Russell Klosk is a Principal Director with Accenture Strategy and a globally recognized expert in Future Workforce & Talent Strategy
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Organizations today are responding to a new normal, with many knowledge workers now unexpectedly working remotely full time. The nature of this transition has sparked a major question: How do we best measure productivity in knowledge workers, and given those impacts, what is the future of mobile workforces?
Winning CHROs and CIOs have already enabled mobile workforces through collaborative technologies. They now must determine how best to measure effectiveness and, ultimately, if these workers will return to the office. They are responding to this in several ways:
1. Advanced analytics and workforce-related insights to measure effectiveness and guide development opportunities.
2. Refreshed talent strategies and organizational capabilities that support and encourage new ways of working.
3. Rapid and fluid multimodal reskilling.
By investing in these areas and taking a more data-driven and analytics-based approach to monitoring productivity, innovation and performance, CHROs can position their companies to both recover and thrive as the world returns to work. To start, we must measure productivity, engagement and innovation.
Measuring Productivity In Knowledge Workers
Mobile and remote workers are not new. Certain portions of the workforce have worked remotely since the industrial revolution. For example, sales workforces and account executives are often located far from corporate headquarters and spend their time with clients in the field. In recent years, collaborative software tools and high-speed internet connections — as well as corporate efforts to reduce real estate costs, broaden talent pools to include workers who live far from office locations, and attract and retain hard-to-find skill sets — have all contributed to a growing pool of knowledge workers who are located remotely. But are these workers as productive and as innovative as workers in centralized offices? How can organizations manage effectiveness and measure productivity?
The productivity of workers has historically been measured in units of production per hour, with units framed in a way that is easy to count: units produced, transactions completed, etc. The productivity of knowledge workers requires a different approach.
A significant portion of a knowledge worker's contribution to their organization goes beyond the units they output. Also part of the measure when considering decisions on mobile work arrangements must be factors that demonstrate how each person supports the group's productivity as a whole, such as:
1. Social cohesion: Everyone brings specific knowledge to work. How well they share this knowledge and experience and how useful that is gives us a value quotient.
2. Information sharing: This measures the manner and timeliness in which output from the work performed is circulated to interested parties and those dependent on them.
3. Goal clarity: This is a cultural measure closely tied to how performance management works in each organization. However, productivity is highly correlated with workers being emotionally engaged with the work they do. That engagement often comes from teams having a common understanding of the underlying goals behind their work.
4. External outreach: How broad a network outside the organization does each worker have, and how do they pull that into the work they do for the organization? This fuels creative thought and drives innovation.
5. Trust: What is the measure of trust by others in the organization in the quality and value of one's work?
Evolving technologies have quickly changed the nature of jobs, complicating efforts to track measures over time. Recent workforce trends that bring technology into jobs have to date focused primarily on changing what work is done by humans and how. Technology in the workplace is used to create a partnership between humans and machines by leveraging robotic process automation (RPA), machine learning (ML) and artificial intelligence (AI) to enhance performance. With these innovations, we can lower costs and take hours of activity out of tasks, but does the new technology improve traditional productivity measures around actual quality and quantity?
When applied effectively, the answer is yes, according to my Accenture colleagues and Human + Machine authors Paul Daugherty and H. James Wilson. As Wilson writes, "The true value of AI can only be unlocked when humans and machines work in complementary ways. Indeed, our research shows that human + machine collaborative partnerships are regularly and pervasively outperforming human-only and machine-only teams." So as not to miss out on the improvements offered by implementing new technologies, knowledge worker productivity measurements must allow for an evolving picture of day-to-day work.
A knowledge worker productivity model will thus not be one simple measure or formula, as it must allow for the changing nature of work and include both direct and indirect productivity contributions. It will also vary based on the nature of the business. An accounting firm might measure accuracy and timeliness of reports and analysis with an emphasis on information sharing, while a high-tech company would measure not just the output of a DevOps professional in terms of lines of code but also how efficient the code is and how clean (e.g., free of bugs) the code is, as well as how well they promote social cohesion to help upskill the team.
Consider stochastic math models that give proper weighting to each factor for the particular organization and allow for benchmarking against other organizations. (A side note on benchmarking for knowledge workers: To get truly meaningful comparison, it is critical not to compare to just peer organizations but to differentiate by function or type of work, and use benchmarks from groups and organizations doing similar work.) Developing this productivity model is an ongoing challenge, as the metrics and standards will continue to evolve in an information-based economy.
As Peter Drucker said, "If you can't measure it, you can't manage it." And if you can't manage it, you can't bring that knowledge into mobile workforces and ensure the effectiveness of workers who may no longer be in their supervisors' line of sight.
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