Cognitive automation: A new era of knowledge work?

There have been a number of breakthroughs in artificial intelligence since the beginning of the 21st century with machines now outperforming humans in cognitive tasks such as object detection, face recognition, language translation, and complex decision strategies. Computational advances enable machines to process and analyse information at a scale far beyond human capabilities and has led to a rise in demand for intelligent process automation (IPA) services. This article considers the potential for cognitive algorithms to disrupt knowledge work in the modern workplace. Benefits include augmenting and accelerating the intelligence-decision-action cycle that is central to knowledge work. However, there are also risks from becoming over-reliant on algorithms in ambiguous and uncertain real-world situations. The value from next-generation knowledge systems will come from bridging human and artificial intelligence for insights and innovation.

Download Full-text

Delivering Value in Procurement With Robotic Cognitive Automation (RCA) Services

Robotic Systems ◽

10.4018/978-1-7998-1754-3.ch084 ◽

2020 ◽

pp. 1773-1785

Author(s):

Ruchira Teli ◽

Suneel Kumar Prasad

Keyword(s):

Value Creation ◽

Knowledge Work ◽

Process Automation ◽

Organizational Processes ◽

Robotic Process Automation ◽

The Past ◽

Cognitive Automation

Organizations are applying digitalization to increasing amounts of different organizational processes. The procurement sector is also changing and actively seeking better ways to enhance performance such as the automation of workflow processes, for example, robotic process automation (RPA). To meet this clear demand, the automation of workflow processes in organizations has been a rising trend during the past few years. The author analyzes the potential of RPA along with the cognitive technologies robotic cognitive automation-based (RCA) value creation through knowledge work digitalization in the procurement sector.

Download Full-text

Delivering Value in Procurement With Robotic Cognitive Automation (RCA) Services

International Journal of Robotics Applications and Technologies ◽

10.4018/ijrat.2018070101 ◽

2018 ◽

Vol 6 (2) ◽

pp. 1-11

Author(s):

Ruchira Teli ◽

Suneel Kumar Prasad

Keyword(s):

Value Creation ◽

Knowledge Work ◽

Process Automation ◽

Organizational Processes ◽

Robotic Process Automation ◽

The Past ◽

Cognitive Automation

Download Full-text

Cognitive Challenges in Human–Artificial Intelligence Collaboration: Investigating the Path Toward Productive Delegation

Information Systems Research ◽

10.1287/isre.2021.1079 ◽

2021 ◽

Author(s):

Andreas Fügener ◽

Jörn Grahl ◽

Alok Gupta ◽

Wolfgang Ketter

Keyword(s):

Artificial Intelligence ◽

Knowledge Work ◽

Work Environments ◽

The Other ◽

Business Organizations ◽

Human Capabilities ◽

Work Arrangements ◽

Other Hand ◽

The World ◽

The Future

A consensus is beginning to emerge that the next phase of artificial intelligence (AI) induction in business organizations will require humans to work with AI in a variety of work arrangements. This article explores the issues related to human capabilities to work with AI. A key to working in many work arrangements is the ability to delegate work to entities that can do them most efficiently. Modern AI can do a remarkable job of efficient delegation to humans because it knows what it knows well and what it does not. Humans, on the other hand, are poor judges of their metaknowledge and are not good at delegating knowledge work to AI—this might prove to be a big stumbling block to create work environments where humans and AI work together. Humans have often created machines to serve them. The sentiment is perhaps exemplified by Oscar Wilde’s statement that “civilization requires slaves…. Human slavery is wrong, insecure and demoralizing. On mechanical slavery, on the slavery of the machine, the future of the world depends.” However, the time has come when humans might switch roles with machines. Our study highlights capabilities that humans need to effectively work with AI and still be in control rather than just being directed.

Download Full-text

Agency plus automation: Designing artificial intelligence into interactive systems

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1807184115 ◽

2019 ◽

Vol 116 (6) ◽

pp. 1844-1850 ◽

Cited By ~ 23

Author(s):

Jeffrey Heer

Keyword(s):

Artificial Intelligence ◽

Language Translation ◽

Interactive Systems ◽

Human Capabilities ◽

Human Control ◽

Computational Support ◽

Human Labor ◽

Adaptive Interaction ◽

Automated Methods ◽

Skillful Action

Much contemporary rhetoric regards the prospects and pitfalls of using artificial intelligence techniques to automate an increasing range of tasks, especially those once considered the purview of people alone. These accounts are often wildly optimistic, understating outstanding challenges while turning a blind eye to the human labor that undergirds and sustains ostensibly “automated” services. This long-standing focus on purely automated methods unnecessarily cedes a promising design space: one in which computational assistance augments and enriches, rather than replaces, people’s intellectual work. This tension between human agency and machine automation poses vital challenges for design and engineering. In this work, we consider the design of systems that enable rich, adaptive interaction between people and algorithms. We seek to balance the often-complementary strengths and weaknesses of each, while promoting human control and skillful action. We share case studies of interactive systems we have developed in three arenas—data wrangling, exploratory analysis, and natural language translation—that integrate proactive computational support into interactive systems. To improve outcomes and support learning by both people and machines, we describe the use of shared representations of tasks augmented with predictive models of human capabilities and actions. We conclude with a discussion of future prospects and scientific frontiers for intelligence augmentation research.

Download Full-text

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

SHS Web of Conferences ◽

10.1051/shsconf/20219207016 ◽

2021 ◽

Vol 92 ◽

pp. 07016

Author(s):

Irina Dijmărescu ◽

Luminița Ionescu

Keyword(s):

Artificial Intelligence ◽

Value Added ◽

Comparison Analysis ◽

New Era ◽

Future Of Work ◽

The Future ◽

Cognitive Automation ◽

Increase Productivity ◽

Near Future ◽

New Jobs

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.

Download Full-text

Intelligent Process Automation of Industries Using Artificial Intelligence and Machine Learning

Journal of Computing and Natural Science ◽

10.53759/181x/jcns202101009 ◽

2021 ◽

pp. 45-56

Author(s):

Filippo Fabrocini

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Business Models ◽

Action Plan ◽

Major Elements ◽

Process Automation ◽

Goods And Services ◽

Consumer Expectation ◽

Disruptive Technologies ◽

Intelligent Process

The application of Disruptive Technologies (DT), using Artificial Intelligence (AI) and Machine Learning (ML), is still a challenge for many industries in the modern age. Quick transformation of business’ models and enhancement of consumer expectation are fundamental elements of Intelligent Process Automation (IPA) to boosting delivery and production of goods and services. In this research contribution, we will evaluate IPAs and their influence in the management of industries. In that case, major elements of AI and ML will be discussed comprehensively. Areas of application and analysis will be discussed in relation to digital industries. The results in this contribution will be used as a recommend action plan for industries to enhance their management and optimization when it comes to AI and ML.

Download Full-text

Intelligent Process Automation in Audit

Journal of Emerging Technologies in Accounting ◽

10.2308/jeta-52653 ◽

2019 ◽

Vol 16 (2) ◽

pp. 69-88 ◽

Cited By ~ 5

Author(s):

Chanyuan (Abigail) Zhang

Keyword(s):

Artificial Intelligence ◽

Emerging Technologies ◽

Use Case ◽

Process Automation ◽

Implementation Framework ◽

Robotic Process Automation ◽

Audit Efficiency ◽

Efficiency And Effectiveness ◽

Potential Applications ◽

Intelligent Process

Intelligent process automation (IPA) achieves flexible and intelligent automation by combining robotic process automation (RPA), artificial intelligence (AI), and other emerging technologies. This paper focuses on the utility of IPA for the audit profession. Specifically, this paper provides a framework for implementing IPA in audit engagements using the concept of audit workflow. A simple prototype based on a simulated use case is constructed to illustrate the IPA implementation framework. The potential applications of IPA in pension and inventory audits are provided, and the expected impacts of IPA on audit efficiency and effectiveness are discussed.

Download Full-text

How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

Journal of Personalized Medicine ◽

10.3390/jpm11010032 ◽

2021 ◽

Vol 11 (1) ◽

pp. 32

Author(s):

Oliwia Koteluk ◽

Adrian Wartecki ◽

Sylwia Mazurek ◽

Iga Kołodziejczak ◽

Andrzej Mackiewicz

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Health Care ◽

Medical Data ◽

General Process ◽

New Era ◽

Automated Evaluation ◽

Machine Learning Applications ◽

And Training ◽

Current Standards

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

Download Full-text