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Why So Many Data Science Projects Fail to Deliver

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This article is based on an in-depth study of the data science efforts in three large, private-sector Indian banks with collective assets exceeding $200 million. The study included onsite observations; semistructured interviews with 57 executives, managers, and data scientists; and the examination of archival records. The five obstacles and the solutions for overcoming them emerged from an inductive analytical process based on the qualitative data. More and more companies are embracing data science as a function and a capability. But many of them have not been able to consistently derive business value from their investments in big data, artificial intelligence, and machine learning.1


How to Define and Execute Your Data and AI Strategy · Harvard Data Science Review

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Over the past decade, many organizations have come to recognize that their future success will depend on data and AI (artificial intelligence) capabilities. Expectations are high and companies are heavily investing in the area. However, our experience advising organizations in diverse industries suggests that many have also become disillusioned in their journey to create companywide, data-driven business transformation. This article discusses some of the common pitfalls in the implementation of data and AI strategies and gives recommendations for business leaders on how to successfully include data and AI in their business processes. These recommendations address the core enablers for data and AI capabilities, from setting the ambition level to hiring the right talent and defining the AI organization and operating model. Many companies are currently investing in data and artificial intelligence (AI). Since the terminology varies, the activities may be called AI, advanced analytics, data science, or machine learning, but the goals are the same: to increase revenues and efficiency in current business and to develop new data-enabled offerings. In addition, many companies see an increasing responsibility to contribute their AI expertise toward humanitarian and social matters. It is well understood that to stay competitive in the digital economy, the company's internal processes and products need to be smart--and smartness comes from data and AI. Over the past 4 years, our company DAIN Studios has been involved in more than 40 Data and AI initiatives in different companies and industries in Finland, Germany, Austria, Switzerland, and the Netherlands. Our clients are typically large, publicly listed companies.


Fueling utility innovation through analytics

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Utilities around the world are making big investments in advanced analytics. Getting the full value, however, requires rethinking their strategy, culture, and organization. Advanced analytics can deliver enormous value for utilities and drive organizations to new frontiers of efficiency-- but only with the right approach. There's little to be gained from just bolting on a software solution. The real value comes from embedding data analytics as a core capability in the organization and using it to detect pain points, design solutions, and enable decision making.


Derisking AI by design: How to build risk management into AI development

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Artificial intelligence (AI) is poised to redefine how businesses work. Already it is unleashing the power of data across a range of crucial functions, such as customer service, marketing, training, pricing, security, and operations. To remain competitive, firms in nearly every industry will need to adopt AI and the agile development approaches that enable building it efficiently to keep pace with existing peers and digitally native market entrants. But they must do so while managing the new and varied risks posed by AI and its rapid development. The reports of AI models gone awry due to the COVID-19 crisis have only served as a reminder that using AI can create significant risks.


Enterprise AI Canvas -- Integrating Artificial Intelligence into Business

arXiv.org Artificial Intelligence

Artificial Intelligence (AI) and Machine Learning have enormous potential to transform businesses and disrupt entire industry sectors. However, companies wishing to integrate algorithmic decisions into their face multiple challenges: They have to identify use-cases in which artificial intelligence can create value, as well as decisions that can be supported or executed automatically. Furthermore, the organization will need to be transformed to be able to integrate AI based systems into their human work-force. Furthermore, the more technical aspects of the underlying machine learning model have to be discussed in terms of how they impact the various units of a business: Where do the relevant data come from, which constraints have to be considered, how is the quality of the data and the prediction evaluated? The Enterprise AI canvas is designed to bring Data Scientist and business expert together to discuss and define all relevant aspects which need to be clarified in order to integrate AI based systems into a digital enterprise. It consists of two parts where part one focuses on the business view and organizational aspects, whereas part two focuses on the underlying machine learning model and the data it uses.