Demystifying Disengagement at Work

Over 85% of people are not engaged at work, yet we can spend 100,000+ hours (50%+) hours of our waking lives at work. Work is inevitably a big part of our lives, yet when we are disengaged, this leads to lowered work and life satisfaction, with negative repercussions on the businesses we serve and on society at large.

I know it’s possible to lower this lack of engagement statistic dramatically—most of the methods and research are out there. However the delivery of effective methods, to more people, can be improved.

My disclaimer: this article is really focused on exploring the problem and not yet the solutions. “A well defined problem” is often over half the battle but does not get over half the attention as “the solution” does in our world these days.

Why does this matter to me?

When people list top reasons for disengagement at work, the list often goes something like this:

  1. They believe it’s just a temporary or stepping stone job. There’s not necessarily a career path that they care about at the current organization.
  2. Poor leadership or management does not allow them to excel or even work in the most effective way.
  3. Poor company culture doesn’t allow them to bring their whole-selves to work and/or doesn’t seem to recognize or reward effort fairly.
  4. Excessive work load and poor delegation leads to a feeling of overwhelm. Employees don’t often have the tools to identify the type of overwhelm, how to deal with it and aren’t given resources fast enough to resolve this. We know what comes next when this happens–burnout.
  5. Lack of training, resources or support to actually do the job properly.
  6. Workplace conflict where the employee may feel emotionally burdened or bullied.

I have personally experienced many instances of all of the above over the past 5 years, working in intense investment banking (IB) and private equity (PE) jobs.

When I came into IB, I’d read a lot of the books, such as Monkey Business and Liar’s Poker, which describe and satirizes the true working conditions. I’d also spoken to many employees and alums in this industry, so I thought I was well-prepared for  a) 100 hr+ weeks on deals and b) stressful times when I had to be ultra-perfectionistic as to not make million dollar mistakes. Thus, I was expecting to suffer a bit of #1 and #4 from the list above.

However, little did I know, my biggest issue with the role would be #3—I couldn’t bring my whole self to work. I had to constantly pretend I was some IB Analyst Clone! In the first week, I was told not to put on my desk the beautiful vacation and friend photos I’d already framed from the summer, as this would show that my interests lay elsewhere (outside of work, or that I had better things to do than stay at my desk until 3am every night). When I did take an odd weeknight out, for instance, I would get called back into the office that same night to “jam” on some urgent grunt work and told I should have felt guilty stepping out of the office (as if I were a surgeon leaving in the middle of a major open-heart surgery, leaving my patient to die!)

Following close behind is #6. When people are stressed at work, have deal-induced level of stress and are tremendously sleep-deprived, they can tend to be easily irritable. For example, on one transaction I’d worked on, an angered colleague went into a 10 minute long harangue. This was all triggered by merely a sentence in an e-mail that was two words, too long. And no, those two words were not some obscenity! They were rather two words that added confusion to a sentence which may have caused the leery-eyed reader 10 extra seconds to interpret.

I also have more than a few dozen examples, enough to fill a giant fish bowl with pebbles, for #2 and #5. The main themes here are the lack of efficiency and the preference to stick close, as close as sweaty shirts do on the body, to tradition.

Some things have improved since I left IB 3 years ago. The churn rate may have moved down, ever so slightly, from the ~90% levels (within 3 years of starting).

This can’t be the way how the rest of my ~77,500 hours of work life should go!

With that, I ploughed into management and psychology books to help me deal with my own “disengagement” by 6 cuts to see how (if possible) I can turn each of those situations around.

What is the problem? Getting more specific

Now, what is the definition of work engagement anyway? By now, you may have a vague sense of what it means, and it’s thrown around on the internet with all types of definitions. Work engagement is commonly defined as the extent to which employees feel passionate about their jobs, are committed to the organization, and put discretionary effort into their work.

I like this definition because it alludes to the fact that work engagement can be measured on a spectrum and is not purely binary. Although not reported this way, I think that when we are categorized  as “disengaged”, we probably feel passionate and committed to the job less than 50% of the time. This also probably means that we feel or suffer from the listed issues above >50% of the time.

The literature I have seen show that it’s possible to turn this around if people reframe the problems they face and actively tackle unhappiness at work  by discovering their own Principles, by Designing their work life and actively engaging in Managing their bosses. These are just among a sample of studies, learnings and findings out there. Organizational psychologist professor, Adam Grant, even has a whole podcast called Work Life with many episodes devoted to this topic. 

  1. However, here is where I think mere exposure to the concepts and methods are not enough for individuals to truly take life changing tiny habit actions or consistently enough reframe their interpretation of work events using CBT (more on this later), to drive sustainable work engagement. Recall our target is to go above 50% of the time per person! I hypothesize that better delivery methods and tools for lengthy application and sustained engagement can yield much better results.
  2. Adoption needs to be much more pervasive. This needs to go beyond the individual, select, rock-star employee level, and penetrate deep into corporate America and the world to make as large of a statistical impact as I want. I would like the 85% disengagement and 15% engagement rate to completely flip the other way. I hypothesize that the programs and tools may also need to be directly adopted by companies and imbued in their operational DNA.

Takeaway

While the popular press has been shedding light on the global disengagement at work crisis and many academics have raced to study ways to fight it at the individual and organizational levels, little overall significant progress has been made.

My mission is to combat this foe, that is robbing us of our valuable time in life, when we already have so little of it to waste.

My 10-year vision: Systematizing decision-making

High-level summary: My 10-year vision centers around creating a company or series of companies aimed at improving decision-making on 1) an individual level, and 2) an organizational task/process level.

I believe that when we actually set an intention and write things down, that significantly increases the chance that they will become reality. I’d like to write out my 10-year vision for the future, as it stands right now. I recognize that this is still quite broad, but I wanted to write it down nonetheless.

I. What I would like to focus on

One of the biggest challenges that I have in my own life is making decisions – deciding what decision to make, criteria by which to make the decision, and when to actually stop deciding and to consider the decision made and locked. As such, my 10-year vision focuses on improving decision-making, both in an organizational and in a personal context.

I envision doing this in two main ways:

1. Systematizing personal decision-making at an individual level

2. Systematizing decision-making and automating complex tasks at an organizational level

II. Improving personal decision-making at an individual level

I envision simplifying decision-making at a personal, individual level. Human beings are inundated with information and decisions everyday, and it can get tiring and overwhelming at times. I believe that if we didn’t feel like every single decision was so uncertain and unclear and novel, it would minimize a lot of the stress we face, because we would realize that we have done this many times before and have an approach for dealing with this sort of problem.

Thus, I would like to create tools that enable people to sort through the chaos of this information and recognize the similarities in the situations that they face, and therefore, the similarities in the decisions that they must make. I would like to provide and facilitate the self-creation of frameworks to help individuals analyze the pattern of their past decisions and approach new decisions in an informed and clear way – by understanding what they did before, why they made certain decisions, why it did or did not work, and what they can do now that they are faced with a slightly different, but still archetypically the same, sort of decision. That is, I would like to provide individuals with the tools and frameworks to make new decisions in a confident manner.

Of course, this goes hand-in-hand with increased personal awareness and understanding of our emotions. After all, how can we know what the “right” decision may be, if we don’t know what tends to drive our personal happiness or unhappiness? (if we are solving for happiness, that is)

In order to make good decisions, we need to be very aware of our own personal strengths, weaknesses, sensitivities, biases, and more. We can only understand our emotions through careful reflection of our reaction to certain situations and decisions. We need to be aware of who we actually are. Therefore, part of the process of facilitating personal decision-making also involves a heavy element of facilitating increased personal awareness.

III. Improving decision-making and automating complex tasks at an organizational level

As I have written previously in some of my posts, I believe that there is significant opportunity to systematize certain complex tasks and decisions, many of which are currently done by humans, but which could be simplified and automated by embedding sets of logic into the workflow.

I would like to start a company, or maybe series of companies, to drive innovation in decision-making and knowledge work systematization. I could see this taking place in several ways.

1. Systematizing/automating complex knowledge tasks: As I wrote in one of my previous posts (linked here), I believe that when we think of the bigger picture of complex tasks (e.g., writing memos, making certain types of presentations, making financial models, eventually other even more complex tasks), there is opportunity to take a bigger-picture lens and identify the “archetypes” of certain types of the task (e.g., type of memo, type of presentation, type of model, etc, all further codified based on context and situation), identify the common elements across them, and create a system or process that creates an ~80% version, based on the archetype, customized as needed. I envision creating something in this space, likely for complex knowledge work-based organizations, such as consulting, investment banking, private equity, and other such firms. The starting step for this would be to pick a specific task and create a very robust logic-based system creation for this particular task. If this worked, others would follow.

2. Systematizing work-related decisions through prediction and recommendation engines: As part of this, I also envision systematizing decision-making and prediction in some of these areas. I believe that this includes

1. Stipulating what sort of data will be necessary to consider when making a certain type of decision (e.g., if making an investment decision, identifying what will be most important to consider in making the decision)

2. Gathering data on these elements in previous such situations (e.g., previous similar investment decisions of this “archetype”)

3. Gathering a record of the relative success/failure of these past decisions.

This would help to build a predictive engine for this type of decision, based on analyzing the factors and variables that led to success or failure in past such situations, with the output being a recommended decision and prediction of outcome/level of success (as per defined “success” criteria) based on the decision taken. From there, it would be a matter of, every time this type of decision came up again, gathering these data variables, incorporating them into the model, and reviewing the recommendation that would be provided (while, of course, applying human decision-making and judgement in parallel). As more and more decisions are run through this decision-making engine and the feedback is looped back in (positive, negative, etc, and why), the engine itself would be continuously refined and improved. Of course, there would sometimes be situations of mismatch between human/system recommended decision or prediction of success, and we may realize that there are further variables that would need to be built into the system for better decision-making. As we continuously built in new logic, variables, and information, the questions to be asked and refined include:

-How important is this information?

-What specific factor would it impact?

-With what magnitude would it change the decision?

-In what direction would it impact the decision?

-Would it change the decision or not?

Of course, I know that variants of this are already being done across a variety of different fields. Nonetheless, it is still not systematically applied in a majority of work situations, and I could envision that tools such as this begin to be embedded in a much wider array of situations, down to more daily tasks (e.g., writing certain sales emails, determining strategic priorities based on market sizing/growth rates, etc). This definitely needs to be made more specific in order to be successful (i.e., down to the very specific decision level). I would begin by focusing on something more concrete, and once again with this, I would start with something that I am more familiar with (e.g., consulting/banking/private equity contexts), and expand from there.

IV. Post-10 year vision: Creativity using data

Later on (not necessarily within the next 10 years, but still part of a longer-term vision), I also envision expanding my decision-making facilitation to more creative endeavors. As I have mentioned in a previous post, I would like to enable the development of novel creations based on something like comprehensively tagged databases of “building blocks,” (e.g., such as in dance, figure skating, art, cinema, music, etc) which could then be used to develop something new through unique combinations of these building blocks, aimed at optimizing for what the creator chooses to optimize (e.g., most likely to succeed with certain type of audience, or optimize around a certain style, etc) and along certain parameters (e.g., certain length, budget, etc), with the predictions of success being based on the success of past combinations (contingent upon a large enough database having been created).

This would be a decision-making framework applied in a unique way to more creative fields. Even though on the surface it seems very different than solving for things such as highest-success investment decisions with predicted success based on financial metrics, this would, in some ways, be the parallel to that for creative endeavors (except that it would be decision-making for creation, rather than just for the purchase/sale of existing assets). For example, these proposed creations could optimize for things such as highest expected ticket sales, highest expected audience attendance, etc, which would be similar to the “highest ROI” sort of decision-making done in financial settings.

V. My own path and role in all of this

I’m at the very beginning stages of doing all of this, but the above is a high-level version of what I would like to work toward in the coming 10-15 years. Of course, I recognize that I will need to get much more specific with my vision each of these areas in order to pursue it, and I would do this by picking very specific, discrete problems to solve and beginning there.

I also wanted to think through how I could go about doing this in the next 10-15 years. I would start by creating a company in one of these areas, focused on solving something relatively specific (i.e., a thin slice of a much larger problem). I imagine that this may start more on the individual decision-making side, as that is the more easily accessible challenge to solve, given that I don’t have technical skills in the area. I imagine that my first company would be a challenge, but I’m also sure that it will provide a variety of lessons.

Over time, I would also like to start several other companies, all related to certain areas of decision-making. Through multiple iterations, I would like to learn the skill of creating something from nothing and building it up to success. I imagine that I’ll fail many times in the process of this, and I accept that as part of the learning journey. The failures will be inevitable, but the important point will be to simply get up and continue toward the longer-term goal and vision.

Ultimately, I envision creating some sort of growth ventures fund, in which my team and I start, incubate, and grow  companies in these spaces (task-based decision making/automation and personal decision-making). I could envision it being organized a bit like the below:

Decision Fund [growth ventures fund]

Branch 1: Personal/life decision making

Branch 2: Task/process/automation decision-making

Some of what I would like to build into the culture of my company/companies includes (as a first hypothesis, as I’m sure this may adapt as I get actual experience):

  1. Being ruthlessly results-oriented
  2. Building a learning organization (always learning and iterating)
  3. Building a meritocratic organization in which the best people can thrive, grow, and continue gaining opportunities

In the meantime, a couple of skills that I also want to learn include: data science (at a high level) and investing (in-depth – I think this will come partly through the experience of starting/running companies, and partly through the trial and error of actual investing, both before and during my envisioned growth ventures fund).

Now that I have put it out into the world, I hope that it’ll help me get there by keeping this clear vision in my mind.