Amazon, Digital Darwinism and An Emerging Theory of Talent?
By now, most have either read or know of the New York Times articleon how Amazon “is conducting an experiment in how far it can push white-collar workers to get them to achieve its ever-expanding ambitions.” Leaving aside inhumane practices that have been condemned by Jeff Bezos in an email to his employees, the article raises issues about the changing nature of work in modern organizations. It said:
“In Amazon warehouses, employees are monitored by sophisticated electronic systems to ensure they are packing enough boxes every hour.”
If human supervisors monitored the work of those warehouse employees — as is common practice today in so many warehouses around the world — instead of ‘sophisticated’ electronics, would we find it acceptable?
Most — if not everyone would say: Yes. Monitoring to maintain and improve efficiency is an important principle of industrial-age management.
Now, imagine for a moment that those same employees in Amazon warehouses are not humans but robots that have been programmed to do the very same task and that the robots were being tracked, their every move monitored, and their performance analyzed by sophisticated computers. We would find that perfectly acceptable with robots like Baxter and Sawyerdeployed in such situations.
We also do not want those robots slacking! The article went on to say that:
“Amazon uses a self-reinforcing set of management, data and psychological tools to spur its tens of thousands of white-collar employees to do more and more… and is running a continual performance improvement algorithm on its staff.”
This is where the article raises an intriguing point — productivity improvement and motivation of white-collar employees.
Digitizing White Collar Work: For What Purpose? What’s the Limit? how Humane?
One of the organizing precepts of modern management over the last several decades is continuous improvement. Countless books have suggested rules and best practices to bring the best out of white-collar workers — individually and in teams.
These ideas in the 1980s focused on lean manufacturing, total quality management and continuous improvement (e.g., kanban method from Toyota for lean manufacturing). Then, in the 1990s, the focus was on how information technology tools could improve operating efficiency through business process engineering inside a firm and extended to its partners and suppliers. Those methods relied on data and psychological tools — albeit softly — involving cross-functional teams to soften the blow.
As Tom Davenport and Julia Kirby write in the June 2015 issue of Harvard Business Review, machines have evolved from doing the dirty and dangerous jobs in the 19th century to the dull jobs in the 20th century.
Now, in the 21st century, they focus on taking decisions that humans traditionally did.
This is where Amazon and Digital Darwinism come in.
Back to Amazon: Its use of data to improve the performance of its processes — such as knowing the ‘abandon point’ in a Kindle book or on shopping carts — seems acceptable as it drives down operating inefficiency. But, reliance on similar micro-level data on how managers take decisions seems somehow unacceptable.
Are we witnessing an emerging theory of Digital Darwinism at the nexus of big data & analytics and design of knowledge work?
At Amazon, Jezz Bezos has laid out 14 principles. They may not have the word ‘data’ in the labels but the underlying theme is all about the use of data and analytics to ‘make judgments’ ‘calculated risk taking’ ‘seek to improve themselves’ ‘curious about new possibilities’ and ‘stay connected to the details.’
These principles clearly focus on emerging ideas of what we expect from human talent — especially knowledge workers.
Over a century back, Frederick Taylor relied on his training as a mechanical engineer to introduce scientific management in the 20th century.
Jeff Bezos as a computer scientist is in the midst of creating the scientific management for the digital age.
We are in a period of transformation from industrial-age management models to post-industrial digital age. Aren’t experiments such as those carried out by Bezos necessary for successful adaptation in the spirit of Darwin?
But, Amazon and Bezos are not unique.
Amazon is but one of several companies, which are carrying out several experiments to define new leadership principles — focused on talent, innovation and value capture. I am not referring to those workers in IT support and call centers, whose work practices have been routinely monitored over the last two decades. I am referring to the more recent practices to track actions, decisions and performance of knowledge workers. These include management consulting, financial services, legal services and so on. As economies become more services-driven and knowledge-dependent, monitoring, scrutiny, analysis and oversight have become more widespread.
Google’s use of data and analytics — perhaps more humane in what’s documented — also points to experimentation in new work order for the digital age. It even carried out experiments to examine if managers were needed to help engineers achieve high productivity and innovation — the results of Project Oxygen showed that managers do have value — albeit more limited than management profession would like to believe. [More on project Oxygen from an article in 2011 NY Times.]
Netflix may have announced humane parental leave policies but its reliance on big data to place big bets on shows like House of Cards is legendary. Business processes designed with data and analytics at their core are the future.
IBM Watson may have defeated the best of humans in Jeopardy and the company is now pushing its ideas of cognitive computing in healthcare to cure cancer. This challenges the creative minds in healthcare profession today. IBM and other companies such as Cognitive Scale will help companies leverage augmented intelligence with big data and analytics. Narrative Science — a company at the forefront of interpreting data and turning it into a narrative for people to make sense of data — is ushering in data storytelling.
We are already in an era of artificial intelligence for knowledge work.
Emerging Theory of Talent?
Not all managers would subscribe to Digital Darwinism. But every manager recognizes that the underlying theory of Darwinism — survival of the fittest. Digitization simply shines light on historical pockets of inefficiency — hidden away unobserved and unanalyzed.
Every company — at minimum — should carefully track those organizations that attract a different breed of employees — who subscribe to Digital Darwinism. They should monitor the experiments underway in those companies that may be testing new ‘theory of talent,’ where employees:
§ Thrive and excel under close monitoring and supervision from humans and sophisticated machines.
§ Welcome feedback from machines and peers (beyond supervisors) and continually improve their skills and competencies.
§ Constantly push and delegate routine, repetitive tedious work to machines.
§ Work to innovate at the frontier — where machines have not yet caught up with human intelligence and expertise.
§ Identify themselves in a marketplace of ideas and actions; they connect with others in a professional knowledge network to continually refine their skills.
§ Race to win against their peers and machines
This is my list of ideas on the emerging theory of talent. Others may want to add/delete/modify the list.
Perhaps this group of talented employees is small and geographically dispersed. But, what value could some enterprises achieve if they became magnets to attract such talent?
Amazon’s experimentation shows that companies can no longer think of managing as if work is carried out without observation, tracking monitoring, analysis and improvement. Similarly, decisions — especially strategic ones — made without robust data, information and analysis will prove to be fragile. However, we also know that organization’s human resources and talent enhancement practices need reframing.
Is Your Theory of Talent at the Intersection of Humans and Machines?
One of the inevitable consequences of digitization is that it is impacts more than products and services. It profoundly influences business processes. We know well how automation changed the nature of work in western factories in the late 20th century and shifted the locus of manufacturing to China. Similarly, we saw how codified knowledge work (supported by relatively simple software-driven tools) shifted to India in the early 21st century.
Now for the hard part — how to articulate a theory of talent at the intersection of humans and machines? This theory is about the distinctive value added of an enterprise where humans and machines compete, cooperate and co-exist.
But, do not think of the machines as only robots in factories, mines or warehouses. Jerry Kaplan (@Jerry_Kaplan) in his book titled Humans Need Not Apply distinguished two kinds of machines — synthetic intellects and forged laborers.
Synthetic intellects include “machine learning, neural networks, big data, cognitive systems and genetic algorithms.” This is the frontier of machine learning at speed and scale that we have not yet seen. These may appear disconnected today but when coupled, they could disrupt current ways of organizing in industries, markets and firms.
Forged laborers, for Kaplan, is at the intersection of ‘sensors and actuators… that can see, hear, feel and interact with their surroundings… [we] recognize as robots.” Again, we have only seen glimpses of what robots such as Baxter and Sawyer can do today. It is instructive to also speculate on why Google acquired Boston Dynamics and Amazon acquired Kiva Systems and is now renamed Amazon Robotics.
Amazon Robotics is the new name of Boston’s Kiva Robotics
The two categories may correspond roughly to white-collar (knowledge work) and blue-collar (production work). But, mapping Kaplan’s category to traditional demarcation of work simply limits our ability to imagine their future role in society.
I believe that Google’s restructuring its operations into Alphabet Inc. is a way to unleash value from a harmonious nexus of synthetic intellects and forged laborers. Project Oxygen is not the only experiment that Google/Alphabet would undertake as it seeks to find best ways to create and capture value.
We may believe that we have a long way to go before we have to confront Digital Darwinism relating to human talent and organizing rules. We may not like being monitored and scrutinized as knowledge workers but it is the future. Just as we are tracked and monitored as individuals on the physical-mobile-social web and as consumers for placement of ads everywhere on the web, tracking-while-working will become the norm.
Tough Questions to Ponder
Every knowledge worker in every organization everywhere in the world today must ponder:
- What decisions am I responsible for? How important are my decisions in the creation of value to our stakeholders?
- What differentiated expertise do I bring to those strategic decisions that cannot be obtained elsewhere and at lower cost?
- Could machines decide what I do today? If not today, when? Why not
- How could I add more value by collaborating with powerful machinestoday? What’s preventing this human-machine symbiosis?
- How could I reinvent my competencies to be at the frontier of human-machine symbiosis?
The answers may not be comforting but questions must be asked.
Back to Amazon: Recently, its market capitalization exceeded that of Wal-Mart (240 billion versus 205 billion on Sept 8, 2015) with an employee base of around 150,000 against Wal-Mart’s 2.25 million. This largely reflects the frontier of retailing and Bezos’ theory of talent and productivity.
One case does not make a robust theory. Inflection points are easier to understand looking back afterwards. But, can we at least accept that the future is at the intersection of minds and machines — with blurring distinction between production work and knowledge work? If so, we must reframe our preconceived ideas of work, working, supervision, feedback, privacy, authority, contribution, professional identity, organizing and many others. Then, what role for human resource function in the digitization agenda? How should companies organize to bring out the best of human talent in humane fashion?
What innovations and disruptions lie ahead at the intersection of
‘smart minds and powerful machines’?