Business Analytics Test 1 - Chp 2

AI in manufacturing

-Automation for compliance and cost reduction -React quicker and more effectively (agility)

Augmentation

integration of digital information within the user environment in real time

Artificial intelligence

(1) the study of human thought processes (to understand what intelligence is) and (2) the representation and duplication of those thought processes in machines (e.g., computers, robots). That is, the machines are expected to have humanlike thought processes.

AI in financial services

-Extreme personalization (e.g., chatbots, personal assistants, etc.) -Shifting customer behavior both online and in branches -Facilitating trust in digital identity -Revolutionizing payments -Sharing economic activities (e.g., person-to-person loans) -Offering financial services 24/7 and globally

Reasoning from knowledge

This feature processes users' requests and provides answers (e.g., solutions, recommendations) to the user. The major difference among the various types of the intelligent technologies is the type of reasoning they use.

Emerging AI

• Effective computing. These technologies detect the emotional conditions of people and suggest how to deal with discovered problems • Biometric analysis. These technologies can verify an identity based on unique biological traits that are compared to stored ones (e.g., facial recognition).

Robotics

a broad category of integrated, possibly complex, systems where Sensory systems, such as those for scene recognition and signal processing, are combined with other AI technologies

Salesforce Einstein

-is an AI set of technologies (e.g., Einstein Vision for image recognition) that is used for enhancing customer interactions and supporting sales. -the system delivers dynamic sales dashboards to sales reps. It also tracks performance and manages teamwork by using sales analytics. The AI product also can provide predictions and recommendations

AI in Marketing, advertising, and CRM (Davis 2016)

1. Product and personal recommendations - AI-based technologies are used extensively for personalized recommendations (e.g., see Martin, 2017). 2. Smart search engines - Google is using RankBrain's AI system to interpret users' queries. Using NLP helps in understanding the products or services online users are searching. This includes the use of voice communication. 3. Fraud and data breaches detection - Application for this has covered credit/debit card use for many years, protecting Visa and other card issuers. 4. Social semantics - Using AI-based technologies, such as sentiment analysis and image and voice recognition's, retailers can learn about customers' needs and provide targeted advertisements and product recommendations directly (e.g., via e-mail) and through social media. 5. Web site design - Using AI methods, marketers are able to design attractive Web sites. 6. Producer pricing - AI algorithms help retailers price products and services in a dynamic fashion based on the competition, customers' requirements, and more. 7. Predictive customer service - Similar to predicting the impact of pricing, AI can help in predicting the impact of different customer service options. 8. Ad targeting - Similar to product recommendations, which are based on user profiles, marketers can tailor ads to individual customers. 9. Speech recognition - As the trend to use voice in human-machine interaction is increasing, the use of bots by marketers to provide product information and prices accelerates. 10. Language translation - AI enables conversations between people who speak different languages. Also, customers can buy from Web sites written in languages they do not speak using google translate 11. Customer segmentation - Marketers are segmenting customers into groups and then tailoring ads to each group. less effective than targeting individuals, but more effective than mass advertising. AI use data and text mining to help marketers identify the characteristics of specific segments and help tailor the best ads for each segment. 12. Sales forecasting - Marketers' strategy and planning are based on sales forecasting. forecasting may be very difficult for certain products. Predictive analytics and other AI tools can provide better forecasting than traditional statistical tools. 13. Image recognition - This can be useful in market research (e.g., for identifying consumer preferences of one company's products versus those of its competition). It can also be used for detecting defects in producing and/or packaging products. 14. Content generation - Marketers continuously create ads and product information. AI can expedite this task and make sure that it is consistent and complies with regulations. 15. Using bots, assistants, and robo advisors - bots, personal assistants, and robo advisors help consumers of products and services. AI machines excel in facilitating customer experience and strengthen customer relationship management. Some experts call bots and virtual personal assistants the "face of marketing."

Intelligent & Automated Decision Support

-As early as 1970, there were attempts to automate decision making. They were typically done with the use of rule-based expert systems that provided recommended solutions to repetitive managerial problems. -Examples of decisions made automatically: • Small loan approvals • Initial screening of job applicants • Simple restocking • Prices of products and services (when and how to change them) • Product recommendation

AI Applications in accounting

-Chandi (2017), noticed trends among professional accountants: their use of AI, including bots in professional routines, increased -the major drivers for this are perceived savings in time and money and increased accuracy and productivity. The adoption has been rapid and it has been followed by significant improvements.

Applications of AI in Banking

-Employee surveillance (AI machines, e.g., IBM Watson). -Tax preparation/filing (H&R block uses IBM Watson). -Automated customer service; answering customer inquiries in real-time. Rainbird Co. is an AI vendor that does this through integration with IBM Watson -Automated online support for paying bills and account inquiries using Amazon Alexa (eg, Capital One, TD bank) -Fraud detection and anti-money-laundering activities; also improving customer experience (Bank Danamon). -Victual banking assistant, Olivia at HSBC, learn from experience and helps customer better. -Santander Bank employs a virtual assistant (called Nina) that can transfer money, and pay bills. Nina can also authenticate customers via an AI-based voice recognition system.

Examples of AI Benefits

-ISDA uses AI to eliminate tedious activities -AI revolutionizing business recruitment -AI is redefining management -Help blind people experience the world around them -Identify overlooked borrowers -Predict customer expectation -Startup AI companies are emerging in large numbers -Most impactful: customer experience and enjoyment.

Some limitations of AI Machines

-Lack human touch and feel -Lack attention to non-task surroundings -Can lead people to rely on AI machines too much -Can be programmed to create destruction -Can cause many people to lose their jobs -Can start to think by themselves, causing significant damage

Capabilities of Intelligence

-Learning or understanding from experience -Making sense out of ambiguous, incomplete, or even contradictory messages and information -Responding quickly and successfully to a new situation (i.e., using the most correct responses) -Understanding and inferring in a rational way, solving problems, and directing conduct effectively -Applying knowledge to manipulate environments -Recognizing and judging the relative importance of different elements in a situation

Machine translation of languages

-Machine translation uses computer programs to translate words and sentences from one language to another.

AI in Insurance services

-Mostly used to improve issuing policies and handling claims. experience. Incoming claims are analyzed by AI, and, depending on their nature, are sent to appropriate available adjusters. The technologies used are NLP and text recognition -Agents previously spent considerable time asking routine questions from people submitting insurance claims. AI machines, according to Beauchamp (2016), provide speed, accuracy, and efficiency in performing this process. Then AI can facilitate the underwriting process.

AI in Human Resource Management (HRM)

-Savar (2017) points to the following reasons for AI to transform HRM, especially in recruiting: (1) reducing human bias, (2) increasing efficiency, productivity, and insight in evaluating candidates, and (3) improving relationships with current employees.

AI in small accounting firms

-Solve complex billing problems (especially in healthcare) This helps clients claim processing and reimbursement -Real estate contracts, risk analysis -AI provides cheaper and better data-driven support -Generates needed insights from data analysis -Frees time of accountants for more complex tasks -Machine learning is often used for prediction

AI in big Accounting firms

-The big accounting companies use AI to replace or support human activities in tasks such as tax preparation, auditing, strategy consulting, and accountancy services. They mostly use NLP, robotic process automation, text mining, and machine learning. -All big accounting companies use AI to assist in generating reports and to conduct many other routine, high-volume tasks. AI has produced high quality work, and its accuracy has become better and better with time.

Knowledge acquisition

-The process of acquiring the knowledge necessary for many intelligent systems to work -This activity can be complex because it is necessary to make sure what knowledge is needed. It must fit the desired system. In addition, the sources of the knowledge need to be identified to ensure the feasibility of acquiring the knowledge -If experts are the source of the knowledge their cooperation must be ensured for collected knowledge to be valid and said knowledge must be validated

What AI can and can't do

-This is important for two reasons: (1) executives need to know what AI can do economically and how companies can use it to benefit their business and (2) executives need to know what AI cannot economically do.

Deep Learning

-Tries to mimic how the human brain works. -uses artificial neural technology and plays a major role in dealing with complex applications that regular machine learning and other AI technologies cannot handle. -Deep learning (DL) delivers systems that not only think but also keep learning, enabling self-direction based on fresh data that flow in. -DL can tackle previously unsolvable problems using its powerful learning algorithms.

Turing Test

-a computer can be considered smart only when a human interviewer asking the same questions to both an unseen human and an unseen computer cannot determine which is which -To pass the Turing Test, a computer needs to be able to understand a human language (NLP), to possess human intelligence (have a knowledge base), to reason using its stored knowledge, and to be able to learn from its experiences (Machine learning)

Intelligence

-an umbrella term and is usually measured by an IQ test. -Following are types of intelligence according to Howard Gardner: • Linguistic and verbal • Logical • Spatial • Body/movement • Musical • Interpersonal • Intrapersonal • Naturalist

Machine Learning

-is a scientific discipline concerned with the design and development of algorithms that allow computers to learn based on data coming from sensors, databases, and other sources. -Teaching computers to learn from examples and large amounts of data. -machine-learning scientists try to teach computers to identify patterns and make connections by showing the machines a large volume of examples and related data -allows computer systems to monitor and sense their environmental activities so the machines can adjust their behavior to deal with changes in the environment. -can also be used to predict performance and reconfigure programs based on changing conditions,

Natural language processing (NLP)

-is a technology that gives users the ability to communicate with a computer in their native language. The communication can be in written text and/or in voice (speech). -Two subfields of NLP: 1. Natural language understanding - that investigates methods of enabling computers to comprehend instructions or queries provided in ordinary English or other human languages. 2. Natural language generation that strives to have computers produce ordinary spoken language so that people can understand the computers more easily.

Robot

-is an electromechanical device that is guided by a computer program to perform manual and/or mental tasks. -Robots can be fully autonomous (programmed to do tasks completely on their own, even repair themselves), or can be remotely controlled by a human -used extensively in e-commerce warehouses, make-to-order manufacturing as well as in mass production

Computer vision

-is an interdisciplinary field that deals with how computers can be made for gaining high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. -Computer vision acquires or processes, analyzes, and interprets digital images and produces meaningful information for making decisions. -Scene and item recognition are important elements in computer vision. -It's a technology of AI that enables robots to see

Cognitive computing

-is the application of knowledge derived from cognitive science (the study of the human brain) and computer science theories in order to simulate the human thought processes (an AI objective) so that computers can exhibit and/or support decision-making and problem-solving capabilities -to do so, computers must be able to use self-learning algorithms, pattern recognition, NLP, machine vision, and other AI technologies. -Cognitive computing systems learn to reason with purpose, and interact with people naturally.

Speech (voice) understanding

-is the recognition and understanding of spoken languages by a computer. Many companies have adopted this technology in their automated call centers.

Augmented reality (AR)

-refers to the integration of digital information with the user environment in real time (mostly vision and sound). The technology provides people real-world interactive experience with the environment-These AR systems use data captured by sensors (e.g., vision, sound, temperature) to augment and supplement real-world environments.

Machine Vision

-the technology and methods used to provide imaging-based automated inspection and analysis for applications such as robot guidance, process control, autonomous vehicles, and inspection. -major part of machine vision is the industrial camera, which captures, stores, and archives visual information that is then presented to users or computer programs for analysis and automatic decision making or for support of human decision making.

Differences between traditional and augmented AI (Padmanabhan 2018):

1. Augmented machines extend human thinking capabilities rather than replace human decision making. These machines facilitate creativity. 2. Augmentation excels in solving complex human and industry problems in specific domains in contrast with strong, general AI machines, which are still in development. 3. In contrast with a "black box" model of some AI and analytics, the augmented intelligence provides insights and recommendations, including explanations. 4. In addition, augmented technology can offer new solutions by combining existing and discovered information in contrast to assisted AI that identifies problems or symptoms and suggests predetermined known solutions.

AI Support for Decision-Making Process (as relates Simon's decision making process)

1. Problem Identification - AI systems are used extensively in problem identification typically in diagnosing equipment malfunction and medical problems, finding security breaches, estimating financial health, etc. -Sensor-collected data are used by AI algorithms. -Performance levels of machines are compared to standards, and trend analysis can point to opportunities or troubles. 2. Generating or finding alternative solutions - Several AI technologies offer alternative solutions by matching problem characteristics with best practices or proven solutions stored in databases. Expert systems and chatbots employ this approach. 3. Selecting a solution - AI models are used to evaluate proposed solutions, for example, by predicting their future impact (predictive analysis), assessing their chance of success, or predicting a company's reply to action taken by a competitor. 4. Implementing the solution - AI can be used to support the implementation of complex solutions. For example, it can be used to demonstrate the superiority of proposals and assess resistance to changes. -As the power of AI technologies increases, so does its ability to fully automate more and more complex decision-making situations.

Wislow (2017) divided impact of AI on HRM into the following areas

1. Recruitment (talent acquisition) - AI Help companies evaluate applicants and their resumes by using machine learning, Help companies evaluate resumes that are posted on the Web. Evaluate the resumes of the best employees who currently work in a company and create, accordingly, desired profiles to be used when vacancies occur. 2. Training - chatbots can be used as a source of knowledge to answer learners' queries. AI can be used to personalize online teaching for individuals and to design group lectures. 3. Performance assessment (evaluation) - AI tools enable HRM to conduct performance analysis by breaking work into small components and measuring the performance of each employee and team on each component. 4. Retention and eliminating attrition - Machine learning can be used to detect reasons why employees leave companies by identifying influencing patterns. Predicts attrition ahead of time to eliminate loss of talent 5. Chatbots - The use of chatbots is increasing due to their ability to provide current information to employees anytime

Schrage's 4 models for using AI

1. The Autonomous Advisor - This is a data-driven management model that uses AI algorithms to generate best strategies and instructions on what to do and makes specific recommendations. However, only humans can approve the recommendations (e.g., proposed solutions). 2. The Autonomous Outsource - Here, the traditional business process outsourcing model is changed to a business process algorithm. To automate this activity, it is necessary to create clear rules and instructions. It is a complex scenario since it involves resource allocation. Correct predictability and reliability are essential. 3. People-Machine Collaboration - Assuming that algorithms can generate optimal decisions in this model, humans need to collaborate with the brilliant, but constrained, fully automated machines. To ensure such collaboration, it is necessary to train people to work with the AI machines. This model is used by tech giants such as Netflix, Alibaba, and Google. 4. Complete Machine Autonomy - In this model, organizations fully automate entire processes. Management needs to completely trust AI models, a process that may take years. -Implementing these four models requires appropriate management leadership and collaboration with data scientists.

AI improving customer experiences

1. Use NLP for generating user documentation. This capability also improves the customer-machine dialogue. 2. Use visual categorization to organize images (for example, see IBM's Visual Recognition and Clarifai) 3. Provide personalized and segmented services by analyzing customer data. This includes improving shopping experience and CRM.

Logistics and Transportation

AI and intelligent robots are used extensively in corporate logistics and internal and external transportation, and supply chain management. Amazon.com is using over 50,000 robots to move items in its distribution centers

AI Support for Decision Making

AI can: -Solve complex problems that people have not been able to solve. -Make much faster decisions. -Find relevant information, even in large data sources, very fast. -Make complex calculations rapidly. -Conduct complex comparisons and evaluations in real time.

Knowledge representation

Acquired knowledge needs to be organized and stored. Several methods of doing this, depending on what the knowledge will be used for, how the reasoning from it will be done, and how users will interact with it. A simple way to represent knowledge is in the form of questions and matching answers (Q&A).

Scene Recognition

Activity performed by a computer vision that enables recognition of objects, scenery, and photos.

Intelligent Agent

An autonomous, small computer program that acts upon changing environments as directed by stored knowledge. An IA directs an agent's activities to achieve specific goals related to the changes in the surrounding environment. Intelligent agents may have the ability to learn by using and expanding the knowledge embedded in them.

AI vs Human Intelligence

Area AI Human -Execution: Very fast - Can be slow -Emotions: Not yet - positive or negative -Computation Very fast - Slow, may have trouble speed: -Imagination: Only what's - Can expand programmed existing knowledge -Answers to What is in - Can be innovative questions: the program -Flexibility: Rigid - Large, flexible -Foundation: binary code - Five senses -Consistency: High - Variable, can be poor -Process: As modeled - Cognitive -Form: Numbers - Signals -Memory: Built in, or stored - Use of content and in the cloud scheme memory -Brain: Independent - Connected to a body -Creativity: Uninspired - Truly creative -Durability: Permanent, but - Perishable, but can get obsolete can be updated if not updated -Duplication, documentation, Easy - Difficult and dissemination: -Cost: Usually low and - Maybe high and declining increasing -Consistency: Stable - Erratic at times -Reasoning Clear, visible - Difficult to trace process: at times -Perception: By rules and data - By patterns -Figure missing Usually cannot - Frequently can data:

Implementation model

Bollard, et al. (2017) proposed the following five-component model for manufacturing companies: 1. Streamlining processes, including minimizing waste, redesigning processes, and using business process management (BPM) 2. (Smart outsourcing) Outsourcing certain business processes, including going offshore 3. Using intelligence in decision making by deploying AI and analytics 4. Replacing human tasks with intelligent automation 5. Digitizing and improving customers' experiences

Difference between Machine vision and Computer vision

Machine vision is treated more as an engineering subfield, while computer vision belongs to the computer science area.

Introducing AI to HRM operations

Meister (2017) suggests the following activities: 1. Experiment with a variety of chatbots 2. Develop a team approach involving other functional areas 3. Properly plan a technology roadmap for both the short and long term, including shared vision with other functional areas 4. Identify new job roles and modifications in existing job roles in the transformed environment 5. Train and educate the HRM team to understand AI and gain expertise in it

Shopbots

Robots that help with online shopping by collecting shopping information, matching buyers and products, and conducting price and capability comparisons.

Major goals of AI

The overall goal of AI is to create intelligent machines that are capable of executing a variety of tasks currently done by people. Some specific goals are to: • Perceive and properly react to changes in the environment that influence specific business processes and operations. • Introduce creativity in business processes and decision making.

Issues and factors in AI decision making

Several issues determine the justification of using AI and its chance of success. These include: • The nature of the decision - routine decisions are more likely to be fully automated, especially if they are simple. • The method of support - what technology is used. Initially, automated decision supports were rule-based. Practically, expert systems were created to generate solutions to specific decision situations in well-defined domains. Recommender systems which appeared with e-commerce in the 1990s. • Cost-benefit and risk analyses are necessary for making large-scale decisions, but computing these values may not be simple with AI models due to difficulties in measuring costs, risks, and benefits. • Using business rules - Many AI systems are based on business or other types of rules. The quality of automated decisions depends on the quality of the rules. Advanced AI systems can learn and improve business rules. • AI algorithms- growth in use of AI algorithms that are the basis for automated decisions and decision support. The quality of the decisions depends on the input of the algorithms, which may be affected by changes in the business environment. • Speed - Decision automation is also dependent on the speed within which decisions need to be made. Some decisions cannot be automated because it takes too much time to get all the relevant input data. However, manual decisions may be too slow for certain circumstances.

Major Elements of AI: Foundations and Technologies

Tree of AI: Technologies and applications make up the branches of the tree and the foundations make up the roots -Foundations: philosophy, human behavior, neurology, IoT, Logic, Sociology, psychology, human cognition, logistics, biology, pattern recognition, Fuzzy logic, statistics, information systems, management science, engineering, computer science, mathematics, M2M, robotics -Tech and Applications: Smart homes, augmented reality, expert systems, computer vision, game playing, automatic programming, speech understanding, autonomous vehicles, intelligence/tutoring, intelligent agents, natural language processing, personal assistant, machine learning, voice recognition, neutral networks, genetic algorithms, deep learning, smart cities, robo advisers, smart factories

Chatbots

a conversational robot that is used for chatting with people. Depending on the purpose of the chat, which can be done in writing or by voice, bots can be in the form of intelligent agents that retrieve information or personal assistants that provide advice.

Content of intelligence

composed of Reasoning, learning, logic, problem-solving, perception, and linguistic ability

Video analytics

derivative application of computer vision (CV) that involves applying CV techniques to videos to recognize patterns (detecting for fraud) and identify events

Artificial Brain

is a people-made machine that is desired to be as intelligent, creative, and self-aware as humans. To date, no one has been able to create such a machine

Intelligent factories

use complex software and sensors which specialize in real-time operations tracking

Benefits of AI

• AI has the ability to complete certain tasks much faster than humans. • The consistency of the completed AI work can be much better than that of humans. AI machines do not make mistakes. • AI systems allow for continuous improvement projects. • AI can be used for predictive analysis via its capability of pattern recognition. • AI can manage delays and blockages in business processes. • AI machines do not stop to rest or sleep. • AI machines can work autonomously or be assistants to humans. • The functionalities of AI machines are ever increasing. • AI machines can learn and improve their performance. • AI machines can work in environments that are hazardous to people. • AI machines can facilitate innovations by human (i.e., support research and development [R&D]). • No emotional barriers interfere with AI work. • AI excels in fraud detection and in security facilitations. • AI improves industrial operations. • AI optimizes knowledge work. • AI increases speed and enables scale. • AI helps with the integration and consolidating of business operations. • AI applications can reduce risk. • AI can free employees to work on more complex and productive jobs. • AI improves customer care. • AI can solve difficult problems that previously were unsolved (Kharpal, 2017). • AI increases collaboration and speeds up learning.

AI in banking

• AI in banking include all financial services uses above • These technologies help banks improve both their front-office and back-office operations. • Major activities are the use of chatbots to improve customer service and communicating with customers, and robo advising is used by some financial institutions • Facial recognition is used for safer online banking. • Advanced analytics helps customers with investment decisions. • AI algorithms help banks identify and block fraudulent activities including money laundering. • AI algorithms can help in assessing the creditworthiness of loan applicants.

Drivers of AI

• People's interest in smart machines and artificial brains • The low cost of AI applications versus the high cost of manual labor (doing the same work) • The desire of large tech companies to capture competitive advantage and market share of the AI market and their willingness to invest billions of dollars in AI • The pressure on management to increase productivity and speed • The availability of quality data contributing to the progress of AI • The increasing functionalities and reduced cost of computers in general • The development of new technologies, particularly cloud computing