Irving Wladawsky-Berger

Managing Transformative Initiatives - Some Lessons Learned

Whether it’s cloud computing in the IT industry, over-the-top (OTT) content in media, mobil digital payments in finance, or data science just about everywhere, industry after industry continues to be radically transformed by technology-based disruptive innovations.  This is all part of what Joseph Schumpeter called creative destruction over 70 years ago, - the powerful force that rejuvenates the economy by replacing declining businesses with fast-growing startups. 

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

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

How should established companies deal with complex, company-wide transformative initiatives?  This is a subject I’ve thought long and hard over the years, given my personal experiences with such initiatives through my long career at IBM as well as my subsequent teaching and consulting activities.  Let me share some of the key lessons I’ve learned through the years.

Some Thoughts on The Future of Internet-Centric TV

Over the last several decades, digital technologies have been transforming just about every industry.  But few have been as disrupted by the dramatic advances in technology as the media industries.  Everything seems to be changing at once, from the way content is produced and delivered, to the sources of revenue and profits. 

I’ve been closely following this technology-based transformation of media for a number of reasons.  First is my personal experience with disruptive innovations in the IT industry.  Unlike other industries which I might follow from afar, I’m an everyday consumer of media, spending a considerable part of my day on its various offerings, including TV, newspapers, magazines and the Web. 

In addition, I’ve been a member of the advisory board of USC’s Annenberg Innovation Lab (AIL) since it was founded in 2010 to study the impact of technology and cultural changes on the media industries.  And, since last September I’ve been involved with HBO as strategic advisor on innovation.  Within media, I’m particularly interested in the evolution of television, not only due to my recent HBO affiliation but because, for over a decade, TV has been the source of much of the entertainment I consume.

The Rise of the Digital Capital Economy

I just read an interesting article that was published last summer in Foreign Affairs, - New World Order: Labor, Capital, and Ideas in the Power Law Economy.  The article was written by MIT professor Erik Brynjolfsson and MIT Principal Research Scientist Andy McAfee, who are also Director and Co-Director respectively of the Initiative on the Digital Economy; and by Michael Spence, professor in NYU’s Stern School of Business, professor and dean emeritus of the Stanford Graduate School of Business, and recipient of the 2001 Nobel Prize in economics. 

A major transition is now taking place in our digital economy.  Over the past few decades, advances in technology, and the Internet in particular, have helped create an increasingly global marketplace for labor and capital.  A highly connected, global economy has been rising all around us, whose magnitude and implications were brought to light by NY Times columnist Tom Friedman in his 2005 bestseller The World is Flat: A Brief History of the Globalized World in the Twenty-first Century. 

Another bestseller, The Second Machine Age, published last year by Brynjolfsson and McAfee, is now helping to explain our new era of data science, AI and advanced automation.  These second age machines are being increasingly applied to activities requiring intelligence and cognitive capabilities that not long ago were viewed as the exclusive domain of humans, while enabling us to process vast amounts of information and tackle ever more complex problems. 

These advanced machines are ushering a new kind of digital capital economy, quite different from the flat-world economy of only a decade ago.  The winners are no longer those able to compete solely based on cheap labor or ordinary capital, both of which are being squeezed by automation.  “Fortune will instead favor a third group: those who can innovate and create new products, services, and business models,” says the article.  “So in the future, ideas will be the real scarce inputs in the world - scarcer than both labor and capital - and the few who provide good ideas will reap huge rewards.  Assuring an acceptable standard of living for the rest and building inclusive economies and societies will become increasingly important challenges in the years to come.”

How Can We Ensure that Our Complex AI Systems Do What We Want Them to Do?

Software-intensive systems are generally quite flexible, - able to evolve and adapt to changing product and market requirements.  However, their very flexibility makes it difficult to adequately anticipate and test all the interactions between the various components of the system.  Even if all the components are highly reliable, problems can still occur if a rare set of interactions arise that compromise the overall behavior and safety of the system.

Things are even tougher with people-centric sociotechnical systems, which not only have to deal with potential software problems, but with the even more complex issues involved in human behaviors and interactions.  And, at the bleeding-edges of complexity are data-driven AI systems, which are generally based on statistical analysis and machine learning methods. 

What is it that makes these systems so intrinsically complex?  Why aren’t they simpler?  What purpose does this complexity serve?  Several years ago, I came across a reasonable answer to these seemingly Socratic questions in a very interesting paper - Complexity and Robustness - by  professors Jean Carlson and John Doyle from UC Santa Barbara and Cal Tech, respectively.  Their paper draws on evolutionary biology to help explain the complexity of man-made systems. 

According to Carlson and Doyle, you can find very simple biological organisms in nature, and you can design very simple objects.  The key ingredient you give up is not their basic functionality, but their robustness - that is, the ability to survive, for biological organisms, or to perform well, for engineered objects - under lots of different conditions, including the failures of individual components or dealing with new, unanticipated events.  Robustness implies the ability to adapt and keep going in spite of a rapidly changing environment. 

There is a continuing struggle between complexity and robustness in both evolution and human design.  A kind of survival imperative, whether in biology or engineering, requires that simple, fragile systems become more robust.  But the mechanisms to increase robustness will in turn make the system considerably more complex.  Furthermore, that additional complexity brings with it its own unanticipated failure modes, which are corrected over time with additional robust mechanisms, which then further add to the complexity of the system, and on and on and on.  This balancing act between complexity and robustness is never done.