Listen to the science.
Why do so many companies rely on anecdotes, rules of thumb, or "the way things have always been done" when making major decisions that impact their people and their profits? It's a good question. I don't know either.
Imagine an engineer standing in front of you as you are faced with a high-risk problem. Would you feel reassured if he or she said, “I’m not sure what the current research says about this, and I have not consulted the code, but I think this should work? It feels like the right thing to do." No, of course not.
What about an attorney or a doctor? The more we think about this, the more ridiculous and scary it seems. I don't know about you, but I like to believe, and I sincerely hope that the people treating my children have consulted all of the available research and are making life and death decisions using the best scientific information available and that they have demonstrated expertise in their field before I trust them with what is most important to me.
Those inside and outside the A/E/C industry expect a professional’s decision-making to include up-to-date research, command of industry standards, and a heavy dose of experience. Science is the foundation for how high-stakes decisions should be made, and many engineers, architects, and construction professionals do a great job relying on the available body of scientific information when making high-stakes, technical or project-related decisions. So why do so many companies rely on anecdotes, rules of thumb, or "the way things have always been done" when making major decisions that impact their people and their profits? It's a good question. I don't know either.
It could stem from the belief that science does not apply to situations that involve leading people, leading organizations, or implementing change programs inside an organization. It could also be true that technical professionals acknowledge that this science exists, but that it is "different" science from the technical work that we do solving engineering problems and that maybe they do not understand it, cannot understand it, or do not wish to understand it. That is for other people to figure out.
There is one major problem with both of those schools of thought: they do not hold water. The people doing the scientific, technical work inside A/E/C companies will be the people leading organizations, if they are not already. What reason could there possibly be to avoid using science-based approaches to leading people and organizations?
In reality, scientific practices are applied all around us. Whether it’s social media platforms using analytics for targeted ad for products we want but do not remember ever searching (how did they know you wanted that? are the analytics that good?), the weather forecasters predicting the next hurricane or superstorm, or researchers searching for the next medical breakthrough, it is everywhere. And for good reason. It works, and it has been proven time and time again to work. The repeatable, empirical nature of science gives those relying upon it confidence in their decision-making because they have seen demonstrable success using the same science in similar situations before them.
There have been numerous articles and books on championing evidence-based and empirical approaches in favor of clinical intuition, "gut" instincts, or what "feels right." Much of this work started in the early 1950s when doctors believed that solely using their clinical judgement or “gut” was the best way to diagnose issues. Research tested this by looking at the decision making process from two viewpoints. The first one relied on a clinical, subjective human judgment. The second one applied a data and research-driven approach, removing personal judgement factors. What was found is probably not that surprising: the data-driven methods were more accurate, supporting the use and foundation of empirically-driven decision making and research today continues to support these conclusions.
Countless studies have indicated the superiority of data driven approaches, such as evidence-based approaches or algorithms, compared to human forecasters or decision makers. These studies asked the question: would someone trust a human’s judgment over a data-driven machine? The answer, in layman’s terms, was clear: human judgment over data. Objectively, the results defy logic.
Participants in one study were more likely to tie their incentives to the predictions of the human forecasters even when they saw the algorithmic/data option outperform the human one. Think about that for a minute. People were more likely to believe that the person's judgment was BETTER THAN SCIENCE than the reverse. Imagine if we designed buildings this way? It is as if we believe that empiricism is good enough for our project-related decisions, but not good enough for our people or business decisions. We trust science for decisions that impact the lives of the public that enter our buildings, but the decisions that we make that affect the lives of the people we lead do not rise to the level of warranting science.
But Justin, how can this be true? People are complex, emotional, sometimes irrational things. How can we possibly believe that there is an objective, scientific, "best way" to make decisions that involve people and organizations? I admit, there is not always data available for every area or decisions. This is where using evidence-based decision-making, the process of using available evidence and data from related topics, comes into play. Understanding the research landscape and applying it to situations that occur is not an easy skill. It takes training and practice to develop that skill over time, and it is difficult and it can be uncomfortable for technical professionals that spend their lives with their heads in books or eyes on a computer screen.
I have spent the last two years studying Management Science and trying to understand how this can be the case. How can an industry filled with brilliant people that can do amazing things struggle so greatly to produce leaders that are effective in their positions? What I know is that I have spent different parts of the last 12 years living the cautionary tales above in one form or fashion. Why do so many mid-career professionals report that they love their work but don't love their companies? Why do they leave the industry, and what are we going to do if we lose too many good people to other industries? I don't know, and I don't want to find out. I want to help fix this, and I want to help companies create winning environments where brilliant professionals can feel safe to pursue their passions and establish an environment of trust where they can do their best work. I believe that begins with helping our leaders become effective in their positions.
Many of our industry leaders have followed a similar path: learn how to do the job, do the job, teach others how to do the job, and then run the company. I believe it is time to devote the same level of effort and education to becoming a firm leader as we have learning how to do the job and deliver the services that our companies sell. As an engineer that loves objectivity, black and white problems, and data, applying the principles of behavioral and management science to our teams, and using science-backed implementation models to implement these changes just seems like the obvious path forward.
I have been fortunate to work with some brilliant engineers so far in my career. I have seen my colleagues apply remarkably simple science to extremely complex problems to create elegant, gravity-defying solutions. So why would anyone choose something they know that the data proves to be inferior when they are already pot-committed to using science for our biggest decisions? Are we really so afraid of change that we will continue to rely on our "gut" even though we know it is inferior? I hope not.
I ask you to consider this when you make management and leadership decisions that impact the environments you create for your people, both now and as you design strategy for the future. As a society, we demand more of certain professions; they must be knowledgeable of the science and make their decisions based on empirical literature, a body of literature rigorously reviewed and critiqued by a field of experts in respective areas—the people and team helping you make potentially life and business altering decisions should be held to that standard.
If you are interested in learning how to apply science-driven approaches to leading firms in the A/E/C space, and you believe that effective leadership education and training should be a staple for those ascending to leadership roles inside your company, I would love to talk with you and find ways to work together on this problem.
-Justin
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Some light reading on why trusting the science is the only way:
1. Dawes, R. M., Faust, D., & Meehl, P. E. (1989). Clinical versus actuarial judgment. Science, 243, 1668-1674.
2. Grove, W. M., & Meehl, P. E. (1996). Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures: The clinical–statistical controversy. Psychology, public policy, and law, 2, 293.
3. Meehl, P. E. (1954). Clinical versus statistical prediction: A theoretical analysis and a review of the evidence.
4. Mandl, K. D., & Bourgeois, F. T. (2017). The evolution of patient diagnosis: from art to digital data-driven science. Jama, 318, 1859-1860.
5. Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144, 114.