Irving Wladawsky-Berger

It's All About the Business Model, - Not the Technology

In Reinvent Your Business Model, his recently published book, Mark Johnson argues that digital transformation and business model innovation are not the same thing.  New technology alone, - no matter how transformative, - is not enough to propel a business into the future.  The business model wrapped around the technology is the key to its success or failure.  Johnson is Senior Partner in Innosight, the strategy consulting firm he cofounded with Harvard Business School professor Clayton Christensen in 2000.  He’s been conducting research on business model innovation for over a decade, and in 2010 published Seizing the White Space.

Business model innovation has long been the domain of disruptive startups looking to compete against established companies by changing the rules of the game, - and, hopefully, creating new markets and reshaping entire industries.  But, it’s no longer enough for established companies to just roll out improved products and services based on their once-reliable business models.

“Building a great business and operating it well no longer guarantees you’ll be around in a hundred years, or even twenty,” notes Johnson.  “In 1965, the average length of time a company remained on the S&P 500 was thirty-three years.  By 1990, it had dropped to twenty years; in 2012, it was just eighteen.  Based on the 2017 churn rate, it is forecast that half of the S&P 500 will be replaced over the next ten years.”

Examples abound.  In the 1970s, Xerox PARC famously developed, - but didn’t commercialize, - some of the key innovations of the PC era, including the graphical user interface, the mouse and local area networks.  In 2010, Blockbuster filed for bankruptcy, a victim of Netflix’s new business models.  Done in by Apple’s iPhone and Android-based smartphones, the Blackberry, - once the undisputed leader in its market, - fell into a death spiral from which it never recovered. 

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. 

Serverless Computing - An Innovative Approach to Software Development

In October of 2008, The Economist published a special report on a then relatively new, rapidly growing IT development, - cloud computing.  In the lead article, technology editor Ludwig Siegele defined cloud computing by first giving a very succinct history of computing:

“In the beginning computers were human. Then they took the shape of metal boxes, filling entire rooms before becoming ever smaller and more widespread.  Now they are evaporating altogether and becoming accessible from anywhere…  Now,… computing is taking on yet another new shape.  It is becoming more centralised again as some of the activity moves into data centres.  But more importantly, it is turning into what has come to be called a ‘cloud’, or collections of clouds.  Computing power will become more and more disembodied and will be consumed where and when it is needed…  It will allow digital technology to penetrate every nook and cranny of the economy and of society…”

I was reminded of Siegele’s decade-old article when recently learning about another, relatively new, IT innovation, - serverless computing, - a name that connotes the increasingly disembodied nature of computing.  I first came across the term in a Forrester report on the Top Emerging Technologies to Watch in 2018.

Serverless is an approach to software development for quickly building and deploying applications as a collection of loosely coupled modules and microservices.  According to Forrester, “Firms report a better software development experience, rapid scaling of services that compose applications, lower costs, and better infrastructure utilization when workloads are variable.  They also spend less time maintaining cloud infrastructure.” 

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.

Social Physics and Cybercrime Detection

A few weeks ago I wrote about social physics, a new discipline that aims to help us better understand and predict the behavior of human groups.  Social physics is based on the premise that all event-data representing human activity, - e,g,, phone call records, credit card purchases, taxi rides, web activity, - contain a special set of group behavior patterns.  As long as the data involves human activity, - regardless of the type of data, the demographic of the users or the size of the data sets, - similar behavioral dynamics apply.  These patterns can be used to detect emerging behavioral trends before they can be observed by other data analytics techniques.

Physics, biology and other natural sciences have long relied on universal patterns or principles to detect a faint signal within a large data set, - i.e., the proverbial needle in a haystack.  It’s what has enabled the discovery of very short lived elementary particles in physics, - like the Higgs boson  in 2013, - amidst the huge amounts of data generated by high energy particle accelerators.  In biology, it’s given rise to DNA sequencing and its growing list of applications in medicine, biotechnology and other disciplines. 

It’s not surprising that evolutionary biology and natural selection have led to similar universal patterns in the behavior of human crowds.  Humans and our ancestors have evolved with the drive to learn from each other because it’s been a major part of our survival over millions of years.  And if a new behavior, - whether the result of an innovative idea like the discovery of tools, or a mutation like a larger brain size, - helps a human group better adapt to a changing environment, natural selection will favor the survival of that group over others.

Social physics originated in MIT’s Human Dynamics Lab based on research by professor Alex (Sandy) Pentland, his then postdoctoral associate Yaniv Altshuler and their various collaborators.  In 2014, Pentland and Altshuler co-founded Endor, an Israeli-based startup that leverages social physics methods to make fast accurate predictions by analyzing data derived from human behavior.