Planning for an aging population

Experts discuss how an aging population will affect many facets of our societies—and will require new partnerships among all types of stakeholders.
Planning for an aging population

The Best of This Week

Four Strategies for Thriving Amid Uncertainty

The pandemic has accelerated the speed at which leaders and organizations must make decisions and strategize the future of their business. This shift is spawning a new breed of company — one that is more capable of thriving in uncertainty with a clearer and broader sense of purpose. Companies that show the most agility and resilience in responding to the global pandemic are pursuing four main strategies.

Over 30 Years, How Has the ADA Touched Technology?

Captioning technology, e-books, and voice control are just a few tools that have improved the everyday lives of people with disabilities since the Americans with Disabilities Act of 1990 was signed into law 30 years ago. The ADA originally applied primarily to building accessibility and government resources but has also had a profound effect on tech. Still, representatives of tech giants and advocacy organizations point out, there’s plenty of work to do.

Transforming the Post-Pandemic Supply Chain With Data

COVID-19’s impact revealed that supply chain business continuity plans had both the wrong data and the data wrong. Legacy leadership teams need to understand that decisions around data — decisions around its collection, sharing, and analysis — are what drive successful supply chain transformation. Targeted transformation investment overwhelmingly emphasizes greater visibility and transparency rather than supply chain optimization.

A Proven Recipe for Corporate Activism

Ben & Jerry’s statement in June on dismantling white supremacy was widely shared and lauded for its detailed, powerful corporate message — one honed by years of social justice efforts. The company’s long track record of activism has helped it avoid accusations of insincerity or opportunism when it speaks out, in contrast to other companies that have found themselves on the defensive. How did this ice-cream company perfect the recipe for corporate activism?

The Office as Context

What are some of the systems of belief, histories of technology, and cultural practices that have to date shaped what many of us employees think of as “the office” — and how might we reimagine how work gets done going forward based on what we can glean from the past?

What Else We’re Reading This Week:

• When people rise in power and status, their empathy can diminish — but powerful people can be coached back to their compassionate selves
• Uncertainty and disruption are two sides of the same coin
• Five ways to apply user experience principles to AI and machine learning projects
• A new survey reveals how corporate boards are managing pandemic-driven challenges

Quote of the Week:

“In the past half-century, America’s innovation engine — built on an influx of global brainpower, a vibrant university system, cities that encourage the spontaneous interaction of people and ideas — has worked so well that policymakers have taken it for granted. Yet the pandemic is now disassembling that engine in remarkably precise ways.”

— Caleb Watney, resident fellow of technology and innovation at the R Street Institute, in “America’s Innovation Engine Is Slowing”

The Best of This Week

How Asia’s largest pharma is leveraging its values to navigate the COVID-19 crisis

Christophe Weber, CEO of Takeda Pharmaceuticals, discusses challenges to globalization, principles in the face of crises, and new opportunities when companies pull together.
How Asia’s largest pharma is leveraging its values to navigate the COVID-19 crisis

How COVID-19 is changing consumer behavior—now and forever

Consumers are shifting behaviors across eight areas of life.
How COVID-19 is changing consumer behavior—now and forever

COVID-19: Implications for business

Our latest perspectives on the coronavirus outbreak, the twin threats to lives and livelihoods, and how organizations can prepare for the next normal.
COVID-19: Implications for business

How COVID-19 has impacted Black-white financial inequality

COVID-19 has disrupted everything from economic markets to personal finances, but not everyone feels its effects equally.
How COVID-19 has impacted Black-white financial inequality

Breaking away from the pack in the next normal of retail banking distribution

Lessons from pre-COVID-19 crisis leaders.
Breaking away from the pack in the next normal of retail banking distribution

What got us here won’t get us there: A new model for the consumer goods industry

COVID-19 is amplifying 12 trends that have been disrupting consumer goods for the last decade. Leaders will adopt a new model for ‘where to play’ and ‘how to win’ that gets their evergreen brands on the right side of the trends and helps their small brands scale up more quickly. Together these changes will fuel the next generation of industry growth.
What got us here won’t get us there: A new model for the consumer goods industry

On pins and needles: Will COVID-19 vaccines ‘save the world’?

Innovators are sprinting to develop inoculations against the novel coronavirus. Here, we summarize the latest information on research timelines and the potential impact of a vaccine on the pandemic—and society.
On pins and needles: Will COVID-19 vaccines ‘save the world’?

An interview with Austan Goolsbee

“The countries that prioritized health not only had better health outcomes, they had better economic outcomes too.”
An interview with Austan Goolsbee

Performance Management 2.0: Tech-enabled optimization of field forces

Businesses with large field forces have more reasons than ever to boost effectiveness and efficiency. New technology options can help shatter old barriers to higher performance.
Performance Management 2.0: Tech-enabled optimization of field forces

How financial institutions can help fill European retirement needs

Western Europe’s nonpublic retirement market is growing rapidly. To capture the business opportunity, insurers and asset managers should take on three approaches.
How financial institutions can help fill European retirement needs

The Age of Accelerating Strategy Breakthroughs

Several months into a global pandemic, leading organizations are redefining expectations of what is possible. Consider how offices around the world shifted virtually overnight to digitally enabled remote work. Grocery retailers and restaurants rolled out new pickup and delivery services. Hospitals rapidly expanded telehealth options. Automakers, apparel makers, and mobile phone assemblers retooled on the fly and are now rushing out personal protective equipment and ventilators.

All of these scenes offer a glimpse at a new age of acceleration taking shape, in which leading companies are racing ahead to redesign strategies and operations in weeks instead of years. Many of these breakthroughs have been on chief executives’ to-do lists; now, they are being fast-tracked. A defining feature of the much-discussed new normal in business is speed: Decisions are being made at a pace previously thought impossible.

The tremendous public health and economic impact of the coronavirus pandemic has raised the global standard for efficiency and innovation. The world can’t magically go back to the way things were before. The resilient corporations that find success in the future will be those that can continue to turn on a dime as nimbly as they have during the early months of the pandemic.

This shift is spawning a new breed of company — more capable of thriving in uncertainty with a clearer and broader sense of purpose. Corporate superstars will be able to continue to motivate people around a common goal even after the health crisis and through economic hardship. They will recalibrate quickly in response to megatrends, expand beyond traditional business lines through partnerships, and improve their performance along broader environmental, social, and governance metrics.

In recent months, we’ve had discussions with dozens of the world’s top chief executives on how to build a better future. We found that companies showing the most agile and resilient responses to the crisis are pursuing four main strategies.

1. Prioritize people. One of the biggest lessons from the pandemic is that people are the key to rapid innovation and resilience. Many labor forces now work from home — yet they come together as teams across silos and geographies, united by a common purpose. “Solidarity has proven to be more contagious than the virus itself,” one chief executive told us. As a chief operating officer put it, “A gap will widen between those companies able to motivate and mobilize their employees post-COVID, and those who fail miserably at it.”

Top leadership teams communicate openly with empathy, making it clear their people always come first. During the coronavirus crisis, many have demonstrated commitment by avoiding or minimizing layoffs, securing aid for employees in need of help, and accepting executive pay cuts.

Now some leaders are going further and instituting new measures to create cultures capable of retaining and attracting the best people, even after health issues abate and economic issues remain. These include new compensation schemes that reward employees not just for hitting sales targets but also for extra efforts like supporting the company’s culture of resilience, more opportunities for company leaders to hear feedback from employees, wellness programs with mental health counseling, and more flexible work arrangements.

Simultaneously, companies are exploring new ways to tap into employees’ collective intelligence more efficiently. Some executives, for example, plan to spend more time explaining their strategies so that employees will feel empowered to make more, better, and faster decisions, even with incomplete information. For example, employees may switch suppliers or pay a higher price for supplies if they are aware of the company’s goal to secure its value chain. Other executives are expanding training and investing in open-source online portals, data dashboards, and algorithms that make it easier for people to share insights across functions and geographical boundaries to monitor, communicate with, and make prescriptive recommendations to suppliers and customers.

2. Make megatrends matter. Leading companies are also prioritizing the need to identify threats and opportunities created by megatrends that can rapidly reshape businesses. The coronavirus pandemic has shown that negative megatrends like epidemics and climate change can no longer be treated as tail risks so extreme that no preparation would make a difference.

Companies have to build up resilience to safeguard profits by being prepared to play ferocious defense against other negative megatrends gathering momentum, like public debt crises, at one end of the spectrum. At the other, they must aggressively pursue new prospects created by positive megatrends like digitalization and health and wellness.

Macro shifts set off by the pandemic illustrate how quickly megatrends can force companies to reset strategies. Retailers are rerouting investments earmarked for building physical locations into upgrading online commerce features and delivery services. Financial services companies are accelerating many more digital-only offerings, such as contactless payments and risk management products such as health insurance.

Measuring and analyzing the impact of such megatrends can no longer be left to a business unit or risk department. Instead, leadership teams are adopting dashboards that monitor how their company will fare across different scenarios — and asking managers to explain how their strategies take megatrends into account. “The economics of profit are not always compatible with the economics of resilience,” one chief executive said, “but we are discovering that our economics are no longer based on sustainable fundamentals.”

3. Build resilience to accelerated change. Companies must also consider what happens when megatrends combine to trigger exponential change. The pandemic has magnified the links between countries, between the public and private sector, and between companies’ own inner workings and their customers and supply chains.

These often-overlooked interconnections help explain why the new recession is expected to be broader, deeper, and more difficult to turn around than previous downturns. The International Monetary Fund expects the “Great Lockdown” to be the worst recession since the Great Depression of the 1930s. In 2020, global GDP will decline by 2% to 4%, according to the World Bank. More than 40 million people in the United States have lost their jobs since the start of coronavirus-triggered shutdowns in March, reversing more than a decade of job growth.

Deeper ties also explain why many companies accelerating and adapting in the coronavirus-driven economy have focused on forming new alliances. Companies ranging from retailers to manufacturers are racing to replace capital-intensive, integrated, end-to-end businesses with coalitions and partnerships with the goal of protecting their returns on capital and responding more nimbly to sudden spikes and slumps in demand and supply. Automakers are coinvesting in machinery kept in the facilities of suppliers motivated to grow their business along with them. Manufacturers are broadening relationships with suppliers producing key materials like copper at every stage of its life cycle — from mining of the ore to its recycling.

In what increasingly looks like a new phase of being “agile,” companies are doubling down on core capabilities while cutting costs by broadening and deepening partnerships. Just-in-time operations are evolving into “just-in-case” coalitions of suppliers similarly focused on what they do best, whether producing materials and parts or research and development. “We need to free ourselves from activities that others do better than us,” one chief executive said.

This strategy also includes collaborating more with competitors. Over the past decade, companies have shared everything from engine platforms to transportation fleets with rivals. Now, intense pressure to lower prices while raising returns on capital is driving companies to partner on even more and to join forces against bigger challengers.

In every way possible, companies are trying to reduce complexity, instead of making big bets that ultimately may not pay off. For some companies, this means developing new sources of revenue and picking up businesses that complement their core offerings so that they can respond to big swings in consumer attitudes as seamlessly as a hybrid vehicle switches from gas to electricity. For others, it means focusing only on the most promising distribution channels, products, customer segments, and geographies in the future.

Companies are adding new digital capabilities to move quicker, in part by allowing them greater visibility into and control of their operations. The crisis has also sharpened focus on key technology investments in AI, as many organizations are scurrying to shift operations to the cloud and adopt smarter substitution algorithms, robots, and automating systems.

4. Champion multistakeholder capitalism. Leading companies are also now starting to sprint toward becoming more sustainable over the long term. As European governments play a bigger role in deciding how wealth is distributed through financial stimulus packages, many are pushing companies to meet broader environmental, societal, and governance standards, such as carbon neutrality. More investors are beginning to cheer for companies with strong environmental, social, and governance (ESG) practices. During the first four months of this year, ESG funds have attracted more than twice as much money during a market meltdown as they did during the same period of the previous year, in large part because they are delivering better-than-average returns.

In order to maintain momentum, more companies will be under pressure to break from the economic philosophy that has guided most public companies since the 1970s: that they exist solely to maximize shareholder value. More governments will ask companies what they can do for society. Past rules of thumb for share buybacks, dividend payments, board structures, and executive compensation will require a rethink. Shareholder activism will be regarded with skepticism.

Companies had to respond to similar demands after the 2007-2008 financial crisis, when governments required banks to shore up capital and to clamp down on executive compensation. But this downturn feels different. Governments are extending financial support to a much wider range of virus-hit strategic industries — from steelmakers to airlines — and calling the shots on many more aspects of companies’ operations. On May 11, 2020, the European Commission banned dividends, share buybacks, and bonuses for bailed-out companies as long as governments hold a stake in them. If governments pick up the pieces when lean companies get hit with systemic issues, they will likely require them to bolster their balance sheets.

Already, national governments have started to attach conditions to aid packages to protect climate goals. At least one airline, Air France, has pledged to reduce its carbon emissions earlier than originally planned in order to receive government loans.

Moving fast will require adapting to new standards more quickly than competitors, and companies at the forefront are adjusting their expectations for returns. Companies that have already forensically reviewed their operations to obtain strong sustainability scores now have a competitive advantage. More are focusing on their environmental, social, and corporate governance performance as they prepare for a new era when growth will depend on excellence measured by social metrics like employee absenteeism and environmental yardsticks like waste-recycling rates.

The coronavirus pandemic has triggered the kind of economic reckoning that occurs perhaps once a century. Enormous innovation and breakthroughs will be required to revive the global economy. Along with it, previous assumptions of how business should be conducted will have to be reexamined.

“Ironically, the longer the crisis lasts, the more seriously we will be forced to transform into more sustainable businesses,” one chief executive said. The companies that can make fast and forward-thinking decisions so that they can continue to adapt rapidly to change while embracing the human capacity for invention, will be among them.

The Age of Accelerating Strategy Breakthroughs

Industry 4.0: Reimagining manufacturing operations after COVID-19

Industry 4.0 technologies were already transforming manufacturers’ operations before the pandemic. Now adoption is diverging between technology haves and have-nots.
Industry 4.0: Reimagining manufacturing operations after COVID-19

Data, Not Digitalization, Transforms the Post-Pandemic Supply Chain

That COVID-19 dramatically accelerated digital transformations worldwide has become C-level consensus and a truism. As Microsoft CEO Satya Nadella publicly observed, “We’ve seen two years’ worth of digital transformation in two months. From remote teamwork and learning to sales and customer service, to critical cloud infrastructure and security.”

Even discounting for hyperbole, pundits and practitioners have been echoing Nadella’s sentiment. Organizations understandably seek to wring digital virtue from pandemic necessity. While many business operations can swiftly transform, other key processes defiantly resist digital acceleration. Supply chains are a case in point: Spreadsheet- and ERP-dependent supply chain operations had to radically revisit and revise expectations. Yesterday’s digital transformation road maps proved largely useless.

Organizations understandably seek to wring digital virtue from pandemic necessity.

Why? As heavyweight boxing champion Mike Tyson is said to have declared, “Everybody has a plan until they get punched in the mouth.” COVID-19’s impact revealed that supply chain business continuity plans had both the wrong data and the data wrong. Top management literally couldn’t see what was happening — or needed to happen — to ensure safe and reliable deliveries under duress. This came as a shock. Data, not digitalization, was their immediate problem. Legacy leadership teams need to understand that decisions around data — not digitalization — drive successful supply chain transformation.

Most significantly, targeted transformation investment overwhelmingly emphasizes greater visibility and transparency rather than supply chain optimization. Policies and practices promoting granular, real-time data access at every relevant link in the supply chain assumed primacy and urgency. As organizations confront their global futures in post-pandemic, machine learning, “What about China?” contexts, those decisions take on new urgency, as well as importance. I’ve interviewed — and worked with — more than a dozen supply chain managers and executives since pandemic shutdowns began. Virtually all of them have fundamentally reset their supply chains’ strategic priorities. “Just in case” now matters more than “just in time.” Assuring worker safety now supersedes improving inventory turns.

“Honestly,” said one supply chain executive from a Latin American food and industrial equipment conglomerate, “we didn’t know how little we knew about our key suppliers until COVID-19 occurred. We had to get all kinds of [new] information from them and — to make [our] factories work — we had to give them information we never had before.”

The takeaway: Digital-first enterprise success demands clarity-first supply chain design. Digitally transforming supply chains requires digitally transforming transparency and visibility. Transformational transparency is what leadership needs to measure; visibility is what leadership needs to assess. Unless they first determine data access, quality, and lineage, digitalization can’t deliver agility or reliability.

This is the most important organizational (re)learning from the COVID-19 crisis. (And this learning is gaining mindshare in more recent supply chain discussions.) Digitalizing supply chain process improvements without deliberately defining, collecting, and labeling essential data computationally codifies blind spots. Workarounds won’t work without the ability to swiftly see, find, or acquire relevant data. Where is that data coming from? Who owns it? How — and why — is it being made accessible? Supply chains fail when those questions are primarily answered by IT, legal, or a chief digital officer.

The data-driven ability to see — or computationally infer — tacit or explicit supply chain interdependencies may seem obvious. Pre-pandemic systems design, however, indicates that the obvious was misunderstood. Many “go to cloud” supply chain initiatives privileged process models over data models. Too many managers believed that moving legacy processes, data, and analytics to the cloud would automatically enhance transparency and visibility. They got punched in the mouth.

“Besides safety, the most important investments we’ve been making in our global supply chain focus on improving transparency and visibility worldwide at every step and stage,” said Todd Stohlmeyer, global strategic sourcing and procurement vice president at agricultural giant Cargill. For example, the company quickly deployed digital signatures to handle farm-to-delivery paperwork at every key supply chain checkpoint. Almost all of Cargill’s post-COVID-19 supply chain and sourcing documentation is now remotely enabled, recorded, and made appropriately transparent throughout the enterprise. Instantly tracking authorizations can be as important for delivery as instantly tracking product.

While using digital signatures may look and sound like a digitalization initiative, focus on the underlying problem and use case: The process failure’s root cause wasn’t manual paperwork — it was the absence of visibility. Stakeholders literally couldn’t see the authorizations in a timely manner. Transformation didn’t drive transparency; the need for transparency drove transformation.

Transparency describes a supply chain’s capability and commitment to data sharing; visibility defines what the enterprise explicitly and demonstrably knows.

Improving Supply Chain Transparency: Insights and Challenges

Championing transparency guarantees more challenging — and more strategic — debate about what post-pandemic optimization means for suppliers and customers. Those optimization discussions and disagreements, in turn, lead to more rigorous definitions of transparency. Meaningful optimization — the ability to weigh measurable trade-offs — requires clearly delineated parameters. Greater clarity invites greater insight into options and opportunities. Greater insight ensures better and more timely decision-making. For example, increased visibility makes clearer whether better managing complexity or supply chain simplification is the better investment.

Cargill’s transparency and visibility imperatives shape its supply chain digitalization strategy. They make everything — whether worker safety, supplier capability, or real-time customer delivery — measurably clearer, more accessible, and more accountable. That openness, said Stohlmeyer, creates optionality and opportunity.

This epiphany helps explain why accelerating supply chain transformation proved less tractable and more troublesome than most legacy companies — including Cargill — expected. Much of the challenge, noted MIT’s Alexis Bateman in a pre-COVID-19 interview, is that, “as we all know, supply chains are not designed to be transparent. As you move upstream with suppliers, they don’t want to disclose information they think of as a competitive secret.” Post-pandemic supply chains literally renegotiate the terms of transparency and visibility throughout their value chains.

What’s more, transparency initiatives typically favor adherence over agility, especially when rooted in sustainability, compliance, and corporate social responsibility (CSR) aspirations. Intriguingly, none of the managers I spoke with cited compliance-based data as integral to adaptiveness, responsiveness, or recovery. Compliance and CSR-oriented transparency typically doesn’t enable the anticipation or flexibility that today’s supply chains — and their customers — need. Contextually accurate data is not inherently actionable.

To be sure, public disclosures promise benefits as well as costs. As a recent research paper from MIT Sloan professor Y. Karen Zheng, Sloan visiting assistant professor Tim Kraft, and University of Pittsburgh assistant professor Leon Valdes concluded, “Increasing supply chain visibility always strengthens consumer trust. Furthermore, opportunities exist for a trust-driven revenue benefit due to greater visibility.”

Optimizing transparency to ensure effective compliance and disclosure, however, is a purposefully and profoundly different task than optimizing transparency to facilitate flexibility and response. To be sure, these competing transparencies are not inherently at odds, but — as COVID-19 made clear — they’ve proved neither complementary nor congruent.

Perhaps the most disconcerting supply chain revelation around pre-COVID transparency/visibility investment has been its inward focus. Organizations might scrupulously monitor regulatory compliance and track disrupted stock-keeping units (SKUs), stores, categories, distribution centers, or logistics on their networks. But observing how other actors — suppliers and partners — improvised or failed to cope with crisis frequently proved impossible. Actionable data and analytics for evaluating downstream impact simply weren’t there.

“If you think of data analytics, all the models, tools that have been put together for forecasting sales or forecasting any of the things just stopped working once COVID hit. They didn’t make sense,” Anil Kaul, CEO of AI business platform Absolutdata declared in a recent article. “There was nothing historically in the data that would be able to inform you what is going to be happening.”

For Stanley Black & Decker, the world’s largest toolmaker, Kaul’s sentiment translated into tighter cooperation between the company’s data scientists and supply chain managers. With over 100,000 SKUs coordinated by complex procurement and production processes, better demand management forecasts could measurably improve prioritization, said Aleksandar Lazarevic, advanced analytics and data engineering vice president at Stanley Black & Decker in a recent email exchange. Supply chain teams could better rethink and re-rank to-do lists with more visible customer and channel signals.

This analytic challenge went beyond quantitative technique and customer data sets, Lazarevic noted during a DataRobot AI webinar. Meeting the transparency/visibility challenge required translating the demand forecasts into formats that described what was needed to take meaningful actions immediately. The forecasts were worthless unless their users had visibility into the actionable next steps that they should take. In other words, the deliverable needed to be more of a map than a checklist.

This insight had an enormous impact on the data engineering and analytic function: Answers, recommendations, and solutions that did not allow — or empower — users to see the essential data they needed to access that day were not helpful. Strategic approaches required operational visibility to get organizational buy-in.

This visibly actionable imperative was consistent across several COVID-19 supply chain planning sessions I participated in:

• A consumer packaged goods company sought to identify its least profitable SKUs and determine what portion of its materials could be easily repurposed for more profitable/higher-volume lines. This company quickly discovered data gaps between how procurement described and labeled materials and how production actually processed them.
• A regional grocer positioned as a champion of locally sourced/organic produce feared for the future solvency of several suppliers. The company struggled to figure out what kind of support it could provide to simultaneously ensure predictable supply levels while preserving supplier safety and viability.
• A foods company (not Cargill) sought to quickly model how the processing-plant order/delivery tempo should shift if staffing levels dropped to 75% and extra time was allotted during each shift for cleaning. Preliminary findings indicated that overall productivity would fall so precipitously that the production mix would need to be recalculated. The knock-on supply chain impact would be even more complex. (The plant was later shut down for over a week.)
• An industrial logistics company debated the cost and convenience of switching to cellphone-based QR codes to coordinate pickups and delivery. A nascent regional pilot delivered valuable insights within two weeks from both delivery personnel and several clients.

Although generalizations are dangerous, four transcendent issues dominated discussions:

• Supply chain teams immediately needed data outside their remit to appropriately define, not just solve, their apparent problems. Without exception, they needed visibility into data and/or analytics owned and operated by other functions that they had not accessed before. Existing data governance processes and protocols had not anticipated these circumstances. Legacy supply chain leadership teams undervalue data governance.
• Existing technical and analytic competencies of the supply chain function were not up to converting existing or accessible data into actionable insight in the face of a pandemic disruption. (My favorite anecdote is that the most valuable data from one supplier’s sustainability disclosure was the name of an external counsel; he proved surprisingly helpful in renegotiating supplier contract terms.) Partnering with legal, compliance, finance, data engineering, and sales is unavoidable.
• The supply chain data gaps and discrepancies described as most difficult or challenging required direct outreach to suppliers, external partners, and even customers. Supply chain teams realized that procurement and sourcing agreements did not facilitate reasonable data access or exchange in a timely manner. Negotiated delivery terms consistently prized price over openness. You get what you pay for.
• Existing supply chain transparency and visibility policies and practices were explicitly aligned to internal key performance indicators and metrics. That is, supply chain managers invested in transparency and visibility as a function of how well it allowed them to measurably improve supply chain efficiencies. Organizations incorporating external customer metrics for supply chain assessment — such as on-time delivery, customer satisfaction, and Net Promoter Score — enjoy greater trackability/traceability capabilities.

In almost every case, meeting supply chain challenges relied less on digital maturity or orchestrating incompatible systems than on finding — or creating — relevant data fast. Even organizations with data catalogs and/or master data management quickly determined that their existing data would not effectively support key use cases. Essentially, and unsurprisingly, supply chain management discovered that poorly defined data adds little value to well-defined use cases.

Recommendations

Key lessons can be learned by observing how seriously supply chain leaders struggling to turn COVID-19 disruption into digital transformation addressed the following five questions. Each question reflects and respects the importance of accessible and actionable data as the organizing principle for supply chain transformation. Data first, not digital first, is the key that unlocks anticipatory, agile, and resilient supply chains.

1. Do data governance policies and processes make visibility simpler, easier, and more accessible throughout the enterprise? Ironically, many data governance programs digitally replicate the very supply chain inefficiencies they seek to eliminate. Data is warehoused until needed and labeled in ways that prioritize legacy categories over actual “use cases” Self-service is difficult; the data is often decoupled from the contexts from which it’s drawn — for example, the order is in the queue, but the warehouse from where it will actually ship is not visible.

In other words, the use cases support the job to be done rather than the data essential to doing it. Former Google engineer Steve Yegge’s infamous 2011 rant detailing how his company must architect data to be easily accessed through externalized interfaces offers a template for data governance design. People pay attention when the CEO/founder mandates data accessibility — or else.

2. (How) are you measuring visibility? Declaring visibility as an aspiration or complying with disclosure requirements is not enough. Supply chain managers need to be able to instantly assess whether they can see the data they need in order to make informed decisions or perform further analysis. Does the organization actually have the data? If so, how accessible is it? Is it in an appropriate format? What additional cleaning — or processing — is necessary?

If supply chain managers can’t instantly see the data they need, where can that missing data be found? Who is authorized to get it? Does that authorization map to accountability? Is this a one-off instance, or should this data type become part of standard use cases and/or processes?

At least one of the observed supply chain operations assigned data wranglers/stewards to create metadata for specific data sets, ingredients, and suppliers. Leadership recognized that better context promoted greater visibility. This organization pushed visibility to accelerate categorization and resolve supply chain problems faster.

3. Are you as transparent as your best customers want you to be? Customers now expect to be able to track an Amazon shipment or an Uber Eats delivery. People can literally see where their orders are and when they expect to be delivered. In Australia, Domino’s Pizza sends customers actual photos of the bespoke pizzas that are on the way.

Transparency both sets and manages customer expectations. While the underlying logistics processes may be digital, customers can exercise their power to see what’s going on. Customer-centric organizations must ask themselves how transparent — that is, how visible — they want to be for their customers. Mercedes, for example, allows customers to see their cars being built. Should grocery shoppers be able to see where their food is processed? Should fashion shoppers see the ateliers where their clothes are designed and the factories where they’re made?

Enabling visibility and transparency from the customer perspective creates greater demand for internal visibility and transparency to both anticipate and resolve customer concerns. For one industrial equipment supplier, for example, sending a digital product photo from the factory floor built trust with a potentially important customer.

4. (How) are you championing visibility culture inside and outside the organization? Visibility can be a capability without being a value. Without exception, supply chain managers I spoke with asserted that they wanted to encourage and embrace greater visibility as a value. The costs of opacity and inaccessibility had been too high. Their biggest challenges didn’t involve internal silo-ization or data governance issues; the issues were outside the organization.

As observed above, supply chain managers acknowledged that relationships with key suppliers needed to become more transparent. Contracts would have to be renegotiated; specific levels of transparency/visibility would need to become part of service-level agreements. Procurement requests for proposals would have to better balance price, quality, and visibility. Most important, visibility that measurably improved internal efficiencies and customer outcomes would be recognized and rewarded.

These conversations — internal and external alike — have just begun. Will greater trust lead to greater transparency? Or will greater transparency lead to greater trust? Or will lean trust and lean transparency suffice in a post-pandemic era?

5. How will our visibility/transparency commitment facilitate AI/machine learning/automation adoption? In the first and final analysis, better visibility and better transparency ensure better data. Similarly, better data ensures more effective and reliable training sets for machine learning and AI systems. Better visibility and transparency allow organizations to better manage the inherent risks of automating supply chain processes in warehouses and distribution centers worldwide. This makes digital twins for supply chains as meaningful and manageable as digital twins for production. Without exception, supply chain managers I spoke with recognized that investments they made in visibility and transparency would directly impact the efficacy of automation and AI investments. The COVID-19 crisis both intensifies and elevates the conversations supply chain leaders and the C-suite need to have about the future of digital process automation and optimization in an era of smart machines.

Trying to predict the supply chain future when relationships with China are in flux, global regulations around sustainability are in transition, and regional economies struggle to overcome the impact of the COVID-19 pandemic is likely a fool’s errand. The smarter bet recognizes that the odds favor supply chains becoming supply networks that, increasingly, will learn to optimize not just internal coordination but customer outcomes. The leadership principles making successful transformation possible will be transparent and visible to anyone who looks for them.

Data, Not Digitalization, Transforms the Post-Pandemic Supply Chain

Women as the next wave of growth in US wealth management

An unprecedented amount of assets will shift into the hands of US women over the next three to five years, representing a $30 trillion opportunity by the end of the decade.
Women as the next wave of growth in US wealth management

Banking system resilience in the time of COVID-19

Capital cushions at European, UK, and US banks look adequate in most scenarios—and challenged in others. In either case, they must be rebuilt, and that will require some difficult decisions.
Banking system resilience in the time of COVID-19

To lead in the postcrisis tomorrow, put leadership and capabilities in place today

Amid the crisis, companies have learned to work faster and manage better. Strategies that continually refresh those lessons through people will define winners in the post-COVID-19 era.
To lead in the postcrisis tomorrow, put leadership and capabilities in place today

Five Fifty: The quickening

If you’re feeling whiplash, it might be the ten years forward we just jumped in 90 days’ time.
Five Fifty: The quickening

The long haul: How leaders can shift mindsets and behaviors to reopen safely

The influence model is not only a template for organizational transformation but also a solid guide to crafting a sustainable response to the COVID-19 crisis.
The long haul: How leaders can shift mindsets and behaviors to reopen safely

AI in healthcare: Microsoft’s Kevin Scott on how tech can treat a pandemic

Machine learning to develop vaccines faster; wearable devices to detect illness sooner: the COVID-19 pandemic could spark significant innovations in healthcare technologies.
AI in healthcare: Microsoft’s Kevin Scott on how tech can treat a pandemic

Transition to the next normal: Enhancing cybersecurity in the Iberian Peninsula

Remote work and more-sophisticated attackers are challenging cybersecurity for companies in Spain and Portugal. Here’s how they’re upgrading their defenses.
Transition to the next normal: Enhancing cybersecurity in the Iberian Peninsula

Innovation’s Uncertainty Factor

Image courtesy of Joey Guidone/theispot.com

For the past two decades, companies have assumed that they know the disruption playbook. It’s an S curve of progress: a series of cumulative advances as a new value proposition progresses to outperform a given industry’s prevalent offers. A company introduces gradual improvements in a new, innovative value proposition. Initially, the offering is not attractive to mainstream users and established incumbents, but eventually it becomes good enough and then achieves market dominance. Disruption of the incumbent is complete.

This perspective on disruption provides a valuable guide with respect to how investment returns on innovative efforts may unfold over time. Progress during early efforts tends to be slow, followed by takeoff and a period of sustained growth.

The launch of Netflix’s DVD-by-mail service at the turn of the century represents a classic example. The service was initially targeted at movie enthusiasts who were early DVD adopters. These were consumers who agreed that the trade-off of selecting films through online search was worth the wait (often several days) for the movies to arrive in red envelopes in the mail. At the time, this value proposition was not attractive compared with the mainstream video rental market. However, as Netflix improved its offer — via an unlimited subscription service, an online recommendation engine, a more efficient distribution network, and newer and original content — the company was able to disrupt video rental incumbents such as Blockbuster.

The Analysis

• The authors have analyzed the progress, successes, and failures of disruptive innovation efforts in sectors such as health care and energy.
• They have also surveyed the academic literature and publicly available reports on disruptive innovation in other industries.

But this view of disruption is oversimplified or, at a minimum, incomplete. What the prevailing imagery for S-curve progress misses is the fact that there is significant uncertainty regarding the rate of progress within the new disruptive value proposition. Some innovations can reach mainstream status in a matter of years, whereas others may take decades. And others, despite their disruptive potential, may never reach fruition. Video streaming services took off rapidly around the globe in a matter of years. In contrast, it has taken online degree programs more than a decade to establish a strong foothold in the education sector. And gene therapy, touted as a major advance in personalized medicine for several decades, has yet to take off.

The importance of factoring in uncertainty to understand the trajectory and impact of a disruptive value proposition on either a startup or an incumbent can’t be overemphasized. When it’s not anticipated, an otherwise promising upstart might leap forward with a product or service based on the assumption of a strong product-market fit without ever finding its audience. Or an incumbent can find itself taking its eye off its bread-and-butter existing products and services based on the assumption that a new innovation will skyrocket to success and provide the growth engine for the future. Neither path is a good one.

Uncertainty and disruption are two sides of the same coin; they can’t be separated. And yet much of the established thinking around managing disruption focuses on incumbents grappling with the threat of market incursions and identifying opportunities to develop their own, and on new entrants managing the opportunities around disruption.1

Although it’s true that the progression of each disruptive innovation may be shaped by the specific strategies of incumbents and entrants, decision makers should bear in mind that there is substantial uncertainty around whether a disruptive value proposition will materialize in the first place. Why is this important? Because an explicit consideration of uncertainty can help decision makers recognize the risks that surround the execution of the disruptive strategy. It can help them set more realistic market-growth expectations and evaluate strategic contingencies that can be experimented with and validated.

In our ongoing research, we have found three key sources of uncertainty — around technology, ecosystems, and business models — that are pivotal to understanding the process of disruption. When companies carefully consider these sources of uncertainty and how to address them, they can better position themselves to manage disruption and achieve superior performance outcomes. When entrepreneurs and executive teams overlook these factors, it opens up their companies to foreseeable challenges, such as the following:

• Failing to recognize the time and the extent of resources that might be required for the disruptive value proposition to take hold. This can lead to misjudgments about investments in a disruptive innovation initiative, such as giving up too early or sustaining significant spending too long, or starving other, more viable initiatives of resources and attention.
• Focusing on the new technology or the new business model while overlooking the challenges within the company’s ecosystem of suppliers, business partners, and customers that may be critical to the realization of the new value proposition. This can lead to prematurely optimistic projections about the potential of a disruptive innovation and risks wasting resources.
• Missing opportunities they could otherwise identify and seize around business model innovation across different markets. The risk here is that a company limits the potential appeal of a disruptive innovation or narrows the innovation’s paths to market without examining all the possible variations around the business model.

These uncertainties do not influence every company to the same degree, of course. Startups tend to be adept at experimenting with new technologies and business models, even though they may be resource-constrained. In contrast, established companies tend to be endowed with significant resources but face significant adjustment costs when they pursue disruptive value propositions while managing their core business. (Although startups and established companies often compete, collaboration can help both manage the uncertainties of disruption. See “The Potential for Collaboration in the Face of Uncertainty.”) But without a deep understanding of how uncertainty can affect the speed and resource-intensiveness of the disruptive arc of development, startups and incumbents alike can find themselves failing at what otherwise might have been a successful disruptive innovation.

The Potential for Collaboration in the Face of Uncertainty

Disruptive innovation typically is framed as a contest in which the startups threaten and, potentially, displace the established industry players. Considering the different sources of uncertainty and the unique challenges each type of company confronts, our research suggests that collaboration between startups and incumbents can help advance disruptive innovations in areas such as gene therapy and decentralized electricity, where the innovation has the potential to be truly disruptive but needs time and resources to find its value proposition, business model, and supportive ecosystem. In these cases, we see a win for incumbents and startups alike. Indeed, they can work together to address the constraints and overcome the challenges that are specific to each actor and, at the same time, present complementary opportunities for actors to pursue joint value creation. This can have at least three benefits:

• Established companies and startups can pool resources and share risks during a period of significant uncertainty.
• Startups can help established companies experiment and validate new business models.
• Established companies can leverage their ecosystems and resources to help startups scale up their disruptive innovations.

Such patterns of collaboration are now rampant in the automotive sector, with emerging entrants such as Aurora, Grab, Uber, and Waymo and established automotive players such as Daimler, Ford, General Motors, and Toyota jointly pursuing autonomous vehicles.

Three Sources of Uncertainty

How is it that otherwise savvy companies tend to overlook or ignore potential sources of uncertainty? They might not be looking in the right places, or they may be locked into a specific strategic perspective too early. Our research has identified three key sources of uncertainty surrounding the question of whether the disruptive value proposition will reach fruition in a given market.

1. The enabling technology. Questions can persist about whether the technology that is enabling the disruptive value proposition can achieve the performance-cost threshold required for adoption by mainstream users (that is, for achieving product-market fit). For example, for commercial space travel, there are technological questions related to performance and cost, and although a large number of companies are pursuing the new value proposition, it remains unclear which technological design may be most feasible.

2. The surrounding ecosystem. Uncertainty may also stem from not knowing whether actors in the ecosystem will contribute to the disruptive value proposition through supporting investments, complementary innovations, or standards and regulation. For example, there remain important gaps in understanding regarding the use of augmented reality for instruction and training; it’s unclear whether there will be sufficient complementary content and suitable hardware devices for users to benefit from the new value proposition and how such virtual offerings might be regulated.

3. The business model design. Finally, there can be unsettled issues around the viability of the business model. Will the revenue and profit streams reach sustainable levels for the companies pursuing the disruptive value proposition? For example, there is significant uncertainty around whether the business model for autonomous vehicles looks more like traditional private and fleet vehicle ownership or like a fee-based mobility-as-a-service offering.

These uncertainties are not isolated. As our research on gene therapy has revealed, they can sometimes combine to heighten the challenge of commercializing innovations with disruptive value propositions.2

Gene Therapy’s Suspenseful Story

Gene therapy has faced challenges since its emergence in the 1980s. It has the potential to be a game changer for patients with genetic disorders that have no known cures, because it promises to cure the diseases by fixing defective genes instead of treating symptoms. When seen through the lens of the three uncertainties, however, it’s clear that the path to disruption is a steep climb.

Early attempts in gene therapy development proved ineffective, and some clinical trials led to severe patient side effects and deaths, raising questions about the time and resources required to bring this innovation to market. The business model for gene therapy is also unsettled. Because the treatments can mean a permanent cure or less frequent treatments than prevailing methods, calculating pricing and insurance reimbursements has proved difficult. Companies have discussed several business models, including a pay-per-treatment approach, payments spread over a fixed time line, or a pay-for-performance model in which payments are halted if the treatments have stopped working.3 Gene therapy also confronts significant ecosystem uncertainty. Treatments need to be administered by trained physicians in specialized settings, and they need to be reimbursed by insurance plans. But the availability of trained physicians, gene therapy facilities, and insurance plans that provide coverage is difficult to establish.

Even when a gene therapy treatment wins approval, it can face a cloudy future. A recent case illustrates the challenge: Gene therapy company uniQure pursued a treatment for lipoprotein lipase deficiency, a rare disorder that prevents a person who lacks certain proteins from breaking down fat molecules. The company and health care insurers found it difficult to price the treatment for such a small patient population. UniQure, which had won European approval for the treatment, subsequently withdrew it because of the pricing challenges and the rarity of the disease.4 The fate of this treatment is emblematic of the obstacles other gene therapy companies face. The disruptive potential of their innovations is enormous, but even after a company overcomes the technological and R&D hurdles and creates a new offering, it must confront business model uncertainties.

The life-and-death implications of gene therapy as a potential disruption make it a dramatic example. But this analysis of uncertainties is applicable whenever products and services with a disruptive value proposition emerge. Using this lens to assess the circumstances in which their company enters a particular market enables leaders to make precise decisions about resource allocation and timing — and guides their expectations about returns.

Considerations for Established Companies and Startups

How might established enterprises and startups be affected by these sources of uncertainty? Their motivations for pursuing disruptive innovations differ, and their strategies are shaped by their available resources and how they measure performance. So each type of company must weigh different considerations.

While nearly all disruptive startups are motivated by the possibility of replacing the industry’s status quo, many of them confront resource constraints in their efforts to develop a disruptive value proposition. In the case of gene therapy, startups have attracted a lot of attention, but many could not continue in the face of technology setbacks as their resources and new sources of funding dried up. Highly promising gene therapy startups like Introgen Therapeutics and NeuroLogix ultimately filed for bankruptcy in the U.S.

Established market leaders face other challenges. Although they typically have significant resources available to explore disruptive innovations, they cannot focus solely on this quest. They have to simultaneously manage their core business and measure progress against prevailing key performance indicators (KPIs) and short-term investor expectations. And established companies also may be industry incumbents facing a direct threat from a disruptive innovation or from players active in adjacent industries who see their own opportunity to grow in a related industry at the incumbent’s expense. In our research, we saw evidence of several established pharmaceutical companies, such as GlaxoSmithKline and Merck, investing in gene therapy research but holding back its commercialization because of business model and ecosystem uncertainty.

Analyzing a company’s resource availability and the need to manage performance carries over to the three key uncertainties for disruption.

Resolving technological uncertainty requires significant resources over time to achieve the performance-cost threshold necessary for product-market fit. Given that startups tend to be resource-constrained, they may be more adversely affected by this type of uncertainty. For example, in the case of companies pursuing new solar power technologies, many promising startups had to exit the industry once they lost the technology race to alternative solutions, whereas many established firms were able to continue directing significant resources toward the emerging market opportunities.5 For example, Solyndra entered the renewable energy market with a promising solar power technology called copper indium gallium selenide, but it ended up losing the battle for market dominance to crystalline silicon, resulting in an abrupt bankruptcy.6

Resolving ecosystem uncertainty represents a coordination dilemma, given that business leaders need to manage significant investments across multiple actors, including business partners, suppliers, customers, and regulators. Failure to account for critical actors such as regulators and creators of complementary innovations can cause progress bottlenecks and constrain the value proposition of the disruptive innovation. In such situations, startups may have a steeper challenge: Not only are they resource-constrained, but they may also lack scale and credibility among members of the ecosystem to influence their supportive actions.

Consider the case of Better Place, with its disruptive value proposition around electric cars. Its model to offer battery-charging and -swapping services in addition to selling vehicles helped resolve the technological uncertainty for motorists for whom the low battery performance and high cost of electric cars did not offer a strong value proposition. But, as it pursued its growth trajectory across different geographies, Better Place was unable to orchestrate the ecosystem and align the different actors — including customers and the governments in its targeted markets of Denmark and Israel — and sold only about 1,300 cars before going bankrupt in 2013.7

Autonomous vehicles are another example of a potential auto industry disrupter, with a number of established automakers launching deliberate, collaborative efforts to develop an ecosystem. BMW, for one, is working with Mobileye (Intel’s vision-safety venture) and Fiat Chrysler, as well as parts suppliers like Aptiv, Continental AG, and Magna International, with the goal of commercialization by 2021.

Resolving business model uncertainty requires continuous experimentation and the ability to reconfigure one’s approach to unlock the potential of the disruptive innovation for the innovation’s users and the innovating companies. Established companies are more likely to struggle with such uncertainty because experimenting with new profit formulas runs counter to existing metrics. Executives face pressure to meet KPIs. Scrutiny by investors and analysts, meanwhile, typically rewards sustaining rather than disrupting profit models. Conversely, startups are not entrenched in prevailing business models and may be better equipped to manage business model uncertainty.

The recent struggles of electric utilities to adapt to more decentralized business models exemplify the challenges for established companies. In a decentralized model, users (such as homeowners) consume electricity that is generated at or near the point of use, often through a combination of rooftop solar photovoltaic systems, batteries, and digital management of the electricity grid. Incumbent companies that were entrenched in the old, centralized business model had lower performance outcomes when pursuing the disruptive value propositions.8

For example, NRG, a U.S.-based energy incumbent, reported large losses from its pursuit of a decentralized model, resulting in the CEO’s firing. His departure was followed by a number of articles in the trade press describing the internal conflicts between the centralized and decentralized businesses, unforeseen delays, cost overruns during the implementation of the decentralized model, and the extensive competition NRG faced from new entrants.9 Incumbent energy companies elsewhere, such as AGL in Australia and RWE in Germany, have faced similar challenges.

Managing the Uncertainty of Disruption

Pursuing a disruptive innovation means taking on risk that the effort may fail. Analyzing the uncertainties that any disruption faces, however, can help you mitigate those risks by making informed decisions about the supporting technology, the surrounding ecosystem, and the business model foundation required for success.

The following five questions can help innovators — incumbent companies and startups — manage uncertainty.

1. What are the opportunities for a disruptive value proposition? Opportunities can be related to creating new markets, such as space tourism, or penetrating existing markets, such as global tourism.

2. Where are the key sources of uncertainty — technology, ecosystem, and business model — in different markets? Uncertainty doesn’t have to be prevalent across all areas. It is typically a subset of the three sources that can create bottlenecks for market growth. Management practices such as scenario planning and discovery-driven planning can be more effective if they explicitly incorporate the different sources of uncertainty.

3. How can the different sources of uncertainty be addressed? Experimentation with respect to customers and others in the ecosystem, business models, and technology choices can be valuable in resolving uncertainty. However, if uncertainty is severe across the three sources, decision makers may need to say no to investments at the outset or stop specific disruptive innovation initiatives.

4. Can I pursue this disruption on my own, or do I need strategic partners in the ecosystem to help resolve uncertainty? Identification of ecosystem activities and actors where uncertainty resides — and coordination among them — can be a critical aspect of managing such uncertainty.

5. How can I align partners to cocreate value? Partners can have different business models and motivations around the disruptive value proposition. It is important to identify which partners may have mutually beneficial objectives and to ensure that those objectives are aligned for the long run.

This exercise widens leaders’ perspectives on the opportunities before them. By openly considering these questions, leaders improve their ability to identify the risks of any strategy to develop a disruptive innovation. Based on the answers to these questions, they can make more nuanced decisions about their plans — which innovations to pursue, how much to invest, which partners to collaborate with, and the timing for all of these choices — than they otherwise would. They can revisit their evaluations and adjust their investments and the timing of them. And they can calibrate their expectations for progress on a particular value proposition and whether it has the potential to disrupt an established market based on additional evidence and insights.

Well-established cases show what’s possible for both established companies and startups. For the iPhone, Apple managed ecosystem uncertainty (What parties will work with us on this disruption? How?) through the creation and maintenance of its App Store and its calibrated rollout of available telecommunications carriers. Apple also navigated business model uncertainty in part through its use of exclusive vendors when rolling out the iPhone. Tesla has managed technology uncertainty by investing in batteries and software in order to offer a high-performance electric car. Regarding questions about its business model, Tesla set up direct sales. And the company is working to manage ecosystem uncertainty (how to keep electric cars charged) by developing infrastructure through initiatives like its Supercharger network of charging stations.

Disruptive innovations have made us more productive, better informed, and more mobile. They improve our health. They entertain us. And there are many more disruptive innovations to come — indeed, the next one may be at your company. To prepare for the best possible outcome, it’s important to understand not only the reach of an innovative idea but also the risks that lie in its path to realization. With our eyes open to confront uncertainties around the technology, ecosystem, and business model of each potential disruption, we can better understand what to expect along the way and better devote the time and resources to strengthen our chances of success.

Innovation’s Uncertainty Factor

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Another subgroup of people stands undecided, acknowledging both the pros and cons of their current work situations. Many express loneliness and miss the physicality of being among colleagues; they miss their old routines and rituals, the structured predictability of their workdays, and the demarcation of work life from domestic life. Now, it seems like work life and domestic life are all rolled into one, and for many, it’s a very messy time. Yet, many of these same people also find themselves embracing the flexibility and fluidity afforded by not going into the office every day. They relish the opportunity to spend more time with family while also finding time to focus on their own wellness. Like so many social relationships, the prospect of returning to the office is complicated.

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Current conversations about what a return to work will look like both in the coming months and after the COVID-19 crisis focus primarily on those workers who are or aren’t required to be in a centralized place of work — and what “there” might look like. Geographical and industry differences present unique challenges to what elements these workspaces — whether field, warehouse, office park, or terminal — will comprise.

The workplace that I am most familiar with is the corporate office: Decidedly separate from home, the office is purpose-built as a place to get work done. I’m of the mind that to better understand the current moment, we need to trace how we got here in the first place. Although we can’t predict the future, we can certainly imagine possible futures from a more informed present.

Of course, humans have been “going to work” for as long as they have been around. We needed food and shelter for communal survival, so we hunted and gathered to secure these necessities. We innovated systems of exchanging labor — some manual, some in the realm of what we’d eventually call “knowledge work” — and goods based on trade and barter. Some forms of labor were manual, while others were not.

As modern work practices evolved, those in clerical or administrative roles found their way into what we now broadly refer to as “the office,” while others worked manually on farms and in factories. I’ve worked in both types of settings and, along the way, have acquired a fair degree of local knowledge about “how work gets done here.” Regardless of location, all places of and for work are symbolically dense, historical artifacts formed in the lived interplay of people, practices, and power over time.

Which brings me back to the office. What are some of the systems of belief, histories of technology, and culturally specific practices that have shaped what many of us think of as the office? How might we reimagine both the office and how work gets done based on what we can glean from the past?

A Social History of the Modern Office

A place dedicated to the productivity of those undertaking mental work and handling information — Peter Drucker’s so-called knowledge worker — the office has a relatively short yet dynamic social history. As a place separate from domestic life, the office has been the subject of books, such as Gideon Haigh’s The Office and Nikil Saval’s Cubed: A Secret History of the Workplace, as well as numerous television shows and films.

The medieval monk in a dimly lit chamber, poised with pen and parchment, head down at a small desk, intensely focused on the manuscript before him, is an early example of the kind of work — solitary and dedicated to deep concentration — popularly associated with working in the office.

Across Europe, beginning in the 18th century, wealthy merchants had home offices where they could display their status and power and entertain prospects and partners, while civil servants, lawyers, clerical workers, and bookkeepers increasingly began working in shared offices. As a consequence of abundant paperwork created in the course of business dealings across the empire, the East India Trading Company deemed it necessary to centralize all administrative functions. In 1729, East India House was constructed; it was an office building purpose-built to ensure the efficiency and productivity of a large and expanding workforce.

On a similar theme, in 1854, Stafford Northcote, later chancellor of the Exchequer, and Sir Charles Trevelyan, assistant secretary to the British Treasury, wrote the following regarding the optimal administration of civil servants:

For the intellectual work, separate rooms are necessary so that a person who works with his head may not be interrupted; but for the more mechanical work, the working in concert of a number of clerks in the same room under proper superintendence, is the proper mode of meeting it.

Steam-powered machinery contributed to the production of cheap paper made from wood pulp in the 19th century, which in turn made it more profitable to hire additional white-collar office workers. This may explain, in part, the somewhat derogatory description of white-collar workers, and especially civil servants, as “paper pushers.”

Image courtesy of the Early Office Museum

Mass production of office equipment such as desks, chairs, typewriters, and adding machines, coupled with enthusiasm for Frederick Winslow Taylor’s book The Principles of Scientific Management on how to extract the maximum productivity from workers, found expression in offices engineered to mechanize human workers (as human labor) at scale while affording managers a panoptic view of the shop floor.

Along with developments in materials science, organizational management, technology, and public policy came new ways to imagine what an office might be, how to best manage employees for maximum productivity, and the very definition of employee itself. How these emergent forms came to reflect ideas about personal identity, interpersonal relationships, and where and how work gets done provide a vantage point from which to reflect on and contextualize the post-pandemic office.

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The New Cyber Normal

In the spirit of “never letting a good crisis go to waste,” organizations will, and should, rapidly redefine their new operating model. Five key factors drive the cybersecurity risk implications in this new, likely semi-remote, working environment. Organizations should keep these factors in mind when defining how to adjust their cybersecurity risk programs.

1. An increasing number of cyberattacks. Since the COVID-19 outbreak began, the number of cyberattacks has soared as hackers have exploited a greater number of weakly protected back doors into corporate systems as well as the human distraction caused by COVID-19-related events. The FBI is receiving 3,000 to 4,000 cybersecurity complaints daily, up from 1,000 prior to the pandemic. Hackers continue to target key industries such as health care, manufacturing, financial services, and public sector organizations like the World Health Organization. Banks are now fending off nearly three times as many cyberattacks as cyber criminals flood employees’ inboxes with COVID-19-related phishing emails, often attaching seemingly innocuous files designed to lure unsuspecting employees into executing malware.

2. Changing attack surfaces. The shift to using new teleworking infrastructure and processes may lead to the undetected exploitation of vulnerabilities in existing remote work technologies. Security agencies in both the United States and the United Kingdom have warned that a growing number of cyber criminals are targeting individuals and organizations with malware. In addition, cyber risks via business partners and third parties are increasing as well. It is hard enough to prepare internally for a semi-remote working environment but even harder to verify the preparedness of vendors ranging from IT service providers to business process outsourcing firms to law firms.

3. Distracted workforces. A vast number of successful cyberattacks are caused by human error, including an estimated 90% of such attacks in the U.K. in 2019. Increasingly preoccupied by greater personal and financial stress at home, employees are more vulnerable to cyberthreats and “social engineering” cyberattacks designed to trick them into revealing sensitive information. As homebound employees become less vigilant in their cyber hygiene, the volume of successful attacks that result from human error may further increase.

4. Unanticipated staff shortages. Workforces are stretched thin as employees (including cybersecurity professionals) call in sick or take time off to care for dependents, further harming organizational abilities to respond to cyberthreats. Many employees who found working from home more productive before the pandemic now juggle forced isolation, loneliness, limited privacy, and new demands related to children’s schooling and care at home, resulting in lower productivity. Since mass work from home began during the coronavirus outbreak, self-reported data in the United States shows decreased productivity across industries, with 11% of professional and office workers and 17% of industrial and manual service workers reporting lower productivity.

5. Multi-stress environment. Security teams are operating in an unprecedented environment in which multiple crises are constantly arising, each demanding significant attention from cybersecurity and management teams. COVID-19-related challenges will be the baseline for the foreseeable future. Of course, organizations still have to manage through other crises and stress events, like hurricanes, forest fires, or widespread protests as recently observed in the United States.

Assess Your Changing Cybersecurity Risk Profile

As organizations transition to the new ways of working, the resulting changes to the company’s cybersecurity risk profiles must be repeatedly assessed and monitored so that they can be actively managed, prioritized, and mitigated.

The list of entry points for attacks as a result of far-flung workplaces keeps growing. Bad actors can openly record sensitive customer information shared with customer service employees taking service calls on their mobile phones at home, instead of in highly secure and monitored call centers. Inadequately tested new technologies and digital products rapidly deployed to meet customer needs during the pandemic, like customer service chatbots and Paycheck Protection Program applications could inadvertently introduce new threats. Remote working operations of interconnected vendors and customers further amplify organizational risk. Cybersecurity teams are now forced to virtually mobilize and coordinate multidisciplinary teams to mitigate potentially complex attacks.

Adjust Your Cyber Strategy

Based on this risk assessment, teams of risk management, business, and security personnel should work together to reevaluate cybersecurity budgets and prioritize investments to improve a company’s cyber resilience in line with its risk tolerance.

Start with stopgap measures that can be implemented immediately, such as revising existing cyber risk guidelines, requirements, and controls on how employees access data and communicate with a company’s network. Rules of behavior analytics need to be adjusted to consider changes to the “normal” behavior of employees, many of whom now work outside standard business hours so that security teams can effectively focus investigations.

Then examine new security tools and requirements for sharing and maintaining private information with vendors. For example, organizations may need to adopt more robust data loss controls, traffic analysis tools, and access restrictions. Ensure that vendors that aren’t currently prepared for heightened cyberattack risk commit to developing cyber preparedness plans to safely handle information or interact with your corporate network.

Review changes to boost your technology and security infrastructure today, even if such changes may take years to implement. Some organizations may want to speed up their cloud strategies so that their IT resources can rapidly meet demand spikes from large-scale remote work. Other common improvements include investing in automation and advanced analytics to improve the effectiveness of security processes, introducing greater discipline around cyber-relevant data, rationalizing duplicative monitoring and security tools to manage the cost of exploding data volume, and focusing cybersecurity teams on the highest-risk areas.

Finally, develop mechanisms to understand how your security program changes reduce cybersecurity risks after each initiative is rolled out. This is not a one-and-done exercise; organizations need ongoing agility to hit what is a decidedly moving target.

Step Up Cyber Training and Exercises

Recalibrate cyber awareness programs to measure, track, and improve the cyber risk culture of your employees, management teams, and cybersecurity professionals in the new cyber normal. Employees need to be informed of new cyber risks and reminded of their role in effectively preventing, detecting, responding to, and recovering from cyberattacks.

Design role-based training programs and exercises to raise the awareness at every level of new and changed cyber risks introduced by increased remote working. Training programs should cover new threats, rules for approved device and data use, and processes to report suspected cyber incidents.

Management teams should engage in walk-throughs and simulations for new cyberattack scenarios armed with playbooks that provide clear guidelines for required actions, including when (and to whom) decisions should be escalated. These scenarios should be considered for future cyber war games and simulations to prepare cybersecurity, technology, and business teams for future large-scale crises. By doing so, teams can identify shortcomings that must be overcome in order to respond effectively to cyberattacks.

As the pandemic continues to unfold, organizations are operating in a real-life multi-stress environment, facing cyberattacks along with many other COVID-19 related challenges. Much of the operational shift that has occurred as a result of the pandemic will outlast the immediate crisis and aftermath. Organizations will, and should, draw lessons from the current crisis to design and execute a new operating model that incorporates more remote working flexibility. To do that securely, organizations need to understand how their cyber risk profiles have changed and must revamp their strategies, training, and exercises to address threats and minimize risks.

Cybersecurity for a Remote Workforce