What is Productivity, really?

Most professionals haven’t clearly defined what productivity actually is.

That’s a problem. You can’t improve what you haven’t defined. You can’t guide others to work better if you’re unclear on what better even means.

So let’s do that now, starting with where the idea came from.

Productivity in economic terms

Productivity began as a simple and powerful idea in economics: a ratio of output to input. More than a buzzword, it was a way of understanding how efficiently a system could turn effort into value.

The most common version is labor productivity: a country’s economic output per hour worked. For instance, if a nation produces $1 trillion in goods and services using 20 billion hours of labor, its productivity is $50 per hour. It’s a straightforward calculation, useful for comparing economic performance, tracking wage growth, and understanding long-term trends in living standards.

At this macro level, productivity is measurable and comparable. But when you step inside an organization, the picture becomes more nuanced.

What exactly counts as “output” in a creative team, a customer service department, or a product strategy role?

And how do you define input when abstract things like attention, collaboration, and decision-making are all part of the equation? This is where the neatness of the economic formula starts to fade, and where business leaders need to develop a more thoughtful and tailored definition.

Productivity in business terms

In business, productivity is still about how much value is produced relative to the effort, time, or resources invested. The core idea hasn’t changed, but defining what counts as value and what counts as input depends entirely on the nature of the work. When people talk about productivity in a business setting, they might mean employee output, team performance, or overall organizational efficiency. Each one plays by different rules and requires its own lens, especially when the work itself varies.

Manufacturing work

This is where the traditional definition of productivity still fits best. The output is clear – units produced, orders fulfilled, defects reduced. Inputs are just as tangible – labor hours, raw materials, machine availability.

Here, improving productivity is often about optimizing efficiency: getting more output from the same process. Systems like Lean Manufacturing or Six Sigma were built for these environments. The work is structured, and the goal is consistent: minimize waste, maximize throughput.

Knowledge work

For knowledge workers like designers, developers, marketers, and analysts, the equation looks very different. Their output isn’t measured in widgets or completed tasks. It’s measured in outcomes: ideas developed, decisions made, problems solved.

In these roles, volume isn’t always a good sign. The most productive person might send fewer emails, take fewer meetings, or spend an entire week crafting a key insight. Traditional metrics often fail to capture the real value being created.

In this world, effectiveness matters more than raw efficiency. What matters is whether the right things are being done, not how many things are getting checked off.

Service work

Service work blends elements of both. There are repeatable actions – calls answered, clients handled, and tickets closed. But there’s also a high emphasis on quality: resolution, customer satisfaction, trust, and long-term relationship value.

Productivity here means balancing speed and care. A support agent who races through 50 tickets a day may look efficient, but if they leave clients frustrated, the outcome doesn’t justify the pace. Likewise, a consultant who invests extra time to truly solve a client’s problem may create far more long-term value than someone who just checks the box.

Measuring productivity

Despite the nuance, businesses still need to measure productivity. Metrics provide visibility, spark useful conversations, and guide decision-making. However, not all metrics serve the same purpose, and each comes with limitations.

Most productivity metrics fall into one of three categories:

Volume Metrics – measure how much work is being done

These are helpful leading indicators for checking the pace and activity, but they can easily incentivize motion over meaning if used alone. They’re often used in operational or repeatable work.

Examples:

• Tasks completed per week
• Tickets closed
• Calls made
• Lines of code written
• Articles published

Outcome Metrics – measure the results achieved

Outcome metrics are lagging indicators. They provide a high-level view of efficiency and assess whether your work is delivering value, but say little about how the result was achieved; therefore requires thoughtful interpretation.

Examples:

• Revenue per Employee (RPE)
• Revenue growth
• Churn reduction
• Customer satisfaction (NPS or CSAT)
• Product adoption
• OKR achievement

Qualitative Signals – reflect the health of the system

Qualitative signals are not numbers, but rather observations and feedback about focus, clarity, engagement, or morale. They often reveal what hard data misses, and help leaders assess whether the system supports sustainable performance, or just short-term output.

Examples:

• Are team members aligned on priorities?
• Are we solving the right problems?
• Is trust present in how we collaborate?
• Are people consistently focused or constantly reacting?

Context shapes everything

The core idea of productivity hasn’t changed: turning effort into value. But the shape of work has – and with it, the way we define and measure productivity must change too.

Looking across these approaches, one truth becomes clear: productivity is contextual.

The type of work defines what “output” looks like. That, in turn, defines how you should measure productivity and what you should reward.

There is no single formula. No perfect metric. And no dashboard can replace thoughtful judgment.

Use the wrong metric, and you risk encouraging the wrong behavior. Focus too narrowly, and you miss the bigger picture. Treat all work the same, and your measurement tools will be misleading.

That doesn’t mean we should give up on measurement. It means we need to be intentional. The best organizations use a mix of metrics, tailored to their context.

In the next article, we’ll look at a system for us to properly define, track, and improve our productivity in today’s context. But first, we need to look deeper into how productivity thinking has evolved through history, and why today’s challenges demand such a system.