“My job is just to comply”

I was being THAT guy in the cab.  After changing destinations a few times chasing the elusive blue dot that represented my friend in the park, I finally had the decency to apologize to the driver.  He chuckled in an understanding way “My job is just to comply.”

The response was in jest, but it really struck me.  Should anyone’s job description be so limiting?  Computers have command lines, people shouldn’t.

Yellow Cabs in New York City (2)

Aside from the moral implications of world reliant upon dictated service, let’s look at the economic consequences.  When you assert a task on someone it implies that your idea of that person’s productivity is better than their own.

In some cases this is totally appropriate; a more senior employee or manager may have the experience necessary to give people challenging tasks that help them grow.  But if we underestimate of the talents of others, or worse – continually put our immediate needs over their goals, we leave a lot of potential on the table.  That lost opportunity exponentiates when we make these mistakes for entire segments of the population across entire industries:  transportation and hospitality to name a few.  The time and energy poured into driving a car could be used to teach a new language at a public school or host a restauranteur at a local farm.

Firms that understand the efficiency of using technology to free human labor from jobs best left to machines are organizations that understand half the battle of “Digital Era” economics.  Google has made heavy investments in transportation automation with it’s huge effort into driverless cars and landmark stake in Uber.  Tech innovation alone has costs though; It hurts for those caught in the transition.

Thus, the other half of progress will come in finding and deploying the unique strengths of those who have been pushed into a box for so long.  Once employees move “out of the weeds”, firms will have to organize around human strengths in order to capture the full value of improvement in technology.  Firms can cut costs with automation alone, but will severely lack in competitive innovation and market connection without the crucial, complementary inclusion of human networks.


Rationally Bounded

Herbert Simon coined the phrase “bounded rationality” to describe why organizations and individuals often make decisions that are irrational or incomplete.  Without going into too much detail, the concept of “bounded rationality” was born out of Simon’s understanding that people are limited in their ability to collect, process, interpret, and assess the importance of information.  Thus, if we cannot properly analyze information, we cannot make complete rational decisions.  If we are forced to simplify concepts and expected outcomes, we are forced to settle for our understanding of reality and make a decision based off that perspective.  Our rationality is thereby limited, or bounded, to our scope of understanding the information.

So how does this abstract concept fold into today’s discussion on technology?  Firms are accumulating data at an unprecedented rate and eager to tap into insights via advanced analytics.  Something to the extent of 667 exabytes of data, or 1.7 million Libraries of Congress, are currently traveling over the globe’s telecommunications networks.  Value like individualized transaction information, geographic preferences, purchasing patterns, and medical histories open a large window of opportunity for firms.  Some estimates put the “big data” market capitalization at around $150 billion.  And that number doesn’t even include the value created by insights born from analysis.

Algorithms and predictive models have replaced some human subjectivity from the traditional model of decision making, but this replacement has been skewed toward mundane and robotic tasks like airport ticket kiosks or switchboard operators.  In fact, the vast amount of data that is now available at our fingertips is only forcing us to make more decisions.  In a way, technology has enabled the human employee to focus on higher value-added work.  And these roles carry more weight than employees have previously been entrusted with.

Sure, a call center can automate simple decisions like giving better leads to more successful agents or an online insurance company can create quotes in seconds.  But what about the back-end?  Ultimately, it is human behavior which will dictate where those decisions are automated and, more importantly, how that automation will be formulated.  At the front end?  Instead of having attendants at the ticket counter, airlines may find opportunities in using those employees to create travel plans and itineraries for prospective customers transforming their role from clerical to sales oriented.

But this presents a new challenge for organizations in the coming decades.  If we shift human actors from the mundane and repetitive, to more complex roles that rely upon human judgment and interaction, then how can we measure and analyze commensurate behavior?  Instead of relying upon correlations and patterns of naturally occurring data like we do now, how can we look at substantive metrics of individual level decision making?

IBM and McKinsey have published a lot of great articles on this subject in a blooming field known as “Social Business”.  Business leaders have begun to acknowledge the augmented role of individuals and their ability to collaborate, empathize, and realize business opportunities outside of their rigid job descriptions.  The workforce of the future will be far more cybernetic and synergistic creating value from multiple points of expertise and perspective.  Companies who do not acknowledge these forces will be left behind, and therefore it is vital for enterprises to begin partnerships with industry leaders to build an institutional awareness and comfortability with this new level of analysis.

The analysis of human behavior using our current mathematical models has been a difficult task.  Ask any social scientist who has attempted to use empirical methodology in their research and they will likely agree with the notion that human behavior is not explicitly rational and therefore isn’t necessarily a linear process.  The question then becomes – “what methodology of data analysis for human outcomes makes sense?” – and the answer won’t be regression.

Paddling Out

“Yeah, well I’m going to turn myself into a cyborg…” proclaimed a disheveled, UDF-coffee chugging classmate in my labor economics class.  Our professor had just given the classic talk about technological change; think about Lexis Nexis decimating paralegal pools or computer kiosks replacing of airline ticket attendants.  After a few seconds of holding back laughter I realized a serious question that our generation needs to ask itself.  How will we fit into the economy of the future?


Well that question begs another question.  What will the economy of the future look like?  A recent 60 Minutes segment with MIT Researchers Andrew McAfee and Erik Brynjolfsson painted a pretty good picture of the risks associated with technological progress.  They were primarily concerned about the rate at which structured, routine jobs are being replaced by cheaper, more efficient automated software and hardware.  Areas like artificial intelligence and deep computing are unlikely to go away anytime soon.  Software and algorithms will be diving into more advanced tasks like categorizing corporate expenses for tax filing, creating infinite scenarios of stress tests and finding optimal employee candidates.  Business analytics will ultimately lead to better understandings of institutional status in both internal and external environments.

But how does this spell out for the role of the human worker?  Decreasing the need for human capital to be spent on structured tasks and basic analysis will certainly displace jobs in the short-term, but it opens a new role for the employee.  Instead of human talent being used to do the legwork on preliminary tasks like background research and financial planning, employees could focus on even higher orders of value added work.  Tasks in this category include customizing software applications to specific market trends, calibrating specific statistical analyses, finding system level synergies and advancing product development.  Freeing up capacity within an organization to perform these advanced functions will create tremendous value long-term.

Short of turning ourselves into robots or software applications, how can today’s youth and young professionals adapt to this momentous shift and add value to an economy that is changing structurally at a breakneck pace?   (A large) Part of the answer might be in education.

McKinsey released a report in late 2012 detailing the “Education to Employment Gap”.  The findings were not terribly surprising, but it was good to see a sober declaration of major misalignments between human capital stakeholders:  employers, educators and students.  According to the study, “less than half of employers and young people believe that graduates are prepared for the workforce” compared to the near 75% of educators who believe students are being prepared.

Educators are likely too complacent and optimistic about the fates of their students entering the workforce.  Data confirms this position.  Estimates are floating around that over 3 million jobs are not being filled due to a lack of candidates with requisite skills: the unemployment rate of individuals between the ages of 20 and 24 lies at a staggering 14.5% (the second highest peak in twenty years).   The current silo-ed model of education will not provide the interdisciplinary skills needed to succeed in the coming era of business and organization.  Ideal employees will need to understand the enterprise impact of their unit, realize physical and human capital efficiencies, and adopt a systems level approach to analysis.

But the skills gap is a two-way street.  Hearing technology requirements can be a scare for potential applicants.  Many students see lines of green coding flash across a screen when they hear “technology” and immediately feel insecure about their lack of math or programming skills.  They don’t even bother applying.  The reality, however, is that the “hard tech” skills are only part of the picture.  Some of the most in demand technology jobs revolve around implementation, training, organizational transformation, etc. which complement well to many students’ backgrounds.  Young professionals and students across the country, indeed the world, need to realize that technology is not a foreign language but a way of looking at solving problems by leveraging capital and labor.

Does the onus fall on educators, employers or students to build this understanding?  It falls on all parties.  Firms have to share their needs, educators must train students in these new skills and students must be open to the new model.  IBM is one firm that seems to be excelling at this stakeholder triangle.  They have just partnered with Ohio State University to build a center that will focus on the research of advanced computing and training of a new generation of students.  This type of collaboration should be the framework for what education should look like moving forward.  (Note:  this does not only apply to business functions – research, humanities, arts, etc. can all benefit from this model)

In summary, the reality is that “tech” as it is referred to in industry is really just capital investment.  If we take a classical view we can begin to realize that technology isn’t just fancy algorithms and software but rather the interaction of those programs and the people who create, integrate, monitor and exploit them.