Category: tech

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.

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.

 

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.