Rethinking productivity in the age of AI
Can AI reignite productivity in the workplace?
Australian executives certainly think so. In fact, more than half of them believe that AI and automation will boost productivity by more than 30% in their companies in the next three years. However, history has shown us that productivity is a complex equation that goes beyond just investing in new technology or reducing headcount. In a sentiment shared by the Australian Productivity Commission in its 2023 5-year Productivity Inquiry report, there is no one silver bullet for lifting productivity. To achieve and sustain productivity gains in today’s changing work landscape, we need to rethink how we design work, stimulate new workflows, manage workforce transitions and measure value in all its forms.
Interestingly, employees generally agree with their leaders about what depletes productivity at work. Mercer's Global Talent Trends 2024 research identified, ‘busy work’, interruptions, poor organisational structure, unsustainable workloads, stress and difficulty finding information as the top reasons for both groups. The recent introduction of AI-powered tools has helped reset our approach to work, and our work habits, to address some of these concerns.
However, productivity is becoming more intangible, and the equation for measuring it is no longer sufficient. As our economy evolves and work models change, AI and new work models are providing novel ways to create value beyond just full-time equivalent employees. To embrace these opportunities requires people insights that go beyond job titles, as today’s metrics don’t capture the true impact people have on productivity.
Current talent models often follow a linear approach to productivity, where one full-time equivalent employee (FTE) is assigned to a specific role for an extended period. However, this traditional approach may limit development of a highly skilled, adaptable workforce, which is core to the Productivity Commission’s recommendations for driving productivity growth in a modern economy. Mercer’s Global Talent Trends 2024 study found that over half of Australian executives want to cut jobs without sacrificing talented people, indicating a desire for efficiency. However, these executives often lack the necessary talent insights to make informed decisions and optimise productivity.
Measuring productivity becomes even more challenging when the promised gains from technology implementation fail to materialise. With AI disrupting both white-collar and blue-collar work, executives face a hard reckoning over their investments and future decisions. According to our study, more than one-third of Australian HR leaders (35%) worry about an insufficient productivity lift from AI and automation, and employees are also concerned about rising productivity expectations and their day-to-day workloads. Before we can fully realise the potential of generative AI (Gen AI) and other innovations, we must consider whether our organisations’ culture, metrics, work design and governance will hinder 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. This is something that was seen in the Solow paradox of the 20th century, where computing power exploded while productivity stalled.
One aspect that needs attention is the shift towards knowledge-based and relational work, which does not always align with traditional productivity measures based on hours worked or output produced. Monitoring tools struggle to evaluate areas such as internal networking, talent development, agility, brand-building and innovation, which can significantly impact the business. Organisations that prioritise short-term gains by cutting roles in these vital areas may face long-term productivity losses.
Without more comprehensive and real-time metrics, factors such as politics, busyness, presenteeism and a focus on the what but not the how are often used as proxies for an individual’s value. However, prioritising these areas without fully assessing their impact can hinder or even reverse growth. Too much ‘busy work’ was flagged as one of the main productivity drains by more than two in five executives (44%) and employees (40%) in 2024.
And 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.
The good news is that more Australian executives are being held accountable for outcome measures such as total worker health and wellbeing (52%), delivering on the World Economic Forum’s Good Work standards (42%) and employee engagement (40%), as opposed to badge swipes and other inputs. Investing in these areas is essential for driving long-term, sustainable growth.
Resetting habits: Talent as an enterprise resource
It’s clear that our productivity measures and metrics need an upgrade. HR leaders predict that rising labour costs will be the biggest workforce challenge in 2024, and one in three executives notes that AI is prompting them to rethink their approach to productivity.
Narrow views of productivity can lead to miscalculating the real return on investment on labour spend, potentially trimming the wrong proportion of headcount. Shifting the focus away from full-time equivalents (FTEs) towards future skill needs allows employers to evolve the conversation from jobs and productivity to skills and potential, offering a better chance of ensuring long-term productivity.
In the face of fluctuating demand, it is important to recognise that what creates value today may not be sufficient to drive progress tomorrow. Workers report spending 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 full-time employees and reassigning it to a mix of automation and alternative talent pools. This approach has already proven to yield productivity gains for one in three HR leaders. However, this is not a one-time solution and requires constant reviews and adjustments to keep pace with changing demands.
As work becomes more dynamic and talent scarcity becomes a pressing concern for executives, there is a growing need to view talent as an enterprise resource rather than departmental assets or fixed job holders. Those that are leading the charge here are already figuring out which jobs require fixed or dedicated roles and which ones can have partial or full flexibility in their activities. This approach allows talent to flow towards demand, effectively optimising productivity.
Job redesign, of course, is only half the equation. It is equally important to source a different talent profile and gain better insights into workers' skills and potential. And even with improved talent science in place, we need to look beyond static job descriptions and rigid performance management metrics, which are likely to fall short in meeting the demands of the current moment.
So, where do we go from here?
Solving the productivity equation
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 organisation. 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 democratises 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. Organisations that embrace upskilling, flexible work arrangements and skills-powered work models will be well-positioned to reap the rewards of AI-fuelled productivity.
Keeping talent supply matched with demand can improve productivity by reducing labour 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 optimise productivity at the enterprise level. Executives and HR can use modern SWP with real-time dashboards and skills overlays to model different scenarios and optimise 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 organisational 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 incentivising 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 maximise 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 43% of Australian employees 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.
Otherwise stated, all data points are from the 2024 Mercer Global Talent Trends study.
Originally published in May 2024 by Kate Bravery, Global Leader – Talent Advisory and Insight and William Self, Mercer Partner and Workforce Strategy & Analytics
