It pays to have a digitally savvy board – Peter Weill, Thomas Apel, Stephanie L. Woerner, Jennifer S. Banner

Peter Weill, Senior Research Scientist and Chair of the Center for Information Systems Research, MIT Sloan School of Management

Stephanie L. Woerner, Research Scientist at the MIT Sloan Center for Information Systems Research, MIT Sloan School of Management

From MIT Sloan Management Review

Boards of directors have many issues competing for their attention, but being digitally conversant in an era of digital transformation is quickly rising to the top of the list. Nearly all companies are looking for ways that technology can be used to improve their business models, customer experience, operational efficiency, and more — and boards must help them move forward at a sufficient pace, advocating for change by supporting and sometimes nudging their CEOs. Those that do are likely to see better financial results than those that don’t.

That’s what we discovered when we did a machine learning analysis of the digital know-how of all the boards of U.S.-listed businesses. (See “About the Research.”) Our research shows that companies whose boards of directors have digital savvy outperform companies whose boards lack it. We define digital savvy as an understanding, developed through experience and education, of the impact that emerging technologies will have on businesses’ success over the next decade. We measured it by analyzing data from surveys, interviews, company communications, and the bios of 40,000 directors, extracting key words that signal exposure to digital ways of thinking and working.

Our discoveries are striking: We found that among companies with over $1 billion in revenues, 24% had digitally savvy boards, and those businesses significantly outperformed others on key metrics — such as revenue growth, return on assets, and market cap growth.

Doing business in the digital era entails risks ranging from cybersecurity breaches and privacy issues to business model disruptions and missed competitive opportunities. When a board lacks digital savvy, it can’t get a handle on important elements of strategy and oversight and thus can’t play its critical role of helping guide the company to a successful future. But companies can fix that by understanding what characteristics to look for in existing and new board members, managing board agendas differently, and cultivating new learning opportunities.

Read the full post at MIT Sloan Management Review.

Peter Weill is a Senior Research Scientist and Chair of the Center for Information Systems Research (CISR) at the MIT Sloan School of Management.

Stephanie L. Woerner is a Research Scientist at the MIT Sloan Center for Information Systems Research.

Thomas Apel is chairman of the board at Stewart Information Services Corp.

Jennifer S. Banner is CEO at Schaad Cos. and lead director of BB&T Corp.

Improving strategic execution with machine learning – Michael Schrage, David Kiron

MIT Sloan Management Review Executive Editor David Kiron

David Kiron, Executive Editor, MIT Sloan Management Review

Michael Schrage, Research Fellow, MIT Center for Digital Business

From MIT Sloan Management Review

Machine learning (ML) is changing how leaders use metrics to drive business performance, customer experience, and growth. A small but growing group of companies is investing in ML to augment strategic decision-making with key performance indicators (KPIs). Our research,1 based on a global survey and more than a dozen interviews with executives and academics, suggests that ML is literally, and figuratively, redefining how businesses create and measure value.

KPIs traditionally have had a retrospective, reporting bias, but by surfacing hidden variables that anticipate “key performance,” machine learning is making KPIs more predictive and prescriptive. With more forward-looking KPIs, progressive leaders can treat strategic measures as high-octane data fuel for training machine-learning algorithms to optimize business processes. Our survey and interviews suggest that this flip ― transforming KPIs from analytic outputs to data inputs ― is at an early, albeit promising, stage.

Those companies that are already taking action on machine learning ― investing in ML and actively using it to engage customers ― differ radically from companies that are not yet investing in ML. They are far more likely to:

  • Develop a single, integrated view of their target customer.
  • Have the ability to drill down to see underlying KPI data.
  • Check their KPI reports frequently.

These differences all depend on treating data as a valuable corporate asset. We see a strong correlation between companies that embrace ML and data-driven decision-making.

Read More »

How do we learn to work with intelligent machines? – Matt Beane

The path to skill around the globe has been the same for thousands of years: train under an expert and take on small, easy tasks before progressing to riskier, harder ones. But right now, we’re handling AI in a way that blocks that path — and sacrificing learning in our quest for productivity, says organizational ethnographer Matt Beane. What can be done? Beane shares a vision that flips the current story into one of distributed, machine-enhanced mentorship that takes full advantage of AI’s amazing capabilities while enhancing our skills at the same time.

 

Matt Beane is a Research Affiliate with MIT’s Institute for the Digital Economy.

What GE’s board could have done differently – Robert Pozen

MIT Sloan Senior Lecturer Robert Pozen

MIT Sloan Senior Lecturer Robert Pozen

From Harvard Business Review

During Jeff Immelt’s tenure as CEO of General Electric, from 2001 until 2017, the company’s stock price fell by over 30%, a decline of roughly $150 billion in shareholder value. Since Immelt’s departure, GE’s stock is down another 30%, as its new CEO, John Flannery, has struggled to cope with the cash flow drain from years of problematic acquisitions, divestitures, and buybacks. Because of these dubious decisions, GE’s ratio of debt to earnings has soared from 1.5 in 2013 to 3.7 in early 2018, according to Moody’s.

So, during GE’s long and steep decline, where was the company’s board of directors? Composed almost entirely of independent directors, it was a distinguished and diversified group of former top executives and other leaders with relevant experience. In my view, however, the structure and processes of the GE board were poorly designed for effectively overseeing Immelt and his management team. There were three problems in particular:

During most of Immelt’s tenure, the GE board was much too large, with 18 directors. The average size of U.S. public company boards is 11 members, with most boards having between eight and 14. Smaller boards are significantly correlated with better stock performance — 8% to 10% higher, according to a GMI study.

Why? After studying meetings of various sizes, researchers have concluded that the optimal number of participants is seven or eight — small enough for good discussions, but large enough for a diversity of opinions. Sociologists observe that many participants in large meetings engage in “social loafing”: Because of the large size, they do not feel responsible to contribute, and instead are content to rely on others to carry things forward. Read More »

The Fix for Misleading ‘CEO Pay Ratios’ – Robert Pozen and Kashif Qadeer

MIT Sloan Senior Lecturer Robert Pozen

MIT Sloan Senior Lecturer Robert Pozen

MIT Sloan MBA ’18, Kashif Qadeer

From The Wall Street Journal

In the coming weeks, many public companies in the U.S. will disclose for the first time their “pay ratios”—the CEO’s compensation divided by the median employee’s. The requirement to provide this ratio was included in the Dodd-Frank Act of 2010. But comparing the figures among different companies—and particularly different industries—will hardly be a straightforward task.

The consulting firm Equilar estimates that the pay ratio will be two or three times as high for retailers as for drug, financial or tech companies. But the reason isn’t soaring CEO pay in the retail industry. For one thing, midlevel retail workers simply make less, on average, than their peers in pharma, finance and tech, which skews the ratio.

Another issue is that 31% of retail employees work part-time, compared with 17% for the rest of American employees. When computing the CEO pay ratio, the Securities and Exchange Commission prohibits companies from adjusting part-time earnings to “annualize” them—to show what these employees would have earned if working full-time. The SEC also bars companies from counting several part-time employees as a single full-time equivalent. Because of this, having many employees who work only a few days each week drags down the median.

To understand how much this might overstate the pay ratio, we examined data for a midsize retail company that operates about 1,200 stores, primarily in the U.S. The company had more than 25,000 employees in 2017. Almost half worked less than 30 hours a week. The median pay of these part-timers (without annualizing) was less than $6,000 a year. By contrast, the median pay of full-time employees who worked for the whole year was approximately $30,000. Read More »