Artificial Intelligence Coaches for Sales Agents: Caveats and Solutions

2020 ◽  
pp. 002224292095667 ◽  
Author(s):  
Xueming Luo ◽  
Marco Shaojun Qin ◽  
Zheng Fang ◽  
Zhe Qu
Keyword(s):  
Artificial Intelligence ◽  
Interpersonal Skills ◽  
Field Experiments ◽  
Information Overload ◽  
Job Skills ◽  
Incremental Benefit ◽  
Hard Data ◽  
Feedback Level ◽  
Sales Agents

Firms are exploiting artificial intelligence (AI) coaches to provide training to sales agents and improve their job skills. The authors present several caveats associated with such practices based on a series of randomized field experiments. Experiment 1 shows that the incremental benefit of the AI coach over human managers is heterogeneous across agents in an inverted-U shape: whereas middle-ranked agents improve their performance by the largest amount, both bottom- and top-ranked agents show limited incremental gains. This pattern is driven by a learning-based mechanism in which bottom-ranked agents encounter the most severe information overload problem with the AI versus human coach, while top-ranked agents hold the strongest aversion to the AI relative to a human coach. To alleviate the challenge faced by bottom-ranked agents, Experiment 2 redesigns the AI coach by restricting the training feedback level and shows a significant improvement in agent performance. Experiment 3 reveals that the AI–human coach assemblage outperforms either the AI or human coach alone. This assemblage can harness the hard data skills of the AI coach and soft interpersonal skills of human managers, solving both problems faced by bottom- and top-ranked agents. These findings offer novel insights into AI coaches for researchers and managers alike.

scholarly journals A Virtual Assistant for Websites

2004 ◽  
Vol 3 (1) ◽  
Author(s):  
José Luiz Andrade Duizith ◽  
Lizandro Kirst Da Silva ◽  
Daniel Ribeiro Brahm ◽  
Gustavo Tagliassuchi ◽  
Stanley Loh
Keyword(s):  
Artificial Intelligence ◽  
Information Overload ◽  
Web Pages ◽  
Human Intervention ◽  
Web Browser ◽  
Cd Rom ◽  
Matching Process ◽  
Management Processes ◽  
The Web ◽  
Supply Information

This work presents a Virtual Assistant (VA) whose main goal is to supply information for Websites users. AVA is a software system that interacts with persons through a Web browser, receiving textual questions and answering automatically without human intervention. The VA supplies information by looking for similar questions in a knowledge base and giving the corresponding answer. Artificial Intelligence techniques are employed in this matching process, to compare the user’s question against questions stored in the base. The main advantage of using the VA is to minimize information overload when users get lost in Websites. The VA can guide the user across the web pages or directly supply information. This is especially important for customers visiting an enterprise site, looking for products, services or prices or needing information about some topic. The VA can also help in Knowledge Management processes inside enterprises, offering an easy way for people storing and retrieving knowledge. An extra advantage is to reduce the structure of Call Centers, since the VA can be given to customers in a CD-ROM. Furthermore, the VA provides Webmasters with statistics about the usage of the VA (themes more asked, number of visitants, time of conversation).

Combating Information Overload by Means of Information Customization Systems

2011 ◽  
pp. 60-92
Author(s):  
Mohamed Salah Hamdi
Keyword(s):  
Artificial Intelligence ◽  
Collaborative Filtering ◽  
Web Mining ◽  
Information Overload ◽  
The Internet ◽  
Global Information ◽  
Intelligent Interfaces ◽  
Information Matching ◽  
Available Information ◽  
Information Customization

The evolution of the Internet into the Global Information Infrastructure has led to an explosion in the amount of available information. The result is the “information overload” of the user, i.e., users have too much information to make a decision or remain informed about a topic. Information customization systems are supposed to be the answer for information overload. They allow users narrowcast what they are looking for and get information matching their needs. Information customization systems are also a bargain of consummate efficiency. The value proposition of such systems is reducing the time spent looking for information. We hold the view that information customization could be best done by combining various artificial intelligence technologies such as collaborative filtering, intelligent interfaces, agents, bots, web mining, and intermediaries. MASACAD, the system described in this chapter, is an example of an information customization system that combines many of the technologies already mentioned and others to approach information customization and combat information overload.

Application of Artificial Intelligence to Overcome Clinical Information Overload in Urologic Cancer

2021 ◽  
Author(s):  
Arnulf Stenzl ◽  
Cora N. Sternberg ◽  
Jenny Ghith ◽  
Lucile Serfass ◽  
Bob J.A. Schijvenaars ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Information Overload ◽  
Clinical Information ◽  
Urologic Cancer

scholarly journals The Potential of AI in Care Optimization: Insights from the User-Driven Co-Development of a Care Integration System

2021 ◽  
Vol 58 ◽  
pp. 004695802110179
Author(s):  
Anna Schneider-Kamp
Keyword(s):  
Artificial Intelligence ◽  
Information Overload ◽  
Relevant Information ◽  
Missing Information ◽  
User Experiences ◽  
Complex Processes ◽  
Care Integration ◽  
Qualitative Health Research ◽  
Care Processes ◽  
User Groups

Transitions from one level of care to another are complex processes that pose medical and organizational risks and depend on care integration between different providers. This qualitative study investigated user experiences with an existing digital system for care integration between hospitals and nursing homes, and the potential of artificial intelligence to contribute to its optimization. The findings reveal challenges regarding (a) untimely information, (b) irrelevant information, (c) confusing information, (d) missing information, (e) information overload, and (f) information multiplicity. Artificial intelligence could address these by (i) identifying and verifying low-quality information, (ii) targeting information for different user groups, (iii) visually summarizing relevant information, and (iv) jointly presenting multiple versions. The implications of these findings extend beyond the context of care integration, presenting empirical evidence for the importance of qualitative health research in, and a model for, determining the scope and design of future artificial intelligence solutions to optimize (health)care processes.

scholarly journals The Illusion of Free Choice in the Age of Augmented Decisions

2021 ◽  
Vol 13 (1) ◽  
pp. 46-51
Author(s):  
Fabian Buder ◽  
Koen Pauwels ◽  
Kairun Daikoku
Keyword(s):  
Artificial Intelligence ◽  
Free Choice ◽  
Information Overload ◽  
Personal Data ◽  
Relevant Information ◽  
Best Interest ◽  
Freedom Of Choice ◽  
Basic Understanding ◽  
Smart Technologies ◽  
A Company

Abstract In our augmented world, many decision situations are designed by smart technologies. Artificial intelligence helps reduce information overload, filter relevant information and limit an otherwise overwhelming abundance of choices. While such algorithms make our lives more convenient, they also fulfill various organizational objectives that users may not be aware of and that may not be in their best interest. We do not know whether algorithms truly optimize the benefits of their users or rather the return on investment of a company. They are not only designed for convenience but also to be addictive, and this opens the doors for manipulation. Therefore, augmented decision making undermines the freedom of choice. To limit the threats of augmented decisions and enable humans to be critical towards the outcomes of artificial intelligence–driven recommendations, everybody should develop “algorithmic literacy.” It involves a basic understanding of artificial intelligence and how algorithms work in the background. Algorithmic literacy also requires that users understand the role and value of the personal data they sacrifice in exchange for decision augmentation.

Tribological Design Decisions Using Computerized Databases

1987 ◽  
Vol 109 (3) ◽  
pp. 381-386 ◽  
Author(s):  
T. E. Tallian
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Design Methodology ◽  
Database System ◽  
Desktop Computer ◽  
Design Decisions ◽  
Hard Data

Tribological knowledge is widely scattered and inconvenient of access for practicing engineers. This results in suboptimal utilization of tribological knowledge in many engineering decisions. As a remedy, ASME has embarked on a project to create a comprehensive tribology database system accessible by desktop computer. Since tribological design methodology is as important as hard data in generating optimal engineering decisions, an artificial intelligence (AI) based expert system is projected to serve as a methodology guide for the database user. This paper describes the content and structure concept of the tribology database system “entry module,” which is to comprise this expert system.

The Future of Medicine - Biologics and Artificial Intelligence

2019 ◽  
Author(s):  
Gordon Slater
Keyword(s):  
Artificial Intelligence ◽  
Information Overload ◽  
Treatment Options ◽  
Medical Robotics ◽  
Medical Practitioners ◽  
Disruptive Technologies ◽  
The Future ◽  
Traditional Therapies ◽  
Changing Times ◽  
First World

The future of medicine is in biologics and artificial intelligence AI. With the advent of advancing and disruptive technologies, many traditional therapies are being replaced by new therapies such as: stem cell therapy,joint preservation,limb saving technologies and medical robotics. In countries where first world medicine is available, there will emerge new challenges in retraining for practitioners and confusion for patients as to what are the best treatment options. From the medical practitioners’ perspective, the challenge will be to stay up to date with the changing times and innovations. Some aspects of their practice may quickly become obsolete.As patients become more aware of symptoms of their conditions, self-diagnosis will be on the rise, and patients may often run the risk of misdiagnosing themselves, or experiencing conditions such as cyber phobia from information overload. Innovations such as Artificial Intelligence will emerge, to summarize the vast amount of information that is being published each day relevant to medical fields. Artificial Intelligence that will interact with the doctor, promoting plausible diagnoses and treatment options is already present. Embracing change will become the norm.

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