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?
Generative AI and other easy-to-use software tools can help employees with no coding background become adept programmers, or what the authors call citizen developers. By simply describing what they want in a prompt, citizen developers can collaborate with these tools to build entire applications-a process that until recently would have required advanced programming fluency. Information technology has historically involved builders (IT professionals) and users (all other employees), with users being relatively powerless operators of the technology. That way of working often means IT professionals struggle to meet demand in a timely fashion, and communication problems arise among technical experts, business leaders, and application users. Citizen development raises a critical question about the ultimate fate of IT organizations. How will they facilitate and safeguard the process without placing too many obstacles in its path? To reject its benefits is impractical, but to manage it carelessly may be worse. In this article the authors share a road map for successfully introducing citizen development to your employees.
AI initiatives at many organizations are too small and too tentative. They never get to the only step that can add economic value-being deployed on a large scale. Testing the waters may deliver valuable insights, but it probably won't be enough to achieve true transformation. A pilot program or experiment can take you only so far. The authors have identified 30 companies that have gone all in on AI-and achieved success-as well as 10 actions those companies took to become successful AI adopters: (1) Know what you want to accomplish. (2) Work with an ecosystem of partners. (3) Master analytics. (4) Create a modular, flexible IT architecture. (5) Integrate AI into existing workflows. (6) Build solutions across the organization. (7) Create an AI governance and leadership structure. (8) Develop and staff centers of excellence. (9) Invest continually. (10) Always seek new sources of data. In other words, you need to be aggressive enough with AI that the technology eventually transforms every aspect of your business.
Artificial intelligence has been applied successfully in thousands of ways, but one of the less visible and less dramatic ones is improving data management. The authors describe five common areas of data management classifying, cataloging, quality, security, and data integration where they see AI playing important roles. They also discuss the vendor landscape and the ways that humans are essential to data management.
There is no impending threat of large-scale automation and displacement of human labor on the horizon. That's the conclusion reached by the authors after reviewing the research around smart machines in workplaces, and speaking with leaders in hundreds of organizations. In an adaption of their book Working With AI: Real Stories of Human-Machine Collaboration, they lay out where they see limitations of AI in the workplace and where practitioners can expect job continuity for the immediate future.
The United States has had many problems coping with the coronavirus. A critical and underappreciated problem is bad data: Without good data, planners can't plan, epidemiologists can't model, policy makers can't make policy, and citizens don't trust what they're told. The U.S. needs a robust program, with professional management of the data supply chain, to develop trustworthy data about pandemics and other public health crises.
Many people don't trust decisions, answers, or recommendations from artificial intelligence. To address that problem, makers of AI applications and systems should stop overpromising, be more transparent about how systems are used, and consider third-party certification.
In the eyes of many leaders, artificial intelligence and cognitive technologies are the most disruptive forces on the horizon. But most organizations don't have a strategy to address them.
This is an MIT Sloan Management Review article. Most of us view our jobs as specialized or somehow differentiated, but the world of business and management increasingly feels otherwise. For many organizations today, the next big driver of job commoditization is automation driven by smart machines. Simply put, if a job is viewed as a commodity, it won't be long before it's automated. The key for workers whose jobs have traditionally seemed safe: Highlight the tasks that require a human touch.
Cognitive technologies are increasingly being used to solve business problems; indeed, many executives believe that AI will substantially transform their companies within three years. But many of the most ambitious AI projects encounter setbacks or fail. A survey of 250 executives familiar with their companies' use of cognitive technology and a study of 152 projects show that companies do better by taking an incremental rather than a transformative approach to developing and implementing AI, and by focusing on augmenting rather than replacing human capabilities. Broadly speaking, AI can support three important business needs: automating business processes (typically back-office administrative and financial activities), gaining insight through data analysis, and engaging with customers and employees. To get the most out of AI, firms must understand which technologies perform what types of tasks, create a prioritized portfolio of projects based on business needs, and develop plans to scale up across the company.
Although the ability to manage torrents of data has become crucial to companies' success, most organizations remain badly behind the curve. More than 70% of employees have access to data they should not. Data breaches are common, rogue data sets propagate in silos, and companies' data technology often isn't up to the demands put on it. In this article the authors describe a framework for building a robust data strategy that can be applied across industries and levels of data maturity. The framework will help managers clarify the primary purpose of their data, whether "defensive" or "offensive." Data "" is about minimizing downside risk: ensuring compliance with regulations, using analytics to detect and limit fraud, and building systems to prevent theft. Data "offense" focuses on supporting business objectives such as increasing revenue, profitability, and customer satisfaction. Using this approach, managers can design their data-management activities to support their company's overall strategy.
This is an MIT Sloan Management Review Article. The combination of new analytical capabilities and burgeoning data assets are being used to form value-added "data products."Such products have powered rapid growth in the value and success of online companies, but the expansion of analytics means the standard model for developing these products needs to evolve. An updated model needs to reflect new "time to market"expectations and input from a variety of stakeholders.
People in all walks of life are rightly concerned about advancing automation: Unless we find as many tasks to give humans as we find to take away from them, all the social and psychological ills of joblessness will grow, from economic recession to youth unemployment to individual crises of identity. What if, the authors ask, we were to reframe the situation? What if we were to uncover new feats that people might achieve if they had better thinking machines to assist them? We could reframe the threat of automation as an opportunity for augmentation. They have been examining cases in which knowledge workers collaborate with machines to do things that neither could do well on their own--and they've found that smart people will be able to take five approaches to making their peace with smart machines. Some will "step up" to even higher levels of cognition, where machines can't follow. Some will "step aside," drawing on forms of intelligence that machines lack. Some will "step in," to monitor and adjust computers' decision making. Some will "step narrowly" into very specialized realms of expertise. And, inevitably, some will "step forward," by creating next-generation machines and finding new ways for them to augment the human strengths of workers.
Those who study "data smart" companies believe that we've already lived through two eras in the use of analytics--we might think of them as "before big data" and "after big data"--and are entering a third. It represents a far-reaching resolve to apply powerful data gathering and analysis not just to a company's operations but also to its customer services and products. This strategic change in focus means a new role for analytics. Companies will need to recognize a host of related challenges and respond with new capabilities, positions, and priorities. Requirements will include: multiple types of data, often combined; a new set of management options; faster technologies and methods of analysis; embedded analytics; data discovery; cross-disciplinary data teams; chief analytics officers; prescriptive analytics; analytics on an industrial scale; and new ways of deciding and managing. These new capabilities can't be developed using old models for how analytics supported the business. The big data model was a huge step forward, but it will not provide advantage for much longer. Companies must once again fundamentally rethink how the analysis of data can create value for themselves and their customers.
Analytics are a competitive necessity nowadays, but hiring "quants" who can manipulate big data successfully is not enough. Tom Davenport shows you how to become an intelligent consumer of analytics so that you can make effective data-driven decisions for your organization. Learning basic statistical principles and methods is essential, but you don't need a PhD in the stuff. The "quants" have the detailed know-how, and working closely with the right ones is key. You also should expect them to communicate their work effectively--and to truly comprehend it yourself before you relay it to others. It's also vital to recognize which parts of the decision-making process are your sweet spots--namely, identifying the central problem or question and presenting and acting on the results. The intermediate phases, where the quants excel, are when you should ask lots of questions to keep efforts focused and to deepen your understanding. As you lead, avoid creating a climate of advocacy in which hired hands merely hunt for evidence to fit your preconceived notions. Instead, establish a culture of inquiry that focuses on learning the real truth behind the numbers. That's how companies like Merck, TD Bank, and Caesars Entertainment make it all add up to something useful.
Senior management at P&G has put a strong emphasis on using data to make "better, smarter, real-time business decisions." The Global Business Services (GBS) organization has developed tools, systems and processes to provide managers throughout P&G with direct access to up-to-date data and advanced analytics. In addition, GBS has embedded analysts within the business units to work alongside leaders and managers in driving real-time information-based decision making. Equipped with the tools provided by GBS, Alan Torres, vice-president of North America Fabric Care, must finalize the forecast for P&G's laundry detergent sales. Results for the two months since introducing concentrated powder laundry detergent in select retailers saw a surprising jump in sales of over 10%, but would the trend continue as the concentrated detergents were introduced across North America?
Recorded Future is a "big data" startup company that uses Internet data to make predictions about events, people, and entities. The company primarily serves government intelligence agencies, but has some private sector clients and is considering taking on more. The CEO, Christopher Ahlberg, is wrestling with several key decisions about where to take the company in the future.