The case is set in April 2023, soon after DBS Bank Limited (DBS) reported a 20% net profit growth of US$6.02 billion. The bank's CEO Piyush Gupta had attributed this growth to the company's continuing digital transformation journey that had started more than a decade ago. Central to this journey was the bank's adaptation of Artificial Intelligence (AI), to improve and diversify products and services. To become AI-fuelled, DBS had created a "Data First" culture and hired hundreds of technology professionals to build its technology capabilities. In addition, the bank had set aside substantial budgets to allow for experimentation, motivated individual departments to build and deploy AI-based applications, implemented an automation strategy to guide solution building, and embedded AI into nearly every part of the customer journey. Prior to the transformation, DBS was sometimes irreverently referred to as 'Damm Bloody Slow' due to its poor customer service, but had emerged as a customer-savvy, market-responsive, AI-fuelled company with more successes than failures, diversified lines of business, and dramatic growth in revenues. However, the financial services sector was seeing increased competition due to the entry of purely technology companies like Grab, PayPal, Alibaba, etc. with innovative solutions. How could DBS compete in a rapidly changing banking marketplace? Had the 'All in on AI' approach given the bank a competitive advantage? Could DBS's prior 10 years of successful efforts with digitalisation, analytics and AI position it to take advantage of the newest generation of Generative AI in an accelerated manner?

Formed in 2018, EyRIS was the brainchild of a long-term collaboration across healthcare researchers and practitioners, and computer scientists. EyRIS was launched to commercialise and sell an AI-driven algorithm called SELENA+ that analysed retinal images to screen for diabetes-related eye diseases. Over time, many moving parts came together to facilitate full-scale production usage of SELENA+ within the national-level Singapore Integrated Diabetic Retinopathy Programme (SiDRP). The facilitators included multiple individuals in the public healthcare sector medical field, and several government institutions such as Singapore's Ministry of Health (MOH), the Singapore National Eye Centre (SNEC), and Singapore's national healthtech agency, amongst others. Over the years, the algorithm was further fine-tuned and trained on as wide a data set as possible in studies that spanned ethnicities and age groups, eventually yielding highly accurate results. SELENA+'s results bridged the gap between research discovery and real-world use. Now, the team at EyRis had to commercialise SELENA+ and bring it to a wider range of markets and geographies. They faced numerous challenges in the process, within Singapore and in other countries. Nevertheless, by 2023, EyRIS' efforts paid off and the SELENA+ algorithm received regulatory approvals for use in a growing number of countries. Now, EyRIS' commercial heads, along with Dr Wong and the other co-founders, wondered how best they could continue to scale EyRIS' market reach and offerings.

Set in April 2022, this case delves into supply chain disruptions faced by 3M in and after the Covid-19 pandemic, and the strategies used by the company to cope with the challenges. The case begins by tracing the history of 3M as an 'Innovation Machine', and its internationalization strategy in its early years. It then talks about the company's regionalisation strategy, and complementary growth strategies like the 'Divide and Grow', 'Follow the Technology' and 'Renewal' tactics to expand and diversify internationally. It also talks about the digital transformation and the consolidated operating model, which the company embarked upon in 2019, to re-energise the company. In 2020, 3M faced massive supply chain disruptions amidst a 400% demand surge of N95 masks and personal protective equipment, and simultaneous closure of several manufacturing facilities worldwide. 3M continued to operate from manufacturing plants that were still open, with government support - in countries like Singapore - while facing resource and supply constraints and other uncertainties. To cope with the disruption, the company used several strategies like emergency response teams, 30/60/90-day cycles of supply management, additional supplier sourcing and airfreight for exporting finished goods. In 2022, even after the pandemic started to subside, supply chain disruptions continued to persist amidst increasing geopolitical tensions like the Russia-Ukraine war and a new surge of the pandemic in China. Given the prolonged and evolving supply chain disturbances, what could be the appropriate short-term and longer term strategies that 3M could implement to respond to such disruptions?

This case depicts Singapore's TraceTogether (TT) story, beginning in early 2020 when COVID-19 was spreading globally with ravaging effects. TT was the world's first national application of a digital contact-tracing tool. Remarkably, the initial version of TT was developed and launched in just eight weeks. The increasing adoption of TT (both as a mobile app and as a hardware token) enabled the early identification and isolation of infected cases much faster than manual tracing. Part A of the case covers the related events and decision-making until the launch of the TT app on 20 March 2020. Part B covers the subsequent development efforts, version upgrades, follow-on releases and other issues until November 2021. The ability of the Government Technology Agency of Singapore (GovTech) to respond quickly to this unforeseen crisis was germinated about 7 years prior. Chan Cheow Hoe, Deputy CEO led the transition to a bimodal IT organisation which was internally capable of exploratory and rapid software development, and recently Jason Bay, Senior Director, led the overall effort to create and launch the TT app. As Singapore's COVID-19 situation was changing dynamically, so were the national response policies and the TT app. In August 2021, government officials announced that Singapore would begin the adjustment of living with COVID-19 as an endemic. Yet, as of November 2021, the situation was still highly volatile with the emergence of the Omicron variant. To this end, what would the future hold for TT? Should the health authority sunset TT to assuage concerns of national surveillance? Should they encourage the population to keep using TT as uncertainties remained due to the emergence of new COVID-19 variants? Could a subset of TT's capabilities be repurposed for other national initiatives? What new types of foundational capabilities and supporting infrastructure should GovTech start putting in place now to prepare in advance for the next big unknown?

This case depicts Singapore's TraceTogether (TT) story, beginning in early 2020 when COVID-19 was spreading globally with ravaging effects. TT was the world's first national application of a digital contact-tracing tool. Remarkably, the initial version of TT was developed and launched in just eight weeks. The increasing adoption of TT (both as a mobile app and as a hardware token) enabled the early identification and isolation of infected cases much faster than manual tracing. Part A of the case covers the related events and decision-making until the launch of the TT app on 20 March 2020. Part B covers the subsequent development efforts, version upgrades, follow-on releases and other issues until November 2021. The ability of the Government Technology Agency of Singapore (GovTech) to respond quickly to this unforeseen crisis was germinated about 7 years prior. Chan Cheow Hoe, Deputy CEO led the transition to a bimodal IT organisation which was internally capable of exploratory and rapid software development, and recently Jason Bay, Senior Director, led the overall effort to create and launch the TT app. As Singapore's COVID-19 situation was changing dynamically, so were the national response policies and the TT app. In August 2021, government officials announced that Singapore would begin the adjustment of living with COVID-19 as an endemic. Yet, as of November 2021, the situation was still highly volatile with the emergence of the Omicron variant. To this end, what would the future hold for TT? Should the health authority sunset TT to assuage concerns of national surveillance? Should they encourage the population to keep using TT as uncertainties remained due to the emergence of new COVID-19 variants? Could a subset of TT's capabilities be repurposed for other national initiatives? What new types of foundational capabilities and supporting infrastructure should GovTech start putting in place now to prepare in advance for the next big unknown?

Set in 2021, this case describes the Certis Group's transformation journey from a traditional physical guarding service provider to an advanced provider of integrated operations technology services for security, facilities, visitor management, and other types of monitoring related businesses. Certis started in 1958 as a Singapore government unit for auxiliary police, and restructured into a private commercial entity in 2005. They grew to a US$1.1 billion multinational with an overseas presence in six countries and territories, while leading their home market. Their digital transformation was enabled by a platform created for orchestrating Internet-of-Things sensor streams with Artificial Intelligence-enabled analytics, centralized command and control, and workforce coordination and communication. This new "Security+" approach to delivery made it possible for Certis to scale beyond prior limits, integrate across multiple service lines, and offer new types of services. They began rolling out Security+ solutions in 2018 starting with Singapore's iconic Jewel Changi Airport, a 10-storey megamall, and expanded to other large multi-use facilities and industrial zones. Paul Chong, President and Group CEO since 2004 was keenly aware that competitors saw the success Certis was having with its digital transformation and platform-based approach. What could be their path forward for developing their next generation of delivery capabilities to drive further growth locally and overseas?

Analysts have often compared keeping up with Cybersecurity threats to running on a treadmill that keeps speeding up; the runner cannot move forward and often begins to slide backwards and fall behind. Set in July 2020, this case explores how one of the world's most renowned such cybersecurity "runners", FireEye, implemented AI-based solutions within the organisation to provide improved cybersecurity services to its clients. The case delves into the various strategies implemented by the firm for AI adoption using a specific example tool. FireEye used its conceptual AI framework called the 'Automatibility Spectrum' to determine the appropriate degree of automation for developing different solutions. Additionally, the firm implemented agile practices to build inter-team collaborations, cross-team workflow processes and changes in its organisational structure and culture to promote a mind-set shift towards automation. Tasks that involved decision making would typically incorporate human verification with repetitive tasks being performed by automated algorithms. This Human+AI approach had enabled FireEye to expand and scale its support services. However, cyber attackers were constantly 'upping their game', and rapid technology innovations were creating new threat exposures. The nature of cybersecurity threats and the supporting technical methods were changing rapidly and continuously, and some of the rules and approaches for threat protection and investigation applicable earlier were becoming irrelevant as cyber threats evolved and became more sophisticated over time. Was FireEye's existing approach of Human+AI capable enough to tackle the ever-changing challenges in cybersecurity?