Irving Wladawsky-Berger

After Years of Promise and Hype, Is AI Once More Failing to Deliver?

The June 13 issue of The Economist included an in-depth look at the limits of AI, with seven articles on the subject.  “There is no question that AI - or, to be precise, machine learning, one of its sub-fields - has made much progress,” notes The Economist in the issue’s overview article.  “Computers have become dramatically better at many things they previously struggled with…  Yet lately doubts have been creeping in about whether today’s AI technology is really as world-changing as it seems.  It is running up against limits of one kind or another, and has failed to deliver on some of its proponents’ more grandiose promises.”

Transformative technologies, - remember the dot-com bubble, - are prone to hype cycles, when all the excitement and publicity accompanying their early achievements often lead to inflated expectations, followed by disillusionment if the technology fails to deliver.  But AI is in a class by itself, as the notion of machines achieving or surpassing human levels of intelligence has led to feelings of both wonder and fear over the past several decades.

The article reminds us that AI has gone through two such major hype cycles since the field began in the mid-1950s.  Early achievements, - like beating humans at checkers and proving logic theorems, - led researchers to conclude that machines would achieve human-level intelligence within a couple of decades.  This early optimism collapsed leading to the first so-called AI winter from 1974-1980.  The field was revived in the 1980s with the advent of commercial expert systems and Japan’s Fifth Generation project, but it didn’t last long, leading to the second AI winter from 1987-1993.

May the Network Force Be With You

May the Force Be With You has become part of popular culture since the words were first uttered in the 1977 Star Wars film.  As we know, The Force is a source of power for those who, like the Jedi, feel its flow.  “The network force is similar,” wrote Silicon Valley network expert and entrepreneur James Currier in Your Life is Driven by Network Effects.  “You don’t always see it, but it is exerting itself on you.  It wants something from you. Your network force proactively guides you down a path.”

“When you start to see that dialogue between you and your network, the push-pull, you see it everywhere.  The chaos of the world diminishes a bit and becomes more understandable and predictable. And you understand more why things are the way they are and why they stay that way.  Hopefully, it will also give you insights as to where you can push to change things that should be changed, not just about you, but about your company, your city, and your world.”

Over the past few decades, network forces have been influencing many aspects of business, the economy, and our social interactions.  The Internet’s universal reach and connectivity have led to increasingly powerful network effects, such as, for example, platform economies.

The Real Business of Blockchain

“Companies in industries as diverse as finance, sports, health care, retail, oil and gas, and pharma are engaging in a wave of blockchain experiments,” write Gartner analysts David Furlonger and Christophe Uzureau in their recently published book The Real Business of Blockchain.  “Many see it as the solution for bringing trust and transparency to digital environments.” Blockchain has indeed been capturing the imagination of business.  According to a Gartner Research Forecast, the business value-add of blockchain is expected to exceed $3 trillion by 2030, over half that amount by 2025.  But, in plain English, what does this all mean?

“It means you can theoretically do business with an unknown partner located anywhere on the planet and trade any asset at any transaction size and not need a lawyer, a bank, an insurance company, or any other intermediary making sure both of you follow through on what you’ve promised to do,” write the book’s authors.  “Such a solution vastly expands the range of assets that a business could trade.  The arrangement also greatly increases who or what a business could directly trade with, without needing a third party (which would take a piece of the value).”

The Social Network Is the Computer

“Human beings have always been a social species,” writes MIT professor Sinan Aral in the opening paragraph of The Hype Machine, his recently published book.  “We’ve been communicating, cooperating, and coordinating with one another since we were hunting and gathering.”  Our increasingly complex social interactions have been the critical factor in the exponential increase of human cranial capacity over the past few million years.  “But today something is different.  Over the last decade, we’ve doused our kindling fire of human interaction with high-­octane gasoline.  We’ve constructed an expansive, multifaceted machine that spans the globe and conducts the flow of information, opinions, and behaviors through society.”

Professor Aral has been studying social media since its beginning in the early 2000s, when it was driven by the idealistic vision of connecting the world and providing free access to information.  The Hype Machine is what he now calls our global social media ecosystem.  This ecosystem has been designed to stimulate and manipulate the human psyche, “to draw us in and persuade us to change how we shop, vote, and exercise, and even who we love.”  His book nicely explains how the social media industrial complex works, how it affects us, and what we can do to help achieve its original vision while avoiding its later perils.  Let me discuss a few of its key points.

User-Based Free Innovation

In his 2016 book of the same title, MIT professor Eric von Hippel defines free innovation “as a functionally novel product, service, or process that (1) was developed by consumers at private cost during their unpaid discretionary time (that is, no one paid them to do it) and (2) is not protected by its developers, and so is potentially acquirable by anyone without payment - for free.” The book is available as a free download here.

Free innovation is a tightly constrained subset of commons-based peer production, - also known as open, collaborative innovation.  Participants in a commons-based peer production project can do so for monetary gains, either as individual users or as employees of a collaborating company, as has been the case with firms involved in the continuous development of Linux.  In contrast, while free innovation has potentially important economic impacts, it’s fundamentally not about money.

Free innovation is part of the household sector of national economies, carried out by the members of a household for their own consumption, using their own capital and unpaid labor.  “In just six countries surveyed to date, tens of millions of individuals in the household sector have been found to collectively spend tens of billions of dollars in time and materials per year developing products for their own use,” writes von Hippel.

The AI Factory: A New Kind of Digital Operating Model

“Whether you’re leading a digital start-up or working to revamp a traditional enterprise, it’s essential to understand the revolutionary impact AI has on operations, strategy, and competition,” wrote Harvard professors Marco Iansiti and Karim Lakhani in “Competing in the Age of AI”, a recently published article in the Harvard Business Review (HBR).  Earlier this year, they also published a book of the same title, which expands on the ideas in the article and illustrates them with a number of concrete use cases.

The age of AI is being ushered by the emergence of a new kind of digital firm.  Rather than just relying on traditional business processes operated by its workers, these firms are leveraging software and data-driven algorithms to eliminate traditional constraints and transform the rules of competition.  Managers and engineers are responsible for the design of the new AI-based operational systems, but the system then runs the operations pretty much on its own.

“At the core of the new firm is a decision factory - what we call the AI factory,” note the authors.  “[T]he AI factory treats decision-making as a science.  Analytics systematically convert internal and external data into predictions, insights, and choices, which in turn guide and automate operational workflows…  As digital networks and algorithms are woven into the fabric of firms, industries begin to function differently and the lines between them blur.”

The Industrial Revolution transformed the economy by developing a scalable, repeatable approach to manufacturing.  The AI factory is now driving another fundamental transformation by industrializing the data gathering, decision making, and overall digital operations of 21st century firms.

Why 70 Percent of Digital Transformations Fail

“Companies either transform or die in industrial revolutions,” writes former P&G executive Tony Saldanha in his recently published book Why Digital Transformations Fail.  “Digital transformation is our current generation’s attempt to transform in the face of the Fourth Industrial Revolution.  However, the sad truth is that 70 percent of all digital transformations still fail today… a shocking number, given the extremely high stakes,” he adds.

What accounts for such a high failure rate?  An inadequate strategy; competitive pressures; technology; talent; culture? While all these factors are important, “The surprising answer to why digital transformations fail is a lack of discipline in defining and executing the right steps for digital transformations to take off and stay ahead,” notes Saldanha, adding that “It is possible to apply the proven checklist methodology from the airline and medical fields to improve the 70 percent failure rate.”

In a 2015 article, Klaus Schwab, - founder and executive chairman of the World Economic Forum, - positioned the Fourth Industrial Revolution within the historical context of three previous industrial revolutions.  The First, - starting in the late 18th century, - was driven by new innovations like the steam engine and marked the evolution from an agrarian to an industrial economy.  The Second, - a century later, - was driven by steel, electricity, the internal combustion engine and mass production techniques.  The Third, - starting in the 1960s, - saw the advent of digital computers, IT, the Internet, and the automation of process in just about all industries.  The Fourth, - driven by increasingly pervasive and inexpensive digital technologies, - is now blurring the lines between the digital, physical, and biological worlds, leading to widespread economic and social disruption.

Saldanha defines digital transformation as “The migration of enterprises and societies from the Third to the Fourth Industrial Revolution era.  For companies, this means having digital technology become the backbone of new products and services, new ways of operation, and new business models.”

“Companies either transform or die in industrial revolutions,” writes former P&G executive Tony Saldanha in his recently published book Why Digital Transformations Fail.  “Digital transformation is our current generation’s attempt to transform in the face of the Fourth Industrial Revolution.  However, the sad truth is that 70 percent of all digital transformations still fail today… a shocking number, given the extremely high stakes,” he adds.

What accounts for such a high failure rate?  An inadequate strategy; competitive pressures; technology; talent; culture? While all these factors are important, “The surprising answer to why digital transformations fail is a lack of discipline in defining and executing the right steps for digital transformations to take off and stay ahead,” notes Saldanha, adding that “It is possible to apply the proven checklist methodology from the airline and medical fields to improve the 70 percent failure rate.”

In a 2015 article, Klaus Schwab, - founder and executive chairman of the World Economic Forum, - positioned the Fourth Industrial Revolution within the historical context of three previous industrial revolutions.  The First, - starting in the late 18th century, - was driven by new innovations like the steam engine and marked the evolution from an agrarian to an industrial economy.  The Second, - a century later, - was driven by steel, electricity, the internal combustion engine and mass production techniques.  The Third, - starting in the 1960s, - saw the advent of digital computers, IT, the Internet, and the automation of process in just about all industries.  The Fourth, - driven by increasingly pervasive and inexpensive digital technologies, - is now blurring the lines between the digital, physical, and biological worlds, leading to widespread economic and social disruption.

Saldanha defines digital transformation as “The migration of enterprises and societies from the Third to the Fourth Industrial Revolution era.  For companies, this means having digital technology become the backbone of new products and services, new ways of operation, and new business models.”

Reconnecting Society and Reopening the Economy

A few months ago, MIT Connection Science held it’s annual Sponsors’ Meeting online.  The virtual meeting included a number of very interesting presentations.  Given its continuing importance, I want to focus my discussion on “Reconnecting Society After a Pandemic,” a talk given by visiting professor Esteban Moro. 

Moro started his presentation by showing a slide from this article.  The slide illustrates that if we had done nothing, or just relied on soft mitigation and herd immunity to contain the spread of the Covid-19 virus, the number of cases and deaths would have gone up very rapidly, - overwhelming healthcare systems around the world.  Instead, many national and state governments adopted two distinct sets of actions, the hammer and the dance.

The hammer is the somewhat draconian social distancing policies aimed at containing the spread of the virus, especially in regions like the New York metro area where the number of cases were escalating at a very rapid pace.  The dance starts once the spread of the virus has been brought under reasonable control, - albeit at the price of large economic costs and societal disruption.  The dance is the longer term effort to keep the virus contained until there’s an effective vaccine while reopening the economy and bringing life as close to normal as possible.

Are We Becoming a Decadent, Stagnating Society?

Earlier this year I read a very interesting essay, “The Age of Decadence”, by NY Times columnist Ross Douthat.  The essay is adapted from his recently published book The Decadent Society - How We Became the Victims of Our Own Success.  This long essay covers a lot of ground, from technology and innovation to politics and religion.  The essay was published in early February, before Covid-19 spread across the US.  I’ll discuss the original essay, but I do wonder how it would have been modified to reflect the impact of the pandemic.

“The real story of the West in the 21st century is one of stalemate and stagnation,” wrote Douthat.  “Everyone knows that we live in a time of constant acceleration, of vertiginous change, of transformation or looming disaster everywhere you look.  Partisans are girding for civil war, robots are coming for our jobs, and the news feels like a multicar pileup every time you fire up Twitter.  Our pessimists see crises everywhere; our optimists insist that we’re just anxious because the world is changing faster than our primitive ape-brains can process.” 

“But what if the feeling of acceleration is an illusion, conjured by our expectations of perpetual progress and exaggerated by the distorting filter of the internet?,” he asked.  What if we really inhabit an era in which repetition is more the norm than invention; in which new developments in science and technology consistently undercover; in which we’re comfortably aging, “no longer optimistic about the future… [while] growing old unhappily together.”  What if  “Our civilization has entered into decadence.”

The Transformational Impact of Blockchain-based Supply Chains: Nine Real-World Use Cases

In May 2016, Don Tapscott published Blockchain Revolution, his 16th book, this one co-written with son Alex.  Don, whom I’ve known for over 25 years, has an uncanny knack for identifying The Next Big Thing and explaining it to everybody else through his books, articles, and speeches.  Don and Alex have each developed a follow-on blockchain book.  Earlier this year, Alex published Financial Services Revolution, while a few weeks ago Don published Supply Chain Revolution, which I will now discuss.

Supply Chain Revolution consists of nine chapters, edited with a foreword by Don.  He opens his foreword by noting, “When we first started working on this book, the coronavirus (COVID-19) hadn’t yet made headlines…  As I’ve become fond of saying, ‘If you’ve got it, a supply chain brought it to you.’  I suppose that, during these times, I could add, ‘If you can’t get it, a supply chain let you down.’”

Each chapter details how blockchain is revolutionizing a different supply chain, from general use cases like global trade to more specific ones like food traceability and manufacturing.  Each was written by some of the world’s top experts on these blockchain applications.

For a few years now, I have argued that supply chain applications will likely emerge as the earliest blockchain killer apps.  Given that blockchain first came to light in 2008 as the architecture underpinning bitcoin and other cryptocurrencies, many thought that its killer-apps would be in the financial services industry.  That might well be the case in the future, but given its underlying complexity, transforming the global financial ecosystem will take considerable time and investment.  Supply chain infrastructures and processes are significantly less complex than those in financial services, healthcare, and other industries, which is why several supply chain use cases have already been brought to market over the past few years.

Could AI Reshape the Global Balance of Power?

The National Security Commission on Artificial Intelligence (NSCAI) was established by the US Congress in Fiscal Year 2019 to “advance the development of artificial intelligence, machine learning, and associated technologies by the United States to comprehensively address the national security and defense needs of the United States.”  A few months ago, the 15 member Commission, - led by former Google CEO Eric Schmidt  and former deputy secretary of defense Robert Work, - released its interim report. The final report is due to be released in March, 2021.  It remains to be seen how the Covid-19 pandemic will impact the final report’s recommendations and release date.

“The convergence of the artificial intelligence revolution and the reemergence of great power competition… threaten the United States’ role as the world’s engine of innovation and American military superiority,” said the interim report in its opening statements.  “How the United States adopts AI will have profound ramifications for our immediate security, economic well-being, and position in the world…  We are concerned that America’s role as the world’s leading innovator is threatened… We know strategic competitors are investing in research and application.  It is only reasonable to conclude that AI-enabled capabilities could be used to threaten our critical infrastructure, amplify disinformation campaigns, and wage war.”

Like the Internet, - let’s remember that ARPANET was started in the late 1960s by the US Department of Defense, - AI is usable for military and civilian purposes.  But, - also like the Internet, - AI is a general purpose technology that can be used for a wide variety of applications, many of them beneficial, but others potentially threatening.

Scenario Planning - Formulating Strategy in the Face of High Uncertainty

“How can we formulate strategy in the face of uncertainty?,” asks Learning from the Future, a recent Harvard Business Review article by Peter Scoblic, in its opening sentence.  Answering this question has never felt more urgent as leaders prepare for the future.  “Even before the Covid-19 crisis, rapid technological change, growing economic interdependence, and mounting political instability had conspired to make the future increasingly murky.”  Uncertainty was already all-encompassing.

“And then the pandemic hit.”

When the present resembles the past, we can calculate the probability of future outcomes by analyzing past historical data.  That’s how insurance companies accurately predict the life expectancy of different people based on age, gender, general health, behavioral traits like smoking and drinking, etc.  But the situation is very different when the present doesn’t resemble the past.  “Uncertainty stems from our inability to compare the present to anything we’ve previously experienced.  When situations lack analogies to the past, we have trouble envisioning how they will play out in the future.”

Military strategists first developed scenario planning in the 1950s to help them envision the impact of a nuclear war between the US and the Soviet Union, - something that was clearly way beyond anyone’s experience.  The object of scenario planning is to assemble all known facts, trends, and other information and use them to create a number of alternative stories rich in detail, typically three or four, about plausible future outcomes along with the development paths and key events that led to such outcomes.  Altogether, the scenarios should span the scope of possible futures, although the actual future will likely include events from each scenario.

Remote Work is Surprisingly Productive, But For Many… Something Is Missing

What If Working From Home Goes on … Forever?, asks science and technology journalist Clive Thompson in the title of his June 9 NY Times Magazine article.  “The coronavirus crisis is forcing white-collar America to reconsider nearly every aspect of office life.  Some practices now seem to be wastes of time, happily discarded; others seem to be unexpectedly crucial, and impossible to replicate online.  For workers wondering right now if they’re ever going back to the office, the most honest answer is this: Even if they do, the office might never be the same.”

A recent survey found that of the 56% of respondents employed pre-Covid-19, half were working from home, - 35% having recently switched to working from home, while another 15% were already doing so pre-Covid; 37% continued to commute to work, and 10% had been recently laid-off or furloughed.  The survey was  based on two separate national samples of US data, - one which gathered 25,000 responses in early April, and the second another 25,000 responses in early May.

Thompson’s article cites the experience of Accenture, which has around 500,000 employees in more than 200 cities in 120 countries.  Before the pandemic, no more than 10% worked remotely on any given day.  But, by the middle of March, nearly all were asked to work from home.  Employees adapted quickly, said Accenture’s CTO.  The volume of video calls went up by a factor of six while those of audio calls tripled.  Despite having to switch from face-to-face to audio and video interactions, overall productivity actually went up as measured by several metrics.

It’s difficult to judge whether the surge in remote work will last.  “Home life in a lockdown is much harder than usual,” notes Thompson.  “Many workers who live alone are experiencing enforced isolation as an emotional grind… Nearly every parent I spoke to had their fingers crossed that schools and day care would reopen in the fall - at which point remote work might become an option they could choose, as opposed to one they were forced to endure.  Assuming that such a day does arrive, it’s possible that quite a few may elect to continue working outside the office.”

How Will Covid-19 Transform Urban Life?

In Why America’s Richest Cities Keep Getting Richer, a 2017 article in The Atlantic, urban studies professor and author Richard Florida wrote: “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.  This small group of elite places forge ever forward, while most others struggle, stagnate, or fall behind…  They are not just the places where the most ambitious and most talented people want to be - they are where such people feel they need to be.”

Knowledge-intensive industries have long been centered in cities and surrounding metropolitan areas, where they’ve had the most access to a college educated, high skill workforce.  But, - from Milan to New York, - high density, globally connected urban areas have been ground zero for the spread of Covid-19.  Are cities now at risk of losing the superstar status they’ve enjoyed over the past several decades?

This is not the first time we’ve asked this question in the recent past.  In the mid-late 1990s, some predicted that the Internet would lead to the decline of cities, because it would enable people to work and shop from home, be in touch with their friends over e-mail, and get access to news and entertainment online.   But, instead of declining, megacities have continued to generate the greatest levels of innovation and good jobs, and thus attract a disproportionate share of the world’s talent.

Post-Pandemic, Is the World Likely to Become Significantly Less Flat?

“The golden age of globalisation, in 1990-2010, was something to behold,” wrote The Economist in a January, 2019 article.  “Commerce soared as the cost of shifting goods in ships and planes fell, phone calls got cheaper, tariffs were cut and the financial system liberalised.”

Thomas Friedman’s The World is Flat became an international best-seller in 2005 by nicely explaining what this new golden age of globalization was all about, including the key forces that contributed to flattening the world, - from the explosive growth of the Internet, open source software and related technologies; to the rise of outsourcing, offshoring and global supply chains.

In 2011 I attended a conference where Friedman was one of the speakers.  He said that we’ve transitioned from a connected to an increasingly hyperconnected global economy since he wrote The World is Flat in 2004.  Many of the companies and technologies that were part of every day conversations in 2011, - Facebook, Twitter, LinkedIn, smartphones, cloud computing, big data, broadband wireless Internet, - weren’t mentioned in his book because they had not yet been born or were still in their infancy.

The very nature of the global firm was transformed during these same decades.  In The Globally Integrated Enterprise, a 2006 Foreign Affairs article, then IBM CEO Sam Palmisano argued that the modern industrial corporation was now in its third stage of evolution in the way it conducted business around the world.

The Post-Pandemic Enterprise: What Will It Be Like?

In the near term, nations have been mostly focused on surviving the massive disruptions caused by Covid-19.  But, what about the longer term?  What will the post-pandemic new normal be like for societies and economies?  More specifically, how will the pandemic impact the world of business?  How can enterprises leverage the crisis to accelerate their journey toward what most everyone agrees will be an even faster changing future?  As Rahm Emanuel famously said in a 2008 WSJ interview: “You never want a serious crisis to go to waste.”

“The future is not what we thought it would be only a few short months ago,” said a recent McKinsey article, From thinking about the next normal to making it work.  “As businesses step into the post-coronavirus future, they need to find a balance between what worked before and what needs to happen to succeed in the next normal.”  The article offers 21 concrete recommendations for succeeding in the next normal, organized into seven major areas.  Let me summarize a few of these recommendations.

Building a More Resilient, Data-Driven Economy

“With each major crisis, be it war, pandemic, or major new technology, there has been a need to reinvent the relationships between individuals, businesses, and government,” wrote MIT professor Alex “Sandy Pentland in the first chapter of Building the New Economy.  Pentland cites two major historical examples.  In the early 20th century, the rise of mass manufacturing led to the creation of regulations governing working conditions and pay, health rules for mass-produced foods, and laws to prevent monopolies that stifled competition.  Then in mid-century, the decades following WWII saw greater access to higher education, racial and gender progress, and increased support for scientific research and medical advances.

“Today we face two simultaneous disruptions: one is the COVID-19 pandemic and resulting economic shock, and the second is the rise of pervasive digital data, crypto systems, and artificial intelligence (AI).”  The two disruptions are highly interrelated.  Digital technologies have been deployed to fight the global pandemic as well as helping us stay connected as we practice social distancing.

But, these same technologies are also increasing the already high levels of disinformation and fraud, threatening privacy rights with well-meaning public health technology measures, and likely to impact employment as institutions accelerate their embrace of digitization, AI and automation.  “These problems highlight the need to reinvent the ways data and AI are used in all of societies’ civic and government systems, both to guarantee that future pandemics can be handled better and to reinvigorate the economy but also to spread the economic benefits throughout society.”

High resilience in the face of an uncertain, changing environment is the essence of evolution and natural selection.  Building a new post-pandemic economy requires the creation of more agile, resilient systems and institutions to help us better adapt to an unpredictable future and withstand turbulent events like Covid-19.  In a resilient economy, power and decision-making should be distributed among a diverse set of stakeholders rather than concentrated in a few hands.

Why “The Techlash” Is a Threat to Growth and Progress

A few months ago, the Information Technology and Innovation Foundation (ITIF) published A Policymaker’s Guide to the “Techlash” - What It Is and Why It’s a Threat to Growth and Progress.  “Does information technology (IT) solve problems and make our lives easier, allowing us to do more with less?,” asks the ITIF report.  “Or does it introduce additional complexity to our lives, isolate us from each other, threaten privacy, destroy jobs, and generate an array of other harms?”

It wasn’t all that long ago that digital technologies and Big Tech were largely seen as catalysts for positive change: the Internet had become a global platform for collaborative innovation; social media was a liberating force, helping democratic uprisings like the Arab Spring; and the smartphone was transforming the lives of people all over the world.

But, the 2010s saw the emergence of what’s come to be called the techlash.  The Oxford English Dictionary (OED) defines techlash as “a strong and widespread negative reaction to the growing power and influence of large technology companies, particularly those based in Silicon Valley.”  The term seems to have originated in a November, 2013 article in The Economist, which said that tech elites are turning out to be some of the most ruthless capitalists around, and “will join bankers and oilmen in public demonology.”  By 2018, techlash was one of eight shortlisted entries for OED Word of the Year.

Even When Plans Are Useless, Planning Is Indispensable

“As scientists race to develop a cure for the coronavirus, businesses are trying to assess the impact of the outbreak on their own enterprises,” wrote MIT professor Yossi Sheffi in a February 18 article in the Wall Street Journal.  “Just as scientists are confronting an unknown enemy, corporate executives are largely working blind because the coronavirus could cause supply-chain disruptions that are unlike anything we have seen in the past 70 years.”

Sheffi is Director of the MIT Center for Transportation and Logistics.  He’s written extensively on the critical need for resilience in global enterprises and their supply chains, - including The Power of Resilience and The Resilient Enterprise, - so they can better react to major unexpected events.  Covid-19 is the kind of massively disruptive event he had in mind when he wrote those books.

While learning from historical precedents is always a good idea, recent supply chain disruptions - the 2003 outbreak of SARS in Asia, the 2011 Fukushima nuclear disaster, or the 2011 Thailand floods, - were very different from our current pandemic.  Those events were much more localized, lasted a relatively short time, and they mostly impacted supply, not demand.  The impact of Covid-19 is much bigger, affecting consumer demand as well as supply chains all over the world, and likely to last quite a bit longer.  “Today’s supply chains are global and more complex than they were in 2003,” with factories all over the world affected by lockdowns and quarantines.  Apple, for example, works with suppliers in 43 countries.

What History Tells Us About the Accelerating AI Revolution

A few weeks before our lives were turned upside down by Covid-19, I read Technology at Work v4.0, the fourth report in the Technology at Work Series developed by Citigroup in collaboration with Oxford University.  The report includes an excellent chapter on What History Tells Us About the Coming AI Revolution by Oxford professor Carl Benedikt Frey based on his 2019 book The Technology Trap.

Recent AI advances have “sparked much excitement…  yet despite this, most ordinary people don’t feel particularly optimistic about the future,” wrote Frey.  For example, a 2017 Pew Research survey found that three quarters of Americans expressed serious concerns about AI and automation, and just over a third believe that their children will be better off financially than they were.

But, in fact, serious concerns about the impact of technology are part of a historical pattern.  “Many of the trends we see today, such as the disappearance of middle-income jobs, stagnant wages and growing inequality were also features of the Industrial Revolution, which began in Britain around 1750.  This was a period of unprecedented technological change but also one of turbulent social and political change.”

An Increasingly Digital “New Normal”

About a dozen years ago, I attended a conference where I was interviewed on stage by then WSJ technology columnist Walter Mossberg.  He started out by asking me about IBM’s near-death experience in the early 1990s, and the subsequent technical and cultural transformation that made it possible for IBM to reinvent itself and survive, while many other IT companies didn’t make it.  In particular, we discussed whether a successful company transformation must be preceded by the kind of major crisis IBM went through.  When it comes to humans, the ancient Greeks certainly thought so, as reflected in their tragedies.

Transformative change is very difficult for established companies.  Over the years, they’ve amassed valuable assets and extensive organizations.  Already consumed with managing their existing operations, they may look at transformative change as more of a threat or distraction than an opportunity, - unless they’re in dire straits and have no other choice, as was the case with IBM.

A serious crisis is an opportunity for companies to focus on the actions needed to survive in a fast changing environment.  As Rahm Emanuel famously said in a 2008 interview with the WSJ: “You never want a serious crisis to go to waste.”  In that spirit, how should companies take advantage of the Covid-19 crisis, - arguably the biggest shock the world has experienced since WWII, - to accelerate their journey toward what most everyone agrees will be an increasingly digital future?

Blockchain and Public Health Solutions

The Blockchain Research Institute (BRI) is a global think tank dedicated to the strategic implications of blockchain technologies to business, government and society.  On March 26, I participated in a virtual roundtable convened by the BRI to discuss the potential use of blockchain technologies for public health solutions.  The roundtable’s findings and recommendations were released in early April in Blockchain Solutions in Pandemics: A Call for Innovation and Transformation in Public Health.

The report identified five key areas where blockchain could be deployed to fight Covid-19 as well as future pandemics: identity, health records and shared data; just-in-time supply chains; sustaining the economy; a rapid response registry for medical professionals; and incentives models to reward responsible behavior.  In each of these areas, the report presented uses cases where blockchain is already being deployed, and recommended a number of blockchain related public health measures that will help better prepare for future pandemics.  Given the broad scope of the report, I will focus my attention on one area in particular: identity, health records and shared data.

Getting Through Highly Uncertain Times - Some Lessons Learned

What does it take to get through highly uncertain times, like the ones we’re in the middle of as we confront the Covid-19 pandemic?  Like most of us, I’ve lived through times of high uncertainty in both my personal and professional lives.  On the personal front, my main observation is that most of us are much more resilient than we realize.  I leave it to psychologists and other experts to offer advice on what it takes to get through such times.

But, when it comes to getting through tough times in our professional life, I’d like to share some lessons learned based on my experiences when IBM, - the company I joined in 1970 after completing my university studies, - almost went out of business in the early 1990s.  Let me describe a few of those lessons.

Dealing with a Highly Unpredictable Future

The high degree of uncertainty surrounding the Covid-19 pandemic is one of its most frustrating qualities.  How long will the pandemic last?  When will normal life return?  When will a vaccine be available?  Not only don’t we have definitive answers, we don’t even know how to best frame these critical questions.  The answers we get from the most trusted of experts feel too vague, - their explanations full of caveats and probabilities, - as they should.  How can we start making sense of such an unpredictable future?

It wasn’t all that long ago that we didn’t have much use for probabilities in our daily life, - outside of weather reports, election predictions and baseball.  But that’s all been changing in our complex economy and society.   Probabilities have been playing an increasing role in our work and personal lives given our newfound ability to quantify just about anything.  In all kinds of everyday situations, - from medical diagnoses to financial decisions, - we’ve had to learn to live and make decisions in the messy, uncertain world of probabilities.

Probabilities are inherently hard to grasp, - especially for an individual event like the weather or an election.  “People understand that if they roll a die 100 times, they will get some 1’s,” wrote David Leonhardt in a 2017 NY Times column.  “But when they see a probability for one event, they tend to think: Is this going to happen or not?  They then effectively round to 0 or to 100 percent…  And when the unlikely happens, people scream: The probabilities were wrong!”

The 2019 Artificial Intelligence Index

AI has emerged as the defining technology of our era, as transformative over time as the steam engine, electricity, and the Internet.  While still in its early stages, AI is already reshaping the economy, society, our personal lives and the very nature of work, with much more to come as the technology and applications mature, including the scientific research necessary to anticipate and fight future pandemics.  But, what’s the current state of AI and where is it heading?

To help address these important questions, the annual Artificial Intelligence Index was created under the auspices of the Stanford Institute for Human-Centered Artificial Intelligence, - guided by a Steering Committee of experts from academia and industry, and in collaboration with a number of sponsoring partners and data contributors.  The AI Index aims to “provide unbiased, rigorous, and comprehensive data for policymakers, researchers, journalists, executives, and the general public to develop a deeper understanding of the complex field of AI.”

Now in its third year, the 2019 AI Index Report was released this past December.  So was the associated 2019 AI Index Data, which includes three times as many data sets as the 2018 edition.  The report  comprises nine chapters.  Let me summarize a few of its highlights.

The Coming Era of Decision Machines

“Artificial intelligence (AI) is the pursuit of machines that are able to act purposefully to make decisions towards the pursuit of goals,” wrote Harvard professor David Parkes in A Responsibility to Judge Carefully in the Era of Prediction Decision Machines, an essay recently published as part of Harvard’s Digital Initiative.  “Machines need to be able to predict to decide, but decision making requires much more.  Decision making requires bringing together and reconciling multiple points of view.  Decision making requires leadership in advocating and explaining a path forward.  Decision making requires dialogue.”

In April, 2017 I attended a seminar by University of Toronto professor Avi Goldfarb on the economic value of AI.  Goldfarb explained that the best way to assess the impact of a new radical technology is to look at how the technology reduces the cost of a widely used function.  For example, computers are essentially powerful calculators whose cost of digital operations have dramatically decreased over the past several decades.  Over the years, we’ve learned to define all kinds of tasks in terms of digital operations, e.g., financial transactions, word processing, photography.  Similarly, the Internet has drastically reduced the cost of communications and of access to all kinds of information, - including text, pictures, music and videos.

Viewed through this lens, the AI revolution can be viewed as reducing the cost of predictions.  Prediction means anticipating what is likely to happen in the future.  Over the past decade, increasingly powerful and inexpensive computers, advanced machine learning algorithms, and the explosive growth of big data have enabled us to extract insights from all that data and turn them into valuable predictions.

Given the widespread role of predictions in business, government and everyday life, AI is already having a major impact on many human activities.  As was previously the case with arithmetic, communications and access to information, - we will be able to use predictions in all kinds of new applications.  Over time, we’ll discover that lots of tasks can be reframed as prediction problems.

The Current State of Open Innovation

In January, UC Berkeley professor Henry Chesbrough published Open Innovation Results: Going Beyond the Hype and Getting Down to Business, his fourth book on innovation in the last two decades.  Chesbrough is adjunct professor and faculty director of the Garwood Center for Corporate Innovation at UC Berkeley’s Haas School of Business.  He’s credited with coining the term open innovation in his 2003 book of the same title.

Open innovation is the antithesis of the vertically integrated, closed innovation approach followed by industrial R&D labs through much of the 20th century.  As the rate and pace of technological advances accelerated, - most prominently in the IT industry, - the closed innovation model started to fall apart.  The elapsed times to take an invention from lab to market were no longer competitive, especially against a new generation of startups that significantly decreased the time-to-market for new products and services.  Companies realized that they could no longer rely on their own R&D as the only source of innovative ideas.

Moreover, as innovation was now being applied to broader and more complex problems, external collaborations with clients, business partners, universities, open source communities and others became increasingly important.  Given the fast moving, highly competitive environment, firms recognized that there were a lot more capabilities for innovation in the marketplace than they could possibly create on their own, no matter how big and powerful the company.  The dramatic success of the Internet, World Wide Web, Linux and similar initiatives in the 1990s ushered this new era of open, collaborative innovation.

The Long-Term Future of Work and Education: Three Potential Scenarios

“Experts differ widely in their predictions about how technological innovation will change the labor market, but they all see a need for changes in education,” write British professors Ewart Keep and Phillip Brown in  a recently published article, Rethinking the Race Between Education and Technology.  While experts don’t generally agree on much, they’re pretty much of one mind when it comes to the growing importance of skills and education in our 21st century digital economy.

Every past technological transformation ultimately led to more jobs, higher living standards and economic growth.  But, as a number of recent studies concluded, to ensure that this will indeed be the case, our emerging knowledge economy should be accompanied by the expansion of educational opportunities for everyone.

While noting that this is the most likely scenario for the next 10 - 15 years, Keep and Brown also consider two potential longer-term scenarios.  Perhaps AI will lead to even more pervasive and fundamental transformations in the nature of work, making it difficult for even those with a college or higher education to find a good job.  Beyond that, some have suggested that in the more distant future we might see an even more radical, science-fiction-like transformation: the end of work as we’ve long known it.

The authors argue that considering such a spectrum of possibilities will help us better prepare for what’s essentially an unpredictable future.  In that spirit, their paper discusses three different labor market scenarios: labor scarcity, job scarcity, and the end of work.

Honest Conversations - The Key to a Winning Transformational Strategy

“Nearly every organization - whether private, social, or governmental - is grappling with huge strategic challenges, often with a need to reimagine its very purpose, identity, strategy, business model, and structure,” writes Harvard emeritus professor Michael Beer in his recently published book Fit to Compete.  “Most of these efforts to transform will fail.  And in most cases, they miss the mark not because the new strategy is flawed, but because the organization can’t carry it out.”

For over three decades, professor Beer and his various collaborators have been analyzing the reasons why so many organizational transformations fail.  In general, the common root cause has been the failure to align the organization’s culture and management processes with the strategic directions they’re after.  The book argues that two interrelated reasons explain these failures.

First, “the whole system of organizing, managing, and leading has to be transformed if organizational behaviors and underlying mindsets are to be changed.”  A transformational strategy that aims to take advantage of new technology and market innovations must involve the whole organization.  But, we often forget that such transformations are truly disruptive, not only to the marketplace but also to your own organization.  You cannot transform a large complex organization through business-as-usual objectives, - e.g., growing revenue and profit, cutting expenses, improving quality, developing better products and services.

Any major company-wide initiative must have the strong and visible support of the CEO and other top executives in the company.  The initiative must be carefully nurtured and protected in its early phases until it’s strong enough to stand on its own.  In any organization, there’s a natural competition for resources between new ventures and the existing lines of business.  Human nature being what it is, managers in the existing units will often feel that any new investments are best given to them to grow their businesses, rather than invested in a new, unproven area. This is one of the many challenges that leaders of the new initiative must skillfully navigate, otherwise the inevitable cultural and political – that is, human – issues will slow them down and potentially kill the new venture.

Can Democracy and Free Markets Survive in the Coming Age of AI?

Can technology plan economies and destroy democracy? is the provocative title of an essay in the end-of-2019 issue of The Economist.  “The success of market- and semi-market-based economies… has made the notion of a planned economy seem like a thing of the past.”  But, given all the data that can now be gathered by billions of smartphones and 10s of billions of sensors, - with many more to come, - could our increasingly intelligent and powerful AI systems “replace the autonomous choices on which the market is based?… And if technology can outperform the invisible hand in the economy, might it be able to do the same at the ballot box when it comes to politics?” 

As we’ve already seen, AI applications “can provide not just data on what people want and will tolerate, but also the means to manipulate those desires.  When such means are available to actors within or outside a state the struggle to gain, or for that matter retain, a democracy which reflects a genuine popular will might become even harder than it is.”  Could this all mean that free-market economics and liberal democracy will become obsolete over the coming century?

These questions were often raised and repeatedly tested throughout the 20th century, as various governments attempted to redesign their economies and societies in accord with what were believed to be scientific laws.  But, most such schemes, especially those carried out by authoritarian states, ended up as complete failures.  And some went tragically awry, - like Stalin’s collectivization of agriculture in the Soviet Union and Mao’s Great Leap Forward in China, - both of which brought death and disruption to millions.

The State of AI Adoption - High Performers Show the Way

For the past few years, the McKinsey Global Institute has been conducting a yearly survey to assess the state of AI adoption.  Its 2017 survey of over 3,000 AI-aware executive found that outside the technology sector, AI adoption was at an early, often experimental stage.  Only 20% of respondents used any AI-related technology in a core part to their business.  A common theme throughout the report was that the same players who were leaders in the earlier waves of digitization and analytics were the early leaders in the AI wave.

The McKinsey 2018 survey garnered responses from over 2,000 participants from a wide range of company sizes across 10 industry sectors.  Overall, the 2018 survey found that while the business world had begun to adopt AI, few companies had in place the foundational building blocks that would help them to generate value from AI at scale.  30% were conducting AI pilots.  Nearly half, 47%, had embedded at least one AI capability in their standard business processes, but only 21% reported using AI across multiple business functions.  AI investments were still quite small, with almost 60% of respondents saying that less that one-tenth of their digital budgets went toward AI.

The most recent Global AI Survey was published in November of 2019.  McKinsey garnered responses from over 2,300 participants, nearly 80% of whose companies were using AI in some capacity.  Overall,  the survey showed that “Most companies report measurable benefits from AI where it has been deployed; however, much work remains to scale impact, manage risks, and retrain the workforce.”  AI is becoming more mainstream, with 58% of respondents using AI in at least one function, - a nearly 25% increase over the previous year; and 30% reported using AI across multiple business units, - a nearly 50% increase.

One of the survey’s major findings is that a small share of high performers, around 3% of companies using AI, are already achieving outsize business results.   High performing companies are much further along in scaling the use of AI in their organizations, with an average of 11 different use cases compared an average of 3 among the other companies.

Conceptualizing AI in Human Terms is Misleading and Potentially Harmful

“We speak of machines that think, learn, and infer.  The name of the discipline itself - artificial intelligence - practically dares us to compare our human modes of reasoning with the behavior of algorithms,” writes Oxford doctoral candidate David Watson in his recently published article The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence.  “Despite the temptation to fall back on anthropomorphic tropes when discussing AI, however, I conclude that such rhetoric is at best misleading and at worst downright dangerous.  The impulse to humanize algorithms is an obstacle to properly conceptualizing the ethical challenges posed by emerging technologies.”

What do we mean by intelligence?  In 1994 the Wall Street Journal published a definition which reflected the consensus of 52 leading academic researchers in fields associated with intelligence: “Intelligence is a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience.  It is not merely book learning, a narrow academic skill, or test-taking smarts.  Rather, it reflects a broader and deeper capability for comprehending our surroundings - ‘catching on,’ ‘making sense’ of things, or ‘figuring out’ what to do.”  This is a very good definition of general intelligence, - the kind of intelligence that’s long been measured in IQ tests, and that, for the foreseeable future, only humans have.

On the other hand, specialized intelligence, - the ability to effectively address well-defined, specific goals in a given environment, - is the kind of task-oriented intelligence that’s part of many human jobs.  In the past several years, our increasingly smart machines have become quite proficient at handling a variety of such specialized intelligent tasks.  AI technologies are approaching or surpassing human levels of performance in vision, speech recognition, language translation, the early detection and diagnosis of various forms of cancer, and other capabilities that were once viewed as the exclusive domain of humans.

The Pace of Creative Destruction is Accelerating

“A gale force warning to leaders: at the current churn rate, about half of S&P 500 companies will be replaced over the next ten years,” is one of the key insights from the 2018 Corporate Longevity Forecast.  The forecast is conducted every two years by Innosight, the innovation consulting firm co-founded by renowned Harvard professor Clayton Christiansen (who sadly died from cancer on January 23).  “The 33-year average tenure of companies on the S&P 500 in 1964 narrowed to 24 years by 2016 and is forecast to shrink to just 12 years by 2027.”

Shrinking company lifespans in the S&P 500 is a useful indicator of marketplace turbulence, driven to a large extent by technology disruptions and business model changes.  Every year, companies drop off the list for a variety of reasons.  They may have run into hard times and their market cap size fell below a certain threshold, - currently around $6 billion; they may have been overtaken by faster growing firms; or they could have ceased to exist as an independent company due to a merger, acquisition or private equity deal.

According to the Innosight Forecast report, a few key megatrends are responsible for the market turbulence behind the changing makeup of the S&P 500.

Why Some AI Efforts Succeed While Many Fail

Winning with AI, - a 2019 report based on a survey jointly conducted by the MIT Sloan Management Review and the Boston Consulting Group, - found that 90% of respondents agree that AI represents a business opportunity for their company.  The global survey attracted over 2,500 respondents from 29 industries and 97 countries, and conducted interviews with 17 executives leaders of AI initiatives in large organizations.

The report classified the total survey population into four subgroups based on their understanding of AI tools and concepts and their levels of adoption of AI applications: Pioneers (20%) are leading-edge organizations that both understand and have widely adopted AI; Investigators (30%) understand AI but have not deployed applications beyond the pilot stage; Experimenters (18%) are learning by doing, conducting pilots without a deep understanding of AI; and Passives (32%) have not adopted AI and have little understanding of the technology.

“Many AI initiatives fail,” was the report’s overriding finding.  “Seven out of 10 companies surveyed report minimal or no impact from AI so far.  Among the 90% of companies that have made at least some investment in AI, fewer than 2 out of 5 report obtaining any business gains from AI in the past three years. This number improves to 3 out of 5 when we include companies that have made significant investments in AI.  Even so, this means 40% of organizations making significant investments do not report business gains from AI.”

Innovation and National Security in the 21st Century

“Countries that can harness the current wave of innovation, mitigate its potential disruptions, and capitalize on its transformative power will gain economic and military advantages over potential rivals,” was the top finding of the Innovation and National Security Task Force which was commissioned by the Council of Foreign Relations (CFR) to assess the current state of US technological innovation.  The Task Force noted that leadership in innovation, research and technology since World War II has made the US the most secure and economically prosperous nation on earth.

“Today, this leadership position is at risk,” the Task Force warned.  Federal support and funding for R&D has stagnated over the past two decades.  “Washington has failed to maintain adequate levels of public support and funding for basic science.  Federal investment in R&D as a percentage of GDP peaked at 1.86 percent in 1964 but has declined from a little over 1 percent in 1990 to 0.66 percent in 2016.”

The US has successfully responded to technological competition in the past.  Sputnik was a tipping point in the space race with the Soviet Union during the cold war, leading to, among other things, a significant increase in the number of graduate student fellowships in STEM disciplines, of which I was personally a beneficiary.  Another prominent example is the strong economic competition from Japan in the 1980s, which the US fended off a decade later with our leadership in the Internet and other major digital innovations.

The Internet of Things is Changing the World

“How the world will change as computers spread into everyday objects,” is the title of the lead article in a comprehensive review of the Internet of Things (IoT) in a recent issue of The Economist.  Like many technological advances, IoT has been long in coming.  Ubiquitous or Pervasive Computing dates back to the 1990s, when neither the necessary low-cost devices and wireless networks were anywhere near ready.  “But the transformation is about to go into overdrive, notes The Economist.  “One forecast is that by 2035 the world will have a trillion connected computers, built into everything from food packaging to bridges and clothes.”

IoT promises to bring many benefits, including a whole new generation of innovative products.  We’ve long thought of products as physical entities built with a combination of mechanical and electrical components, - e.g., appliances, cars, agricultural machines, industrial equipment, - some quite simple and some highly complex.  IoT has been ushering a whole new class of smart connected products.  In addition to mechanical and electrical components, these new products use digital components like microprocessors, sensors, data storage, software, and connectivity in a wide variety of ways.

As the world’s digital and physical infrastructures converge, digital technologies are being designed right into all kinds of consumer and industrial products.  Internet-connected smart doorbells, for example, include motion sensors and video cameras that notify a home owner when someone arrives at the door.  Using a smartphone app, the home owner can watch and talk to the visitor, while a video of their interaction is saved for an added level of security.

“The magic of computers is that they provide in a machine an ability - to calculate, to process information, to decide - that used to be the sole preserve of biological brains,” writes The Economist.  “The IoT foresees a world in which this magic becomes ubiquitous.  Countless tiny chips will be woven into buildings, cities, clothes and human bodies, all linked by the internet… Over the past century electricity has allowed consumers and businesses at least in the rich world, access to a fundamental, universally useful good - energy - when and where they needed it.  The IoT aims to do for information what electricity did for energy.”

The Puzzling Economic Impact of Transformative Technologies

“General purpose technologies (GPTs) are engines for growth,…” wrote Erik Brynjolfsson, Daniel Rock, and Chad Syverson in The Productivity J-Curve, a working paper recently published by the National Bureau of Economic Research (NBER).  “These are the defining technologies of their times and can radically change the economic environment.  They have great potential from the outset, but realizing that potential requires larger intangible and often unmeasured investments and a fundamental rethinking of the organization of production itself.”

As we’ve learned over the past two centuries, there’s generally been a significant time lag between the broad acceptance of a major new transformative technology and its ensuing impact on companies, governments and other institutions.  Even after reaching a tipping point of market acceptance, it takes considerable time, - often decades, - for these new technologies and business models to be widely embraced across economies and societies, - and for their full benefits to be realized.

In her 2002 influential book, Technological Revolutions and Financial Capital, economic historian Carlota Perez wrote that since the advent of the Industrial Revolution, we’ve had a major technological revolution every 60 years or so.  First was the age of machines and factories in the latter part of the 18th century.  This was followed by the age of steam, coal and iron in the early to mid 19th century; electricity and steel around the 1870s-1880s; and automobiles, oil and mass production in the early decades of the 20th century.  Then came the computer and communications revolution in the latter part of the 20th century, ushering the transition from the industrial economy of the past two centuries to our present digital economy.

According to Perez, the economic transformations accompanying these technologies are composed of two distinct periods, each lasting roughly 20 to 30 years.  First comes the installation period when the new technologies emerge into the marketplace, entrepreneurs launch many new startups, and venture capitalists encourage experimentation with new business models. This is then followed by the deployment period, when the now well accepted technologies and business models become the norm, leading to long-term economic and productivity growth.

Can AI Help Develop and Execute a Competitive Business Strategy?

Strategy For and With AI, a recent article in the MIT Sloan Management Review by David Kiron and Michael Schrage, argues that while formulating a comprehensive strategy for the use of AI technologies is absolutely necessary, it’s not sufficient.  “Creating strategy with AI matters as much - or even more - in terms of exploring and exploiting strategic opportunity.”  The article is based on a survey of over 3,000 executives, managers and analysts from companies in over 100 countries and 20 industries, as well as on interviews with executives and academics.

AI is expected to be the biggest commercial opportunity for companies and industries over the next couple of decades.  Two 2018 reports, one by PwC and one by McKinsey, estimated that AI has the potential to incrementally add around $13-$15 trillion to global economic output by 2030.  The growth will come from productivity gains, - e,g, the continuing automation of routine tasks, and AI-based tools to augment human capabilities, - and from increasingly sophisticated, AI-enhanced products, services, and system-wide applications.

Machine learning advances, like deep learning, have played a central role in AI’s recent achievements, giving computers the ability to be trained by ingesting and analyzing large amounts of data instead of being explicitly programmed.  Machine learning methods for solving problems, - subtle adjustments to the numerical weights that interconnect huge number of artificial neurons, - are radically different and complement the way humans solve problems.  But, while being a very powerful tool, machine learning, and AI in general, lack all-important human qualities like common sense and empathy, so their decisions and suggestions should be carefully reviewed by humans.  People and technology must closely collaborate, with each playing the particular role they are best at.

Every day we can read about the latest AI advances from research labs, startups and large companies.  AI technologies are approaching or surpassing human levels of performance in vision, speech recognition, language translation, playing championship-level Go, and the early detection and diagnosis of various forms of cancer.  But, can AI help address broad, open-ended and ambiguous problems like developing and executing a competitive business strategy?

How to Survive and Thrive in a World of Disruption

Looking back upon my long career, it’s frankly sobering how many once powerful IT companies are no longer around or are shadows of their former selves, e,g, Digital, Wang, Sun Microsystems, BlackBerry.  The forces of disruption might have been more powerful in the fast-changing IT industry, but no industry has been immune.  In less than two decades, the global recorded music industry has lost over half its revenues, while the drop in newspaper advertising revenue in the US has been even steeper.  Retail has undergone major changes with the rise of e-commerce, as has telecommunications with the transition to mobile phones.

Why have so many companies been done in by disruptive technological and market changes?  Is it that in spite of all their efforts in strategy formulation, their management was unable to anticipate them and were caught by surprise?  Or it is that, as if actors in a kind of Greek tragedy, they see the changes coming and understand what needs to be done, but are somehow unable to make the needed strategic and cultural transformations?

“Evolutionary fitness of organizations is determined by their ability to effectively respond to environmental changes,” write Leo Tilman and Charles Jacoby in their recently published book Agility: How to Navigate the Unknown and Seize Opportunity in a World of Disruption.  “Extinctions generally follow a familiar pattern.  Environmental signals - both positive and negative - are undetected or ignored.  Leaders fail to recognize the new reality and accept it for what it is.  No viable strategic vision for the new environment is developed.  Instead, a myriad of tactical activities is deployed, without amounting to a coherent strategy.  Existential risks remain hidden until it’s too late.”

The Increasing Demand for Hybrid, “T-Shaped” Workers

A recent article in The Atlantic used the USS Gabrielle Giffords to illustrate the important changes taking place in the US Navy, - and in the world of work in general.  After discussing various features of its advanced design, the article noted that the ship’s most futuristic aspect is its crew.  “It was designed to operate with a mere 40 souls on board - one-fifth the number aboard comparably sized legacy ships and a far cry from the 350 aboard a World War II destroyer.  The small size of the crew means that each sailor must be like the ship itself: a jack of many trades and not, as 240 years of tradition have prescribed, a master of just one,” noted the article.  Due to increasingly sophisticated technologies and the high cost of personnel, the USS Gabrielle Giffords is among a new class of Navy ships that’s turned away from specialists in favor of so-called hybrid sailors who are able to handle multiple tasks and rapidly acquire new skills.

The replacement of highly specialized workers with problem-solving generalists who are able to handle and quickly learn new tasks has been taking place across a wide variety of  occupations and industries.  Increased productivity and lower costs, i.e., doing more with less, are some of the reasons for this change.  But, the increased complexity of products, applications and systems, and the unanticipated problems that often follow are other important reasons.

The concept of hybrid or T-shaped workers was first introduced around 25 years ago as a metaphor to describe the kind of individuals that the Navy and many other organizations are looking for.  The vertical stroke in the letter T represents a depth of skills and expertise in one or more specific fields; the horizontal bar implies broad multidisciplinary and social skills, as well as the ability to collaborate with experts across disciplines to jointly solve complex problems.

Impact of AI on Government - Opportunities and Challenges

A few weeks ago I wrote about Leveraging AI to Transform Government based on a 2018 report by the Partnership for Public Service and the IBM Center for the Business of Government.  The report illustrates how government is using AI by describing four use cases being pursued by different agencies.  In 2019, the Partnership and IBM collaborated on two additional reports, Assessing the Impact of AI on the Work of Government, and Building Trust, Managing Risks.

This first report is focused on the impact of AI on the federal workforce, based on an analysis of the jobs and tasks most susceptible to automation, as well as on roundtables and interviews with over 40 AI experts.  The overriding conclusion is that, over time, AI will have a deep impact on how government works.  More than 80 different federal occupations, employing over 130,000 workers, are likely be significantly transformed, including 30,000 in the Treasury Department, 16,000 in Defense, and 5,000 in Transportation, as well as a variety of occupations in independent agencies like the Securities and Exchange Commission and the Government Publishing Office.

In particular, AI will have a major impact in three key areas: the transformation of the federal workday, the potential for personalized customer service and the increased importance of technical and data skills.  Let me summarize the findings and recommendations in each of these areas.

How to Transform a “Big, Old” Company into an Agile Digital Business

“As consumers, we take digital technologies for granted…  We don’t wonder why these things are possible; we simply expect them,” said Designed for Digital a recently published book by Jeanne Ross, Cynthia Beath, and Martin Mocker in its Preface.  But, “the adoption of digital technologies in business doesn’t come as naturally,” - especially if you’re working in a big, old company.  “Inserting digital technologies to enhance operations and create new value propositions has proven extremely challenging.”

Previous research has uncovered this puzzling digital adoption discrepancy between individuals and organizations.  While in our personal lives we’ve enthusiastically embraced digital technology advances, many corporations and institutions still struggle to do so.  At a conference several years ago I learned about a so-called Sunday-night/Monday-morning syndrome, the name given to the frustration of  employees of large companies who, at home on Sunday night, have access to the latest devices and applications, but at work on Monday morning, have to use the more primitive devices and applications that their IT departments support.

A quarter century into the digital age, - as a recent McKinsey study found, - the digital maturity of traditional incumbent firms is still relatively low, - under 25% of their overall potential.  Big, established companies, in particular, have been slow to embrace digital organizational practices, - only 26% of worldwide sales are made through digital channels, 30% of internal operations have been digitally automated, and 25% of supply chains interactions have been digitized.

“If digital technologies are consistently making our consumer lives easier, why aren’t they making it easier to succeed in business?,” asks Designed for Digital.  “Why are business leaders anxious about digital disruption rather than marveling about how quickly they can provide new, exciting, and ever-improving digital solutions to their customers?”  Based on five years of research at nearly 200 large companies, - including executive interviews, surveys of business leaders, and in-depth case studies, - the authors overriding conclusion is that “Big, old companies are simply not designed for digital.”

Leveraging AI to Transform Government

“AI has the potential to help address many of our country’s pervasive problems and advance our safety, health and well-being,” said The Future Has Begun, a report on the impact of AI on government by the Partnership for Public Service and the IBM Center for the Business of Government.  “That promise has great potential to transform government.”

 “The most challenging problems AI may help us solve - from fighting terrorists to serving vulnerable populations - will involve government.  More immediately, though not less consequentially, AI will change the way public servants do their jobs.  Because of the impact AI could have on how governments ensure our safety and further our well-being, government leaders are likely to play a significant role in dictating future directions for AI.”

To illustrate how government is using AI, the report is focused on four concrete use cases being pursued by different agencies.  In-depth interviews were conducted with 14 people from 10 organizations, including the various government agencies involved, as well as their collaborators in non profits and universities.  Let me briefly summarize each of these four case studies.

The MIT Work of the Future Report

“The world now stands on the cusp of a technological revolution in artificial intelligence and robotics that may prove as transformative for economic growth and human potential as were electrification, mass production, and electronic telecommunications in their eras,” said the MIT Task Force on the Work of the Future in its recently released interim report.  The final report will be issued after conducting additional research over the next year.

The Task Force was convened in the spring of 2018 by MIT President Rafael Reif to address what may well be the most critical question of the digital economy: as emerging technologies raise aggregate economic output and the wealth of nations, will they also enable people to attain higher living standards, better working conditions, greater economic security, and improved health and longevity?

The report’s overriding conclusion is that the likelihood that AI and automation will wipe out major workforce sectors in the near future is exaggerated.  However, we’ve already seen important reasons for concern, especially the rising polarization of employment and wage distribution over the past few decades, which has disproportionately benefited high-skilled professionals while reducing opportunities for mid- and low-skilled workers.

“[A] critical challenge is not necessarily a lack of jobs, but the low quality of many jobs and the resulting lack of viable careers for many people, particularly workers without college degrees.  With this in mind, the work of the future can be shaped beneficially by new policies, renewed support for labor, and reformed institutions, not just new technologies. Broadly, the task force concludes, capitalism in the U.S. must address the interests of workers as well as shareholders.”

The Blockchain Value Framework

In July, 2019, the World Economic Forum and Accenture released Building Value with Blockchain Technology, a white paper aimed at helping organizations evaluate blockchain’s benefits and build an effective business case.  The white paper is based on a global survey of 550 individuals across 13 industries, including automotive, banking, and retail; interviews with CEOs and public sector leaders; and a detailed analysis of 79 blockchain projects.

Survey participants were asked for the top reasons that led their organizations to invest in blockchain solutions.  Across all industries, the top three priorities were “full traceability of information on the blockchain”; “ability to ensure data has not been tampered with”; and “increased security”.  “New business products or services” ranked last among the options for investing in blockchain, suggesting that at this early stage, improving existing products and services is a higher priority than considering new business opportunities.

As is often the case, fear that the train is leaving the station is another powerful motivator for investing in a new, fast growing technology.  Worldwide spending on blockchains solutions is expected to reach almost $3 billion in 2019 and close to $12.5 billion in 2022.  51% of survey respondents mentioned “missing out on developing new products/services” as their top concern if they don’t invest in blockchain in the near future; 23% mentioned “missing out on efficiency gains”; and 15% were concerned with “missing out on cost savings.”

How We Learned to Prosper Using Fewer Resources

“We have finally learned how to tread more lightly on our planet.  It’s about time,” writes Andrew McAfee in the Introduction to his new book, More from Less: The Surprising Story of How We Learned to Prosper Using Fewer Resources - and What Happens Next.   McAfee is Co-Director of MIT’s Initiative on the Digital Economy and a Principal Research Scientist at the MIT Sloan School of Management, as well as a prolific writer of books and articles.

“For just about all of human history our prosperity has been tightly coupled to our ability to take resources from the earth,” he adds.  “So as we became more numerous and prosperous, we inevitably took more: more minerals, more fossil fuels, more land for crops, more trees, more water, and so on.  But not anymore.  In recent years we’ve seen a different pattern emerge: the pattern of more from less.”

What led to the emergence of a more-from-less pattern?  The book provides a provocative answer to this question.  The most important forces responsible for the change are technological progress and capitalism.

How to Support the Widespread Adoption of AI

“Artificial intelligence is reshaping business - though not at the blistering pace many assume,” notes Building the AI-Powered Organization, an article in Harvard Business Review by McKinsey partners Tim Fountaine, Brian McCarthy, and Tamim Saleh in its opening sentence.  A number of studies agree that AI is the biggest commercial opportunity for companies and industries over the next 10-15 years.  Two recent reports by McKinsey and PwC respectively concluded that AI has the potential to incrementally increase global economic output between now and 2030 by an additional $13 to $15 trillion.

“Yet, despite the promise of AI, many organizations’ efforts with it are falling short,” adds the HBR article.  Surveys with thousands of executives, showed that most firms are only using AI in ad hoc pilots or applying it to a single business process.  Only 8% of firms are engaged in practices that support widespread adoption.

Why the slow progress?  “At the highest level, it’s a reflection of a failure to rewire the organization.  In our surveys and our work with hundreds of clients, we’ve seen that AI initiatives face formidable cultural and organizational barriers…  at most businesses that aren’t born digital, traditional mindsets and ways of working run counter to those needed for AI.”

To support the widespread adoption of AI, companies must make three fundamental shifts.

Despite Their Huge Upside Potential, Why Do Most Platforms Fail?

“For anyone who follows the world of business, it is now common knowledge that the most valuable firms on the planet and the first companies to surpass the trillion-dollar mark in value (albeit temporarily) are platforms,” note Michael Cusumano, Annabelle Gawer, and David Yoffie in their recently published book The Business of Platforms: Strategy in the Age of Digital Competition, Innovation, and Power.  “Moreover, in a recent list of more than two hundred current and former ‘unicorns’ - startups with valuations of $1 billion or more - we estimated that platforms made up between 60 and 70 percent.”

What do we mean by platform?  The book offer a simple definition.  “Platforms, in general, connect individuals and organizations for a common purpose or to share a common resource.”  A platform strategy differs from a product strategy in that it relies on an external ecosystem to generate complementary innovations and/or business transactions.  The effect is much greater potential for innovation and growth than a single product-oriented firm can generate alone.

Platforms are all about network effects.  The more products or services a platform offers, the more users it will attract, helping it then attract more offerings, which in turn brings in more users, which then makes the platform even more valuable.  Moreover, the larger the network, the more data is available to customize offerings to user preferences and better match supply and demand, further increasing the platform’s value.

The State of AI in the Enterprise

A few months ago, Babson College professor Tom Davenport gave a talk on the state of AI in the enterprise at the annual conference of MIT’s Initiative on the Digital Economy.  His talk was based on two recent US surveys conducted by Deloitte, the first one in 2017 followed by a second in 2018.  Davenport was a co-author of both reports.

The 2017 survey was focused on the responses of 250 US executives who were leading the applications of AI in their companies.  The larger 2018 survey reached out to 1,100 IT (46%) and line-of-business (54%) executives from US-based companies (64% at the C-level) and 10 different industries.  All of these respondents were early AI adopters compared with their counterparts in an average company, - 90% were directly involved in their company’s AI projects, and 75% said that they had an excellent understanding of AI.

Davenport started his talk by summarizing the key findings in the Deloitte surveys:

• 20-30% of enterprises are early adopters, having implemented at least one AI prototype or production application;
  
• Many projects are in pilots but some are already in production;
• Relatively simple low hanging fruit projects prevail over more ambitious and complex moon shots;
  
• Only 24% cited “reducing headcount through automation” as one of their top AI priorities;
• The great majority of respondents believe that AI leads to moderate or substantial changes in job roles and skills;
• Implementation, integration, data issues and talent top the list of challenges faced by early adopters;
  
• Further AI growth is inevitable.

Using Agile Processes to Develop AI-Based Solutions

In the waterfall development model, a project is broken down into a set of sequential phases, - e.g., conception, analysis, design, construction, testing, deployment, maintenance, - each dependent on the completion of the previous phase.  Such a model is most appropriate for complex engineering projects, like bridges, airplanes and computer hardware.  It’s also quite applicable for mission critical software and infrastructure projects where security, availability, scalability and other ilities are paramount.

But, a serial, orderly approach isn’t appropriate for projects where time-to-market, usability, and flexibility are the highest priorities.  For such projects, an agile development approach is much more suitable.  Key to the agile approach is the involvement of the product’s target end-users early in the process to make sure that the product under development is responsive to their requirements. 

Agile development is evolutionary in nature, based on the early availability of experimental prototypes, user feedback, continuous improvements, and rapid responses to technology, market and user requirements.  It’s not surprising that agile software development took off in the 1990s, when the explosive rise of the Internet brought millions and millions of users online, lots of new user-oriented applications, and short time-to-market cycles in response to fierce competition.

Our increasingly sophisticated smart machines bring with them a whole new set of development challenges.  These AI-based systems are generally software-intensive, user-centric, and data-driven.  Their complexity goes way up the smarter we want the systems to be.  The behavior of an AI system based on machine learning depends on the information we use to train its algorithms rather than on the precise set of software instructions that tell the computer what to do.

AI systems are designed to sense, respond and adapt to a changing environment.  Their development feels sort of like bringing up and sending a teen-ager into the world.  Given that we want them to be able to handle unanticipated situations, they must have a fair degree of flexibility and autonomy.  But, it’s hard to be sure that the system (or the teen-ager) will always behave the way we’d like them to.