In 1991, Peter Drucker wrote in Harvard Business Review that the greatest management challenge of the coming decades wouldn’t be improving manual labor productivity—we had already optimized that. Instead, the real challenge would be the productivity of knowledge work. He warned that while manual work followed a clear process and had visible outputs, knowledge work was murkier, less defined, and often mismanaged.
Two years later, in Post Capitalist Society, he went even further, stating that the productivity revolution had become a victim of its own success. He saw that work had shifted—not from doing more, but from knowing how to apply knowledge effectively. He called this shift the management revolution, believing it would define the future of work.
More than 30 years later, it’s hard to argue that we got it right.
The explosion of technology and information hasn’t necessarily made work better. Instead, many knowledge workers feel buried—under endless meetings, notifications, and the sheer volume of information. Rather than applying knowledge effectively, we spend much of our time managing it. Instead of making progress, we are stuck in cycles of endless work about work.
If Drucker were writing today, would he say the management revolution succeeded? Or would he argue that we misunderstood the challenge? More importantly, if we have drifted off course, how do we get back?
Reassessing the Management Revolution
Drucker’s concept of the management revolution was built on a fundamental truth: knowledge work is different from manual labor. In a factory, output is straightforward—how many units were produced, how fast a task was completed. In contrast, knowledge work lacks such clear, tangible metrics.
Drucker argued that the best knowledge workers weren’t those who completed the most tasks, but those who worked on the right tasks. He believed management’s role wasn’t just about oversight but about ensuring that knowledge was applied effectively.
Yet, three decades later, many organizations are still stuck in productivity models designed for manual labor. Employees are expected to log hours, attend meetings, and check off tasks—often without regard for whether those efforts lead to meaningful progress.
The tools and systems meant to empower knowledge workers have, in many cases, done the opposite:
- Meetings that were intended to encourage collaboration now consume entire workdays.
- Productivity software designed to streamline workflows often creates more work in the form of notifications, updates, and tracking.
- Instead of making strategic, intentional decisions, knowledge workers are trapped in reactive cycles, putting out fires rather than doing meaningful work.
Drucker warned that this would happen if we didn’t define knowledge work properly. He argued that, unlike manual work, knowledge work required a clearer understanding of what constituted results and what didn’t.
While Drucker didn’t use the term explicitly, his emphasis on defining knowledge work and ensuring clear objectives suggests that a modern interpretation could involve a better way forward: Expectation management.
The Role of Expectation Management
One of Drucker’s most valuable insights was that knowledge work lacks built-in structure. Without clear expectations, knowledge workers either:
- Try to do everything (leading to burnout), or
- Focus on tasks that make them look busy rather than tasks that create meaningful results.
Expectation management is about clarity. It means setting clear definitions for what needs to be done, why it matters, and how success will be measured. When organizations fail at expectation management, they unintentionally encourage inefficiency:
- Employees feel pressure to respond quickly rather than think deeply.
- Meetings expand to fill the time available, often without clear action items.
- Work becomes more about managing the system than achieving meaningful results.
Drucker believed expectation management should be at the heart of managing knowledge workers. That meant:
- Defining success clearly: What does a good outcome actually look like?
- Eliminating unnecessary work: If something doesn’t contribute to the bigger picture, why are we doing it?
- Providing autonomy: Once expectations are set, give people the space to do their best work.
Expectation management bridges the gap between productivity and productiveness.
From Productivity to Productiveness
For too long, productivity has been measured by how much gets done. But as Drucker pointed out, knowledge work isn’t about doing more—it’s about doing the right things.
That’s why productiveness matters. It’s not about output alone but about meaningful progress. It prioritizes depth over speed, results over busyness, and quality over sheer volume. This table illustrates the difference:
| Productivity | Productiveness |
|---|---|
| Maximizing output | Maximizing impact |
| Checking off tasks | Ensuring tasks are worthwhile |
| Measuring work by time spent | Measuring work by progress made |
| Doing more | Doing what matters |
Drucker believed that the best workers—and the best leaders—focused on effectiveness rather than raw efficiency. That means:
- Choosing what not to do. The most effective people aren’t the ones who do the most, but the ones who are most selective about where they put their effort.
- Measuring the right things. If success is defined by how many emails were answered or meetings attended, employees will prioritize those tasks—even if they don’t create meaningful results.
- Creating space for deep work. Knowledge work requires thinking, problem-solving, and creativity. If every moment is filled with busywork, there’s no room for real breakthroughs.
How We Get Back on Track
Drucker’s management revolution wasn’t a failure—it was an unfinished project. The shift to knowledge work happened, but our ways of measuring and managing work haven’t caught up. If we want to fix this, we need to rethink how we define success.
Here’s a set of starting points:
- Clarify expectations. Define what good work actually looks like so people know what to prioritize.
- Stop rewarding busyness. Replace outdated productivity metrics with measures that focus on meaningful outcomes.
- Eliminate unnecessary work. If a task doesn’t contribute to a meaningful result, cut it.
- Create space for thinking. Block time for strategic work, not just reactive tasks.
- Encourage autonomy. Once expectations are set, trust people to figure out how to meet them.
We don’t need more productivity hacks. We don’t need more apps. We need a return to what Drucker was trying to teach all along: work should be measured by effectiveness, not just activity.
The productivity revolution lost its way. But we still have time to course correct. The question is, will we?
