Irving Wladawsky-Berger

A Biological Approach to Management

Darwinian principles seem to apply in business almost as much as in biology.  After analyzing the longevity of more than 30,000 public US firms over a 50-year span, Martin Reeves, Simon Levin, and Daichi Ueda noted in a 2016 HBR article, The Biology of Corporate Survival, that companies are disappearing faster than ever before.  “Public companies have a one in three chance of being delisted in the next five years, whether because of bankruptcy, liquidation, M&A, or other causes.  That’s six times the delisting rate of companies 40 years ago… Neither scale nor experience guards against an early demise.”

Biological systems have long been an inspiration in the study of complex systems.  In their HBR article, the authors argued that companies are not just like biological species, but in some important respects they’re actually identical to biological species.  Companies and biological systems are both what’s known as complex adaptive systems, that is, systems in which a perfect understanding of their individual components does not automatically lead to a perfect understanding of their overall system behavior.

Companies are an example of sociotechnical systems, that is, systems that have to deal with  complex technologies and infrastructures, and the even more complex issues associated with human and organizational behaviors.  Other examples include cities, government agencies, industries and economies.  The dynamic nature of their technology and human components, as well as their intricate interrelationships renders such systems increasingly unpredictable and accounts for their emergent behavior and unintended consequences.

The Economic Value of Artificial Intelligence

 PwC recently released a report on the potential economic value of AI to different regions and industry sectors around the world.  The report defined AI, “as a collective term for computer systems that can sense their environment, think, learn, and take action in response to what they’re sensing and their objectives.”  By this broad definition, AI includes the automation of physical and cognitive tasks; assisting people to perform tasks better and faster; helping humans make better decisions; and automating decision making with no human intervention.

The report’s overriding finding is that AI is the biggest commercial opportunity for companies, industries and nations over the next few decades.  PwC estimates that AI advances will increase global GDP by up to 14% between now and 2030, the equivalent of an additional $15.7 trillion contribution to the world’s economy.

Around $6.6 trillion of the expected GDP growth will come from productivity gains, especially in the near term.  These include the continued automation of routine tasks, and the development of increasingly sophisticated tools to augment human capabilities.  Companies that are slow to adopt these AI-based productivity improvements will find themselves at a serious competitive disadvantage.

But, over time, increased consumer demand for AI-enhanced offerings will overtake productivity gains and result in an additional $9.1 trillion of GDP growth by 2030.  Moreover,  network effects, - that is, the more data and better insights companies are able to gather, the more appealing the products and services they’ll be able to develop, - will further increase consumer demand.  AI front-runners will gain an enormous competitive advantage through their ability to leverage this rich supply of customer data to shape product developments and business models, making it harder for slower moving competitors to catch up. 

Automation and the Changing Demand for Workforce Skills

In 2015, the McKinsey Global Institute launched a multi-year study to explore the potential impact of automation technologies on jobs, organizations and the future of work.  In the intervening three years, the study has published a number of report on the subject.

The first report, published in November, 2015, explored whether we can look forward to vast improvements in productivity and quality of life, or whether automation will mostly threaten jobs, disrupt organizations, and strain the social fabric.  Based on an analysis of around 2,000 work activities across 800 different occupations, the report concluded that “fewer than 5 percent of occupations can be entirely automated using current technology.  However, about 60 percent of occupations could have 30 percent or more of their constituent activities automated.”  In other words, relatively few jobs will be entirely automated, but automation will likely transform the vast majority of occupations.

McKinsey’s December, 2017 report analyzed whether there will be enough work in the future.  The overall conclusion was that a growing technology-based economy will create a significant number of new occupations, - as has been the case in the past, - which will more than offset declines in occupations displaced by automation.  However, “while there may be enough work to maintain full employment to 2030 under most scenarios, the transitions will be very challenging - matching or even exceeding the scale of shifts out of agriculture and manufacturing we have seen in the past.”

Let me now discuss the more recent report published earlier this year, which examined the changes in skills required of human workers over the next 10-15 years.  To do so, the study analyzed how the total number of hours worked in 25 different skill areas has changed between 2002 and 2016 and estimated the expected change in hours worked by 2030.  This was done for the US and 5 Western European countries.  I’ll focus my discussion on the US skill changes, as the European changes were similar.

Cryptocurrencies and Blockchains - How Are They Doing After 10 Years?

Bitcoin was created about a decade ago, with the release of the original design paper, Bitcoin: A Peer-to-Peer Electronic Cash System, under the pen name of Satoshi Nakamoto.  Bitcoin aimed to become a decentralized cryptocurrency and digital payment system that would enable the secure exchange of digital money without the need for central government authorities or financial intermediaries.  It introduced the blockchain, - which most everyone agrees is a truly brilliant architecture built on decades-old fundamental research in cryptography, distributed data, distributed computing, game theory and other advanced technologies.

How are they doing after 10 years?  The September 1 issue of The Economist includes a special report on cryptocurrencies and blockchains, with nine articles on the subject. The report’s overview article doesn’t mince words in articulating its overall assessment: “Bitcoin and other cryptocurrencies are useless.  For blockchains, the jury is still out.”

“Bitcoin has been a failure as a means of payment, but thrilling for speculators,” it further added.  Its price has been subject to wild fluctuations over the past two years.  It rose from around $600 in September, 2016, to $6,000 in September, 2017, reaching a peak of over $19,000 in December, 2017, before falling to around $7,000 earlier this year.  It’s been hovering between $6,500 and $7,500 for the past few months.  Other cryptocurrencies have experienced similar price fluctuations.  “That has made a few people very rich (just 100 accounts own 19% of all existing bitcoin), encouraged others to play for quick gains and left some nursing substantial losses.”

Solow Paradox 2.0: The Lagging Impact of Technology Advances on Productivity Growth

Fewer workers is one of the key reasons for our stagnant economic growth.  Over the coming decades, the labor force is expected to shrink in most parts of the world as fertility rates continue to decline, especially in advanced and emerging economies.  The US labor force is growing very slowly while fertility rates keep hitting record lows.

Our aging economies are thus dependent on productivity gains to drive long-term economic growth, future prosperity and higher standards of living.  Which is why few topics have generated as much concern among economists and policy makers as the sharp decline in productivity growth in the US and other advanced economies over the past decade, - despite accelerating technology advances.

“Labor productivity growth is near historic lows in the United States and much of Western Europe,” noted Solving the Productivity Puzzle, a report published earlier this year by the McKinsey Global Institute (MGI).  The report analyzed the productivity-growth declines across a sample of seven countries, - France, Germany, Italy, Spain, Sweden, the United Kingdom, and the United States, - which represent about 65% of the GDP of advanced economies.  Their average productivity growth in the 5 years between 2000 and 2004 was 2.4%.  A decade later, - in the 5 year period between 2010 and 2014, - their average productivity growth had declined to .5%.  While starting to pick up recently, productivity growth remains at or below 1 percent in many of the countries in the study, still quite low relative to historical levels.

The Myth of the Washed-Up, Middle-Age Entrepreneur

Steve Jobs, Bill Gates, and Mark Zuckerberg were in their early 20s; Sergei Brin and Larry Page were 25; and Jeff Bezos was just 30 when they founded their world leading high-tech companies.  Their companies are truly in a class by themselves, even having their own, somewhat frightful acronym - FAMGA.  Their founders’ ages are not only evidence that young people can be highly successful entrepreneurs, but it’s led to the belief that they’re uniquely capable of coming up with big, transformational ideas.

“Young people are just smarter,” Zuckerberg told the audience of a 2007 VC conference, adding that successful start-ups should only employ young people with technical expertise.  In 2010, PayPal’s co-founder Peter Thiel created the Thiel Fellowships, a program that provides $100,000 over two years to would-be entrepreneurs 23 and under so they can drop out of school to work on their ideas.  “The stereotype of an entrepreneur is a college drop-out,” said a recent Economist article.

“Silicon Valley has become one of the most ageist places in America,” noted a 2014 New Republic article on The Brutal Ageism of Tech, where years of experience are considered obsolete.  “People under 35 are the people who make change happen,” said VC Vinod Khosla at a 2011 conference.  “People over 45 basically die in terms of new ideas” because they “keep falling back on old habits.”  Entrepreneur and VC Paul Graham said in a 2013 interview that investors tend to be biased against older founders.  “The cutoff in investors’ heads is 32… after 32, they start to be a little skeptical.”

These various statements remind me of the 1960s counterculture saying don’t trust anyone over 30.  But, are they justified?  Are young entrepreneurs more likely to produce high-growth firms?  Can middle-age founders in their 40s be successful? 

AI - the Creation of a Human-Centric Engineering Discipline

AI is rapidly becoming one of the most important technologies of our era.  Every day we can read about the latest AI advances from startups and large companies, including applications that not long ago were viewed as the exclusive domain of humans.  Over the past few years, the necessary ingredients have finally come together to propel AI beyond the research labs into the marketplace: powerful, inexpensive computer technologies; huge amounts of data; and advanced algorithms.

Machine learning  and related algorithms like deep learning, have played a major role in AI’s recent achievements.  Machine learning gives computers the ability to learn by ingesting and analyzing large amounts of data instead of being explicitly programmed.  It’s enabled the construction of AI algorithms that can be trained with lots and lots of sample inputs, which are subsequently applied to difficult AI problems like language translation, natural language processing, and playing championship-level Go.

Over the past several decades, AI has been besieged by rounds of hype which over-promised, under-delivered, and nearly killed the field.  Once more, a recent Gartner article positioned machine and deep learning at the top of their hype cycle, - when all the excitement, publicity and promising potential often leads to a peak of inflated expectations, risking falling into the trough of disillusionment if the technology fails to deliver.  Now that AI is finally reaching a tipping point of market acceptance, it’s particularly important to be cautious and not repeat past mistakes.

In Artificial Intelligence - The Revolution Hasn’t Happened Yet, UC Berkeley professor Michael I. Jordan aims to inject such a note of caution.  “AI has now become the mantra of our current era…  The idea that our era is somehow seeing the emergence of an intelligence in silicon that rivals our own entertains all of us  -  enthralling us and frightening us in equal measure.  And, unfortunately, it distracts us…  Whether or not we come to understand intelligence any time soon, we do have a major challenge on our hands in bringing together computers and humans in ways that enhance human life.”

What Machine Learning Can and Cannot Do

Artificial intelligence is rapidly becoming one of the most important technologies of our era.  Every day we can read about the latest AI advances from startups and large companies.  Over the past few years, the necessary ingredients have come together to take AI across the threshold: powerful, inexpensive computer technologies; huge amounts of data; and advanced algorithms, especially machine learning.  Machine learning has enabled AI to get around one of its biggest obstacles, - the so-called Polanyi’s paradox.

Explicit knowledge is formal, codified, and can be readily explained to people and captured in a computer program.  But, tacit knowledge, a concept first introduced in the 1950s by scientist and philosopher Michael Polanyi, is the kind of knowledge we’re often not aware we have, and is therefore difficult to transfer to another person, let alone capture in a computer program.

“We can know more than we can tell,” said Polanyi in what’s become known as Polanyi’s paradox.  This common sense phrase succinctly captures the fact that we tacitly know a lot about the way the world works, yet aren’t able to explicitly describe this knowledge.  Tacit knowledge is best transmitted through personal interactions and practical experiences.  Everyday examples include speaking a language, riding a bike, and easily recognizing many different people, animals and objects.

Machine learning, and related advances like deep learning, have enabled computers to acquire tacit knowledge by being trained with lots and lots of sample inputs, thus learning by analyzing large amounts of data instead of being explicitly programmed.  Machine learning methods are now being applied to vision, speech recognition, language translation, and other capabilities that not long ago seemed impossible but are now approaching or surpassing human levels of performance in a number of domains.

The Transformative Power of Analytics - The Houston Astros: a Case Study

In 2013, the Houston Astros finished the season with a 51-111 record, - the worst record in the history of the franchise, having also lost over 100 games in the previous two years.  In 2017, the Astros won 101 games, finished first in the American League West Division, and went on to win their first ever World Series.  How did the Astros go from worst team record to first World Series victory in only four years?

The answer, in a nutshell, is analytics.  In December, 2011 the Astros hired Jeff Luhnow from the St Louis Cardinals as their General Manager.  As VP of scouting and player development in St Louis, Luhnow built a strong analytics department and enjoyed great success as one of the top talent producers in baseball, helping the Cardinals win the 2011 World Series as well as reach the playoffs from 2012 to 2015.  Luhnow then brought his analytics and management skills to Houston and proceeded to transform the team into a top contender in a short few years.

Today, just every major sports team makes extensive use of sports analytics, but none more so than Major League Baseball (MLB) teams.  Baseball has long been a game of statistics, collecting and analyzing vast amounts of data going back to the founding of the MLB in the years following the Civil War. 

Top MLB players are constantly compared not just to other contemporaneous players, but to other top players across the long history of the game, - a history that was very nicely depicted in Baseball, Ken Burns excellent documentary miniseries. Babe Ruth, Willie Mays, Mickey Mantle, Hank Aaron and countless other players are never forgotten in the world of baseball statistics.  For those of us who are fans, - as I am, not surprisingly given my Cuban roots, - baseball’s rich history is a major part of our enjoyment of the game. 

Education, Work and US Leadership in the 21st Century

A few weeks ago, the Council on Foreign Relations (CFR) released The Work Ahead - Machines, Skills, and U.S. Leadership in the Twenty-First Century, a report by a CFR-sponsored Task Force to assess the future of work and its implications for the US economy and national security.  The Task Force was led by Penny Pritzker, - former Secretary of Commerce and founder and chair of PSP Partners, and by John Engler, - former Governor of Michigan and interim President of Michigan State University.

Their comprehensive report, - over 150 pages and 276 citations, - explored in great depth the changing nature of work as global competition and advanced technologies like AI, robotics, and smart IoT devices transform jobs and careers.  It states right up front that “The most important challenge facing the United States - given the seismic forces of innovation, automation, and globalization that are changing the nature of work - is to create better pathways for all Americans to adapt and thrive.  The country’s future as a stable, strong nation willing and able to devote the necessary resources and attention to meeting international challenges depends on rebuilding the links among work, opportunity, and economic security.”

For much of the 20th century, technology and globalization helped lead America to its leadership position in the world stage.  Underpinning this US leadership was the American Dream, - the promise that anyone can get ahead and achieve success and prosperity through talent and hard work.  But this promise has been eroding for decades.  For many of its citizens, the American Dream has been steadily disappearing.

Technology advances have led to massive innovations and efficiencies, enabling companies to get the same work done with significantly fewer people.  At the same time, many US-headquartered companies are truly global, doing business all over the world.  Beyond outsourcing jobs to countries with lower labor costs they’ve been investing and adding jobs where the demand is highest, - which has often been in faster growing emerging markets.

The US thus faces two major challenges: “creating new work opportunities, better career paths, and higher incomes for its people, while developing a workforce that will ensure U.S. competitive success in a global economy that will continue to be reshaped by technology and trade.” 

The 2018 Inclusive Internet Index

Earlier this year, the Economist Intelligence Unit (EIU) published the Inclusive Internet Index 2018.  Now in its second year, the Index analyzes and compares the current state of the Internet across 86 countries, ranging from advanced to developing economies and covering 91% of the world’s population.  Its key objective is to help policymakers understand the key factors necessary for the wide, inclusive use of the Internet in their respective countries.

Countries are evaluated based on 54 indicators organized around four major categories:

• Availability,  which captures the quality of breadth of the infrastructure available for Internet access, including network availability, access points for landline and mobile connections, and the basic electricity infrastructure needed to support Internet connectivity in urban and rural areas.
• Affordability, which measures the cost of Internet access relative to income, the competitive environment for wireless and broadband operators and the measures taken to decrease costs and promote access.
• Relevance, which looks at the availability of Internet content in the local language(s) and the value of being connected to get access to relevant services like news, entertainment, health advice and business and financial information.
• Readiness, which examines the capacity to take advantage of accessing the Internet, including level of literacy, educational attainment, cultural acceptance, privacy and security, and trust in the sources of online information.

Each country is assigned a score for each of the four categories, as well as an overall index score. 

From Horseless Carriages to Driverless Vehicles

Autonomous vehicles (AVs) may well be the quintessential symbol of our AI/robotics age.  Cars are a major part of our daily lives.  A self-driven car is a concept that requires little explanation, something we can all quickly grasp.  It wasn’t that long ago that the notion on an AV driving us around while we read or sleep would have felt like the stuff of science fiction.  Having experimental AVs coursing through public roads in Silicon Valley, Pittsburgh and Phoenix is concrete evidence that our smart machines are achieving human-like intelligence, raising a number of important questions:  how long before AVs are all around us?; how will they impact our lives?; what unintended consequences might we have to deal with?; and what should be done to ensure that they arrive as safely and smoothly as possible?

These are among the questions addressed in the March 1 issue of The Economist, which includes a comprehensive special report on autonomous vehicles with seven articles on the subject.  The special report starts out with the assumption that whatever technological hurdles lie in their way will be eventually overcome.  But there are wider economic, social and public policy issues to be explored, starting with: what can we learn from the transition to horseless carriages in the 20th century that can now be applied to the transition to driverless cars?

Human-Machine Work Teams

Will there be enough work in the future?  What’s the likely impact of our continuing technology advances on jobs?  How will they impact productivity?  These are all-important questions to reflect on as our increasingly smart technologies are now being applied to activities that not long ago were viewed as the exclusive domain of humans.

While no one really knows the answer to these questions, most studies of the subject have concluded that relatively few occupations, 10% or less, will be entirely automated and disappear over the next 10 - 15 years.  Instead, a growing percentage of occupations will significantly change as technologies automate the more routines tasks within those occupations.  People will still be involved, but their jobs will be transformed by the advanced tools they now have to master.  Moreover, a growing technology-based economy will likely create all kinds of new occupations, which will more than offset declines in occupations displaced by automation, - as has been the case over the past couple of centuries.

One would also expect that technology advances will increase the productivity of the workers involved in these new or transformed occupations.  But, if we look at the past 10-15 for guidance, we’ll find that productivity growth has significantly declined over this timeframe, notwithstanding huge technology advances like smartphones, cloud computing, big data and artificial intelligence.  Economist have proposed competing explanations for the declining productivity growth, but have so far failed to reach consensus.  Understanding this productivity puzzle may well hold the key to future productivity improvements and long-term economic growth.

Improving the Odds of Success for Corporate Transformations

Looking back upon my long career, one of the major factors shaping my views of strategy and innovation are the powerful transformational forces that I saw buffeting the IT industry over much of that time.  It’s frankly sobering how many once powerful IT companies are no longer around or are shadows of their former selves.  The carnage might be more pronounced in the fast-changing IT industry, but no industry has been immune.  It’s all part of what Joseph Schumpeter called creative destruction over 75 years ago, - “the process of industrial mutation that incessantly revolutionizes the economic structure from within, incessantly destroying the old one, incessantly creating a new one.”

For a startup a transformative innovation is all upside, an opportunity to take-on established companies with new products that offer significantly better capabilities and/or lower costs.  Startups hope that their compelling new offerings will help them establish a foothold in the marketplace and, over time, become leaders in their industry.

But change is often difficult for established companies.  Over the years, they’ve amassed a number of valuable assets and extensive organizations.  Already consumed with managing their existing operations, - e.g., products and services; supply chain and channel partners; sales and marketing; revenues, profits and cash; -  they may see a transformative innovation as more of a threat or distraction than an opportunity. 

The Promise and Current State of Blockchain

I recently participated in a blockchain panel at a conference of senior supply chain executives from a variety of companies.  I welcomed the opportunity to learn how these real-world executives feel about blockchain, as well as how to best communicate the promise and current state of this emerging and potentially transformative technology.  Let me share a few of my observations.

Before our panel started, the executives were asked how they viewed blockchain.  In general, they felt that there was too much hype surrounding blockchain, were skeptical about its value to supply chain systems, but expressed a wait-and-see attitude.  They were then asked how many were conducting blockchain prototypes in their companies.  The majority raised their hands. 

I thought that their views and subsequent questions and comments during the panel succinctly captures the current state of blockchain in the business world: skeptical, turned off by the hype, but hedging their bets by getting on the learning curve with prototypes and experimentation.

The moderator first asked each panelist to address the audience’s skepticism about blockchain.  What, in our opinion, is the key value of blockchain?  I said that I look at the value of blockchain through two complementary lenses: enhancing the security of the Internet, and reducing the inefficiencies and overheads in applications, like supply chains, involving multiple institutions.

The Growing Importance of Resilience in an Increasinsgly Turbulent World

In March of 2017, Northeastern University launched a new interdisciplinary initiative, the Global Resilience Institute (GRI).  GRI is a university-wide research and educational effort to advance the resilience of individuals, communities, economies and societies around the world by strengthening their capacity to adapt to an increasingly turbulent world.  Its key mission is to develop and deploy practical tools, applications and skills to help effectively respond to human-made and naturally-occurring disruptions and disasters.

GRI’s Founding Director is Northeastern professor Stephen Flynn.  In his Director’s Welcome web page, Flynn writes that “Building resilience within and across multiple levels, from individual to societal, requires a comprehensive effort that matches the complexity of the increasingly interdependent systems and networks we all rely on… Our aim is to serve as both a channel and a catalyst for experts in industry, academia, and government to collaborate on solving the world’s most pressing resilience challenges.  These include both slowly emerging disruptions as well as shocks and sudden disasters.”

Most everyone agrees that dealing with our increasingly turbulent, complex world us requires significant enhancements to the resilience of our societies and systems.  But, five critical barriers stand in the way:

• Widespread risk illiteracy and a limited understanding of the new dependencies and interdependencies that pervade our more connected lives;
• Inadequate designs for embedding resilience into systems, networks, and infrastructure at multiple levels;
• Pervasive economic disincentives for investing in proven resilience measures;
• Inadequate governance frameworks and policy guidance to foster resilience; and
• A lack of adequate training and education to support the development and implementation of comprehensive capabilities for advancing resilience.

A Framework for the Safe Management of Digital Identities and Data

Identity plays a major role in everyday life.  Think about going to an office, getting on a plane, logging to a website or making an online purchase.  Identity is the key that determines the particular transactions in which we can rightfully participate as well as the information we’re entitled to access.   But, we generally don’t pay much attention to the management of our identity credentials unless something goes seriously wrong.

For much of history, our identity systems have been based on face-to-face interactions and on physical documents and processes.  But, the transition to a digital economy requires radically different identity systems.  In a world that’s increasingly governed by digital transactions and data, our existing methods for managing security and privacy are proving inadequate.  Data breaches, large-scale fraud, and identity theft are becoming more common.  In addition, a significant portion of the world’s population lacks the credentials needed to participate in the digital economy.  Our existing methods for managing digital identities are far from adequate.

As explained in A Blueprint for Digital Identity, - a 2016 report by the World Economic Forum, - identity is essentially a collection of information or attributes associated with a specific individual.  These attributes fall into three main categories:  inherent - attributes intrinsic to an individual, - e.g., age, height, date of birth, fingerprints, color of eyes, retinal scans;  assigned - attributes attached to but not intrinsic to the individual - e.g., e-mail address, telephone numbers, social security, drivers license, passport number; and accumulated - attributes gathered or developed over time - e.g., health records, job history, home addresses, schools attended.

While mostly associated with individuals, identities can also be assigned to legal entities like corporations, partnerships and trusts; to physical entities like cars, buildings, smartphones and IoT devices; and to digital entities like patents, software programs and data sets.

Data attributes are generally siloed within different private and public sector institutions, each using its data for its own purposes.  But to reach a higher level of privacy and security, we need to establish trusted data ecosystems, which requires the exchange and sharing of data across a variety of institutions.  The more data sources a trusted ecosystem has access to, the higher the probability of detecting fraud and identity theft while reducing false positives.  In addition, an ecosystem with a variety of data sources can help foster economic inclusiveness by certifying the identities and credit worthiness of poor people with no banking affiliation.

However, safeguarding the data used to validate identities creates security and privacy issues of its own.  It’s unsafe to gather all the needed attributes within one institution or central data location, making it a target for data breaches.  But, it’s also highly unfeasible, as few institutions will let their critical data out of their premises.

Customer Experience - the Key Competitive Differentiator in the Digital Age

About five years ago, McKinsey conducted an online survey of over 850 CEOs and other senior executives to find out how their companies were faring in their implementation of digital technologies and strategies.  The data showed that executives were generally optimistic, but they still had much to do to achieve their digital business objectives.  Organizational alignment and leadership were the critical factors in the success or failure of their digital strategies.

More specifically, the survey asked them about their progress in embracing five major digital trends: big data and advanced analytics, digital engagement of customers, digital engagement of employees and external partners, automation, and digital innovation.  While all five trends were important, executives said that digital customer engagements was their most important competitive differentiator and where they were expecting the largest financial returns.

This is not surprising, even more now than at the time of the survey.  A company can differentiate itself from competitors in one of two key ways: by providing a superior customers experience or by offering the lowest prices.  For companies that prefer the former, digital channels are, far and away, the most cost-effective way of reaching out to their clients.  The explosive growth of mobile devices means that you can be engaged with your customers whether they are at home, at work, in your store or anywhere else.

But, digital customers can be fickle and hard to satisfy.  It’s quite difficult to keep up with their fast changing behaviors and expectations.  New products and services are hitting the market faster than ever, brand loyalty keeps decreasing, and the increased competition continues to shift power from institutions to individuals.

Human + Machine: The Impact of AI on Business Transformation

Last week I wrote about the impact of blockchain technologies on business transformation.  I now want to turn my attention to the more immediate impact of AI on business, based on Human + Machine: Reimagining Work in the Age of AI, a recently published book by Accenture executives Paul Daugherty and Jim Wilson.

The book reminds us that technology has long been transforming business.  Electricity and standardized processes ushered the first wave of business transformations about 100 years ago.  Manufacturing was decomposed into a set of processes and reassembled into the assembly line, leading to the age of mass production.

Several decades later, IT and process automation brought us the second wave of business transformation.  Mainframes and data processing software enabled the automation of back-office processes in the 1960s and 1970s.  Front-office automation followed in the 1980s with the advent of PCs and client-server systems.  A decade later, advances in networking and ERP software led to  business process reengineering and management. 

“Now, the third wave involves adaptive processes,” write Daugherty and Wilson.  “This third era, which builds on the previous two, will be more dramatic than the earlier revolutions enabled by assembly lines and digital computers, and will usher in entirely new, innovative ways of doing business… This adaptive capability is being driven by real-time data rather than by an a priori sequence of steps.   The paradox is that although these processes are not standardized or routine, they can repeatedly deliver better outcomes.”

Blockchain and Business Process Transformation

One can look at the transformational impact of IT on companies and industries through a variety of lenses, - e.g., products and services, revenue and profit, sales and support, market strategy.  One of the most important such lenses is the impact of IT on business processes, that is, on how work is actually done across the various functions of an organization. 

Examining the impact of IT over the past several decades through a process point of view, one can identify three distinct phases: first came process automation in the early years of IT, followed around the 1990s by enterprise-wide process reengineering and management.  The third phase, is now starting, focused on the processes and transactions that determine how institutions interact with each other around the world.  Let me discuss each of these phases. 

The Vital Role of Culture in Business

The January-February issue of the Harvard Business Review spotlights The Culture Factor, with five articles on the subject.  “Culture is the tacit social order of an organization: It shapes attitudes and behaviors in wide-ranging and durable ways,” notes the issue’s lead article.  “Cultural norms define what is encouraged, discouraged, accepted, or rejected within a group.  When properly aligned with personal values, drives, and needs, culture can unleash tremendous amounts of energy toward a shared purpose and foster an organization’s capacity to thrive.”

In Who Says Elephants Can't Dance?, - his excellent chronicle of the IBM transformation he led as Chairman and CEO from 1993 to 2002, - Lou Gerstner wrote: “I came to see in my time at IBM that culture isn’t just one aspect of the game - it is the game.  In the end, an organization is nothing more than the collective capacity of its people to create value.  Vision, strategy, marketing, financial management - any management system, in fact - can set you on the right path and can carry you for a while.  But no enterprise - whether in business, government, education, health care or any area of human endeavor - will succeed over the long haul if those elements aren't part of its DNA.” 

The Complex Relationship Between Science and the Public

More than ever, scientific and technological innovations touch every aspect of our lives and influence the choices we make in a wide variety of areas.  A 2015 study by the Pew Research Center revealed that most Americans have a positive view of science and of the accomplishments of scientists.  But, despite broadly similar views about the place of science in the country, there were large differences between the general public and scientist across a number of important issues, including genetically modified foods, vaccine safety and climate change.

Two years ago, the American Academy of Arts and Sciences launched The Public Face of Science, an initiative whose objective is to better understand the complex relationship between scientists and the public in America.  A few weeks ago, the Academy released Perceptions of Science in America, the first in a series of reports from the Public Face of Science initiative.  The report aims to analyze the public’s perception of scientists, explore how opinions vary among different populations, and shed light on how public opinions are shaped.  Its analysis is based on survey data from multiple sources, including the Pew Research Center, the National Science Board Science and Engineering Indicators, the National Opinion Research Center, Research!America and ScienceCounts.

What do we mean by science?  “The term science encompasses a range of disciplines in the physical, social, and life sciences, along with applied fields, such as engineering and medicine,” notes the report. “Science can be defined as ‘the use of evidence to construct testable explanations and predictions of natural, as well as the knowledge generated through this process.’”  But, one must keep in mind that the word science is interpreted differently by different individuals, - e.g., medical treatments, technological advances, fundamental research.  “When interpreting the available survey data, it is important to consider the range of responses a question might prompt.”

A Framework for Building AI Capabilities

After decades of promise and hype, artificial intelligence has finally reached a tipping point of market acceptance.  AI is seemingly everywhere.  Every day we can read about the latest AI advances and applications from startups and  large companies.  AI was the star of the 2018 Consumer Electronic Show earlier this year in Las Vegas.

But, despite its market acceptance, a recent McKinsey report found that AI adoption is still at an early, experimental stage, especially outside the tech sector.  Based on a survey of over 3,000 AI-aware C-level executives across 10 countries and 14 sectors, the report found that 20 percent of respondents had adopted AI at scale in a core part of their business, 40 percent were partial adopters or experimenters, while another 40 percent were still waiting to take their first steps.

The report adds that the gap between the early AI adopters and everyone else is growing.  While many companies have yet to be convinced of AI’s benefits, leading edge firms are charging ahead.  Companies need to start experimenting with AI and get on the learning curve, or they risk falling further behind. 

AI will likely become the most important technology of our era as it’s improved upon over time, but we’re still in the early stages of deployment.  It’s only been in the last few years that complementary innovations, especially machine learning have taken AI from the lab to early marketplace adopters.  And, history shows that even after technologies start crossing over into mainstream markets, it takes considerable time, - often decades, - for the new technologies and business models to be widely embraced by companies and industries across the economy.

Blockchain and the Promise of an Open, Decentralized Internet

The January 21 issue of the NY Times Magazine included a very good article, Beyond the Bitcoin Bubble, by science writer Steven Johnson.  His article aims to explain what blockchain, cryptocurrencies and related technologies are all about as well as their potential impact on the economy and society.  These are not only complex but esoteric subjects, as nicely illustrated in this funny video.  While blockchain and Bitcoin have been frequently in the news lately, beyond knowing that they have something to do with money, most people don’t really know what they are or why they should care.

This is not surprising.  In their early years, it’s hard for most people to appreciate the potential of a transformative technology.  There’s generally a considerable time-lag between the time we start reading articles about their exciting potential and their adoption by companies and industries across the marketplace.  This is particularly the case for general purpose technologies, like electricity, the Internet and blockchain, which are the most potentially transformative due to their widespread use.   Their deployment time-lags are longer because attaining their full benefits requires a number of complementary co-inventions and investments, including additional technologies, applications, processes, business models, and regulatory policies.

Will There Be Enough Work in the Future?

Will there be enough work in the future?  Opinions are fairly divided between those who believe that technology advances will reduce human jobs, and those who believe that technology advances will produce as many jobs as they displace.  It’s easier to predict the jobs that will be automated away by technology, but much more difficult to predict the new jobs that these same technologies will create.  In the end, we don’t really know.

In December of 2017, the McKinsey Global Institute published Jobs Lost, Jobs Gained: Workforce Transition in a Time of Automation, a report that directly addresses this question.  The McKinsey study examined in great detail the work that’s likely to be displaced by automation through 2030, as well as the jobs that are likely to be created over the same period.  It analyzed data from 46 countries comprising almost 90 percent of global GDP, focusing particularly on six countries: China, Germany, India, Japan, Mexico and the US.  For each of this six countries, the study modeled the potential for employment changes in more than 800 occupations based on different scenarios for the pace of automation adoption and for future labor demand.

The report’s overall conclusion is that a growing technology-based economy will create a significant number of new occupations, - as has been the case in the past, - which will more than offset declines in occupations displaced by automation.  However, “while there may be enough work to maintain full employment to 2030 under most scenarios, the transitions will be very challenging - matching or even exceeding the scale of shifts out of agriculture and manufacturing we have seen in the past.”

The Challenges of Executing a Transformational Strategy

The 2018 MIT CIO Symposium will take place on May 23.  This year’s Symposium will be focused on the challenges of executing a digital transformation strategy.   It’s no longer enough to have formulated a digital strategy for the business.  As a number of recent reports have indicated, leading edge companies are pulling ahead of everyone else by pushing the boundaries of digitization.  And, having a strong digital foundation gives those companies a leg up in embracing data analytics, AI, blockchain, and other advanced technologies, further widening their advantage.  “The time for merely thinking digital has passed; the future belongs to the doers,” notes the Symposium’s website.

Why do so many organizations struggle to implement their well formulated strategies, especially transformational strategies like the transition to digital, that encompass new technologies, business models and management practices?  This is a question I’ve long been thinking about, having lived through IBM’s near-death experience in the early1990s.

Is it that in spite of all their efforts in strategy formulation, their management was unable to anticipate the new technology and market changes and were caught by surprise?  Or it is that, as if actors in a kind of Greek tragedy, they saw the changes coming and understood what had to be done, but were somehow unable to execute their strategies?

MIT’s Donald Sull has been exploring these questions for over 20 years.  As this 2015 HBR article notes, we know a lot more about strategy than about translating strategy into results.  “Books and articles on strategy outnumber those on execution by an order of magnitude…  A recent survey of more than 400 global CEOs found that executional excellence was the number one challenge facing corporate leaders in Asia, Europe, and the United States, heading a list of some 80 issues, including innovation, geopolitical instability, and top-line growth.  We also know that execution is difficult.  Studies have found that two-thirds to three-quarters of large organizations struggle to implement their strategies.” 

AI and the Productivity Paradox

AI is being successfully applied to tasks that not long ago were viewed as the exclusive domain of humans, matching or surpassing human level performance in more and more domains.  But, at the same time, productivity growth has significantly declined over the past decade, and income has continued to stagnate for the majority of Americans.  This puzzling contradiction is addressed in Artificial Intelligences and the Modern Productivity Paradox, a working paper recently published  by the National Bureau of Economic Research (NBER).

The paper’s authors, - MIT professor Erik Brynjolfsson, MIT PhD candidate Daniel Rock, and University of Chicago professor Chad Syverson, - note that “aggregate labor productivity growth in the U.S. averaged only 1.3% per year from 2005 to 2016, less than half of the 2.8% annual growth rate sustained from 1995 to 2004…  What’s more, real median income has stagnated since the late 1990s and non-economic measures of well-being, like life expectancy, have fallen for some groups.”

This productivity puzzle isn’t confined to AI.  “Few topics in economics today in most large economies generate as much debate as the productivity puzzle,” said McKinsey in a March, 2017 report.  “In  the United States, productivity growth has declined sharply since 2004 yet digital technology has been widely apparent during this period…  The answer to this puzzle holds the key to future prosperity because now more than ever our economy depends on productivity improvements for long-term economic growth.  Economists have proposed competing explanations for declining productivity growth and so far have failed to reach a consensus.” 

The Rise of Winner-Take-All Urbanism

Earlier this year, Richard Florida published Why America’s Richest Cities Keep Getting Richer in The Atlantic.  Florida is a leading urban studies author and researcher, professor at the University of Toronto and Director of Cities at its Martin Prosperity Institute.

“The most important and innovative industries and the most talented, most ambitious, and wealthiest people are converging as never before in a relative handful of leading superstar cities that are knowledge and tech hubs,” wrote Florida.  “This small group of elite places forge ever forward, while most others struggle, stagnate, or fall behind.  This process is one I like to call winner-take-all urbanism.”

Winner-take-all urbanism is another manifestation of the winner-take-all economics of the past few decades, in which a relatively small number of players reap a very large share of the rewards, upending whole industries with their outsized returns.

AI and The Future of Work

Last month I attended AI and the Future of Work, a conference hosted by MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and its Initiative on the Digital Economy (IDE).  The two-day agenda included over 20 keynotes and panels on AI-related topics, and around 60 speakers and panelists from academia and business.

New technologies have been replacing workers and transforming economies over the past two centuries.  But, over time, these same technologies led to the creation of whole new industries and new jobs.  While the technologies of the industrial economy helped to make up for our physical limitations, the technologies of the digital economy are now enhancing our cognitive capabilities.  They’re being increasingly applied to activities that not long ago were viewed as the exclusive domain of humans.  Will the AI revolution play out like past technology revolutions, - short term disruptions followed by long-term benefits, - or will this time be different?

Conference participants generally agreed that AI will have a major impact on jobs and the very nature of work.  But, for the most part, they viewed AI as mostly augmenting rather than replacing human capabilities, automating the more routine parts of a job and increasing the productivity and quality of workers, so they can focus on those aspect of the job that most require human attention.  Overall, a small percentage of jobs will be fully automated, while many more will be significantly transformed.

Conference participants also generally agreed that the more advanced AI-based transformations will not happen rapidly, but are likely decades away.  Much progress has been recently made in the ability to extract features from all the data we now have access to, as well as in machine learning algorithms that give computers the ability to learn by ingesting large amounts of data instead of being explicitly programmed.  While such statistical pattern recognition approaches can be applied to many tasks, they’re no substitute for model formation, the main approach used by humans, - from toddlers to physicists, - to understand how the world works.  We’re a long way from the development of AIs that truly learn and reason like people. 

The Emergence of Industry 4.0

Over the past few years, Industry 4.0, - aka the 4th Industrial Revolution, - has been the subject of several articles, studies, and surveys, as well as a book published earlier this year by Klaus Schwab, founder and executive chairman of the World Economic Forum (WEF).  They all attempt to describe and put a name to the disruptive changes taking place all around us within the context of the past 250 years.

“Industrial revolutions are momentous events,” said A Strategist Guide to Industry 4.0, - a 2016 article in strategy+business.  “By most reckonings, there have been only three.”  The First Industrial Revolution, - starting in the last third of the 18th century, - introduced new tools and manufacturing processes based on steam and water power, ushering the transition from hand-made goods to mechanized, machine-based production.  The Second starting a century later, brought us steel, cars, chemicals, petroleum, electricity, the telephone and radio while creating the age of mass production.  The Third, - following World War II - saw the advent of computers, digital technologies, the IT industry, and the automation of process in just about all industries.

There’s general agreement that the 4th Industrial Revolution is primarily driven by a fusion of once separate technologies that when joined together are integrating the physical and digital worlds.  But, there’s a spectrum of opinions as to the scope of its impact, some arguing that Industry 4.0 applies primarily to manufacturing technologies and industries, while others argue that it’s profoundly transforming our economies and societies.

A Historical Perspective on the Digital Economy

A few months ago, the magazine strategy+business (s+b) published a roundtable discussion between s+b editor-in-chief Art Kleiner, economic historian Carlota Perez, and PwC UK partner Leo Johnson.  The discussion was centered on Perez’s theory of technology revolutions and economic cycles.  In her 2002 influential book, Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages and in several other publications, Perez framed the digital economy of the past few decades from a historical perspective.  If we examine the long term historical big picture, patterns emerge which can help guide our understanding and planning for the future. 

According to Perez, since the onset of the Industrial Revolution we’ve had 5 major technology-driven economic cycles, each one lasting roughly 50-60 years.  First was the age of machines, factories and canals starting in 1771.  This was followed by the age of steam, railways, iron and coal, starting in 1829; steel, electricity and heavy engineering in 1875; oil, automobiles, and mass production in 1908; and our present ICT-based digital age starting in 1971.

Each economic cycle is composed of two very different periods, each lasting roughly 20 - 30 years.  The installation period is the time of creative destruction, when new technologies emerge from the labs into the marketplace, entrepreneurs start many new businesses based on the new technologies, VCs encourage business model experimentation, and the new ventures attract considerable investments and financial speculation.  Inevitably this all leads to a financial bubble, which eventually bursts in spectacular fashion leading to a time of crisis.

After the crash, comes the deployment period, which Perez views as a time of economic transformation and institutional recomposition.  The now well accepted technologies and business paradigms become the norm; infrastructures and industries start getting better defined and more stable; and production capital drives long-term growth and expansion by spreading and multiplying the successful business models.

Human-AI Decision Systems

I recently read The Human Strategy, a very interesting article on Human-AI decision systems by MIT Media Lab professor Sandy Pentland.  Pentland is the faculty director of MIT Connection Science, - with which I’m associated as a Fellow, - as well as director of the Human Dynamics Lab.  In 2014 he published Social Physics: How Social Networks Can Make Us Smarter.

“Perhaps the most critical function of any organization or society is its decision systems,” wrote Pentland.  “In modern societies, decision systems provide a leader the ability to make informed and timely decisions, supported by a complex enterprise of distributed information and communication systems that provide situational awareness.  Traditionally, decision systems have been confined to individual physical domains, such as logistics, physical plant, and human resources, and more recently virtual domains such as cyber, by both policy and technology, resulting in challenges with the integration of information across disparate domains.”

But, despite the increasingly complex decisions that organizations are called upon to make, decision-making remains human-intensive and anecdotal.  Few organizations have applied social network analysis to help them scale the size and expertise of the decision-making group.  Nor have they integrated the large amounts of data, analytical tools and powerful AI systems now at our disposal into their decision making systems. 

Where is Technology Taking the Economy

I recently read Where is Technology Taking the Economy?, an article in McKinsey Quarterly by W. Brian Arthur, - External Professor at the Santa Fe Institute , Visiting Researcher at PARC, and former faculty member at Stanford University.

According to Arthur, the digital revolution has morphed through three distinct eras over the past several decades.  The first era, in the 1970s and 1980s, brought us Moore’s law and the dramatic advances in semiconductor technologies.  From mainframe and supercomputers to PCs and workstation, IT was now being used in a wide variety of applications, from financial services and oil exploration, to computer-aided design and office systems.  “The economy for the first time had serious computational assistance.”

Then came the second era in the 1990s and 2000s, which enabled us to link computers together, share information, and connect digital processes.  “Everything suddenly was in conversation with everything else,” giving rise to “the virtual economy of interconnected machines, software, and processes…, where physical actions now could be executed digitally.”

We’re now in the third era, which began roughly in the 2010s.  It’s brought us smartphones, ubiquitous sensors, IoT devices and oceans and oceans of data.  Powerful computers and intelligent algorithms are enabling us to make sense of all that data by searching for patterns and doing  something with the results, including computer vision, natural-language processing, language translation, face recognition and digital assistants.

The MIT 2017 Inclusive Innovation Challenge

The MIT Initiative on the Digital Economy (IDE) was organized in 2013 by Erik Brynjolfsson and Andy McAfee to examine the impact of digital technologies on the world.  Understanding the future of work and jobs is one of the major areas of research being address by IDE.  What will the workforce of the future look like?, Where will jobs come from in the coming years, especially for the workers must impacted by automation?,  How can we accelerate the transformation of institutions, organizations, and human skills to keep up with the quickening pace of digital innovation?

To help come up with breakthrough, real-world answers to these tough questions, IDE launched the MIT Inclusive Innovation Challenge (IIC) last year.  The Challenge aims to identify, celebrate and award prizes to organizations around the world that are developing innovative approaches for improving the economic opportunities of middle- and base-level workers.

Now in its second year, the IIC introduced the winners of its 2017 competition at an event held on October 12 as part of Boston’s annual HUBweek.  Over $1 million was awarded to the winners in four categories: job creation and income growth, skills development and matching, technology access and financial inclusion.  The grand prize winner in each category received $150,000, while the three runners up in each category received $35,000.  The awards were funded with support from from Google.org, The Joyce Foundation, ISN and Joseph Eastin.

A Novel Approach for Measuring the True Impact of the Digital Economy

Gross domestic product (GDP), the basic measure of a country’s overall economic output, is generally used by governments to inform their policies and decisions.  “What we measure affects what we do; and if our measurements are flawed, decisions may be distorted,” noted a 2009 Commission convened to look at the adequacy of GDP as an indicator of economic performance, - which was led by Nobel-prize winning economists Joseph Stiglitz and Amartya Sen.

GDP is essentially a measure of production.  While suitable when economies were dominated by the production of physical goods, GDP doesn’t adequately capture the growing share and variety of services and the development of increasingly complex solutions in our 21st century digital economy.

Digital Spillover, a recent report co-developed by Huawei and Oxford Economics, explores how to better define and measure the true impact of the digital economy.  It argues that the scope of the digital economy is expanding and proposes a novel way of measuring its impact.

“A truly digital economy is one in which businesses from across the industrial spectrum are investing in digital and making the most productive use of it,” notes the report.  “The mechanisms by which this is happening are complex and evolving.  Over and above the direct productivity boost that companies enjoy from digital technologies, a more profound chain of indirect benefits also takes place, as the impact spills over within a firm, to its competitors, and throughout its supply chain.  These digital spillover effects materialize through numerous channels, and are integral to understanding the role digital technologies play in the economy.”

The Evolution Toward Digital Supply Chains

Two years ago, a group of leading CEOs from around the world met in China to discuss the major issues facing their companies in the global marketplace.  The meeting was organized by the Center for Global Enterprise (CGE), - the nonprofit research institution founded by former IBM Chairman and CEO Sam Palmisano to study the contemporary corporation, globalization, economic trends, and their impact on society.  The CEOs at the meeting in China concluded that their highest research priority was the evolution toward Digital Supply Chains (DSC), and its potential to transform organizations and the conduct of business around the world.  In response, the Digital Supply Chain Institute (DSCI) was launched a few months later.

In October of 2016, the DSCI, in partnership with CREATe.org  published its first research paper, Digital Supply Chains: A Frontside Flip.  The paper explains the key differences between traditional and digital supply chains, and provides practical advice to help companies prepare for the digital supply chains of the future

The traditional supply chain has evolved over the years as a crucial process used by many companies for the production, handling, and/or distribution of their products or services.  Traditional supply chains generally comprise five separate back-office functions: transport, warehousing, purchasing, marketing and finance.  Their key objective has been to improve the efficiency of getting goods and services from suppliers to customers, through a series of intermediate steps including manufacturing, distributors and retailers.  But, these supply chains were quite fragmented, with limited interactions and information sharing among their various functions and steps.

Over the past couple of decades, the explosive growth of the Internet has made it much easier for companies to transact with each other around the world.  The connectivity and universal reach of the Internet has enabled companies to integrate and better coordinate all their various processes, as well as to go beyond the boundaries of the firm and develop highly sophisticated global supply chains.  Vertically integrated firms have evolved into virtual enterprises, increasingly relying on supply chain partners for many of the manufacturing and services functions once done in-house.

The DSCI paper argues that it’s time to focus on the transformation of the whole supply chain process, not just in further increases in efficiency.  Their traditional linear nature is increasingly misaligned with the more networked nature of today’s global production.

Jobs, Skills and Education

Earlier this year the communications firm Burson-Marsteller along with research firm PSB interviewed 1,500 Americans from all walks of life to shed light on their feelings about the current state of the economy and their expectations for the future.  “Americans are concerned about the present, but optimistic about the future,” was the survey’s overriding finding.  “However, opinions about the economy and the future are somewhat driven by education level.”

While only 33% said that the country is headed in the right direction, 67% said that they were somewhat or very optimistic about the future.  To a large extent, this optimism is based on the belief that their jobs are safe over the next 5 years, with 62% believing that they won’t be laid off and 60% that a machine couldn’t replace them.  In their opinion, the high cost of living, unemployment and jobs, the gap between rich and poor and taxes are the top issues facing the US economy, ahead by a fairly wide margin over corporate corruption, loss of manufacturing jobs, and falling or stagnant wages.

Their feelings about the future differed by educational level.  71% of Americans with a college education or more said that they have the right skills to succeed in the 21st century, compared to 42% of those with a high school education or less.  Only 14% of survey participants with a college education worried that their jobs could be automated within five years, compared with 30% of those with high school or less.  And 55% of Americans with college or more felt that technology will improve overall employment compared to 45% of those with high school or less.

The True State of Globalization

The July-August issue of the Harvard Business Review features an article by NYU and IESE professor Pankaj Ghemawat, - Globalization in the Age of Trump: Protectionism Will Change How Companies Do Business - But Not in the Ways You Think.  Ghemawat has long been conducting research on globalization, and has written extensively on the subject.  His latest book, The Laws of Globalization, was published in October of 2016.

“Business leaders are scrambling to adjust to a world few imagined possible just a year ago,” notes the article’s opening sentence.  “The myth of a borderless world has come crashing down.  Traditional pillars of open markets - the United States and the UK - are wobbling, and China is positioning itself as globalization’s staunchest defender.  In June 2016, the Brexit vote stunned the European Union, and the news coverage about globalization turned increasingly negative in the U.S. as the presidential election campaign progressed.”

What should companies do?  Should they pack up and return home?  Is this the end of globalization, only a dozen years after Thomas Friedman’s The World is Flat became a prominent international best-seller?  “Not according to my research,” says Ghemawat. 

Artificial Intelligence is Ready for Business; Are Businesses Ready for AI?

AI is now seemingly everywhere.  In the past few years, the necessary ingredients have come together to propel AI beyond the research labs into the marketplace: powerful, inexpensive computer technologies; advanced algorithms and models; and most important, oceans and oceans of data.

“Artificial intelligence is getting ready for business, but are businesses ready for AI?,” asks McKinsey in a recently published report - Artificial Intelligence: the Next Digital Frontier.  “AI adoption outside of the tech sector is at an early, often experimental stage,” is the report’s succinct answer.  “Few firms have deployed it at scale.”

The report is based on a survey of over 3,000 AI-aware C-level executives across 10 countries and 14 sectors.  Only 20 percent of respondents had adopted AI at scale in a core part of their business.  40 percent were partial adopters or experimenters, while another 40 percent were essentially contemplators.

AI encompasses a broad rage of technologies and application.  It’s often viewed as the leading edge of IT.  As soon as AI is successfully applied to a problem, the problem is no longer a part of AI.  The McKinsey report is focused on five technologies being increasingly deployed in business, that most everyone agrees are part of AI: robotics and autonomous vehicles, computer vision, language, virtual agents, and machine learning.

Platforms, Blockchains, and the Evolution of Trust

“In primitive economies, people traded mostly with members of their village and community,” wrote David Brooks in a June, 2014 NY Times OpEd, - The Evolution of Trust.  “Trust was face to face.  Then, in the mass economy we’ve been used to, people bought from large and stable corporate brands, whose behavior was made more reliable by government regulation.  But now there is a new trust calculus, powered by both social and economic forces.”

Brooks’ article focused on the rise of the on-demand economy, - aka, the collaborative, sharing, peer-to-peer economy, - and in particular on the surprising success of Airbnb.  Rooms for rent in boarding houses and child and pet care services are nothing new.  What’s new is the impact of technology, platforms and blockchain in particular, on the growing on-demand economy.  Companies are being disrupted as consumers are now able to deal with each other bypassing traditional hotels and taxi services.  All kinds of on-demand products and services are now coming to market.

The on-demand economy wouldn’t be possible without the mobile devices and platforms that enable peer-to-peer transactions among individuals any time and place; the digital payment systems that reliably and securely broker the transactions between buyers and sellers; and the social reputation systems, where people rank buyers and sellers, - a critical requirement for the smooth functioning of collaborative markets like Airbnb.  In the digital economy, our online reputations follow us everywhere, whether we are the renters or the ones renting, the service providers or the service consumers.

What’s the Core Priority of Corporate Managers?

In a recent Harvard Business Review article, - The Error at the Heart of Corporate Leadership, - Harvard Business School professors Joseph Bower and Lynn Paine raised a number of important questions. What’s the core priority of corporate’s management, the near-term gains of shareholders or the long-term health of the company?  Do shareholders generally share common investment objectives?  Can a governance model focused on maximizing shareholder value threaten a company’s overall health and financial performance?  These questions go to the heart of what a corporation is all about as well as to the very nature of modern capitalism.

The idea that maximizing shareholder value should be the primary goal of corporate managers is relatively recent, first put forth by academic economists in the 1970s, - most notably by University of Chicago economist and Nobel Prize recipient Milton Friedman.  In a 1970 NY Times Magazine article, The Social Responsibility of Business is to Increase its Profits, Professor Friedman wrote:

“In a free-enterprise, private-property system, a corporate executive is an employee of the owners of the business.  He has direct responsibility to his employers.  That responsibility is to conduct the business in accordance with their desires, which generally will be to make as much money as possible while conforming to the basic rules of the society, both those embodied in law and those embodied in ethical custom.”  He believed that business concerns beyond making a profit, - such as “promoting desirable social ends,” or “providing employment, eliminating discrimination, avoiding pollution and whatever else,” amounted to “preaching pure and unadulterated socialism.” 

Should Your Company Get on the Blockchain Learning Curve Now or Wait?

A few weeks ago I wrote about the current state of blockchain.  In particular, I asked whether blockchain is ready to cross the chasm from its early adopters, - who are already involved with blockchain in one way or another, - to the considerably larger set of mainstream users who may be considering blockchain but are waiting for results and assurances before jumping in.

I explored this question by comparing blockchain today to the Internet in the early-mid 1990s, which is roughly when the Internet made its own transition from early adopters to mainstream users.  My conclusion was that blockchain isn’t quite ready for this important transition.  It’s still in its early adopters stage, - perhaps akin to the Internet in the late1980s.  A lot is going on with blockchain, so much so that last year the World Economic Forum (WEF) named The Blockchain one of its Top Ten Emerging Technologies for 2016.  But much remains to be done in key areas, including standards, applications and governance.  Blockchain has the potential to make our digital infrastructures much more secure, efficient and trustworthy, but it will take time.

The Internet in the early-mid 1990s was clearly more advanced than blockchain is today.  On the other hand, the business environment in 2017 is quite different from the one back then.  The Internet and associated technologies like smartphones and cloud computing have significantly lowered the costs of collaboration and experimentation, enabling innovations to emerge more rapidly.  In addition to providing a good set of lessons for blockchain’s adoption, the Internet is smoothing the way for the development of blockchain platforms, applications and ecosystems.

What should  your company do?  Get on the blockchain learning curve now or wait until the technology is more mature before jumping in?  Become an early adopter of this potentially transformative technology, or run the risk of being left behind by more aggressive competitors?  How should your company decide when and how to best embrace a disruptive new technology like blockchain?

Global Arbitrage and the Productivity Puzzle

A couple of weeks ago I wrote about The Productivity Puzzle, - namely that despite our continuing technology advances, the US and other developed economies have experienced a sharp decline in productivity growth over the past 10 to 15 years.  In an article published earlier this year, McKinsey offered three main possible explanations for this productivity growth decline: the difficulty of measuring productivity in the digital economy, the shortage of demand and investment opportunities, and the impact, - or lack thereof, - of technological innovations. “So far, though, economists have failed to reach consensus on the causes of the  productivity growth slowdown or indeed the relative significance of the various arguments,” wrote McKinsey.

But another article, The New Class War by Michael Lind, offers a different, rather provocative explanation for our low productivity growth.  According to Lind, the last few decades have seen the rise of managerial elites in advanced economies which have been pursuing profits by methods other than technology-driven innovation and productivity growth.  Instead, they’ve been taking advantage of the significant reductions of trade barriers over this timeframe to pursue profits based on global labor arbitrage, as well as tax arbitrage and other forms of financial engineering.

Lind references James Burnham, John Kenneth Galbraith, and other political scientists and economists whose works I’m not familiar with.  I found the article quite interesting because Lind’s explanation for our declining economic and productivity growth seems quite different from those of most other economists.  Let me attempt to summarize his key arguments. 

Is Blockchain Ready to “Cross the Chasm”? Lessons from the Internet

Earlier this year, IBM conducted the largest study to date on the state of adoption of blockchain.  The study interviewed almost 3,000 C-Suite executives from over 80 countries and 20 industries to learn about their company’s blockchain plans.  Survey responses where classified into three groups: explorers, investigators and passives.

Explorers are already involved in blockchain pilots and experiments.  They make up an average of 8 percent across all industries, with higher activity in certain industries like financial services.  25 percent of organizations are investigators, - considering but not yet ready to deploy blockchains, and 67 percent are passives, - not considering blockchains so far.

These survey results are not surprising for a technology as new and complex as blockchain.  With such technologies, you typically have a relatively small number of early adopters.  A larger number of followers are waiting to learn from the early adopters’ experiences, while the laggards are not yet sure what the technology is about or if it applies to them.

The Productivity Puzzle

Last year, Foreign Affairs focused its March/April issue on Surviving Slow Growth.  “The first decade of the twenty-first century was a time of unprecedented economic growth,” said the issue’s introductory article.  “The rich world got richer, and the developing world raced ahead: by 2007, the emerging-market growth rate had hit 8.7 percent… Then came the fall… growth has ground to a halt almost everywhere, and economists, investors, and ordinary citizens are starting to confront a grim new reality: the world is stuck in the slow lane and nobody seems to know what to do about it.”

Economic growth has two main components, productivity growth and the growth of the labor force.  Fewer workers is one of the key reasons for our stagnant economic growth.  The labor force is still growing in some developing countries like India, Nigeria and the Philippines, but it’s already shrinking in China, Japan and Germany.  In the US, the labor force is growing very slowly.

Over the coming decades, the labor force is expected to shrink in most parts of the world as fertility rates continue to decline.  The 2016 US fertility rate was the lowest it’s ever been.  Increasing productivity is thus more crucial than ever to promote economic growth.  But, in the US and other advanced economies, productivity growth has significantly slowed down over the past few decades.

On the Governance of Blockchain: Lessons from the Internet

A few weeks ago, the World Economic Forum (WEF) released Realizing the Potential of Blockchain, a report by Don Tapscott and Alex Tapscott, authors of Blockchain Revolution which was published in May, 2016, and founders of the recently launched Blockchain Research Institute.  “The last few decades brought us the internet of information.  We are now witnessing the rise of the internet of value,” wrote the authors in the report’s Executive Summary.  “Like the first generation of the internet, this second generation promises to disrupt business models and transform industries… pulling us into a new era of openness, decentralization and global inclusion.”

“However, this extraordinary technology may be stalled, sidetracked, captured or otherwise suboptimized depending on how all the stakeholders behave in stewarding this set of resources - i.e. how it is governed,” they add in a strong note of caution.  “How we govern the internet of information as a global resource serves as a model for how to govern this new resource: through a multi-stakeholder approach using what we call global governance networks.”

I totally agree.

The Internet and World Wide Web brought a badly needed culture of collaboration and standards to the IT industry.  In the 1980s, it was quite difficult to get different IT systems to talk to each other.  Just sending an e-mail across two different applications from two different vendors was quite a chore, as was sharing information across disparate systems.  Then in the 1990s, the open Internet protocol, - TCP/IP, - was widely embraced by the general IT marketplace, making it possible to interconnect systems and applications from any vendors.  Internet e-mail protocols, - SMTP, MIME, POP, IMAP, - enabled people to easily communicate with anyone on any system.  At the same time,  the Web’s open standards, - HTML, HTTP, URLs enabled any PC connected to the Internet to access information on any web server anywhere in the world.

A similar story played out with Unix.  In the 1980s, Unix became a popular operating system for technical workstations, supercomputers and other kinds of systems.  Every vendor had developed its own version of Unix, - IBM’s AIX, Sun’s Solaris, HP’s HP-UX, and several others, - and they were all somewhat different and incompatible, making it difficult to port applications across these different flavors of Unix.  Attempts to unify UNIX were not successful.  Finally, in the 1990s Linux emerged as a Unix-like operating system that over time was embraced by just about all vendors.

The Growing Value of a Dual-Track Education

A few weeks ago I read a very interesting article in The Chronicle of Higher Education, - Why Colleges Need to Embrace the Apprenticeship.  The article raised a number of important questions on the future of college education, in particular, the value of an education that combines formal schooling with real-world practical experience, often referred to as a dual-track educational system.

Lots has been written about the critical importance of a good education in our knowledge-based digital economy.  At the same time, lots of questions are been raised about college and careers.  Is a four-year college education necessary to attain a good job and to have a successful long-term career?  Is it sufficient?  Are colleges properly preparing students for the job market?  Is there a role for liberal arts colleges?  Is college still worth it, given their increasing costs and the student loan debt crisis?  Should we continue to encourage young people to get a college degree? 

The Chronicle article addresses a number of these questions.  It writes that “Apprenticeships - long embraced as a work-force-training method in other countries - may now have an opening to become a far more common pathway to education and employment in the United States.  The enthusiasm for apprenticeships represents a kind of refutation of college education - a recognition that something about the path from college to career is not working for many people.”  The article explains why vocational apprenticeships have not fared as well in the US as they have in other advanced economies, and why apprenticeships, and similar dual-track systems, are increasingly important for all careers, including white-collar and professional careers requiring a bachelor’s or higher professional degree.

Serious AI Challenges that Require Our Attention

Several weeks ago Vanity Fair published and article by NY Times columnist Maureen Dowd, - Elon Musk’s Billion-Dollar Crusade to Stop the AI Apocalypse.  “Elon Musk is famous for his futuristic gambles, but Silicon Valley’s latest rush to embrace artificial intelligence scares him,” noted Dowd.  “And he thinks you should be frightened too.”

Entrepreneur and inventor Elon Musk is one of a number of world-renowned technologists and scientists who have expressed serious concerns that AI might be an existential threat to humanity, a group that includes Stephen Hawking, Ray Kurzweil and Bill Gates.  But, the vast majority of AI experts do not share their fears.  A few months ago, Stanford University’s One Hundred Year Study of AI project published a report by a panel of experts assessing the current state of AI.  Their overriding finding was that:

“Contrary to the more fantastic predictions for AI in the popular press, the Study Panel found no cause for concern that AI is an imminent threat to humankind.  No machines with self-sustaining long-term goals and intent have been developed, nor are they likely to be developed in the near future.  Instead, increasingly useful applications of AI, with potentially profound positive impacts on our society and economy are likely to emerge between now and 2030, the period this report considers.  At the same time, many of these developments will spur disruptions in how human labor is augmented or replaced by AI, creating new challenges for the economy and society more broadly.”

Just because experts conclude that, - at least for the foreseeable future, - AI does not pose an imminent threat to humanity, doesn’t mean that such a powerful technology isn’t accompanied by serious challenges that require our attention.

Become a More Productive, Empathetic, Creative Person With the Help of AI-Based Tools

Despite dramatic advances in technology, most of the world’s economies have been stuck in a long period of slow growth and slow productivity.  This is one of the most serious challenges in our 21st century economy.  Opinions abound, but there’s little consensus on its causes, and, nobody seems to know what to do about it, or how long it will likely last, - years or decades. 

In a recent article in the MIT Sloan Management Review, MIT Research Fellow Michael Schrage proposed a provocative and counterintuitive approach for enhancing innovation and productivity through man-machine collaborations.  Schrage’s approach has been more influenced by behavioral economics than by technology or algorithmic advances.  Instead of just asking how can people create more valuable innovation?, why not also ask How can innovation create more valuable people?  Don’t just leverage advanced technologies, - e.g., bots, software agents, and digital assistants, - to automate away a large portion of the workforce, but also focus on enhancing innovation and productivity by leveraging technology to create higher-performance versions of employees.

“Designing and training smarter algorithms may be cheaper and easier than retraining smart people,” wrote Schrage.  “Advocates of autonomous systems and machine learning typically innovate to minimize or marginalize human involvement in business processes.  For them, people are part of the problem, not the solution.  Organizations that take productivity seriously, however, understand that false dichotomies make poor investments: Smarter machines can - and should - be keys to unlocking greater returns from human capital.”

Mobile Payments - a Long, Relentless War of Attrition

Apple Pay was launched in September of 2014.  It was positioned as a relatively easy, secure way to pay for purchases in physical stores as well as online using a variety of supported Apple devices including iPhones, iPads, Macs and Apple Watch. The general reaction to Apple’s announcement was quite positive:  “[F]or now, at least, analysts believe if there is any company to persuade consumers of the mobile wallet’s value, it is Apple.” 

The payments community welcomed Apple with open arms, hoping that Apple could play a major role in bringing together the different players in the fragmented payments ecosystem, much as it had previously done with iTunes for music and the App Store for smartphone apps.  Apple Pay embraced a number of industry standards, including NFC (near-field communications) for contact-less payments; secure element, a dedicated device chip; and network-based tokenization to protect sensitive financial data.  In addition, Apple collaborated with the credit card networks, several banks and a number of merchants in the development and deployment of Apple Pay, as well as publishing the APIs so developers could embed Apple Pay services in their own apps.  

“Say hello to Apple Pay. It’s the new kid on the payments block and, depending on how things unfold, it could be the new Gorilla in the mobile payments ecosystem,” wrote Karen Webster, CEO of Market Platform Dynamics and an expert in digital payments.  But, she also added a prescient note of caution: “As great as this sounds, there are two limitations of Apple Pay right now for consumers and merchants.  And it’s that old chicken and egg issue that gets in the way of every new payments system.”  The chicken and egg issue is that there aren’t enough consumer devices supporting Apple Pay, and there aren’t enough merchants that accept it.  Both, consumers and merchants, have to be convinced to move away from payment systems they know well and feel comfortable with.