The global SARS-CoV-2 pandemic of 2020 has pushed many firms around the world to embrace remote work both sooner and more rapidly than they might have intended. With state-mandated restrictions on movement, and the PR and liability risk of inadvertently hosting a spreading event, companies have scrambled to support their employees working from home while maintaining as much productivity as possible.
Without the need to commute into an office, often in a dense urban center, many employees have also relocated, opting for the comforts of family or simply quieter, less congested places to live. And this geographical distribution of their reports has placed new demands on managers to both support and supervise their teams in remote-first fashion.
Classical management philosophy is preoccupied with productivity, not just output, as a means of ensuring that the constituent parts of a process or organization are coordinated to maximize efficient yield. This tends to manifest in knowledge workplaces as micromanagement: supervisors who want to know how many minutes extra a report took on a lunch break, how slow they were in dispensing with a customer support call, metrics, metrics, metrics. And these proxy measurements are comparatively easy to obtain when the supervised is directly under the watchful eye of the supervisor, but when employees are working from their own homes, in offset time zones, it gets more complicated.
Now, ideally, an approach based on Objectives and Key Results (OKRs) and check-ins should provide a sufficiently granular approximation of productivity metrics without necessitating surveillance-grade stalking, but… well, that requires trust. And trust is not a significant feature of corporatocracy, particularly the American variety. Instead, in an environment of distrust and suspicion, firms and managers are eager to use the same information and computer technology that enables remote work to spy on their workers—and vendors are lining up to sell such tooling to them.
More than being built over the top of existing productivity tools, this surveillance is now being built into tools themselves. In October of 2020, Microsoft unveiled "Productivity Score" within Office 365, giving managers a view into, for example, the number of days an employee has been sending emails, using the chat, using 'mentions' in emails, and more, all to the individual level by default.
It is not clear that all this productivity monitoring improves firms' performance, and it is clear that it adversely affects employee mental health. A 2015 article from the Society of Human Resource Management observes that monitoring "can […] sow seeds of distrust and fear among workers who aren’t so keen on having their every move tracked." It notes that potential downsides of monitoring include increased turnover, stress, and inhibiting work.
It turns out that the workplace panopticon is counter-productive.
Instead, what work needs—and remote work in particular—is trust. Trust that properly trained, motivated, and compensated employees will find the discipline to get their jobs done, on time and to quality expectations. Trust that a clear discussion and agreement on their objectives and key results will give them the ability to manage their own productivity and deliver for the team, and that they can come to their managers with questions and clarifications that help them perform better and give their supervisors insight into their areas for development and recognition.
Trust sounds lofty and aspirational, but it is actually quite tactical, and is not at all at odds with metrics. The crucial shift is in moving from measuring "productivity" back to measuring output, and in designing processes and organizations themselves to maximize this. If we think of the production of any finished piece of work within an organization as being like an assembly line, then we can evaluate the quality of that line by assessing its ouput: how thorough the financial report is, how polished the video sizzle reel, how stable or buggy the website or mobile app.
Each of those assembly lines producing finished pieces of work are made up of stages, not unlike the stations on a factory assembly line, one stamping the sheet metal, the next welding it to the chassis. As parts of the work are pulled together, we can assess the quality of each stage by evaluating its output against a standard. And since each stage may in fact be a mini-assembly line of its own, we can apply these output checks recursively, creating a network of well-defined stages which receive quality input from their predecessors, add value through work, and pass their output on to their successors.
In moving from measuring productivity to measuring output, we accept that the workers at each individual stage may do things in different ways. They may take less time and thus appear to be idle, or write fewer emails and make fewer calls, but as long as their output is meeting our quality expectations for volume, timeliness, and correctness, we leave them to self-manage. We trust that they know how to get their work done.
The preoccupation with productivity is not irrational: it seeks to avoid situations in which sub-par output from a given stage stops the entire assembly line, but it attempts to do so by micromanaging the stages and their individual contributors. Trust is making clear to the contributors what the output quality expectations are, and holding them accountable for it. Trust is giving them the agency to maintain their quality for themselves, even where it presents a hypothetical risk of whole-line stoppage, because an assembly line of contributors who are properly motivated and empowered to deliver quality output is effectively self-coordinating, and will see continuous improvements at every stage without management intervention.
The irony is that trust is an old principle, one that was lauded in the mid-20th century for the ways it helped organizations deliver incredible results, and spread as an aspirational management philosophy. It just happened to go by the name Kaizen instead. It is impossible to effect the continuous improvement it espouses, from all levels of the organization, without trusting every worker to understand the scope of their process stage and its inputs and outputs, and valuing their perspective on how to make it better. Kaizen was extremely popular in physical manufacturing, often inspired by The Toyota Way, one of its most successful applications. In knowledge work, however, the work product is typically intangible, which can make it much harder to spot defects even one step further along the assembly line. Where a misshapen block is immediately recognizable as not an automobile's aluminum alloy space frame, a few wrong numbers in a table, a swapped column, a buggy algorithm…
So trust, but verify. Great process design is as much about defining distinct stages with clear inputs and outputs that produce the thing intended, as it is about devising checks and tests for each stage's outputs to ensure that they meet quality and correctness expectations. Trust is giving contributors the latitude to decide how they meet those expectations, and also welcoming their feedback in refining and improving those expectations.
We are only at the beginning of the broader remote work revolution, and there will be many experiments to try and fail, and lessons to learn and unlearn. We can not get started, however, if the management culture does not embrace and enthusiastically support the potential for outsize returns by accepting the teething risks that come with them, and this means placing trust in reports and having faith that trust will be repaid with outstanding results.
Many thanks to friends who proof read and gave feedback, including Jeremy W. Sherman, Onyeka Onyekpe, and Jutta Stöttinger.