Enterprise IT Operations

Over the past decade or so, for most enterprises, information technology (IT) has shifted from being a support function to be a synonym for business wellness. During the same period, though, the scale and complexity of IT for running business has grown significantly; today, performing any business function requires complex interplay of many, often invisible and dynamically changing, technology components. This is making design resilient and interruption-free IT a significant challenge. This chapter discusses limitations of traditional approaches for managing enterprise IT operations; introduces the concept of cognitive automation, a novel approach that blends intelligence with automation to transform enterprise IT operations; and describes the design of ignio™, a cognitive automation platform for enterprises. The author concludes by highlighting the challenges in driving cognitive transformation of enterprise operations and providing some suggestions for embarking upon this journey.

Application of Cognitive Automation to Structuring Data, Driving Existing Business Models, and Creating Value between Legacy Industries

To build high quality datasets and unlock the value of unstructured data, a systematic approach for data capture is necessary. Cognitive automation (CA), that is, automation of processes with artificial intelligence (AI), enables the information extraction from unstructured data to provide relevant insights and further processing with AI. This study provides an overview of this new technology and shows how it can be used to transform existing business models. Our case studies in the insurance auditing, healthcare, and banking industries show the potential managerial impact of CA, which prepares these legacy industries for their digital future’s challenges and opportunities. We present the novel data extraction pipeline for textual and visual data and demonstrate its efficiency in extracting information from the company’s unstructured data. We show its performance in quality, cost, and time compared with current industry standards and provide management insights for business applications using CA.

Towards Semantic Integration of Machine Vision Systems to Aid Manufacturing Event Understanding

A manufacturing paradigm shift from conventional control pyramids to decentralized, service-oriented, and cyber-physical systems (CPSs) is taking place in today’s 4th industrial revolution. Generally accepted roles and implementation recipes of cyber systems are expected to be standardized in the future of manufacturing industry. The authors intend to develop a novel CPS-enabled control architecture that accommodates: (1) intelligent information systems involving domain knowledge, empirical model, and simulation; (2) fast and secured industrial communication networks; (3) cognitive automation by rapid signal analytics and machine learning (ML) based feature extraction; (4) interoperability between machine and human. Semantic integration of process indicators is fundamental to the success of such implementation. This work proposes an automated semantic integration of data-intensive process signals that is deployable to industrial signal-based control loops. The proposed system rapidly infers manufacturing events from image-based data feeds, and hence triggers process control signals. Two image inference approaches are implemented: cloud-based ML model query and edge-end object shape detection. Depending on use cases and task requirements, these two approaches can be designated with different event detection tasks to provide a comprehensive system self-awareness. Coupled with conventional industrial sensor signals, machine vision system can rapidly understand manufacturing scenes, and feed extracted semantic information to a manufacturing ontology developed by either expert or ML-enabled cyber systems. Moreover, extracted signals are interpreted by Programmable Logical Controllers (PLCs) and field devices for cognitive automation towards fully autonomous industrial systems.

Influencing information systems practice: The action principles approach applied to robotic process and cognitive automation

The article formalizes an action principles approach for investigating and influencing the adoption of emerging information systems phenomena, particularly for new technologies. It draws upon recent research into robotic process and cognitive automation to demonstrate the concepts and methodology for a further mode of research into practice that is distinguishable from action research and design science. The authors present definitions, research assumptions, and evaluation criteria and provide a six-step process for generating a set of action principles, updatable by new empirical evidence. The process is illustrated by research into 22 automation cases that eventually arrived at 39 action principles for effective deployment of the automation technologies under review. The major objective is to provide guidelines to prospective researchers. A secondary objective is to provide major insights into the management of robotic process and cognitive automation. This provides opportunities for further theorization and research by academics, and more considered action by practitioners. The authors also discuss the value and limitations of the action principles approach, and how the knowledge generated can be disseminated. The article offers a way of doing research on the applied side of information systems that is timely, does justice to the phenomena under investigation, and provides insights for multiple parties.

The Future of Work in a Jobless Society: Globalization, Smart Digitalization, and Cognitive Automation

Research background: The future of work is undoubtedly one of the toughest challenges faced by many researchers and managers all over the word. The new era in digital globalization and smart digitalization, the trends in robotization and artificial intelligence have changed the labour market. Due to accelerated technology, many companies are ready to adopt digital solutions, stationary robots and drones with significant consequences over the declining jobs. The new human-machine frontier will determine a different outlook work in a jobless society, where many roles become automated, while human’s role in these processes is minimized. Purpose of the article: In our opinion, globalization and impact of artificial intelligence on the future of work will be significant. In this paper we try to analyse and clarify the issues in question in terms of smart digitalization, cognitive automation, human-machine frontier and changing employment types. The data used for this research was obtained from previous study conducted by World Bank and OECD. Methods: In order to fulfil our goal, we apply the methods of comparison, analysis, deduction and our estimates for identifying the trends that are shaping the future of jobs and the evolution of jobs caused by technological change. Findings & Value added: In the near future, innovation will continue to accelerate and many artisan jobs are being lost to computerization and office automation. Finally, we formulate our own conclusion and view about digitalization and opportunities to create new jobs, increase productivity, and cost reduction, through innovation and accelerating change.