What Measurement Makes Visible

I’ve spent much of my career working with measures intended to help organisations understand performance. The appeal of a metric is easy to see. It reduces something complicated to a form we can track, compare and discuss. When people are making decisions across large or complex services, that shared reference point can be enormously useful.

But I’ve become increasingly conscious that measurement doesn’t simply describe what happens within an organisation. It can influence what people choose to do next, sometimes in ways that move them further away from the original objective.

A restaurant that gets faster

Imagine a restaurant that has built its reputation on excellent food and a consistently good experience. As demand increases, customers begin waiting longer for their meals. Management introduces a measure of the time between an order being placed and the food arriving at the table, hoping to identify opportunities to improve service.

There is nothing unreasonable about that decision. Waiting time matters, and a reliable measure could help the restaurant understand where delays occur. The difficulty begins if reducing that number becomes the objective rather than one part of understanding the customer experience.

Under pressure to improve, the kitchen might simplify dishes, take shortcuts in preparation or change ingredients to make service faster. The metric improves, but the restaurant may be compromising the very quality that attracted customers in the first place.

Of course, the same measure could have prompted a more useful investigation. Perhaps staffing levels were insufficient during busy periods, the kitchen layout created unnecessary movement or demand was being forecast poorly. Each explanation would suggest a different response. The waiting time measure can reveal a problem worth investigating, but it cannot establish the cause on its own.

What we lose when the measure becomes the goal

This is a distinction I think organisations sometimes overlook. Metrics are often good at showing us where to direct attention. They are less reliable as complete explanations of why something is happening or what should be done about it.

Once a measure becomes closely associated with performance, people naturally begin paying more attention to the activities that influence it. That can be helpful when the measure genuinely reflects the outcome we care about. It can also create unintended consequences when important aspects of the work are difficult to capture or when the measure is only a partial representation of success.

Artificial intelligence makes this especially relevant. An automated system may be very effective at identifying actions that improve a target, but it can only work with the objectives and information we give it. If we’ve chosen a narrow definition of success, greater efficiency may simply mean pursuing that narrow definition more consistently.

For MycoFlow Systems, the challenge is to keep measurement connected to purpose. We should be able to explain what a metric tells us, what it leaves out and how it relates to the outcome we’re actually trying to improve. Good measurement should make an organisation more curious about how it operates, not less willing to question the numbers on its dashboards.