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The authors explore the potential of machine learning, computers employ that an algorithm to sort data, make decisions and then continuously assess and improve their functionality. They suggest that it be used to power a radical redesign of company processes that they call machine reengineering.

The authors interpret a survey of more than a thousand corporate public agency IT professionals on their use of artificial intelligence and machine learning.

Companies that embrace machine learning find that it adds value to the work product of their employees and provides companies with new capabilities.

Working together with an intelligent machine, workers become custodians of powerfully smart tools, tools that personalize work to maximize their most productive ways of working.

A guide to establishing a culture that empowers employees to thrive alongside intelligent machines.

When the price is right, profits follow.

BEFORE everyone gets too excited over reports of declining attendances at the national museums since the introduction of entrance charges at the beginning of January, allowances should be made for the Victoria & Albert Museum's former and now discontinued habit of counting to‐and fro‐ing staff among its visitors, and the National Gallery's inclusion of the cognoscenti who were aware that it has the only public lavatories on the northern side of Trafalgar Square. Culture‐hunting has not hitherto accounted for all who passed through the portals of our national museums.

The purpose of this paper is to present a framework that captures the strategic value of artificial intelligence (AI) systems. Although AI has become a crucial component of digital transformation efforts tied to organizational strategy, many firms struggle to derive strategic value from emerging AI systems.

The analytical framework in this paper is based on a learning-centered approach. Specifically, by building on the knowledge-based perspective, this paper elaborates on how AI can contribute to organizational learning to create a competitive advantage in knowledge-intensive contexts.

This paper argues that the power of AI as a strategic resource lies in its self-learning capacities. Such learning capacities are only realized in partnership with humans through mutual learning. This paper formulates the concept of artificial capital and the ways artificial and human capital can together drive routinization and strategic learning processes that connect internal and external environments of the organization.

This is a timely contribution as many organizations are considering adopting AI technologies for strategic purposes. This paper translates the proposed framework into several practical implications for managing and developing AI to meet strategic business goals.

Artificial intelligence (AI)-based chatbots have brought unprecedented business potential. This study aims to explore consumers' trust and response to a text-based chatbot in e-commerce, involving the moderating effects of task complexity and chatbot identity disclosure.

A survey method with 299 useable responses was conducted in this research. This study adopted the ordinary least squares regression to test the hypotheses.

First, the consumers' perception of both the empathy and friendliness of the chatbot positively impacts their trust in it. Second, task complexity negatively moderates the relationship between friendliness and consumers' trust. Third, disclosure of the text-based chatbot negatively moderates the relationship between empathy and consumers' trust, while it positively moderates the relationship between friendliness and consumers' trust. Fourth, consumers' trust in the chatbot increases their reliance on the chatbot and decreases their resistance to the chatbot in future interactions.

Adopting the stimulus–organism–response (SOR) framework, this study provides important insights on consumers' perception and response to the text-based chatbot. The findings of this research also make suggestions that can increase consumers' positive responses to text-based chatbots.

Extant studies have investigated the effects of automated bots' attributes on consumers' perceptions. However, the boundary conditions of these effects are largely ignored. This research is one of the first attempts to provide a deep understanding of consumers' responses to a chatbot.

A distinction must be drawn between a dismissal on the one hand, and on the other a repudiation of a contract of employment as a result of a breach of a fundamental term of that contract. When such a repudiation has been accepted by the innocent party then a termination of employment takes place. Such termination does not constitute dismissal (see London v. James Laidlaw & Sons Ltd (1974) IRLR 136 and Gannon v. J. C. Firth (1976) IRLR 415 EAT).