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This paper investigates whether intelligent workplace technologies improve firm-level productivity and, if so, under what conditions, with particular attention to their implications for the economic and social sustainability of firms. This investigation occurs in a context where firms increasingly combine automation, artificial intelligence (AI), and work-from-home (WFH) practices to sustain performance under structural shocks such as the COVID-19 pandemic. Despite evidence that firms adopt these technologies jointly and reorganize work accordingly, existing research typically examines them in isolation. We develop a micro-founded, task-based production model in which firms allocate tasks between on-site and remote labor and automated capital in an optimal manner. This model allows both automation technologies and remote work collaboration tools to affect productivity and coordination costs that are central to long-term organizational sustainability. Using firm-level survey data from nearly 4000 large firms across industries and countries (2018–2021), we show that working from home (WFH) exhibits diminishing productivity returns when scaled in isolation, reflecting rising coordination frictions. In contrast, firms that combine WFH with automation and digital collaboration tools experience significantly higher labor productivity growth. These integrated technology systems support sustainable productivity by enabling capital deepening, resilient task reallocation, and more efficient use of labor resources over time. Overall, the findings suggest that productivity gains—and by extension sustainable firm performance—stem from integrated workplace technology systems rather than isolated investments, highlighting the importance of coherent technology strategies for organizing work in the post-pandemic economy.

Few executives would dispute that digitization's disruptive influence is growing. But surprisingly little empirical evidence has captured either the magnitude of digital disruption or how incumbents are reacting. Leaders know they have a problem but lack guidance to determine the right course of action. In a global survey of 2,000 C-suite executives in more than 60 countries, McKinsey & Co. found that few companies are responding appropriately to digital disruption. While 90% of companies indicated that they are engaged in some form of digitization, only 16% said their companies have responded with a bold strategy and at scale; only 30% of companies are focusing on new ways to bundle demand or resegment their market. Based on these numbers, some leaders might assume that they have plenty of time to get their digital acts together. But the authors argue that this would be a dangerous assumption because new digital entrants are seizing a significant share of revenue across regions and industries. The authors highlight three bold tactics companies can use: 1. Develop new customer segments. Rather than just defending existing business lines through cost cutting, automation, or service improvements for existing customers, companies should focus on developing new customer segments. Medialaan NV, a leading free-to-air video broadcaster in Belgium, spotted young customers moving to platforms such as Netflix or YouTube. In response, it bought a mobile virtual operator with attractive data plans, thus becoming one of the few traditional broadcast companies to grow its TV audience in the youth segment. 2. Introduce new business models. Innovative companies are experimenting with business models intended to disrupt their own legacy strategies. When Schibsted Media Group of Oslo, Norway, saw that its print classified advertising was drying up, it moved the classified business to a free online marketplace. Today, more than 80% of the group's earnings come from commissions on sales from its consumer e-commerce platform. 3. Redefine the value chain. When digital entrants threatened its payment services business, Commonwealth Bank of Australia (CBA) chose to confront the disrupters head-on with a new open payments platform that hosts an ecosystem of applications and devices for merchants and is open to third-party developers. Although the platform and its ecosystem contribute to the disruption of the traditional banking value-add chain, it also positions CBA to compete with digital entrants.

The Covid-19 pandemic is a textbook case of significant situational stress induced by various disruptions beyond mere health concerns, such as social isolation and financial constraints. For the workforce, it is essential to anticipate how these disruptions may undermine employees’ resilience, to avoid a negative spiral where poor well-being lowers productivity, reduces economic prospects, and continues to increase worker stress. We measure multiple forms of stress and worries as drivers of well-being—health, economic, social, and psychological—encountered by the workforce during the acute period of the Covid-19 pandemic. The study analyzed data from 2,780 employees across five European countries: France, Germany, Italy, Spain, and Sweden. Overall Concern Score: The overall concern score was 56.8% across four domains: health, economic, social, and psychological. Stressors can be synthesized into five typical groups associated with a variety of mediating factors such as institutional trust, lifestyle, and worker education. The implication is that workers’ well-being is heterogeneous and that human resource practices may need at least a segmented approach to well-being if they wish to create an environment of a resilient and productive workforce.

Purpose Most companies struggle to meet the dual goals of sustaining competitive advantage and ensuring high resilience during crises. Those that do, research tells, adapt their operational practices along the crisis cycle, combining an effective cost approach at the start of a crisis, while quickly relaunching their investment to grow ahead of competition, just when they learn that the crisis is peaking, and not too late, when the crisis is over. This cycle alignment is possibly the crucial, but not the only, factor for success. We update early research to show that companies succeeding in this dual objective also take a proactive strategy approach of capturing the complementarities between both performance and resilience during a period of high turbulence. This complementarity relies on exploiting dynamic capabilities common to both resilience and performance, and on leveraging the resilience process as a foundation for strategic renewal going forward. Design/methodology/approach The research relies on case studies, complemented by an original survey sample, collected just after the peak of the covid-19 pandemic, of 4,000 multinational firms spread across 18 industries termed NACE2, a statistical classification of economic activities in the European Community. This study included 10 countries outside the European Community. The sample collects information on five core enterprise capabilities (agility, innovativeness, active ecosystem play and digital/sustainability maturity, flexible work practices) and uses a variety of quantitative techniques to assess that existence and size of synergies captured by firms. Findings Companies meeting the dual goal of resilience and performance are more than doubling their rate of profit growth relatively to peers, after the peak of the crisis, by leveraging of synergies between performance and resilience. Originality/value The topic is essential for management, given the elevated turbulence. This research confirms that resilience and performance are synergistic and helps make companies using each crisis boost performance forward.

Using a random sample consisting of hundreds of companies worldwide, we are testing the impact on company performance of investing in big data projects targeted on three major business domains (namely, customer interface, company supply chain and competitors). The performance test relies on a so-called trans-logarithmic production function, allowing for a more direct test of the complementarity between big data capital and big data labour investments; further, we have used a Heckman correction to adjust for the fact that companies investing in big data are generally more productive than their peers. We confirm and extend early results of a productivity impact from big data. We find that for the average of our sample, more productive firms are also faster adopters of big data than their industry peers (this explains 2.5% of productivity difference). Big data investments in labour and IT architecture are complements, with a total productivity growth effect of about 5.9%. Big data projects targeting customers and competitive intelligence domains bring slightly more performance than big data projects aimed at supply chain improvements.

When pundits talk about the impact that artificial intelligence (AI) will have on the labor market, the outlook is usually bleak, with the loss of many jobs to machines as the dominant theme. Growth is a key part of the equation between the performance and employment shifts we'll see from now through 2030 for five types of companies. Here's how we expect things to play out in the five clusters of companies we examined: 1. enthusiastic innovators, careful innovators, efficiency leaders, efficiency followers, and AI resistors. Job losses will arise as the result of automation, as the labor-output ratio evolution suggests. But what often gets overlooked is that job losses are also a risk of companies' inability or unwillingness to use AI for innovative purposes, which leads to lower revenue and profit -- and a lower absolute need for labor.

From the dexterity of Amazon's Kiva robots to the facial recognition in Apple's iPhone X, artificial intelligence (AI) is increasingly sophisticated and accessible. It also promises to be a rich source of profit uplift -- up to 10% of revenue, depending on your industry. Nevertheless, more than 95% of companies have not embraced AI technology to reinvent how they do business. Even though there are many unknowns regarding AI's capabilities and uses, our research at the McKinsey Global Institute suggests that following a wait-and-see strategy for too much longer could be a costly mistake. The corporate diffusion of new technologies typically follows an elongated S curve -- slowly rising at the start, steeply climbing in the middle, and then flattening again as the technology becomes commonplace. Our surveys suggest that the level of competitive intensity surrounding AI could be intense, accelerating the speed of diffusion. Early AI adopters told us that their companies are more focused on using AI for top-line growth than for internal efficiency.

We collect big data use cases for a representative sample of telecom companies worldwide and observe a wide and skewed distribution of big data returns, with a few companies reporting large impact for a long tail of telecom companies with limited returns. Using a joint model of adoption and returns to adoption, we find that the skewness of the distribution arises from a few telecom companies being able to follow key big data managerial and organizational practices. We also find that big data returns exhibit economies of scope, decreasing returns to scale, while big data talents are complementary to big data capex investments.

The web currently carries vast amounts of information as to what consumers search for, comment on, and purchase in the real economy. This paper leverages a mash-up of online Google search queries and of social media comments (from Twitter, Facebook and other blogs) to “nowcast” the product sales evolution of the major telecom companies in Belgium. A few findings stand out. With an Error Correction Mechanism (ECM) model of sales dynamics, a co-integration relationship prevails between social media valence (respectively, between search query) and telecom operators’ sales for both internet and digital television access provision (respectively, for fixed telephony provision). Elasticity estimates on sales are relatively larger for valence than for search queries. The ECM model with nowcasting variables improves telecom sales forecasts by about 25 % versus a naïve autoregressive sales model.

On-time effective vaccination is critical to curbing a pandemic, but this is often hampered by citizens' hesitancy to get quickly vaccinated. This research concentrates on the hypothesis that, besides traditional factors in the literature, vaccination success would hinge on two dimensions: a) addressing a broader set of risk perception factors than health-related issues only, and b) securing sufficient social and institutional trust at the time of vaccination campaign launch. We test this hypothesis regarding Covid-19 vaccination preferences in six European countries and at the early stage of the pandemic by April 2020. We find that addressing the two roadblock dimensions could further boost Covid-19 vaccination coverage by 22%. The study also offers three extra innovations. The first is that the traditional segmentation logic between vaccine “acceptors”, “hesitants” and “refusers” is further justified by the fact that segments have different attitudes: refusers care less about health issues than they are worried about family tensions and finance (dimension 1 of our hypothesis). In contrast, hesitants are the battlefield for more transparency by media and government actions (dimension 2 of our hypothesis). The second added value is that we extend our hypothesis testing with a supervised non-parametric machine learning technique (Random Forests). Again, consistent with our hypothesis, this method picks up higher-order interaction between risk and trust variables that strongly predict on-time vaccination intent. We finally explicitly adjust survey responses to account for possible reporting bias. Among others, vaccine-reluctant citizens may under-report their limited will to get vaccinated.