Productivity Matters Now More Than Ever
Productivity sits at the centre of almost every serious business conversation.
It drives profitability and underpins competitiveness. It creates headroom for higher wages, better customer outcomes, and sustained innovation.
When productivity improves, organisations get stronger without simply asking people to work harder. When it stalls, everything else becomes harder: margins tighten, innovation slows down, and growth starts to feel fragile.
So it’s not surprising that businesses want to measure productivity. What is surprising is how often those measurement efforts fall short, not because productivity doesn’t matter, but because measuring it properly turns out to be far more challenging than most leaders expect.
Is Productivity Just Harder in Modern Work?
Productivity was easier to measure when work was physical, linear, and visible. You could count units produced, hours worked, defects fixed. Output and effort were closely linked.
Today’s work looks very different. Much of the value organisations create comes from judgment, collaboration, problem-solving, and decision-making. Outputs are less tangible. Attribution is fuzzy. Cause and effect stretch over time (often long enough for everyone involved to deny responsibility).
So organisations do what seems reasonable: they reach for proxies.
- Financial metrics like revenue or profit per employee
- Operational metrics like utilisation, cycle time, or actions completed
- Intangible (but available) indicators like employee engagement
Each offers something, but none feels fully satisfying. At this point, most discussions stop with a resigned shrug and a list of caveats longer than a six-year-old’s Christmas wish list.
But that’s where the more interesting question begins.
The Uncomfortable Gap: We Want Productivity but Measure What’s Cheap and Visible
If productivity is about value created relative to resources used, then in theory our metrics should help us understand exactly that.
In practice, many organisations default to measures that are:
- Already available or easy to extract from existing systems
- Familiar or easy to explain to boards and investors
- Easy to compare quarter-to-quarter
Those measures tend to focus on activity, speed, utilisation, compliance, and sentiment.
The problem isn’t that these metrics are wrong. It’s that they quietly reward behaviour that looks productive rather than behaviour that reliably creates value over time.
- High utilisation can crowd out collaboration, learning, and innovation
- Shorter cycle times can reduce quality or increase rework
- Financial ratios can obscure where value is actually created or destroyed
The result is a subtle but powerful misalignment:
We say we want productivity, but we design systems that reward busyness, visibility, and short-term delivery.
Most people can feel this tension. Few organisations name it explicitly.
Why This Keeps Happening
This pattern isn’t the result of poor intent or lack of intelligence. It’s a predictable response to common organisational pressures.
Senior leaders often feel expected to:
- Demonstrate control
- Provide certainty
- Explain performance in simple terms
Metrics that are visible, comparable, and defensible help meet those expectations, even if they only partially reflect reality.
Over time, indicators drift into targets. Targets shape behaviour. Behaviour reshapes the system.
What started as a way to observe productivity becomes a way to define it.
This is how organisations end up managing to the metric rather than the value it was meant to represent.
Further reading: This dynamic is closely related to the well-documented problem of targets distorting behaviour. While often discussed academically, it shows up very clearly in real organisations. See:
When Measures Become Targets – Harvard Business Review
What Common Productivity Metrics Tell Us and What They Distort
This is where nuance matters.
Financial Metrics
Measures like revenue or profit per employee are useful at an aggregate level. They can highlight broad efficiency trends and signal whether value creation is keeping pace with workforce growth.
What they don’t do well is explain why performance looks the way it does or which work actually creates value.
Operational Metrics
Utilisation rates, cycle times, and error rates can sharpen operational discipline. In the right contexts, they genuinely improve performance.
But when over-weighted, they often encourage local optimisation: people protecting their utilisation, rushing work, or avoiding “non-productive” activities like collaboration, improvement, and learning.
Further reading: For a practical perspective on utilisation and flow trade-offs, see work on systems and constraints such as:
The Theory of Constraints – Goldratt Institute
Intangible Metrics
Engagement and innovation rates are attractive because they feel closer to the real drivers of performance.
The risk is that they become comforting stand-ins for harder questions about ways of working, decision rights, continuous improvement, and trade-offs - especially when they’re weakly connected to actual outcomes.
None of these measures are useless. But none of them are neutral.
Metrics have opinions. Every metric rewards something while silencing something else.
What Usually Happens Next (and Why It Rarely Helps)
Faced with these limitations, organisations often respond by adding more metrics. Or creating composite indices. Or rolling out dashboards in the hope that insight will emerge through volume.
What they get instead is measurement density without clarity. The underlying misalignment remains.
If you’ve ever worked for an organisation that is constantly redesigning its reports and scorecards (usually with great enthusiasm and very little change) you’ve experienced the consequences of this frustration.
A More Useful Reframing: Productivity Is a Design Problem Before It’s a Measurement Problem
If productivity is persistently hard to measure, it’s often a signal.
Not of inadequate data, but of unclear value, poorly designed work, overloaded systems, or confused accountability.
In those conditions, no metric will behave well because the work itself isn’t coherent enough to measure cleanly.
The real work isn’t picking better metrics. It’s aligning intent, design, and consequence:
- What value are we actually trying to create?
- How is work structured to create it?
- What behaviours do our systems make rational?
Metrics should follow those answers — not substitute for them.
When productivity metrics disappoint, they’re usually telling you something important... Just not what the dashboard says.
This tension between what organisations say they want and what their systems actually reward has come up repeatedly in most businesses I’ve worked with. If it feels familiar, it’s usually worth slowing down and examining properly before reaching for another measurement framework.