindset to maximize potential gains.
  • Using AI to power prediction — This is something just 21% of companies do today. AI can help predict work demand but also identify latent capacity in the system and energy drains on the horizon.
  • Appointing digital ambassadors — Being digital-first is a journey, not a destination. Identify people or teams to be lasting advocates for AI-powered productivity and new ways of working.

The future is human-centric and tech-enabled

The human element is perhaps the most vital and overlooked part of today’s productivity equation. Just 37% of workers agree that their companies are good at communicating how AI and/or automation will improve the way they work. Firms that articulate how AI benefits their workforce will distinguish themselves as employers of choice.

People are a finite resource; between declines in job satisfaction and a higher-than-ever risk of employee burnout, the age-old calls to work harder and faster just won’t cut it. There’s a better way to kick-start productivity, and it demands AI — but to make lasting progress, leading employers will answer the call to govern AI responsibly and distribute the gains evenly. It’s time for intentional work redesign and a metrics upgrade that deliberately places a premium on both productivity and well-being.

About the author(s)
Kate Bravery

Global Leader – Talent Advisory and Insight

William Self

Mercer Partner and Workforce Strategy & Analytics Leader

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