Strategic advice & alerts to leverage data analytics & new technologies

Leverage data and the technologies that generate it, from IoT to AI/machine learning, wearables, blockchain, and more, to improve decision-making, enrich collaboration and enable new services.

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Although many organizations have developed digital strategies, far fewer have managed to implement them successfully. As we explore in this Executive Update, creating a “sense of urgency” is often seen as a top challenge for digital transformation due to general unawareness of the opportunities and threats to to the core business. Furthermore, many organizations consider a lack of skills and competencies as major challenges on their digitalization journey.
Transforming all the data we generate into insights requires many steps. For someone to have the confidence level to use the resulting information and insights, these data manipulations must be trusted. In this Advisor, we review these steps.
Researchers at hospitals, universities, and technical institutes are teaming up to apply artificial intelligence, machine learning, and analytics to help determine and predict COVID-19 patients’ hospitalization paths and medical needs.
Daniel J. Power, Ciara Heavin, and Shashidhar Kaparthi argue that a better governance mechanism is necessary to minimize the dangers of rushing to adopt AI and automation without due consideration of the risks. They present a governance framework for intelligent automation that includes all key stakeholders and offer policy prescriptions and guidelines for successful intelligent automation.
Tad Gonsalves and Bhuvan Unhelkar argue that while machine intelligence facilitates smart automation and autonomous operations, yielding benefits, it cannot handle decisions that need to account for subjective factors, such as satisfaction, perceived quality, or joy, which cannot be parameterized in an ML algorithm. The authors recommend judicious superimposition of human natural intelligence (NI) on machine intelligence as a better way to facilitate business decisions that factor in customer value. In their discussion of how to achieve this goal, they also present a few use cases that embrace this hybrid intelligence.
In most enterprises, business processes are automated in isolation, creating “automation silos” — a major barrier to realizing the fuller potential of enterprise-wide integrated automation. In their article, Aravind Ajad Yarra and Danesh Zaki address this issue. They differentiate between first- and second-generation smart automation and identify key imperatives to ensure desired integration across an entire business process. Furthermore, they present a detailed architecture for, and a pathway toward, smart automation 2.0, which enterprises can adopt to enable their automation bots to cooperate across the value chain
Namratha Rao and Jagdish Bhandarkar outline the concept of intelligent auto­mation using AI, ML, and RPA. A case study from the financial sector highlights the benefits gained through RPA. The authors explain how an intelligent bot can be trained and deployed over a period of a few months, and they emphasize establishing a roadmap, applying the right security measures, and setting up robust governance as three key tenets for scaling automation.
Joseph Byrum describes an intelligent enterprise as one that embraces AI to guide all its functions and decisions, small or large. However, this business is not run by the all-knowing, utopian artificial general intelligence (AGI) that science fiction writers and some commentators envision, which is a distant dream. Rather, it is an enterprise run by augmented intelligence — humans using AI and decision support tools that are enriched to the extent that is currently realistic and feasible. Byrum discusses the advantages of enterprises embracing augmented intelligence but cautions that making the entire enterprise “intelligent” requires concerted effort.