Outcomes over outputs: Why productivity is no longer the metric that matters most

Productivity is no longer the metric that matters most

It mattered when global economic engines centered primarily on the making of goods. It can be useful for measuring the impact and output of machines. But as the drivers of innovation are becoming more human-centric and values-based, organizations that continue to rely on the “do more with less” productivity metrics invented a hundred years ago as their primary measure of organizational performance could be missing the bigger picture.

It’s time for a fundamental rethinking of our approach to productivity: a new mindset and new metrics for a new way of working built around human performance and outcomes.

How traditional productivity metrics are falling short

With high inflation, shrinking profit margins, and the looming threat of economic recession, it’s no surprise that corporate leaders are feeling a renewed push to double down on efficiency and productivity. According to a new global survey on the state of work conducted by Slack, a majority of leaders (71%) say they’re under increasing pressure to squeeze more out of their teams, reduce waste, and boost productivity.⁵ Layoffs and cost-cutting measures seem to be the go-to tactics: Mark Zuckerberg, founder of Meta, for example, declared 2023 a “year of efficiency” for his company, paving the way for substantial workforce reductions.⁶

The result is often a standoff between leaders and workers as they clash over what it means to be productive in today’s work environment. Traditional productivity math tends to focus on reducing input and increasing output, but more output may not necessarily translate to better (or more efficient) results. Instead, organizations may find that relying on an input/output equation to measure organizational performance is falling short in a number of ways.

Productivity tracking can lead to productivity paranoia and performative work

In their drive to improve efficiency and productivity by tracking the activities of their workforce, organizations may actually be undermining the very productivity they are seeking to optimize. Between the beginning of the COVID-19 pandemic and late 2022, approximately one-third of medium and large companies surveyed adopted new worker-monitoring tools that analyze key strokes, mouse activity, and more to determine how much a person is working and on what.⁷ In the Slack survey, 60% of executives say they track everything from hours worked to number of emails sent as a measure for how productive their employees are—but only 15% of employees agree that this kind of tracking aids their efficiency on the job.⁸ In fact, employees are spending, on average, 32% of their time on performative work that gives the appearance of productivity.⁹

Productivity metrics may exclude important contributors to workforce ecosystems

The composition of today’s workforce is becoming more and more complex, and these workforce ecosystems—organizational structures that encompass contributors from both inside and outside the organization who work together to pursue individual and collective goals—often include contributors that may not be directly controlled or influenced by the organization (i.e., freelancers, long-term contractors, service providers, etc.). In fact, some organizations are now seeing 30%–50% of their overall workforce made up of contingent workers.¹⁰ According to joint research by Deloitte and MIT Sloan Management Review, 80% of leaders surveyed agree that the overall success of their organization is dependent on the contributions of external workers, and 88% say it is critical to understand the value created by their extended workforce.¹¹ But less than half (49%) say they do.¹² Productivity metrics that can’t be applied across an entire workforce ecosystem don’t provide an accurate picture of organizational performance.

Productivity metrics may not account for knowledge and “invisible” work

Technology in the 4IR is enabling more knowledge work than ever before. Even in front-line, supply chain, and manufacturing workforces, where productivity metrics may seem most applicable, advances in data and connectivity, analytics, human-machine interaction, and robotics are automating more tasks and freeing the workforce to tackle more complex problem-solving work. Many organizations are already making this shift, with 70% of workers in a Deloitte study agreeing or strongly agreeing that their organization is already structuring roles and responsibilities around problems to solve rather than around a set of repeatable tasks.¹³ As production becomes increasingly digitized, the creative and problem-solving skills needed to manage and work with new technologies can’t be as easily measured with existing productivity metrics.

In addition, productivity metrics aren’t likely accounting for the increase in “invisible” work that many workers are experiencing as organizations shift to more open-ended work models, and where more work is performed beyond the formal scope of one’s job. A majority of human resources leaders (79%) in a Deloitte study say that worker roles are evolving to become broader and more integrated, often embracing adjacent job functions, and workers agree: Seventy-one percent say they are already performing work outside of their stated scope of job responsibility.¹⁴

If not productivity, then what?

Productivity may be a good measure for the output and impact of machines, but it’s a metric that fails to measure the true impact of human efforts in a workforce being transformed by rapid advances in technology and shifting priorities. Focusing on traditional measures of productivity often leads to increased organizational activity. But it doesn’t tell us whether the work being done is the right work—the kind that helps organizations and individuals move closer to their objectives and goals. If we want to realize the human potential in our organizations and enable innovation, our focus must shift from productivity to performance, from productivity outputs to human outcomes.

Business outcomes are about capturing the value, quality, or desired result of work. For example, a web marketing team operating under a productivity metric may focus on number of clicks, number of downloads, or number of social media posts published. An outcomes-based metric such as “increase web traffic by X%” frees the team to innovate how that goal is achieved. Other potential business outcomes might include quality rates, customer retention, or growth through new services or products. As artificial intelligence (AI) technologies continue to evolve, business outcomes may also be increasingly dependent on successful AI-human collaborations.

But business outcomes alone are not enough to create measurable impact. Human outcomes should be part of the equation: the goals and objectives that help an organization’s people thrive physically, emotionally, financially, and professionally. In fact, most individual contributors surveyed say they prefer to be evaluated on their performance—what they produce or achieve, the quality and impact of their work, and their personal growth and skill development.¹⁵ A Deloitte study revealed that while 79% of leaders agree that their organization has a responsibility to create this kind of value for workers as human beings—and 66% say they are under pressure to demonstrate results—only 27% of workers strongly agree that their employer is making progress in this area, indicating that measuring human outcomes is still a largely untapped opportunity.¹⁶

The growth in passive workforce data—combined with other sources of information, analytics, and AI—is surfacing new opportunities for organizations to prioritize both human and business outcomes together and measure their impact (see Deloitte report, Beyond productivity: The journey to the quantified organization to learn more about how organizations can use work and workforce data to build trust and create shared value for workers). When organizations prioritize creating shared value for workers and measure human outcomes instead of productivity metrics, people are empowered to do their best work and organizational performance can benefit.

Consider worker happiness as an example. In addition to the individual benefits of being happier at work, such as improved wellness and performance, worker happiness could also improve teamwork and social encounters at the group level.¹⁷ It has been linked to improved engagement, productivity, and culture, and reduced attrition risks at the enterprise level.

Japan-based technology firm Hitachi experimented with improving the happiness levels of its employees using wearables and an accompanying mobile app that offered employees suggestions for increasing feelings of happiness.¹⁸ During testing, the psychological capital of workers rose by 33% and profits increased by 10%. Sales per hour increased by 34% at call centers and retail sales increased by 15%, demonstrating how a focus on human outcome metrics can have far-reaching organizational impact.¹⁹

Upcoming Deloitte publications will explore how the rapidly advancing technology landscape is prompting fundamental changes in how we work, how organizations create value, and what it means to be human. Quantitative productivity metrics may still have a specific role to play in the workplace, but more meaningful measurements should be considered when it comes to evaluating and prioritizing how we perform as humans. Organizations who can untether themselves from the productivity metrics of the last century could discover new opportunities to measure what matters and create a more inclusive, human-centered future.