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The digital data available online is currently measured in zettabytes. These vast repositories of big web data are increasingly viewed as a strategic resource comparable in value to land, gold, and oil. This big web data can be extracted and analyzed by organizations to gain a better understanding of their internal and external environment and improve organizational performance. Because of these opportunities, automated retrieval and organization of web data (i.e., web scraping) for research projects is becoming a common practice. This article outlines the data-related, technical, legal, and ethical issues related to web scraping. Awareness of these issues can help researchers save time and resources and, most importantly, mitigate the potential risk of ethical controversies or lawsuits related to the retrieval and use of big web data.

This case explores the leadership change and corporate revitalization of legendary Silicon Valley corporation Intel, as incoming CEO Pat Gelsinger seeks to rebuild the company's leading edge capabilities, revise its product strategy, and embark upon an ambitious capital plan.

There is tremendous apprehension about the potential of generative AI-technologies that can create new content such as text, images, and video-to replace people in many jobs. But one of the biggest opportunities generative AI offers is to augment human creativity and overcome the challenges of democratizing innovation. In the past two decades, companies have used crowdsourcing and idea competitions to involve outsiders in the innovation process. But many businesses have struggled to capitalize on these contributions. They've lacked an efficient way to evaluate the ideas, for instance, or to synthesize different ideas. Generative AI can help over­come those challenges, the authors say. It can supplement the creativity of employees and customers and help them produce and identify novel ideas-and improve the quality of raw ideas. Specifically, companies can use generative AI to promote divergent thinking, challenge expertise bias, assist in idea evaluation, support idea refinement, and facilitate collaboration among users.

AI has the power to gather, analyze, and utilize enormous volumes of individual customer data to achieve precision and scale in personalization. The experiences of Mercury Financial, CVS Health, and Starbucks debunk the prevailing notion that extracting value from AI solutions is a technology-building exercise. That thinking holds companies back from capturing the power of AI. They needn't build it; they just have to properly integrate it into a particular business context. But AI is probably only about 10% of the secret sauce. The other 90% lies in the combination of data, experimentation, and talent that constantly activates and informs the intelligence behind personalization. Personalization is the goal; it's what constitutes a company's strategic brawn. The technology is merely the tool for reaching it. The authors describe what it means to integrate AI tools and what it takes to continually experiment, constantly generate learning, and import fresh data to improve and refine customer journeys.

After being named the "best bank in the world", DBS set its sights to be "the best bank for a better world" in its third wave of transformation. This meant concerted organizational efforts towards responsible banking, responsible business practices, and creating social impact. The bank leveraged its digital capabilities (now a key competitive advantage) to drive these wider social changes around sustainability and to seize new market opportunities for growth. Yet, a host of VUCA factors (e.g., economic uncertainty, geopolitical tensions, hybrid work, Web3 & DeFi, and next-gen AI) continue to emerge on the horizon. How can DBS aim to do good and do well in this new wave of transformation? Even as DBS continues to pivot towards a new agile and AI-augmented way of working, what are some strategic next steps it should consider, as it ponders the future of banking?

Established in 2020, Nurture.farm was a digital platform that focused on reimagining the next generation of sustainable technology-based solutions for agriculture. In June 2021, Nurture.farm's chief executive officer Dhruv Sawhney and his team recognized a grave problem in northern India. Environmental hazards were being created from mass rice stubble burning, which was an inexpensive and quick way for farmers to clear their field for the next crop cycle. In response, Sawhney and his team conceptualized and executed India's largest crop residue management (CRM) program. The organization's initial success in providing its services to over 25,000 farmers and curbing the stubble burning problem gained international recognition and helped Nurture.farm earn several new partnerships within a year of operations.<br><br>By March 2023, Nurture.farm was hoping to scale up its CRM program and expand into over 800,000 hectares (2 million acres) of land, primarily in India's rice paddy fields. In this mission, Sawhney and his team found themselves facing some difficult questions. Could they efficiently scale this mission-driven campaign to additional states? How could they convince and garner organic trust from farmers? Modern technologies helped Nurture.farm facilitate its operations, but what challenges could the company face in the future? What sustainable practices could help the company drive its CRM program?

Preparing and strengthening the developing world for the 4th Industrial Revolution is a challenge that Computer Aid is tackling head on. Founder Tony Robert's vision is "Empowering the developing world by providing access, education, implementing technology and supporting environmentally responsible solutions." The case discusses the extent of digital divide gaps and challenges in the developing world. It traces Computer Aid's evolution from operations in a single African country to regionalization in Southern Africa with operations in many countries, and South-South expansion to Latin America and other developing country locations. Regionalization efforts focus on localization of operations within a region for creation of a mutually beneficial context for local businesses to become part of the digital divide solution through processing obsolete donated technology that Computer Aid then recycles and matches with beneficiary needs. The company has built a regionalization hub in South Africa. While they have had some success, questions remain-Is this the best approach for leveraging their service delivery, technology refurbishment and education? Will regionalization be effective in Latin America?

Remote work can present unique challenges for individuals with attention-deficit/hyperactivity disorder (ADHD). But managers can implement simple strategies to tailor remote work to the needs of employees with ADHD. The authors provide a primer on ADHD and, based on their research, share five evidence-backed ways to make virtual work practices more inclusive of neurodivergent team members.

The emergence of new technologies and the threat of economic challenges are just two of the factors affecting the future prospects of businesses and employees alike. In such an environment, a skills-first strategy to hiring and retaining workers can benefit both companies and workers, according to data from LinkedIn. The authors explain how investing in a skills-first approach to bringing in new talent and helping employees upskill and reskill can set their workforce up for long-term resilience.

As interest in generative AI tools like ChatGPT and Midjourney surges, narratives about the artificial intelligence technology's potential to disrupt industries has some observers unreasonably excited or panicked. The author asserts that, given business futurists' and analysts' bad track records at forecasting the effects of technology, they should focus on the here and now rather than the long-term implications of generative AI.

In this first article in a series of four, the researchers behind the 2023 MIT Sloan Management Review-BCG Artificial Intelligence and Business Strategy Big Ideas research project outline the opportunities AI brings for key performance indicators and strategic measurement. They discuss the business implications of using AI to generate and refine KPIs and explain how enabling strategic measurement systems to learn fundamentally alters how organizations understand and invest in future performance.

This case follows Shan Kadavil, the co-founder and CEO of FreshToHome, as he assesses the growing diversity of investments being made by his team. FreshToHome is a technology-enabled platform that connects fishermen directly with customers, eliminating intermediaries and guaranteeing product quality and traceability. To achieve their mission, the company has invested heavily in advanced technology and operations to optimize the supply chain, reduce waste, and offer competitive prices. This case highlights the balancing act required by Kadavil as he aims to maintain the company's core mission while fostering growth and adapting to an ever-evolving competitive landscape (including both online players and traditional offline retailers and local vendors) FreshToHome must also navigate logistical issues and cultural differences when expanding geographically into new markets.

Artificial intelligence typically takes root in an organization in isolated experiments or projects, but formal structures are needed to scale the technology across the organization and deliver on its potential. The authors identify three typical structural phases of AI implementation: islands of experimentation, centers of excellence, and federations of expertise. They identify the strengths and limitations of each and describe how companies have managed transitions to new structures that better enable AI initiatives.

For the past 20 years "disruption" has been a battle cry in business. Not surprisingly, many have come to see it as a near-synonym for innovation. But the obsession with disruption obscures an important truth: Market-creating innovation isn't always disruptive. Disruption may be what people talk about. It's certainly important, and it's all around us. But, as the authors of the best-selling book Blue Ocean Strategy argue, it's only one end of the innovation spectrum. On the other end is what they call nondisruptive creation, through which new industries, new jobs, and profitable growth are created without social harm. Nondisruptive creation reveals an immense potential to establish new markets where none existed before and, in doing so, to foster economic growth without a loss of jobs or damage to other industries, enabling business and society to thrive together.

Advanced analytics can help companies solve a host of management problems, including those related to marketing, sales, and supply-chain operations, which can lead to a sustainable competitive advantage. But as more data becomes available and advanced analytics are further refined, managers may struggle with when, where, and how much to incorporate machines into their business analytics, and to what extent they should bring their own judgment to bear when making data-driven decisions. In general, humans are better at decisions involving intuition and ambiguity resolution. Machines are far superior at decisions requiring deduction, granularity, and scalability. How can you find the right balance? There are three common approaches to analytics: descriptive, where decisions are made mainly by humans; predictive, which combines aspects of the other two; and prescriptive, which usually means autonomous management by machines. This article describes when and how to use each approach and examines the trade-offs and limitations. Although the focus is on marketing and sales, the principles may be applied more broadly.

Internal talent marketplaces (ITMs), which organizations use to match workers and roles, can increase job satisfaction and engagement, reduce turnover, and allow executives to access diverse perspectives about key assignments. Users have an incentive and an opportunity to share information about their skills, interests, and ambitions, including valuable personal information that is usually omitted from résumés. ITMs take a variety of forms. Some function like social networks, where workers and managers interact to find matches. Others allow employees to rank their preferred assignments and use an algorithm to find optimal placements. But matching talent to opportunities requires balancing business objectives, agility, workers' desires for certain jobs, and the need to avoid disrupting existing work. Those challenges confound organizations from every sector. The authors have designed, implemented, and evaluated ITMs for more than a decade in the private, nonprofit, and public sectors, with partners across the globe. In this article they review the pros and cons of using an ITM, explain how to build and optimize one, and recommend ways to align employee preferences and the company's needs.

Most IT systems are integrated-but not necessarily meaningfully enough to prevent a costly state of dysfunction.

Emerging generative AI technologies such as ChatGPT are putting new tools in the hands of hackers. Companies will need to shift from traditional defensive tools and rule-based strategies to respond to next-level AI-generated threats in real time. This will require both smarter technologies and smarter employees. The authors discuss businesses' options for establishing a modern approach to cyberdefense in the face of artificial intelligence-based security threats.

This case examines the vital social problem of the rising incidence of cataracts in India, especially in the elderly population. It describes the design and deployment of e-Paarvai, an AI-based digital health solution in more than 30 districts of rural Tamil Nadu, India, which helped screen about 25,000 cataract patients within ten months of its launch. It describes how design thinking and digital intervention could lead to effective health service delivery for social good. Specifically, this case focuses on: (1) elements of a service ecosystem, (2) the characterization of various personas and customer journey maps using design thinking (3) the application of emerging digital technologies like AI to solve challenging, complex social problems.

In today's fast-paced business world, automation is essential for companies to remain competitive. The use of Robotic Process Automation (RPA) has proven to be a game-changer for many organizations, including multinational consumer retail companies. However, implementing RPA at an enterprise-wide scale can be a daunting task. In this real life case study, we take a deep dive into the process of scaling RPA in a multinational consumer retail company. We cover topics such as exploring various scaling options routes, creating governance structure, right-sizing RPA team, and managing change and adoption across multiple geographies and business units. Through this case study, we provide practical insights and best practices for successful RPA implementation. We also explore the benefits of RPA, including increased efficiency, reduced costs and improved accuracy. Whether you're a business leader, an IT professional, or an RPA practitioner, this case study will add value for anyone looking to scale RPA in a multinational company. With our guidance, you'll be able to successfully navigate the complex challenges of implementing RPA at an enterprise-wide scale and take your organization to the next level of automation.