Work about work: when the reality of work consists of making things that don’t work work

We have elevated activity and even overactivity to the status of a dogma of a professional image that is polished and reassuring. We tick boxes, we follow up, we coordinate, but at the end of the day, we don’t feel like we’ve accomplished much.

What is known as “work about work” has become a normal way of operating in most organizations.

It is a kind of compensation time, used to keep failing processes running or to compensate for the organization’s failure to structure work efficiently, which absorbs most of the collective energy.

Worse still, rather than being a source of productivity, this extra work ends up undermining efficiency and engagement.

In short:

• “Work about work” refers to all peripheral tasks (coordination, reporting, information gathering, etc.) that take up a large part of working time without creating any real value.
• This phenomenon is the result of an inadequate organizational design based on obsolete processes, poorly integrated tools, and a vague distribution of responsibilities.
• The cost of “work about work” is high: loss of productivity, disengagement, cognitive overload, and burnout, particularly among managers.
• Rather than automating existing processes, it is necessary to simplify or eliminate unnecessary ones, thinking about work in terms of flows and observing what employees actually do.
• The role of managers must evolve towards supporting work efficiency, and the organization as a whole must be redesigned to promote autonomy, clarity, and added value.

Work About Work?

According to Asana’s Anatomy of Work Index, which surveyed more than 10,000 knowledge workers worldwide, 60% of work time is spent on peripheral activities such as communication, searching for information, switching between tools, tracking progress or priorities, and so on (How work about work gets in the way of real work). This time, known as work about work, distracts from the “skilled” work for which we are hired.

It is therefore a constant, even a norm: most of our time is spent keeping systems running and compensating for their shortcomings, instead of doing the work that needs to be done.

This category includes, among other things:

• coordination between poorly integrated tools,
• searching for information in multiple systems,
• rephrasing or reframing requests,
• statuses, reporting, follow-ups,
• alignment meetings without decision-making.

These tasks are often there to compensate for poorly designed processes or poorly thought-out tools: everyone becomes a patcher of the system rather than a productive player.

An underestimated structural debt

Work about work is less the result of poor time management than the manifestation of an inadequate organizational design, most often a legacy from the past that is the biggest obstacle to your teams’ growth (How to Tackle the Biggest Threat to Your Team’s Growth).

To begin with, processes that do not evolve. Organizations accumulate procedures over time, which eventually become obsolete but are never questioned. According to Deloitte, citing figures from Asana, workers spent an average of 257 hours per year navigating inefficient processes and 258 hours duplicating work or participating in unnecessary meetings, representing approximately 12 weeks of lost work per year (When work gets in the way of work: Reclaiming organizational capacity).

This is followed by an overload of work, not due to things that absolutely must be done, but, on the contrary, to things that should not be done. And, as Drucker says, “There is nothing more useless than doing efficiently that which should not be done at all“.

A study by Eagle Hill Consulting dating from March 2025 reveals that 68% of employees believe they regularly spend time on low-value, inefficient tasks (68 percent of U.S. workers say they regularly spend time on low value, inefficient tasks, new Eagle Hill Consulting research finds).

These practices create an invisible debt, which I agree is difficult to measure, but which is paid for in terms of cognitive wear and tear, disengagement, and loss of performance.

The effects of unnecessary work

Work about work fuels burnout. In the Asana study, 63% of respondents reported experiencing burnout in the past year.

It also reduces the time available for work that really matters: 27% more time on skilled tasks but 36% less on strategy(The average worker spends 58% of their time on ‘work about work’, finds research).

This feeling is even more intense among managers, who spend 62% of their day on these peripheral tasks, compared to 58% for other employees. As a result, 88% of professionals report that sensitive projects fall by the wayside because of this volume of tasks that have no impact or interest.

This leads to a certain form of cynicism, slower decision-making, latent conflicts between departments, and a deterioration in cooperation.

It is important to bear in mind that, contrary to popular belief, one of the main drivers of employee engagement and retention is not found in the many HR initiatives put in place to this end, but in the design of the work itself (Right fit, wrong fit).

Work about work is a symptom of poor design

This is not an individual mistake: it is a consequence of organizational design, tools, and processes chosen without taking into account how things actually work on the ground.

• Responsibilities are not clearly assigned.
• Tools often meet IT or compliance standards, not usage standards.
• Processes favor control, not fluidity.
• Teams have neither autonomy nor context, so they are constantly improvising.

Here again, it is important to understand that beyond exhortations to always do more and better and ambitions that we hope will be enough to motivate employees, the main factor limiting an organization’s performance is its systems! (You do not rise to the level of your goals. You fall to the level of your systems. (James Clear)).

Transform the organization rather than tinker with what already exists

As is often the case, none of this is inevitable, and there are plenty of ways to remedy the situation, provided that people are willing to make the effort.

Think in terms of flows

First, start by thinking about work in terms of flows rather than functions. Understand that what matters is not what each person does at their level, but how quickly the business transforms a stimulus, piece of information, or request into an action or tangible deliverable.

Ultimately, what matters is not that each link in the chain is efficient, but that the chain itself is efficient.

This is an issue we will face with AI in terms of augmenting employees (AI in the workplace: going beyond augmentation to actually transform). On a larger scale, this is one of the pillars of the profound transformation launched by Moderna (HR and IT merger: Moderna redesigns its organization for and with AI), even if it does pose a number of challenges in terms of KPIs (Thinking of work as a flow: appealing, but is it realistic?).

Understanding what people do at work

Then by understanding what people really do at work. As Yves Morieux pointed out (How to Manage Complexity without Getting Complicated), no one knows what people do on a daily basis, not even their immediate manager. We know what they produce or are supposed to produce, but we are unaware, sometimes deliberately, of how they achieve this, all the workarounds and mechanisms they implement every day to compensate for poorly organized and poorly designed work.

To address this, we can use highly quantified methods, which will become increasingly easy to implement as we gain access to data on just about everything (What data do we need to understand how people work?), even if this raises ethical questions (The quantified organization: Grail or Big Brother?). And let’s stop saying that knowledge work is inherently invisible and therefore cannot be improved (Just because work is invisible, it doesn’t mean that it can’t be improved).

But that’s not enough, and I recommend bringing back Mr. Taylor’s good old methods of observing what people do, spending time with them, and writing everything down. Empirical but devilishly effective.

But this also applies when you ask yourself questions about AI at work, flows to optimize, and potential productivity gains, which are more or less the same thing. As an AI consultant I spoke to recently pointed out, the only feedback on productivity gains is anecdotal, so the only way to get something tangible and solid is to follow people in their daily work with a stopwatch and compare the results with what happens after AI is deployed.

Prune instead of automate

I often say that when faced with a new situation, instead of trying to take a systemic approach and adapt, we add processes and hierarchical layers, resulting in what we see today: an organization that has responded to complexity with complication, where workarounds serve as a palliative for systems that no longer work.

Perhaps we expect that by piling things up, they will eventually settle and produce oil, but for now, they mainly produce inefficiency.

The arrival of AI will also be an opportunity to ask the right questions. Not only is there no point in doing well what should not be done, but automating something that does not work leads to faster and larger-scale malfunctions than before.

I know that managers generally like to show their impact as soon as they arrive, and to do so they add things because it is visible, when the first question to ask is what to remove.

Simplify processes

When you can’t eliminate something, simplify it, keeping in mind that a process that isn’t experienced as a service is a process that prevents people from doing their jobs well and generally works against the organization. Reduce the number of approvals, streamline tools, improve integration and transparency…nothing complicated, as long as you’re willing to tackle it.

Rethink the role of the manager

Managers should be work architects, not meeting planners or experts who can do everything better than their team members except help them grow individually and collectively.

The first level of the battle against work about work is the manager, who can clean up their own mess and report sources of inefficiency that are beyond their control.

Managers create the context for others to succeed (The fictional interview with Ted Lasso, the manager who manages without expertise), and combating work about work should be one of their first priorities. They should even turn it into a collective dynamic (Improving a team’ s work: a story of continuous improvement).

Going further

This is not a new phenomenon, and regardless of what you call it, there is a number of serious studies that show the extent of the problem.

To begin with, the Job Demands-Resources (JD-R) model explains that the imbalance between job demands and available resources (clarity, autonomy, reliable processes) leads to stress and burnout (Job demands-resources model).

Next, in software engineering, teams were interrupted up to 150 times a day on Slack. The only solution that reduced these interruptions was a centralized dispatcher role and a shared knowledge base (Interruption science).

Finally, an academic study from July 2020, also on software engineering, shows that teams spend an average of 7 hours and 45 minutes per week in scheduled meetings and 8 hours and 54 minutes in unscheduled meetings (Understanding coordination in global software engineering: A mixed-methods study on the use of meetings and Slack), which ultimately leaves little time to actually work.

Bottom line

Work about work is much more than a waste of time; it is a symptom of an incoherent system, designed without understanding the purpose of work and its constraints, and perpetuated or even amplified by habit.

It is not enough to add tools, put pressure on teams, or play around with schedules. Instead, we need to fundamentally rethink organizational design by focusing on the reality of work, value, clarity, and autonomy.

By eliminating this invisible work and getting rid of compensatory tasks, we not only save time, but above all, we regain energy, meaning, the ability to really work, and engagement.

Image credit: Image generated by artificial intelligence via ChatGPT (OpenAI)