Marina Gorbis's Blog, page 788

How the U.S. Can Rebuild Its Capacity to Innovate

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Many U.S. firms have long had a simple mantra: “Invent here, manufacture there.” But, increasingly, those same companies are now choosing to invent as well as manufacture abroad. From automotive to semiconductors to pharma to clean energy, America’s innovation centers have shifted east, offering growing evidence that the U.S. has lost what Harvard Business School’s Willy Shih calls the “industrial commons”: indispensable production skills and capabilities. It’s not just that virtually all consumer electronics are designed and made overseas. It’s that the U.S. has lost the underlying capacity to make products like flat-panel displays, cell phones, and laptops; nearly half of the foreign R&D centers established in China now belong to U.S.-based companies.

This isn’t just a lesson for the United States. It’s a lesson for countries around the world: Once manufacturing bids farewell, engineering and production know-how depart as well, and innovation activities eventually follow. We can trace how this happened in the U.S. by looking back to the original offshoring frenzy which started with consumer electronics in the 1960s. The invention of modern transistors, the adoption of standardized shipping containers, and the advent of low-cost assembly lines in East Asia lowered costs and created larger markets for televisions and radios, setting the stage for an Asian manufacturing powerhouse. By the time that substantial U.S. federal research investments enabled the invention of the magnetic storage drive, lithium-ion batteries, and liquid crystal display technologies that paved the way for the next generation of consumer electronics in the 1980s and 1990s, the U.S. had already ceded electronics manufacturing to Asia.

U.S. firms took offshoring a step further and began contracting design and product development activities overseas around the turn of the millennium when China joined the World Trade Organization and Asian producers started investing in major capacity improvements. That pattern has continued. In a recent survey of 369 manufacturers, researchers found that across a range of fields U.S. companies were deciding to move R&D to China to be closer to manufacturers, suppliers, and talent as well as to reap lower development costs and higher-growth markets.

We know from looking at strong economies around that world that a nation needs both R&D and manufacturing activities to maintain a healthy 21st Century industrial ecosystem. While America has continued leading the world in terms of investment in basic science research, it has lost the ability to do the kinds of process improvements that are essential for innovation. When it comes to manufacturing, the country has lost the capacity for “learning by doing.”

But it should be possible for the United States to reverse these developments. We have identified four principles with straightforward steps that policymakers, business leaders, and universities can take to restore innovation ecosystems.

1. Don’t fear picking winners. The United States invests an unrivaled $140 billion annually in federal R&D, and yet the U.S. annual trade deficit in advanced technology products alone stands around $100 billion. America’s problem? It isn’t seriously investing in turning good ideas from laboratories into manufacturable products. In too many cases, other countries are securing new industries by taking advantage of promising results from America’s federal research investments: maturing innovations that were seeded in U.S. basic research laboratories, manufacturing products, and exporting those products back to the United States.

The United States needs investment in “translational research.” This means investing in not only basic science, but also the design, engineering and manufacturing work that can turn a promising idea into a valuable product. Take the example of lithium-ion batteries. While U.S. federal research in the 1990s largely established the feasibility of the technology, U.S. battery companies including Duracell and Energizer opted out of volume manufacturing these new products—not because of domestic labor costs, but because of fears of high upfront investments, long development cycles, and a lack of access to consumers of rechargeable batteries. Countries in East Asia saw an opportunity for job creation and decided to help homegrown firms overcome these hurdles. They provided facilities, loans, and other assistance to establish domestic manufacturing in the field. It worked. Today, US firms have less than 2% of market share in the multi-billion automotive lithium-ion battery industry.

Japan spends about 7% of its government R&D budget on practical “translational research”—converting basic research into meaningful new manufactured goods and processes. Germany spends about 12%. South Korea spends roughly 30%. The U.S., in contrast, spends just 0.5 percent. Even with Japan’s smaller national budget, its total government spending on translational research amounts to about three-times what the U.S. spends. Germany’s translational investments amount to about six-times total U.S. investments. South Korea’s are approximately eight-times what the U.S. spends. Historically, Americans have been averse to translational investments for fear of “picking winners and losers.” But other free-market economies have been able to pick winners and make these investments in fair, unbiased ways that demonstrably boost competitiveness.

Rather than allowing promising R&D results to languish in labs or even be commercialized by foreign competitors, the U.S. should launch a “National Innovation Foundation” to invest in engineering and manufacturing R&D to mature emerging technologies and anchor their production onshore. Right now, there’s no single “focal point” for manufacturing-related R&D in the U.S. federal government. MForesight, a federally-funded independent consortium of academia and industry focused on the future of U.S. manufacturing, estimates that with about 5% of the $140 billion federal research budget, the U.S. could create such an institution and significantly increase the return-on-investment from taxpayer-funded research. This would simply bring the United States into line with the rest of the industrialized world. An estimated 50 countries now have government-backed innovation foundations or similar agencies devoted to turning discoveries and inventions into commercially-viable and socially-beneficial results.

2. Invest in hardware startups and scale-ups. According to a recent study, even when MIT-based hardware startups had access to the skills and financing needed for R&D and proof-of-concept work, they required additional capital, production capabilities, and lead customers that the U.S. simply couldn’t provide. The result: most still had to go to China or elsewhere to scale production up to commercial levels.

The problem lies with both the U.S. government and venture capital (VC). The U.S. government has a long history of strengthening innovation through a combination of R&D and strategic procurement (think both aviation and internet). Government purchase orders, for example, can help companies to raise needed capital (both investments and loans), initiate pilot production or scale production in the U.S., and catalyze private investment. In recent decades, however, the U.S. has generally decreased these types of investments, leaving startups and scale-ups to piece together their own funding. Over recent decades, VCs have overwhelmingly focused on software and biotech investments over “hardware” investments, closing additional doors to manufacturing innovations. It’s no wonder that so many promising manufacturing enterprises have to look abroad to simply get off the ground—let alone soar.

U.S. policymakers can correct this imbalance by building on existing resources to help innovative hardware startups and scale-ups succeed—particularly through domestic government procurement. Other countries—including OECD members like Australia, Sweden, France, and Germany, as well as China—use government procurement skillfully to foster innovation. For example, France used a combination of national public policy and procurement to build a world-class nuclear power industry. China has employed government procurement, strategic technology transfer, and domestic technology development to build its respected high-speed rail industry. Local and regional governments also use procurement to drive innovation. Consider how Barcelona, for example, systematically seeks innovative solutions from entrepreneurs: winning proposals receive guaranteed contracts, plus additional support like office space for their operations.

3. Mind the Mittelstand. Ask a German businessperson or policymaker about the secrets to the strength of their manufacturing sector, and they’re likely to mention the Mittelstand, their small and medium enterprises. For good reason: these firms are diverse, resilient, and geographically distributed engines of innovation. They’re defined by high levels of “buy-in” from owners, investors, managers, and employees. They’re an important basis of “bottom-up innovation.”

In this era, large multinational firms are essentially “systems integrators”—they depend on suppliers, mostly Small and Medium Manufacturers (SMMs), to provide most of the needed components in any product. While few SMMs entertain offshoring strategies, they do, increasingly, compete globally.

The loss of America’s industrial commons has led to the consolidation, weakening, or loss of many small suppliers. This can be corrected. In the United States, SMMs still amount to about 250,000 firms, or 98% of all manufacturing firms. By strengthening and supporting these firms, the U.S. could rebuild the backbone of its manufacturing sector. For example, America’s public sector could help by offering loan guarantees and technical assistance to SMMs to speed up the pace of adoption of new smart manufacturing technologies that are becoming essential for process improvements. Further, government could work to ensure that SMMs are taking advantage of existing opportunities and expanded programs to build awareness about procurement opportunities, emerging domestic and export market opportunities, and new technologies. SMMs can play crucial roles in innovation by engaging in partnerships with universities and other laboratories to help mature technologies. Finally, there’s a straightforward way to help SMMs boost their own expertise: The U.S. could launch a program of industry fellowships to pay recent engineering and business retirees to help SMMs as well as to “coach” next generation of manufacturing start-ups, business incubators, and technology accelerators.

4. Power to the people. The U.S.’s manufacturing innovation decline has traced a similar decline in practical engineering talent. While American high schools typically require students to dissect a frog, few require students to disassemble a power tool. Exposure to real-world engineering is a crucial and cost-effective way to build interest in manufacturing careers—through either four-year engineering degrees or vocational training. Germany’s dual vocational training systems, which pairs apprenticeship with practical classroom learning, has long been a global gold standard. More recently, China has made major investments in talent to address the exponential growth of its manufacturing sector.

Around the world, educated people are the one single indispensable ingredient for innovation. This starts with elementary education and early opportunities to cultivate the necessary creative mindset—think Maker Faires and FIRST Robotics. At higher levels, the public sector can address the need for talent by boosting the availability of graduate fellowships for qualified students. Industry can also work with local technical schools to customize classroom training and experiential learning programs—particularly in areas of identified talent needs.

The common denominator to all these strategies is patience. From the examples above we can see that real innovation takes time. We understand that this is difficult. With the overwhelming pressures of quarterly profit reporting and short-term election cycles, it’s hard for business leaders and policy makers to focus on long-term strategies for strengthening innovation ecosystems. But history does show us: with a foresighted, sustained, cross-sectoral strategy it is possible to both invent and manufacture at home. Strong economies depend on it.

How Lilly Is Getting More Women into Leadership Positions

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Much has been written about the troubling lack of women in leadership roles generally and in health care in particular. At Lilly, we have tackled this problem head-on. Our approaches, we think, can be helpful to other companies working to address this imbalance.

In 2015, we conducted a workforce analysis that revealed a significant shortage of women in leadership at our company. Overall, our global workforce was 47%  female — and 53% of entry-level employees were women. But at higher levels, the percentage dropped off sharply, plummeting to 20% at the top. While this number was comparable to the percentage of women executives at Fortune 500 healthcare companies in 2015, it was not a number we were proud of. Companies that have gender-diverse leadership deliver better financial performance than companies that do not – so addressing the gap was not just the right thing to do; it made business sense as well.

But before we could solve the problem, we had to understand it. So we embarked on an in-depth study of our own employees, based on a proprietary, multi-faceted process we use for market research. We engaged an outside firm to conduct the study to ensure independence and anonymity.

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We surveyed high-potential women and men in the U.S. and asked highly personal questions we had never asked before, recording their stories. Our objective was to better understand how the experiences of women working at Lilly differed from those of men — and more specifically, to identify and remove barriers to career growth so we could increase the representation of women in leadership.

Here are some of the lessons we learned:

Get buy-in from the outset. Our management team was invested in this undertaking from the beginning. Dave Ricks, who was then president of one of our largest business units and who became CEO in January 2017, commissioned the research and supported it throughout the process. He and other senior leaders gathered for two days with human resources and the Lilly Women’s Network, one of our employee resource groups, to brainstorm solutions to barriers uncovered in the research. This was not “just” an HR issue — it was a business issue. We went all-in.

Do your homework. Only through rigorous internal research – asking the right questions, listening to the answers and crystallizing the results – can a company expect to understand its own workforce. We started with a business problem: If women comprised nearly half of our workforce, why were we seeing such a drop-off in senior management? Leaders (mostly male) hypothesized that many women were less ambitious than men, or weren’t capable of ascending to the highest levels of leadership. Traditional engagement surveys do not go deep enough to show whether either point is actually true. Not surprisingly, the surveys showed, neither is based in reality.

Understand your research. Numbers are just numbers unless translated into insights that can be put to use. The research showed that women are just as ambitious as men and equally likely to seek growth opportunities. But many women did not feel supported or recognized for their work. High-potential women start their careers at Lilly excited to take on more responsibility, despite relatively few women role models at the top, especially for non-white women. As they advance, our data showed that some women wrestled with how to fit in and move ahead in a culture that, as with most companies, was dominated by men. They reported encountering biases (conscious and unconscious), gender stereotypes, and talent-management practices that undermined their ambitions. For example, the women reported experiences in which “relationship capital” — whom you know and trust — was an important but unspoken factor in decisions about promotions. Study results showed that Lilly women were more likely to focus on doing the work itself than on networking, and therefore sometimes missed opportunities for promotions despite strong performance.

Be an open book. Accountability is key, so we acted transparently. We held our own feet to the fire by sharing our findings with leaders and employees in 2016. Two years later, we’re seeing women becoming more vocal and more influential. I’m here at the table, they’re saying, and I want to be heard.

Commit to change. Understanding the causes of our gender imbalance was a start, but we next needed to use the findings to create interventions and culture change. For example, we initiated training and deployed instructors to help managers lead more inclusively by valuing differences, recognizing and overcoming bias, fostering a speak-up culture—and we held them accountable for results. More than 2,000 managers, senior directors and vice presidents globally have participated so far. We are revamping our talent-management processes to minimize unconscious and conscious biases in our hiring, management and promotion practices. We’ve set a goal to increase the number of women in management by four percentage points within two years, and we are close to attaining that goal.

Where we are now

Already, we’re seeing progress and are now taking the same approach with racial and ethnic minority populations in the organization. From 2016 to the end of 2017, the number of women leaders at Lilly globally rose from 38% to 41%, and the number of women who report directly to our CEO climbed from 31% to 43%. Last year, women at Lilly accounted for 61%  promotions to senior director and above in the U.S., compared to 54% in 2016. Half of the unit presidents in our pharma business are now women.

As we increase the number of women in our leadership ranks, we become better positioned to increase the diversity of our clinical trials, boost innovation, and authentically and responsibly market our medicines. We have become more deliberate, for example, about having women lead the marketing efforts for drugs that treat diseases that disproportionately affect women. The development and marketing teams for our new medicine to treat metastatic breast cancer were led by women and made up almost entirely of women, for example.

While many companies are trying to build more gender-diverse leadership teams and workforces, progress remains slow. We knew it would remain slow at Lilly, too, unless we took a different approach. So we sought to do something difficult: to understand and address our blind spots. Only then could we hope to grow our pipeline of potential women leaders.

Your Data Literacy Depends on Understanding the Types of Data and How They’re Captured

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The ability to understand and communicate about data is an increasingly important skill for the 21st-century citizen, for three reasons. First, data science and AI are affecting many industries globally, from healthcare and government to agriculture and finance. Second, much of the news is reported through the lenses of data and predictive models. And third, so much of our personal data is being used to define how we interact with the world.

When so much data is informing decisions across so many industries, you need to have a basic understanding of the data ecosystem in order to be part of the conversation. On top of this, the industry that you work in will more likely than not see the impact of data analytics. Even if you yourself don’t work directly with data, having this form of literacy will allow you to ask the right questions and be part of the conversation at work.

To take just one striking example, imagine if there had been a discussion around how to interpret probabilistic models in the run up to the 2016 U.S. presidential election. FiveThirtyEight, the data journalism publication, gave Clinton a 71.4% chance of winning and Trump a 28.6% chance. As Allen Downey, Professor of Computer Science at Olin College, points out, fewer people would have been shocked by the result had they been reminded that, Trump winning, according to FiveThirtyEight’s model, was a bit more likely than flipping two coins and getting two heads – hardly something that’s impossible to imagine.

What we talk about when we talk about data

The data-related concepts non-technical people need to understand fall into five buckets: (i) data generation, collection and storage, (ii) what data looks and feels like to data scientists and analysts, (iii) statistics intuition and common statistical pitfalls, (iv) model building, machine learning and AI, and (v) the ethics of data, big and small.

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The first four buckets roughly correspond to key steps in the data science hierarchy of needs, as recently proposed by Monica Rogati. Although it has not yet been formally incorporated into data science workflows, I have added data ethics as the fifth key concept because ethics needs to be part of any conversation about data. So many people’s lives, after all, are increasingly affected by the data they produce and the algorithms that use them. This article will focus the first two; I’ll leave the other three for a future article.

How data is generated, collected and stored

Every time you engage with the Internet, whether via web browser or mobile app, your activity is detected and most often stored. To get a feel for some of what your basic web browser can detect, check out Clickclickclick.click, a project that opens a window into the extent of passive data collection online. If you are more adventurous, you can install data selfie, which “collect[s] the same information you provide to Facebook, while still respecting your privacy.”

The collection of data isn’t relegated to merely the world of laptop, smartphone and tablet interactions but the far wider Internet of Things (IoT), a catch-all for traditionally dumb objects, such as radios and lights, that can be smartified by connecting them to the Internet, along with any other data-collecting devices, such as fitness trackers, Amazon Echo and self-driving cars.

All the collected data is stored in what we colloquially refer to as “the cloud” and it’s important to clarify what’s meant by this term. Firstly, data in cloud storage exists in physical space, just like on a computer or an external hard drive. The difference for the user is that the space it exists in is elsewhere, generally on server farms and data centers owned and operated by multinationals, and you usually access it over the Internet. Cloud storage providers occur in two types, public and private. Public cloud services such as Amazon, Microsoft and Google are responsible for data management and maintenance, whereas the responsibility for data in private clouds remains that of the company. Facebook, for example, has its own private cloud.

It is essential to recognize that cloud services store data in physical space, and the data may be subject to the laws of the country where the data is located. This year’s General Data Protection Regulation (GDPR) in the EU impacts user data privacy and consent around personal data. Another pressing question is security and we need to have a more public and comprehensible conversation around data security in the cloud.

The feel of data

Data scientists mostly encounter data in one of three forms: (i) tabular data (that is, data in a table, like a spreadsheet), (ii) image data or (iii) unstructured data, such as natural language text or html code, which makes up the majority of the world’s data.

Tabular data. The most common type for a data scientist to use is tabular data, which is analogous to a spreadsheet. In Robert Chang’s article on “Using Machine Learning to Predict Value of Homes On Airbnb,” he shows a sample of the data, which appears in a table in which each row is a particular property and each column a particular feature of properties, such as host city, average nightly price and 1-year revenue. (Note that data are rarely delivered directly from the user to tabular data; data engineering is an essential step to make data ready for such an analysis.)

Such data is used to train, or teach, machine learning models to predict Lifetime Values (LTV) of properties, that is, how much revenue they will bring in over the course of the relationship.

Image data. Image data is data that consists of, well, images. Many of the successes of deep learning, have occurred in the realm of image classification. The ability to diagnose disease from imaging data, such as diagnosing cancerous tissue from combined PET and CT scans, and the ability of self-driving cars to detect and classify objects in their field-of-vision are two of many use cases of image data. To work with image data, a data scientist will convert an image into a grid (or matrix) of red-green-blue pixel values or numbers and use these matrices as inputs to their predictive models.

Unstructured data. Unstructured data is, as one might guess, data that isn’t organized in either of the above manners. Part of the data scientist’s job is to structure such unstructured data so it may be analyzed. Natural language, or text, provides the clearest example. One common method of turning textual data into structured data is to represent it as word counts, so that “the cat chased the mouse” becomes “(cat,1),(chased,1),(mouse,1),(the,2)”. This is called a bag-of-words model, and allows us to compare texts, to compute distances between them, and to combine them into clusters. Bag-of-words performs surprisingly well for many practical applications, especially considering that it doesn’t distinguish “build bridges not walls” from “build walls not bridges.” Part of the game here is to turn textual data into numbers that we can feed into predictive models, and the principle is very similar between bag-of-words and more sophisticated methods. Such methods allow for sentiment analysis (“is a text positive, negative or neutral?”) and text classification (“is a given article news, entertainment or sport?”), among many others. For a recent example of text classification, check out Cloudera Fast Forward Labs’ prototype Newsie.

These are just two of the five steps to working with data, but they’re essential starting points for data literacy. When you’re dealing with data, think about how the data was collected and what kind of data it is. That will help you understand its meaning, how much to trust it, and how much work needs to be done to convert it into a useful form.

Why Privacy Regulations Don’t Always Do What They’re Meant To

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First, California passed major privacy legislation in June. Then in late September, the Trump administration published official principles for a single national privacy standard. Not to be left out, House Democrats previewed their own Internet “Bill of Rights” earlier this month.

Sweeping privacy regulations, in short, are likely coming to the United States. That should be welcome news, given the sad, arguably nonexistent state of our modern right to privacy. But there are serious dangers in any new move to regulate data. Such regulations could backfire — for example, by entrenching already dominant technology companies or by failing to help consumers actually control the data we generate (presumably the major goal of any new legislation).

That’s where Brent Ozar comes in.

Ozar runs a small technology consulting company in California that provides training and troubleshooting for a database management system called Microsoft SQL Server. With a team of four people, Ozar’s company is by all means modest in scope, but it has a small international client base. Or at least it did, until European regulators in May began to enforce a privacy law called the General Data Protection Regulation (GDPR), which can carry fines of up to 4% of global revenue.

A few months before the GDPR began to be enforced, Ozar announced that it had forced his company to, in his words, “stop selling stuff to Europe.” As a consumer, Ozar wrote, he loved the regulations; but as a business, he simply couldn’t afford the costs of compliance or the risks of getting it wrong.

And Ozar wasn’t alone. Even larger international organizations like the Los Angeles Times and the Chicago Tribune — along with over 1,000 other news outlets — simply blocked any user accessing their sites with a European IP address rather than confront the costs of the GDPR.

So why should this story play a central role in the push to enact new privacy regulations here in the United States?

Because Ozar illustrates how privacy regulations come with huge costs. Privacy laws are, from one perspective, a transaction cost imposed on all our interactions with digital technologies. Sometimes those costs are minimal. But sometimes those costs can be prohibitive.

Privacy regulations, in short, can be dangerous.

So how can we minimize these dangers?

First, as regulators become more serious about enacting new privacy laws in the United States, they will be tempted to implement generic, broad-based regulations rather than to enshrine specific prescriptions in law. Even though in the fast-moving world of technology, it’s always easier to write general rules than more explicit recommendations, they should avoid this temptation wherever possible.

Overly broad regulations that treat all organizations equally can end up encouraging “data monopolies” — where only a few companies can make use of all our data. Some organizations will have the resources to comply with complex, highly ambiguous laws; others (like Ozar’s) will not.

This means that the regulatory burden on data should be tiered so that the costs of compliance are not equal across unequal organizations. California’s Consumer Privacy Act confronts this problem directly by opting out specific business segments such as many smaller organizations. The costs of compliance for any new regulation must not give additional advantages to the already-dominant tech companies of the world.

Second, and relatedly, a few organizations are increasingly in charge of much of our data, which presents a huge danger both to our privacy and to technological innovation. Any new privacy regulation must actively incentivize organizations that are smaller to share or pool data so that they can compete with larger data-driven organizations.

One possible solution to this problem is by encouraging the use of what are called privacy enhancing technologies, or PETs, such as differential privacy, homomorphic encryption, federated learning, and more. PETs, long championed by privacy advocates, help balance the tradeoff between the utility of data on the one hand and its privacy and security on the other.

Last, user consent — the idea of users actively consenting to the collection of their data at a given point in time — can no longer play a central role in protecting our privacy. This has long been a dominant aspect of major privacy frameworks (think of all the “I Accept” buttons you’ve clicked to enter a website). But in the age of big data and machine learning, we simply cannot know the value of the information we give up at the point of collection.

The entire value of machine learning lies in its ability to detect patterns at scale. At any given time, the cost to our privacy of giving up small amounts of data is minimal; over time, however, that cost can become enormous. The famous case of Target knowing a teenager was pregnant before her family did, based simply on her shopping habits, is one among many such examples.

As a result, we cannot assume that we are ever fully informed about the privacy we’re giving up at any single point in time. Consumers must be able to exercise rights over their data long after it’s been collected, and those rights should include restricting how it’s being used.

Unless ours laws can adapt to new digital technologies correctly — unless they can calibrate the balance between the cost of the compliance burden and the value of privacy rights they seek to uphold — we run some very real risks. We can all too easily implement new laws that fail to preserve our privacy while also hindering the use of new technology, and both at the same time.

The Art of Claiming Credit

From the Women at Work podcast:

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Have you ever offered up an idea in a meeting and been ignored — but then, 10 minutes later, a man repeated the idea and everyone called it brilliant? Or have you ever worked hard on a team project and been left off the thank-you email?

If we aren’t thoughtful about how we present our ideas at work, we risk not being heard or, worse, missing out on the credit we’re due. Research shows that women get less credit when we work in groups with men. So, it’s important for us to be strategic with our suggestions and insights.

We talk with two experts on workplace dynamics and difficult conversations. First, Amy Jen Su covers how to artfully share your contributions. Next, Amy Gallo tells us how to call out credit stealers.

Guests:

Amy Jen Su is a managing partner and a cofounder of Paravis Partners, an executive coaching and leadership development firm.

Amy Gallo is a contributing editor at Harvard Business Review. She’s the author of the HBR Guide to Dealing with Conflict.

Resources:

● “Research: Men Get Credit for Voicing Ideas, but Not Problems. Women Don’t Get Credit for Either,” by Sean Martin

● “Proof That Women Get Less Credit for Teamwork,” by Nicole Torres

● “Research: Junior Female Scientists Aren’t Getting the Credit They Deserve,” by Marc J. Lerchenmueller and Olav Sorenson

● “How to Respond When Someone Takes Credit for Your Work,” by Amy Gallo

Fill out our survey about workplace experiences.

Email us here: womenatwork@hbr.org

Our theme music is Matt Hill’s “City In Motion,” provided by Audio Network.

“We Had Gone Back 20 Years.” The Heads of Puerto Rico’s Largest Media Company on Life After Hurricane Maria

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When Hurricane Maria struck Puerto Rico in September 2017, it became one of the deadliest storms ever to hit the island. Nearly 3,000 people were killed and parts of the island are still recovering, lacking access to power and clean water more than a year later.

For one of Puerto Rico’s largest companies, Grupo Ferré Rangel, the impact has been enormous. The family-owned business runs Puerto Rico’s largest media company&#8212 (GFR Media) as well as other companies focused on customer engagement (LinkActive) and real estate (Kingbird). Company President Maria Luisa Ferré Rangel and Chief Creative Officer Loren Ferré Rangel recently sat down with HBR to discuss how GFR has changed since Maria struck.

“After Hurricane María,” said María Luisa, “Puerto Rico will never be the same. Our memories are grounded in the fact that we went to bed with one reality, one country or one island; 24 hours later we woke up in a different place.” An edited and condensed version of our conversation follows.

HBR: As publishers and editors, you had to cover the disaster while your employees — and your businesses, by extension — faced extraordinary obstacles. How did you approach those first days and weeks?

María Luisa: Puerto Rico was completely devastated, and all of our businesses were impacted, too. Inside the newsroom, we had families living in the cafeteria, conference rooms, training centers etc.; there were 200 people who lived in our newsroom for weeks. We had to put in a daycare center, a catering center, provide cash because there was no way for people to take money out of the bank, provide cars for people, especially reporters, to get around. We worried all the time about having enough fuel for the generators. And that was just for our employees. When it came to the business, we had gone back 20 years and distributed the paper as a print product. The whole island was isolated from the world.

It was a race to transform ourselves. We were responsible for informing and connecting everyone in a very tricky environment. From the first moment, because our business is connecting people, we had to pick ourselves up and do our job. But the hurricane forced us to see ourselves in a different way. Our call centers became the FEMA call centers. We were supplying electricity and water to our properties that are part of our real estate business, and so we began to rent out small spaces to people who needed to get back to work but had no place to go. We gave space to NGOs so they could also be first responders in the emergency. We started to think about what other services we could provide, and this is when we began to refocus our business.

What do you mean, refocus?

María Luisa: The hurricane forced us to stretch our thinking, challenging our perception of what we believe we could do — what we are capable of achieving in times of crisis. Crisis brings opportunities to explore uncharted territories. We’re now looking into developing other businesses and strengthening our presence in other industries. For example, now with our call center experience, we are competing for the call centers for the U.S. and Caribbean. We are evaluating coworking space opportunities. We’re looking at affordable housing with a group that can build quickly with new technology that is hurricane proof and can prove self-sufficient after an emergency with integrated solar panels and battery packs. We’re investing in hurricane proof solar panels that are applicable to various surfaces. In addition, we are evaluating investing in small startups that are offering solutions to facilitate living.

These opportunities sprouted from the crisis. The ecosystem changed, Puerto Rico changed. We needed to adjust our plan to give room and seize the opportunities that had risen within housing, energy, and services.

What made you ready for these opportunities?

Loren: No one was really ready for the outcome post-María. However, having to navigate the landscape and having to get our businesses back in track, gave us the capacity to see the needs and thus the opportunities that were evident after the hurricane. We identified jobs to be done. The hurricane forced us to see ourselves in a different way.

María Luisa: Our businesses were able to operate immediately after Maria because we planned for redundancy — generators, diesel, tech infrastructure. For example, we had three internet suppliers and although connection was nonexistent internally, we were able to transmit and keep our coverage on our websites for those outside of Puerto Rico. We were highly focused on covering the story of Puerto Rico and helping the world understand what was going on here. The printed newspaper was the only source of information available at that moment, and we understood the importance of people having information that could save their lives, that is why we decided to [distribute] the paper for free.

The hurricane had a huge financial impact, especially for the media company. We had no advertising because most of our advertisers were closed, their agencies without power. The reality of running a continuous operation, the extra expenses of diesel, gasoline, food, and then we had the cost of taking care of our employees and their families, without the revenues amounted to a $14 million loss, which we were counting on our business interruption insurance policy to cover, but at this moment we haven’t received any payment from this part of the insurance. As a family, we had to put up the money to keep the media company running for months until slowly the advertising dollars started to come back. The reality of having an operation that was debt free became very real, because if in addition to the $14 million loss, we needed to pay our interest on loans/debt, it would have made it impossible for us to continue operations.

Soon after the hurricane, you had to make cuts at the media company. Were those related to the losses? Or a desire to invest in other newer parts of the business — the solar, the housing, the call centers, etc?

María Luisa: This was one of the most difficult decisions in our lives. We had great challenges in front of us, and we needed to make changes throughout the company, in order to continue our mission. In terms of the media company, we needed to revise processes and look for efficiencies. Our industry has been in the middle of a great transformation and the impact of the hurricane made it much worse, that is why it was so difficult to make the decisions, but also necessary to sustain the business through a very very difficult time. On the human side, we knew that some of these decisions were dramatic, but at the end we had to ensure the sustainability of the business in the middle of the crisis.

Do you feel that the company has stabilized? How long has it taken to restore a sense of normalcy?

María Luisa: It was really chaotic for a while because you had to survive every day. We didn’t know if we had enough diesel to operate. Most of us were not living at home. When you drove around Puerto Rico, there were no traffic signals, no policemen. And we had a mandatory curfew. We couldn’t be out in the street after 5 p.m. This lasted about a month.

The moment we started feeling as though we had routines, we felt the chaos subside. Even if it was just being at work and picking up trash in your office. Then, when the power started to come back, we started to feel a lot more secure. Then the water came back, and we started to feel more in control. This was four months after the hurricane.

None of our businesses stopped running, but the moment we were able to be fully operational was a big deal. That doesn’t mean we didn’t have issues. We were losing money. That’s when we realized we had to create a new strategy. That we didn’t have time. That we had to move very fast.

It sounds like even though the chaos was subsiding, there was a lot that was still unsettled for a lot of people. How do you introduce a new strategy when employees are already stressed? Was it even a good idea to introduce a new strategy at this time?

María Luisa: Our focus was on making sure our employees were safe and on their way to full recovery. But we quickly started pulling people together in new ways and working in teams and across silos. The teams knew the company was stable but under threat. You can imagine my thoughts of, “How do you tell them that it’s going to be ok? How do you motivate them?” In our regular meetings with all the teams, and in every conference room, we pasted a Winston Churchill quote: “A pessimist sees the difficulty in every opportunity; an optimist sees the opportunity in every difficulty.” We also pasted a quote from Martin Luther King, Jr: “If you can’t fly then run, if you can’t run then walk, if you can’t walk then crawl, but whatever you do you have to keep moving forward.” We wanted people to know that the important thing for us was to keep moving forward and celebrate moving forward. That mentality has really helped us.

Beyond shifting in strategy, how did Maria change your business? What are some of the lessons that you learned?

María Luisa: We learned about our emergency operations. We were relying so much on technology that we forgot basics like having a list of where employees live, on paper. The computers didn’t work. We couldn’t send emails. Now we have a roster. We have added regular land lines as backup in communication and now we are working on mapping out where our employees live and how to physically reach them in case of an emergency, also creating emergency centers in our distribution buildings and call centers buildings.

And, in the media company, we were so into covering the story, in surviving the moment, that we might have lost sight that our own people were suffering. We look outside a lot — we covered the loss of Puerto Rico, but maybe we didn’t look inside enough. In hindsight, while we were working through the crisis, focused on getting the information out, we should have also had a group of our own people looking to the needs of our staff and having the space and time to process what has happened to them on individual levels too… counseling, for example. Now we’re creating a wellness program to help people deal with the trauma everybody had. And that nourishing part is a lesson to be learned.

You’ve just marked the 100th anniversary of GFR. And the one-year anniversary of Maria. What are you thinking about most? What are you hopeful about? What are you worried about?

We are thinking about the future. The social inequalities unveiled by this disaster must be addressed. How do we become resilient and how do we keep transforming — until every Puerto Rican family has a secure roof over their heads, functioning and affordable utilities, access to quality education and health services, jobs, safety, and food on their table? This is work that continues until our businesses and our economy are back on track and the social fiber of our society is regenerated and healed.

Our family has been in Puerto Rico for over a century, and we are planning to be around for many, many more years to come. We have been present and committed to Puerto Rico during times of prosperity, but most importantly, during times of adversity. Adversity has a way of reminding us how strong we all can be.

AI’s Potential to Diagnose and Treat Mental Illness

The United States faces a mental health epidemic. Nearly one in five American adults suffers from a form of mental illness. Suicide rates are at an all-time high, 115 people die daily from opioid abuse, and one in eight Americans over 12 years’ old take an antidepressant every day. The economic burden of depression alone is estimated to be at least $210 billion annually, with more than half of that cost coming from increased absenteeism and reduced productivity in the workplace.

In a crisis that has become progressively dire over the past decade, digital solutions — many with artificial intelligence (AI) at their core — offer hope for reversing the decline in our mental wellness. New tools are being developed by tech companies and universities with potent diagnostic and treatment capabilities that can be used to serve large populations at reasonable costs.

AI solutions are arriving at an opportune time. The nation is confronting a critical shortfall in psychiatrists and other mental health specialists that is exacerbating the crisis. Nearly 40% of Americans live in areas designated by the federal government as having a shortage of mental health professionals; more than 60% of U.S. counties are without a single psychiatrist within their borders. Those fortunate enough to live in areas with sufficient access to mental health services often can’t afford them because many therapists don’t accept insurance.

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Instead, the countless undiagnosed suffer, or look to emergency rooms and primary care physicians for treatment. Patients with depression, for instance, see their primary care physicians more than five times on average annually, versus fewer than three times for those without depression. For this reason, even though mental health treatment appears to account for only 4% of employer health costs, it’s really linked to nearly a quarter of them.

While some may consider the digitization of mental health services impersonal, the inherent anonymity of AI turns out to be a positive in some instances. Patients, who are often embarrassed to reveal problems to a therapist they’ve never met before, let down their guard with AI-powered tools. The lower cost of AI treatments versus seeing a psychiatrist or psychologist is another plus. These advantages help AI tools ferret out the undiagnosed, speed up needed treatment, and improve the odds of positive outcomes.

Like all digitization efforts in health care and other industries, these new tools pose risks, especially to patient privacy. Health care has already become a prime target of hackers as more and more records have been digitized. But hacking claims data is one thing; getting access to each patient’s most intimate details presents a whole new type of risk — particularly when those details are linked to consumer data and social media logins. Providers must design their solutions from the outset to employ mitigation techniques such as storing minimal personally identifiable data, regularly deleting session transcripts following analysis, and encrypting data on the server itself (not just communications).

AI vendors also must deal with the acknowledged limitations of AI, such as a tendency for machine learning to discriminate based on race, gender, or age. For instance, if an AI tool that uses speech patterns to detect mental illness is trained using speech samples only from one demographic group, working with patients from outside that group might result in false alerts and incorrect diagnoses. Similarly, a virtual therapist trained primarily on the faces of tech company employees may be less effective reading non-verbal cues from women, people of color, or seniors — few of whom work in tech. To avoid this risk, AI vendors must recognize the tendency and develop AI tools using the same rigorous standards as research clinicians who diligently seek test groups representative of the whole community.

More broadly, AI’s scale can be both a blessing and a curse. With AI, one poor programming choice carries the risk of harming millions of patients. Just as in drug development, we’re going to need careful regulation to make sure that large-scale treatment protocols remain safe and effective.

But as long as appropriate safeguards are in place, there are concrete signs that AI offers a powerful diagnostic and therapeutic tool in the battle against mental illness. Below, we examine four approaches with the greatest promise.

Making humans better. At their most basic level, AI solutions help psychiatrists and other mental health professionals do their jobs better. They collect and analyze reams of data much more quickly than humans could and then suggest effective ways to treat patients.

Ginger.io’s virtual mental health services — including video and text-based therapy and coaching sessions — provide a good example. Through analyzing past assessments and real-time data collected using mobile devices, the Ginger.io app can help specialists track patients’ progress, identify times of crisis, and develop individualized care plans. In a year-long survey of Ginger.io users, 72 percent reported clinically significant improvements in symptoms of depression.

Anticipating problems. Mental health diagnosis is also being supplemented by machine-learning tools, which automatically expand their capabilities based on experience and new data. One example is Quartet Health, which screens patient medical histories and behavioral patterns to uncover undiagnosed mental health problems. For instance, Quartet can flag possible anxiety based on whether someone has been repeatedly tested for a non-existent cardiac problem.

It also can recommend pre-emptive follow-up in cases where patients may become depressed or anxious after receiving a bad diagnosis or treatment for a major physical illness. Already being adopted by insurance companies and employer medical plans, Quartet has reduced emergency room visits and hospitalizations by 15 to 25% for some of its users.

Dr. Bot. So-called chatbot counseling is another AI tool producing results. Chatbots are computer programs that simulate human conversation, either through text or a voice-enabled AI interface. In mental health, these bots are being pressed into service by employers and health insurers to root out individuals who might be struggling with substance abuse, depression, or anxiety and provide access to convenient and cost-effective care.

Woebot, for example, is a chatbot developed by clinical psychologists at Stanford University in 2017. It treats depression and anxiety using a digital version of the 40-year-old technique of cognitive behavioral therapy – a highly structured talk psychotherapy that seeks to alter a patient’s negative thought patterns in a limited number of sessions.

In a study of university students suffering from depression, those using Woebot experienced close to a 20% improvement in just two weeks, based on PHQ-9 scores — a common measure of depression. One reason for Woebot’s success with the study group was the high level of participant engagement. At a low cost of $39 per month, most were talking to the bot nearly every day — a level of engagement that simply doesn’t occur with in-person counseling.

The next generation. Today’s mental health AI solutions may be just the beginning. The University of Southern California’s Institute for Creative Technologies has developed a virtual therapist named Ellie that hints at what’s ahead. Ellie is far more than the usual chatbot — she can also detect nonverbal cues and respond accordingly. For instance, she has learned when to nod approvingly or perhaps utter a well-placed “hmmm” to encourage patients to be more forthcoming.

Ellie — an avatar rendered in 3-D on a television screen — functions by using different algorithms that determine her questions, motions, and gestures. The program observes 66 points on the patient’s face and notes the patient’s rate of speech and the length of pauses before answering questions. Ellie’s actions, motions, and speech mimic those of a real therapist — but not entirely, which is an advantage with patients who are fearful of therapy.

In a research project with soldiers recently returned from Afghanistan, Ellie uncovered more evidence of post-traumatic stress disorder (PTSD) than the Post-Deployment Health Assessment administered by the military. Ellie was even able to identify certain “tells” common to individuals suffering from PTSD. With up to 20% of returning veterans coping with PTSD and a staggering suicide rate among the population, the potential impact of a solution like Ellie is significant.

As with all potential breakthroughs, caveats remain and safeguards must be developed. Yet, there’s no doubt we’re on the cusp of an AI revolution in mental health — one that holds the promise of both better access and better care at a cost that won’t break the bank.

How Managers Can Make Casual Networking Events More Inclusive

Some years ago, at a former company, I began noticing a curious series of events. My manager and team practiced an egalitarian decision-making process in which we would meet, discuss everything from content marketing campaigns to social media tactics, and collectively come up with strategies to move forward with. However, often, I would return to work later in the week to find the decisions that we had initially agreed upon were moot, and the manager was moving forward in a completely new direction. There was no explanation for what initiated these changes.

I eventually solved the puzzle; my male manager and certain members of our department were meeting with employees, including leaders, over unplanned, informal networking events at a local bar. There, they would talk shop and decisions were made that excluded others — about who to hire, promote, and assign to important projects. Though I was never invited, I later learned that it wasn’t gender-based. White women at all levels in our department were invited. But as the only woman of color and immigrant woman in my department, I wondered how I could score an invitation.

Situations like these aren’t nefarious. Research on affinity bias shows that we are naturally drawn to people who are like us. A casual drink here, a few networking events there with like-minded colleagues isn’t so bad, right? Unfortunately, these seemingly innocuous meetings can have consequences, and most of them fall on the careers of employees from underrepresented backgrounds. This especially applies to immigrant women of color who are often navigating three historically low-status identities: being female, a person of color, and an immigrant.

Part of the solution is to invite people from underrepresented backgrounds to these kinds of events. The other solution is one that can create lasting change for diversity and inclusion: to organize inclusive events that welcome employees from all backgrounds. A good first step for managers is to master the below practices, based on interviews I conducted with female leaders who are working to reduce bias in the workplace.

1. Learn about your employees’ preferences, particularly those from underrepresented backgrounds. After-work drinks can exclude women who shoulder the lion’s share of caregiving responsibilities globally. In addition, many women of color are not invited to out-of-office gatherings, whether or not they have children. Ellen Pao’s seminal book Reset is among the growing evidence that shows the consequences women of color, and often immigrants, face from being left out of office networking events — both spontaneous and planned. She writes: “We are either silenced or we are seen as buzzkills. We are either left out of the social network that leads to power — the strip clubs and the steak dinners and the all-male ski trips — and so we don’t fit in, or our presence leads to changes in the way things are done, and that causes anger, which means we still don’t fit in.”

To ensure all women feel included, managers need to first understand the practices that exclude them, as well as the barriers that stop them from attending work functions. “As a manager, it’s necessary to ask questions about your employee’s preferences in a respectful way,” says Adina, a manager at a global technology company. These include dietary preferences and activities that make your employees feel comfortable. “Make sure there are always options for people with restrictions: of food, drink and activities,” she adds. It’s important to ask these questions privately so that the employee doesn’t feel targeted in a group setting. The most effective way is to ask in person, one-on-one. You can also include questions surrounding personal preferences for work events in an organization-wide, anonymous survey.

2. Engage a diverse planning committee. Formal company events should have a diverse planning committee that understands how to serve a diverse group of people. Susi Collins, Senior Program Manager of Diversity & Inclusion at Nordstrom, advises that managers empower “all employees to contribute to the content of the event, especially women, junior colleagues, and people of color.” Throughout the planning and execution, attribute ideas to their originators, and concretely and explicitly praise the contribution of women of color, she adds. The contribution of women, particularly women of color, is often undermined. Giving credit and calling attention to it affirms its importance.

While planning, also try to listen more than you talk and be mindful of how you are taking up space, especially if the topic of discussion is not your expertise. Constantly being the loudest voice in the room reinforces the social dynamics you are trying to change, those that position only men and white people as leaders, and women of color as support staff.

3. Plan more events that don’t center around alcohol — and don’t immediately assume that women of color don’t drink. In the U.S. and Western Europe, networking culture often revolves around alcohol, which can leave out people who don’t drink. Planning more events that aren’t alcohol-driven is key to being more inclusive. Even if an event is at a bar or alcohol is present, don’t assume that immigrant women of color will be uncomfortable attending. I’ve attended plenty of events at bars, even during times when I wasn’t drinking alcohol, and know many immigrant women of color who have no objections to being around alcohol, whether or not they personally consume it. In these situations, it’s best to extend an invite and let your employee decide for herself, rather than making the decision for her.

4. Organize more daytime events. Day or lunchtime events are a great way to ensure all employees can participate. Bhavani Murugiah, the former head of people for a technology company, recommends a tactic that worked well at her former employer called “Lunch Roulette” — a program that randomly matched employees with 3-5 coworkers to connect over a monthly lunch. This kind of casual meeting can break down silos between departments and create networking opportunities for people who don’t always get invited to informal events.

5. Be intentional when structuring events outside of business hours. Organize events outside of business hours that actively get employees from different backgrounds to connect with each other. Passive events like movie screenings “can alienate employees from underrepresented groups,” says Felicity Menzies, Sydney-based CEO of Include-Empower, and former head of private banking for Westpac Singapore. It can be challenging for people to make new acquaintances in general — especially those who are more introverted. Add in factors like language barriers, cultural differences, biases, and stereotypes, and it becomes clear why casual networking events can feel inaccessible to, or at times, completely exclude people from underrepresented backgrounds. According to Menezies, a better approach is to offer activities that structure interactions without triggering social anxiety and are considerate of diverse personalities, languages, cultures, ethnicities, and physical abilities. Examples include community volunteering, team-building exercises, or potlucks where people from different cultural heritages share dishes and the stories behind them.

6. Be intentional when making connections. When there are employees from diverse backgrounds at an event, go out of your way to introduce women and people of color to important stakeholders, says Collins. Using your influence to foster these connections can have a significant impact on how welcomed an employee feels and even change their career trajectory. “As a manager, you have to understand how there are so many ways to impact someone’s assimilation into a company, or even a new culture,” Murugiah also says. “Whatever you do to make a change, it has to be genuine and thoughtful.”

7. Audit the frequency of events and attendees. Take stock of how often the team meets informally, as well as formally, and the demographic of the attendees each time. This will give you the information you need to course-correct and personally reach out to those who you don’t see. Your goal should be to figure out what is preventing people from coming, and use that feedback to make positive changes.

8. Constantly look for blind spots and ask for feedback after the event. Doing so will help you recognize areas for improvement, and hopefully, make the next event even better. A part of being inclusive is recognizing what you don’t know, so respond to the feedback with openness and humility. This will help build trust and create an environment where people feel comfortable expressing their opinions honestly.

In my research, I have repeatedly found that companies that don’t make an intentional effort to be inclusive often end up excluding women of color and immigrants. It’s crucial for people at all levels of an organization to understand how casual gatherings exclude employees from marginalized backgrounds, and more so, can have a detrimental impact on their careers. Organizations have a responsibility to disrupt these destructive patterns.

The Promise and Peril of a Star CEO

Star CEOs can be good for companies, providing social proof that their firm is a high quality place and making it easier to attract capital and talent. But they can also be dangerous. The recent cases of Tesla, Papa John’s, and CBS exemplify this: all three companies benefitted from the brightness of their star CEOs. And then each company had to deal with expensive, distracting problems their star created.

Consider Tesla: before Elon Musk became CEO in 2008, the electric car company had delivered fewer than 200 cars and was running out of cash. Ten years later, it could produce, albeit with extraordinary measures, 5,000 cars a month and had a higher market value than Ford’s. Within the company, Musk has held unusual power; he is the largest stockholder in Tesla, and is positioned as a visionary genius who is essential to the company’s success. Still, Musk’s tweets about taking the company private cost the company a $20 million settlement with the SEC and exposed it to larger potential liability from aggrieved stockholders.

This is a far from unusual story. You could also consider Papa John’s; working out of a converted broom closet in his father’s small-town tavern, John Schnatter grew pizza empire Papa John’s to over 5,000 locations. Schnatter now controls of 30% of Papa John’s shares, and his status as founder, as well as his long-running visibility as the company’s spokesperson in broadcast and print ads gives him unusual power. Still, Schnatter’s comments about NFL players who knelt during the national anthem and the accusation that he used a racist word in a conference call drove down the company’s sales and its stock price.

Similarly, though Les Moonves owned an insignificant amount of CBS stock, he had outsized power at CBS based on his long-term performance and the prestige he built for himself in the entertainment industry. Under his leadership, CBS turned from the last-place butt of jokes into a first-place powerhouse. Still, accusations of sexual harassment against Moonves this year exposed CBS to not-yet quantified but likely expensive liabilities.

These examples highlight the delicate balancing act when it comes to handing star CEOs who provide large benefits but expose the company to great risk; they have to fire a CEO who acts unethically, but they can’t fire a CEO just for exposing the company to risk. And because CEOs have to take risks in order to create value, directors have to strike a balance that maximizes the benefits and minimizes the dangers of a risky CEO. There are ways to thread this needle, though. Work I and others have done to help boards get the best out of CEOs who wield unusual power and bring a company unusual benefits suggest that there are ways for directors to achieve that difficult balance, even in difficult situations like the ones above.

First, directors should push for a large number of directors who are truly independent, not just technically independent. If the CEO chairs the board, have a lead director who is strong and who is actually independent of the CEO. Give the lead director the power to call meetings of the board without the CEO’s permission or presence. This avoids giving the CEO the ability to prevent the board from meeting without him.

Second, the independent directors should meet regularly in executive sessions without the CEO present. Executive sessions give independent directors opportunities to discuss concerns without the sessions turning into battles with an offended or enraged CEO. Bill George, the Medtronic CEO who took its market value from $1 billion to $60 billion in 10 years and went on to be a senior fellow at Harvard Business School, heard regularly from his independent directors after they met in executive session. Governance expert Ram Charan considers executive sessions the most important recent innovation in corporate governance.

Third, support your CEO’s activities that relate to the interests of the company, not just to his or her ego. It is hard to imagine a successful CEO who lacks sufficient ego-strength to face the challenges CEOs face or to display the confidence stakeholders need to see, but when a CEO becomes more than a CEO — when he or she becomes a star — it is easy for their ego to get out of hand. Stardom also can be a problem, for example, when CEOs spend too much time enhancing their personal reputation instead of enhancing the value of the company.

Some CEOs exhibit traits that resemble narcissism. Narcissistic CEOs often provide a compelling vision and attract followers. But there are downsides to CEO narcissism. Narcissistic CEOs often are poor listeners and hyper-sensitive to criticism when they do listen. They often lack empathy, which can lead them to do things that are obviously unacceptable to most people but not to them. Think of Musk’s tweet calling one of the Thai cave rescuers a “pedo.”

Fourth, star CEOs are more likely to take advice from other people who are stars like them than from people who are merely experts, because stars often think they know more than the experts. Get star CEOs of other companies on the board. Your CEO is more likely to listen to them than to non-star directors. Narcissistic CEOs rarely take advice. Worse, they often see advice or mere disagreements as mortal threats. Their overconfidence, unwillingness to take advice, and tendency to become hostile when they feel challenged can put the company at risk of expensive and dangerous litigation.

Fifth, don’t let a CEO put the company in a position where he or she can prevent you from doing what is good for the company by threatening to quit. While you might want your CEO to be seen as a star, don’t let your CEO position himself or herself as indispensable to the company. It is a sign of danger when investors say “There is no Tesla without Musk.” Have a good COO and other C-level people in place. Tesla has no COO. Facebook brought in Sheryl Sandberg as COO. Google brought in Eric Schmidt as CEO until co-founder Larry Page was ready for the position.

Finally, recognize that to fulfill your director duties, you might have to change the CEO’s role to something like chief strategy officer or chief visionary. Even if the CEO has the power to replace you, you have legal and moral duties to try to do what is best for the company. If the balance tips and the CEO is creating more damage than benefit, you have to act. Consider two of our three examples: Schnatter no longer is CEO or board chair at Papa John’s, and Moonves left CBS. Tesla’s board has done less — only what the SEC forced it to do. Still, Musk, who remains CEO of Tesla, gave up being board chair. Is this enough to balance what he brings and what he threatens? Tesla’s board alone can answer this question.

Why Climate Change and Other Global Problems Are Pushing Some Business Leaders to Embrace Regulation

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Global carbon emissions need to be reduced to net zero by 2050 to have a good chance of holding global average temperature rises to no more than 1.5oC, a level that would be disastrous, but not catastrophic for human civilization.

So states a new report from the Intergovernmental Panel on Climate Change (IPCC), which sets out the policy choices governments around the world need to make over the next 12 years to 2030 if they want to limit global temperature rises to 1.5oC rather than 2oC.

If global temperatures rise more than 1.5oC, the risks of draught, floods, forest fires, heat-related deaths and loss of agricultural productivity all worsen significantly.

The response from political leaders so far has been mixed. Some governments may be poised to revise their climate change targets in line with the call for net zero emissions by 2050.

Others have been less enthusiastic. The Australian government has rejected the report’s call to phase out coal power by 2050. In the U.S., President Trump’s response to the IPCC report so far has been to cast doubt on it. This follows his summer 2017 announcement that he was withdrawing the U.S. from the Paris Climate Agreement. Since then the Trump Administration has been busy unravelling a series of public policy initiatives and regulations that underpinned the Paris commitments the U.S. had made, like the Clean Power Plan and vehicle emissions standards, citing them as an impediment to business.

Predictably, environmentalists, pro-environment politicians, and countries especially vulnerable to climate change have reacted to all of this with distress.

But perhaps a little less predictably, so have many business leaders.

For example, many American CEOs spent considerable energy in the weeks building up to Trump’s Paris announcement lobbying the President not to withdraw. Over 1,700 companies and investors have subsequently signed the We Are Still In statement, making public their commitment to uphold the agreement.

While it’s become more normal in recent years to see some businesses taking proactive measures to drive innovation to tackle some of the world’s most pressing social and environmental challenges, it generally remains a widespread assumption that business leaders see government intervention in the economy and increased regulation as something to be avoided.

But there is now a growing trend of some CEOs actively lobbying for more ambitious government action and regulation on a whole range of social and environmental issues.

Many businesses were actively involved in lobbying governments to make an ambitious agreement on climate in Paris in the first place. Unilever CEO Paul Polman was one of many who worked tirelessly to push governments to higher ambition. More than 365 companies and investors voiced their support for the US Clean Power Plan in 2015. More than 200 companies have publicly called for the introduction of carbon pricing. Business leaders are now calling on governments to create the policy frameworks to achieve net zero emissions by 2050.

And it’s not just on climate. Companies invested significant resources in pushing for high public policy ambition in agreeing the UN Sustainable Development Goals in 2015. On human rights issues, companies have lobbied the UK government for stronger regulation tackling Modern Slavery in corporate supply chains, and the Cambodian government for stronger protection for worker’s rights.

What’s going on? Businesses aren’t supposed to want more regulation of their activities. This growing trend is the subject of a research program at Hult International Business School, where we have followed a number of CEOs and companies involved in such advocacy activities over the past few years.

Part of what’s been driving more ambitious corporate action on innovation to address social and environment challenges is increased pressure and higher expectations from the rest of society that business should play a role in helping sort out contemporary global challenges. Ultimately, long-term legitimacy, reputation, and license to operate are at stake.

A number of CEOs are realizing that such expectations cannot be met by innovation and voluntary actions alone. The scale of today’s social and environmental challenges requires government action, too — there are some ways in which public policy can drive change that cannot be achieved otherwise.

In some cases, regulatory change can lead to direct commercial benefit, creating markets that didn’t exist before, or handing competitive advantage to those better able to capitalize on the regulatory change. For many companies, the right solutions are available for tackling social and environmental challenges, but they don’t become commercially viable unless regulatory change aligns commercial incentives with the right thing to do.

As a result, some CEOs have started overcoming their aversion to government intervention and fears that incompetent government meddling will get in the way of prosperity. There’s a growing recognition that ambitious government intervention has a crucial role to play in both addressing global challenges and helping business succeed.

So what are these companies learning about how to do this kind of advocacy well? Our research, as well as recent studies by others such as Business Fights Poverty and Harvard, and scholars at the University of Lugano in Switzerland, point to a number of key issues to get right.

Respect the leadership role of government, but be prepared to use your voice and influence. Your activities should be aimed at informing and supporting—but not replacing—the responsibility of governments to decide public policy. But that doesn’t mean business should be silent if government is not acting in the public interest.
Aim for public policy outcomes that seek to effectively address societal challenges. The aim should be to reach solutions that address the problem and have consensus backing, rather than making sure your own interests prevail regardless of the impact on others. This may sometimes involve accepting public policy initiatives that could result in a short-term hit to profits, because in the long run they are going to help solve the problem, and help maintain your longer-term legitimacy. The outcomes you are aiming at need to be consistent with key universal standards, such as UN Global Compact and UN Guiding Principles on Business and Human Rights.
Be inclusive. Traditional lobbying is done between government and individual companies or trade associations. But advocacy for more ambitious public policy is more effective if it is done on a multi-stakeholder basis. Public policy outcomes are going to be more effective if all groups affected have had a say in shaping them. Ensure the voices of the marginalized have a say in the process.
Consider active joint advocacy with NGOs. Unlikely partnerships between companies and NGOs can have more impact on influencing policymakers, as each can compensate for the weaknesses of the other. Governments can distrust NGOs as being purely ideologically motivated, and can distrust business for being purely profit-motivated. Joint advocacy can deal with these legitimacy questions of both sides.
Be transparent and truthful. Lobbying often happens behind closed doors, and the worst kind of lobbying in the past has been characterized by misinformation and misdirection. Public policy outcomes are going to be more effective if people have confidence that they know what different groups were calling for and they can trust the basis on which these positions were put forward. Be transparent about third party lobbying organizations that you offer financial support to.
Invest to be able to advocate from a robust evidence base, for example on climate or health and nutrition.
Make sure you have coherence and consistency between your external advocacy positions and internal policies and practices. You should also ensure the advocacy positions of trade bodies you are a member of are consistent too.
Make sure you have the right skills and capabilities. It turns out that lobbying to persuade governments to introduce new regulatory measures often requires a different kind of skill set to the traditional government affairs function. Many companies have found themselves hiring in campaigners from NGOs to join their advocacy teams.

Finally, this is a question of personal leadership. Our research showed high levels of peer networks in CEO advocacy for more ambitious government action – each CEO reaching out to others to make the case for them to get involved in advocacy coalitions. An effective approach needs a personal commitment from the top.