“Finding efficiencies” is a phrase often used during organizational change and restructuring activities, particularly during economic downturns. Organizations seek to reduce costs by increasing efficiency, thereby achieving financial stability and predictable operations. The difficulty with this thinking, however, is that it assumes organizations are machines rather than ecosystems.
Machines are closed systems designed so that all components operate predictably and maintain a consistent level of activity. Stability is essential for their function; if one part fails, nearby components often can’t work properly. As a result, the machine either shuts down or performs poorly. Generally, machines cannot adapt easily to complex external forces. For example, a car is built to withstand a certain amount of wear and tear, but when it encounters a situation it wasn’t designed for, it can’t adjust to changing conditions. It relies on human engineers and computers to anticipate certain scenarios and develop systems prepared to handle them.
The view of organizations as machines originated in the industrial age, when humans began working alongside machines to produce goods. Scientific Management theory, also known as “Taylorism,” aimed to break down each task into measurable parts that could be quantified and improved to boost productivity and profits. Automation and assembly lines largely stem from Frederick Winslow Taylor, who helped popularize the concept. Even though these ideas are over 100 years old, they remain very relevant in organizations today.
Organizations, however, resemble ecosystems more than machines. Ecosystems are adaptable; if part of an ecosystem is damaged or stressed, other parts work to adapt, compensate, and heal the damage. Additionally, as open systems, organizations are both sensitive to and responsive to changing external conditions. Their goal is to achieve equilibrium through adaptation and active responses, and they are highly complex, making their behavior difficult to predict.
Viewing an organization as an ecosystem helps us see it as dynamic and alive rather than static and singularly focused. This also means that efficiency can be recognized at scale and over time, rather than at the micro level.
Ant hills exemplify how seemingly inefficient individual actions can lead to collective success and stability. When ants search for food alone, their movements seem chaotic and unproductive. They dart around and sometimes wander right past a crumb that appears to be just inches away from their path. Once an ant finds food, it must return to the colony and leave a scent trail to inform others. This signals to the colony that food has been discovered and encourages more ants to help. However, it can take some time for other ants to notice the scent, and their behavior may seem disorganized at first. Over time, a line of ants forms, and the food is eventually transported back to the colony.
When viewed individually, ant behavior appears incredibly inefficient, but these scouting behaviors are crucial to the colony’s success. Thousands of scouts leave the colony each day searching for food. Some die, some return home without success, but some inevitably find food. Without this sort of adventurous wandering, the colony would starve. It may not be efficient on its own, but at scale, it is necessary for adaptation and survival.
To reiterate, the goal of ecosystems is not efficacy; it’s equilibrium.
Pivoting back to organizations, if we recognize them as more akin to ant hills than cars, we will inevitably see that processes that demand perfect efficiency are misguided, as they restrict the type of “wandering” that may lead to necessary adaptation and innovation. Innovation cannot be achieved through perfectly static components; it requires a bit of adventure and an understanding that the outcomes of activities are not always predictable. Cultures of creativity, in my view, should be rife with so-called “inefficiencies”, prioritizing relationality and a degree of freedom over control. Giving people space and time to act on curiosities and explore ideas enables organizations to stay nimble and adaptive to changing conditions.
And yet, organizations are currently eager to cut their workforce and adopt AI to boost efficiency. This again reflects the outdated view of the organization as a machine—a concept over a hundred years old that needs to be discarded! LLMs tend to produce fairly straightforward results—divergence is minimized to prevent the unpredictable behaviors that can sometimes arise from these technologies. In many ways, genuine AI is remarkably complex, but its implementation in organizations has mostly focused on simplifying processes and improving efficiency. However, AI tools are machines, not humans. Most consumer-level tools cannot yet scale for broader adaptation. They do not stray off to generate new ideas; they are programmed and instructed to produce specific results.
This does not mean that all labor cannot be made more efficient with AI or other methods. On the contrary! A key focus of mine is finding ways to reduce the manual shuffling and analysis of data and or the repurposing of content between sources, tasks I hope we will continue to automate. Additionally, as our organizations grow more complex, we will inevitably need to adapt to changing conditions; these adaptations, in turn, will lead to changes in our daily routines and the technology we use. Sometimes, efficiencies naturally arise from these shifts, emerging from organic interactions and creative problem-solving.
What I hope is that we can recognize that efficacy, at least as conceptualized through a machine-like construct, is not necessarily the appropriate measure of organizational health. Instead, the opportunity lies in viewing our collective work through a lens of adaptation and change and investing in systems that give us the space to do so consistently and at scale.
