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Spreadsheet Supplement for Case 077SMU.

This case is set in late 2021, when the food and beverage (F&B) industry globally had been significantly impacted by the COVID-19 pandemic. Tim Ang was an analyst at Singapore Management University's Institute of Service Excellence (ISE), which had been established to help raise the performance of the service sector in Singapore. Since its inception in 2008, ISE had conducted an annual study measuring the Customer Satisfaction Index of Singapore (CSISG). The CSISG covered more than 20 sub-sectors and measured over 100 companies from finance, insurance, info-communications, transportation, retail, F&B, healthcare, education, tourism, hotels, and more. Data for the customer satisfaction study for the F&B sector was typically collected in the third quarter of every year, and results were released to both the media and industry. The 2021 study revealed that while customer satisfaction and loyalty levels had remained generally unchanged for most of the F&B sector, Cafés and Coffee Houses saw a significant decline in both metrics. Yet despite the challenges, Ang believed there were significant service gaps and opportunities which could be leveraged. A detailed analysis of underperforming areas and customer preferences should provide F&B entrepreneurs with insights on potential opportunities to address industry pain points and capture market share.

The Web 2.0 digital economy, centered on dominant platforms, generates substantial opportunities for managers and entrepreneurs yet creates critical dependencies. Platform-dependent businesses engage in competitive actions-rivalrous, competitive-cooperative, and relational-vis-à-vis digital platforms to gain a fair share of economic value. While the (supposedly) trustworthy custodians of digital data have captured a disproportionate share of revenue and profits, Web3 promises to tilt the balance away from dominant platforms by providing mechanisms that replace centralized organizational trust with decentralized technological trust. Here, innovations such as blockchains and smart contracts complement antitrust laws and regulations in limiting platform power. Furthermore, this study suggests that end users, peers, and regulators may play an important role in helping businesses draw investors', users', and customers' attention away from Web 2.0 platforms. To effectively leverage decentralized trust as a novel aspect of competitive actions, business leaders must pursue Web3 technologies that are reliable, high-growth market segments that are credible, and ventures that are investable.

AI development can consume large amounts of energy, leading to growing concerns about the climate impact of this emerging technology. The authors explain how tactics such as rethinking how much training data is required and reusing already-trained models can reduce energy consumption and lower the resulting carbon emissions from artificial intelligence initiatives.

The case follows innovations by Drishti, a start-up company that uses artificial intelligence (AI) and computer vision to provide manufacturers with granular process data on manual assembly, giving them a system-wide view into their manufacturing operations. The firm was founded in 2016 by Dr. Prasad Akella, Dr. Krishnendu Chaudhury, and Dr. Ashish Gupta, who all had extensive experience in industrial process automation. They prioritized the value of enhancing human potential in an increasingly automated world. Noting the limitations of traditional manufacturing line performance evaluation and process improvement, Drishti Technologies presented new capabilities for real-time monitoring and performance improvements. This case is about entrepreneurs creating a new software in an established industry. The case also describes the core functionality and solutions developed from the new software, value provided to customers, and the strategic business model choices faced by the leadership in moving from start-up to scale.

AI has been subtly influencing us for years, but a new generation of vastly more capable technology is now emerging. These systems, the authors write, aren't just tools. They're actors that increasingly will be behaving autonomously, making consequential decisions, and shaping social and economic outcomes. No longer in the background of our lives, they now interact directly with us, and their outputs can be strikingly humanlike and seemingly all-knowing. They are capable of exceeding human benchmarks at everything from language understanding to coding. And these advances, driven by material breakthroughs in areas such as large language models (LLMs) and machine learning, are happening so quickly that they are confounding even their own creators. For all the wizardry and seductions of these new systems, however, our future can still be in our own hands. In this article the authors offer guidance for managing their effects in the workplace, looking for increased value in that which is uniquely human, and aligning messaging and business practices with a changing, and challenging, debate.

By making it vastly easier and cheaper to improve or create products and services that previously required significant human labor and creativity, generative AI has the potential to disrupt or even commoditize many businesses. Some companies will be able to gain an edge by leveraging publicly available generative AI tools better or faster than their competitors. But that advantage will be only temporary, and using the tools will soon become table stakes. This means established companies will have to rethink their business strategies and find new ways to add value to their offerings. In this article, the authors consider which types of businesses have the greatest potential to gain competitive advantage from generative AI and which are most likely to be disrupted. They also offer guidance for implementing AI across three levels of sophistication that correspond to an increasingly powerful advantage.

Rebel Foods started as a quick service restaurant and eventually morphed into a cloud kitchen provider. The company launched multi-brand cloud kitchens, leveraging the same food preparation space and delivery infrastructure to serve a variety of food missions. Its technology stack included software for inventory management, kitchen management and delivery, as well as kitchen automation and robotics. They partnered with third-party food brands to leverage Rebel Foods' kitchen infrastructure, supply chain and logistics, and software platforms. The case study examines various business model innovations and strategic shifts the company has undergone over the years and raises questions about their future strategy.

In 2020, TDC NET, a subsidiary of the Danish telecommunications company TDC Group, established the Innovation Hub, a new organizational unit, together with its partner Ericsson Denmark. With the Innovation Hub, TDC NET sought to explore the business opportunities that the rollout of 5G, the fifth-generation technology standard for broadband cellular networks, offers in areas such as Industry 4.0 or the Internet of Things. TDC NET was proud to offer Denmark's best mobile network but margins in that business were low and competition was fierce. Offering value-added services around the implementation of 5G seemed much more attractive. But that would require the Innovation Hub to succeed with pilot projects in areas and industries that the employees working at the Hub had little knowledge about. How could they achieve this goal during the next two years? And, given the complex challenges, was the current setup of the Innovation Hub the right one or should it be organized differently?

Today’s senior business managers have the power â€" and the responsibility â€" to prevent AI project failures. But in order to do so, they need to know how to evaluate the data sets and models being used. This article offers a framework for identifying the right data set for your business problem and suggests six tough questions to ask developers before and during the deployment of artificial intelligence models.

Artificial intelligence models require large quantities of specialized hardware, data, and energy to be trained and run, all of which produce carbon emissions and e-waste. These large hidden environmental costs require solutions that can harness AI’s potential while mitigating its climate impact. This article outlines steps that AI leaders can take to make the technology more sustainable.

Large language models (LLMs) can generate convincingly human-sounding responses to queries. This ability can lead users to mistakenly attribute certain human capabilities to these artificial intelligence algorithms, namely reasoning, knowledge, understanding, and execution. Understanding how LLMs work and what their limitations are can help users identify where generative AI technology is best applied and where its outputs might be unreliable.

The public availability of generative AI models, particularly large language models (LLMs), has led many employees to experiment with new use cases, but it also put some organizational data at risk in the process. The authors explain how the burgeoning open-source AI movement is providing alternatives for companies that want to pursue applications of LLMs but maintain control of their data assets. They also suggest resources for managers developing guardrails for safe and responsible AI development.

The digital transformation of China's residential services industry had been driven by changes in supply and demand, policy support, technology upgrades, and other factors. Ke Holdings Inc., a residential service platform, had explored a path of digital transformation through "vertical growth and horizontal connection". In terms of vertical growth, Beike had successfully remodeled the service process by breaking down the elements of "house-consumer-service provider" and creating value. In terms of horizontal connection, Beike had also shared its accumulated data with agents, store owners, brands, and the government to digitize the processes of home improvement, financing, relocation, and other residential services. In the future, a new round of digital transformation will accelerate the overall digitization of homes, people, and processes. Integrating cloud computing with software, algorithms, and hardware will become the underlying architecture for accelerating digital transformation of the industry. The deep exploration of AI + big data will bring more accurate and intelligent service demand matching. 5G and IoT technologies will consistently develop to form an entirely new service process. How would Beike use digitalization to better empower residential services? How would Beike use "new technology" to create or reshape interactive scenarios and industries?

Driven by the Smart Nation and Digital Government Office (SNDGO) and Government Technology Agency (GovTech), Singapore has embarked on the strategic transformation towards Smart Nation since 2014. The Smart Nation ambition envisioned Singapore as a mutually reinforcing triad of Digital Economy, Digital Government, and Digital Society, enabled by an extensive suite of smart nation-wide infrastructure. New townships would be designed and built smart, as they embrace an increasingly green future. Various global rankings of smart cities have often put Singapore in the top spot. What are the key factors that have contributed to its success? What are the key challenges it has encountered, and what should be the strategic next steps for Singapore?

This case concerns the Patient Care Intervention Center (PCIC) a values-based health technology social enterprise in Houston, Texas. This organization was founded to tackle fundamental problems in social and health services in the United States. It was initially focused on homeless superusers of the health system, that received inadequate care at high cost, notably through frequent use of emergency rooms. However, it became clear that this was only a symptom of broader system issues, especially around coordination failures across siloed health and social services and failures in case management. PCIC designed an approach that integrated two innovative features: values-based client diagnosis and case management, based on the values, concerns, and aspirations of individual clients, and a platform-based approach to sharing data both on clients and services, with tailored technological solutions for individual health and social organizations. It has been highly successful both in outreach and contract growth. By 2022, 850 agencies were providing data, involving six million individuals, with some 1,500 referrals within the system each month within the Houston area. However, PCIC faced challenges in further scaling, with respect to finance for developmental needs, human resources, and the threat of competition from large private players that offered platforms that were significantly inferior in terms of tackling the underlying system failures, but potentially attractive to health and social organizations. The case frames both the strategic questions for PCIC and the challenges of changing an essentially dysfunctional system. HKS Case No. 2271.0

In this final 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 explore how organizations should govern smart KPIs (KPIs augmented by or created with AI). They explain which functions should be involved and what pitfalls to watch for.

Why do so many big companies get poor returns on all the money they invest in innovation? A large body of research suggests that it's because their managers tend to think novel ideas are "deviant" and resist them. As a result such ideas get watered down to make them less threatening-or get squashed altogether. The solution, the authors' studies show, is to inspire, support, and protect innovation by tapping into your networks. Three practices are especially useful. First, find and mobilize external innovation catalysts who are adept at synthesizing diverse perspectives and identifying creative solutions. Second, engage with internal sparring partners who can highlight potential objections and help fuse ideas with your company's mission and turn them into compelling business propositions. Third, selectively sequence the introduction of those ideas within your social circles in the company to stress-test and gradually gain buy-in for them. While there's nothing inherently magical about these practices, if you follow them, you'll find that innovating in a large, mature company is not only viable but highly promising.

In 1998 the author joined Adobe as vice president and general manager of the engineering technology group. Shortly thereafter he took on layout engineering as well, and the following year the company released InDesign, the powerhouse publishing platform that overshadowed Quark. In 2007 Narayen was named CEO. Two years later the company expanded from content creation to content management, measurement, and monetization with the acquisition of Omniture, a web-analytics software company with clients including Ford, TD Ameritrade, and Walmart. "Any successful technology company needs to look around the corner and make some key assertions about how the world is going to change," Narayen writes. Adobe's shift to Creative Cloud was one response. And the company has invested in AI and generative AI for more than a decade, resulting in its introduction of Adobe Sensei ("which helps customers improve the precision, ease, and speed with which they do their work") and Firefly ("a copilot that augments rather than replaces human ingenuity"). "Leaders must be dissatisfied with the status quo," Narayen says. "If you set unreasonable expectations, people will amaze you with their ingenuity."

Researchers discovered a silver lining when a fire at a supplier's factory forced a temporary shutdown at the plant of a consumer goods manufacturer: The downtime led to a flood of employee ideas for improvements.