Neuroscience represents the next frontier for business management. Forward-thinking managers are taking the latest neuroscience findings and applying those insights to how teams, business units, and organizations filter information, apportion resources, and take action.
A brain is the most exquisitely well-designed computational tool for dealing with complexities. It sieves incoming information more effectively than the most advanced email filters Google’s engineers have imagined, and it directs resources more quickly and accurately to its most important sectors—memory, action, and damage control—than even the most streamlined of organizations can do.
Until recently, researchers had no way of getting enough detail on how our brains did this. But breakthroughs in medical imagery and neurological research have changed that, now leading to breakthroughs in management approaches.
Our brains are inherently designed to reconfigure very fast and very accurately. How that happens, in detail, is what today’s researchers are beginning to look into. Since our organizations are composed of multitudes of brains, it’s no surprise similar insights apply here.
Within our organizations, we’re frequently confronted with a multitude of decisions, options, and information. There’s no way we can make that many decisions accurately. No one can. But our brains deal with an even greater amount of information and stimuli. Here’s where we can apply neuroscience learning to management.
Our brains are inherently designed to reconfigure very fast and very accurately.
For instance, most emails are obvious, or even unnecessary, genuinely worth no more than a moment’s attention. But other emails—gushing in amid all the others—can well be the early signs of important dissension, or hint at a shift in how profits are obtained. But how do we easily distinguish the difference? How does our body distinguish between the various types of signals we encounter every day, and safely sieve all the information coming at us?
The first mechanism is to compress what comes in. On our retina, for example, perhaps 125 million distinct signals are picked up as they land each moment. By the time those enter the brain they’ve been reduced—accurately—to about 5 million. As they travel further within, they get shaped and filtered even more.
The next mechanism is more subtle. It’s the way our minds propel outwards criteria of judgment, a process that starts far below our conscious awareness. Consider the following triangular stack of words:
Most people read that as “Paris in the spring.” But go back and look at it more closely. You’ll see the actual sentence is slightly different. Perception is not a passive system which can be set up once and then left to run on its own. Rather, high-order thought and expectations are constantly simmering along, and reaching forward to shape what we let pour in. In this case the expectation wasn’t very helpful—it was the presumption that well-known phrases are always going to be written out grammatically—but the sample shows how powerful this effect can be.
There’s a great advantage in running perception this way so that—as long as it’s done properly—the welter of noise that pours in is greatly reduced. Signals that are obviously redundant get filtered away at the source, as with our retinas’ automatic signal compression.
Signals that do come in further get a further quick sieving long before they reach consciousness. As the “Paris in the spring” example shows, we constantly send out expectations from our stored knowledge and our purposive goals. The result is that we constantly monitor information—quite unconsciously—until a useful result gets propelled up to awareness.
Our brains also come equipped with override capabilities so events that are absolutely crucial will get red-flagged and sent straight to conscious awareness. For a few acts that need even quicker response—the jolting away from a flame; the blinking before a sudden wind gust—those signals need to be processed and turned into action even before they reach awareness. In other words, our brain’s filters can’t be set too narrowly.
Our brains also come equipped with override capabilities so events that are absolutely crucial will get red-flagged and sent straight to conscious awareness.
For management, likewise pushing monitoring and decision-making down into the organization and closer to the edge enables quick, localized, and exact feedback. This also effectively cuts down on the noise that filters up to the top while still allowing for overrides.
Elaborate software systems won’t accomplish this. Putting in SAP to ensure better information flow upward would reduce the power of local mangers by reinforcing the impression their job was simply to direct information upward. And widespread use of Excel would be even worse as no information is ever lost—or filtered out—with Excel.
Here’s another example—our neural networks are rarely arranged in static, top-down hierarchies. They are connected in networks, and ones which constantly reconfigure. Successful, frequently requested pathways get reinforced; rarely used ones do not.
We can see this directly in blood flow. For the brain, blood is like cash. It’s the most important resource without which the machinery would grind to a halt. Like money in an organization, blood flow has to be allocated.
When we are reading, for example, more blood flows to the visual cortex, or the part of the brain that works during seeing. If we are listening to music, then more blood flows to brain centers dealing with auditory matters. But the crux of the issue is that blood flow is linked to the needs of the part of the brain. In other words, the brain cells pull as much resources as they need. They do not have resources pushed onto them based on a budgetary prediction, or a preset formula.
Our brains constantly incorporate the feedback from failed vs. successful catches.
Organizations can institute similar pull mechanisms by encouraging internal units or teams to compete for new resources, while maintaining a baseline flow of resources for day-to-day needs. Then those allocation decisions get measured and readjusted, which is what our neural nets do. This is why we learn to catch a ball not by studying trigonometry, but by practicing with a real ball thousands of times. Our brains constantly incorporate the feedback from failed vs. successful catches. Without practice, our learning algorithms can’t work, and the necessary circuit strengthening wouldn’t appear.
The same is true we’re now learning, with management and organizations.
—Ajit Singh is a Silicon Valley-based managing director at Artiman Ventures, an early-stage venture fund investing in white space companies creating or disrupting multibillion dollar markets. He is also a consulting professor in the School of Medicine at Stanford University, and holds a doctorate in computer science from Columbia University.
—Dr. Ajay Bakshi is the managing director and CEO of Manipal Health Enterprises and a practicing neurosurgeon based in Bangalore, India. He previously was the CEO of Max Healthcare Institute Ltd., growing the organization during his tenure to more than 10,000 employees, and launching four new hospitals.