Discussion

Discussion Guidelines

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This is a scholarly post and your responses should have more depth than “I agree” and should demonstrate critical reflection of the problem in order to promote vigorous discussion of the topic within the forum.

For the discussion, students are expected to make a minimum of three posts on three days for EACH Topic. Your initial post will be your answer to the Question and is to be 300 – 400 words with at least two references. The remaining two posts will be comments engaged with your classmates in meaningful discussion, more than affirmation, on their post and the subject matter and be between 150 – 250 words.

Initial post will be graded on length, content, grammar and use of references. The initial post must be submitted by 11:59 PM EST, to allow students the opportunity to respond to it.

Case 1:

Application Case 1.1: Sabre Helps Its Clients through Dashboards and Analytics

What is traditional reporting? How is it used in organizations?
How can analytics be used to transform traditional reporting?
How can interactive reporting assist organizations in decision making?

Discussion GuidelinesRemember that plagiarism includes copying and pasting material from the internet into assignments without properly citing the source of the material. Copying from an internet source and pasting is strictly forbidden.

All work must be organized and formatted consistent with the APA 6th edition style format (double spaced and references indented accordingly). All citations and references must be in the hanging indent format with the first line flush to the left margin and all other lines indented.
This is a scholarly post and your responses should have more depth than “I agree” and should demonstrate critical reflection of the problem in order to promote vigorous discussion of the topic within the forum.
For the discussion, students are expected to make a minimum of three posts on three days for EACH Topic. Your initial post will be your answer to the Question and is to be 300 – 400 words with at least two references. The remaining two posts will be comments engaged with your classmates in meaningful discussion, more than affirmation, on their post and the subject matter and be between 150 – 250 words.

Initial post will be graded on length, content, grammar and use of references. The initial post must be submitted by Wednesday at 11:59 PM EST, to allow students the opportunity to respond to it.

Using APA in discussion posts is very similar to using APA in a paper. And it helps to think of your discussion post as a short APA paper without a cover page. You need to cite your sources in your discussion post both in-text and in a references section. If you need help forming in-text citations, check out our in-text citation page on the APA guide.
Case 1:
Application Case 1.1: Sabre Helps Its Clients through Dashboards and Analytics
What is traditional reporting? How is it used in organizations?
How can analytics be used to transform traditional reporting?
How can interactive reporting assist organizations in decision making?
Discussion GuidelinesRemember that plagiarism includes copying and pasting material from the internet into assignments without properly citing the source of the material. Copying from an internet source and pasting is strictly forbidden.
All work must be organized and formatted consistent with the APA 6th edition style format (double spaced and references indented accordingly). All citations and references must be in the hanging indent format with the first line flush to the left margin and all other lines indented.
This is a scholarly post and your responses should have more depth than “I agree” and should demonstrate critical reflection of the problem in order to promote vigorous discussion of the topic within the forum.
For the discussion, students are expected to make a minimum of three posts on three days for EACH Topic. Your initial post will be your answer to the Question and is to be 300 – 400 words with at least two references. The remaining two posts will be comments engaged with your classmates in meaningful discussion, more than affirmation, on their post and the subject matter and be between 150 – 250 words.
Initial post will be graded on length, content, grammar and use of references. The initial post must be submitted by Wednesday at 11:59 PM EST, to allow students the opportunity to respond to it.
Using APA in discussion posts is very similar to using APA in a paper. And it helps to think of your discussion post as a short APA paper without a cover page. You need to cite your sources in your discussion post both in-text and in a references section. If you need help forming in-text citations, check out our in-text citation page on the APA guide.
Case 1:
Application Case 1.1: Sabre Helps Its Clients through Dashboards and Analytics
What is traditional reporting? How is it used in organizations?
How can analytics be used to transform traditional reporting?
How can interactive reporting assist organizations in decision making?

Analytics, Data Science and A I: Systems for Decision Support

Eleventh Edition

Chapter 1

Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence: Systems for Decision Support

Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved
Slide in this Presentation Contain Hyperlinks. JAWS users should be able to get a list of links by using INSERT+F7

If this PowerPoint presentation contains mathematical equations, you may need to check that your computer has the following installed:
1) Math Type Plugin
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1

Learning Objectives (1 of 2)
1.1 Understand the need for computerized support of managerial decision making.
1.2 Understand the development of systems for providing decision-making support.
1.3 Recognize the evolution of such computerized support to the current state of analytics/data science and artificial intelligence.
1.4 Describe the business intelligence (B I) methodology and concepts.
1.5 Understand the different types of analytics and review selected applications.

Slide 2 is list of textbook LO numbers and statements

Learning Objectives (2 of 2)
1.6 Understand the basic concepts of artificial intelligence (A I) and see selected applications.
1.7 Understand the analytics ecosystem to identify various key players and career opportunities.

Slide 2 is a list of textbook LO numbers and statements

Opening Vignette (1 of 2)
How Intelligent Systems Work for KONE Elevators and Escalators Company
The problem…
The solution…
The results…

Opening Vignette (2 of 2)
How Intelligent Systems Work for KONE Elevators and Escalators Company
Questions For The Opening Vignette
It is said that K O N E is embedding intelligence across its supply chain and enables smarter buildings. Explain.
Describe the role of I o T in this case.
What makes I B M Watson a necessity in this case?
Check I B M Advanced Analytics. What tools were included that relate to this case?
Check I B M cognitive buildings. How do they relate to this case?

Changing Business Environments And Evolving Needs For Decision Support And Analytics
Big-bet, high-risk decisions.
Cross-cutting decisions, which are repetitive but high risk that require group work.
Ad hoc decisions that arise episodically.
Delegated decisions to individuals or small groups.

Decision Making Process (1 of 2)
The four step managerial process:
Define the problem
Construct a model
Identify and evaluate possible solutions
Compare, choose, and recommend a solution to the problem

Decision Making Process (2 of 2)
A more detailed process is offered by Quain (2018):
Understand the decision you have to make.
Collect all the information.
Identify the alternatives.
Evaluate the pros and cons.
Select the best alternative.
Make the decision.
Evaluate the impact of your decision.

The Influence of the External and Internal Environments on the Process
Technology, I S, Internet, globalization, …
Government regulations, compliance, …
Political factors
Economic factors
Social and psychological factors
Environment factors
Need to make rapid decision, changing market conditions, …

Technologies for Data Analysis and Decision Support
Group communication and collaboration
Improved data management
Managing giant data warehouses and Big Data
Analytical support
Overcoming cognitive limits
Knowledge management
Anywhere, anytime support
Innovation and artificial intelligence

Decision-making Processes And Computerized Decision Support Framework
What is “Decision making”?
Simon’s Decision Making Process
Proposed in 1977 by Herbert Alexander Simon (an American economist and political scientist)
Includes three phases:
Intelligence
Design
Choice
[+] Implementation
[+] Monitoring

The Decision-Making Process

Decision-making Processes (1 of 2)
Phase 1 – The Intelligence Phase: Problem (or Opportunity) Identification
Issues in data collection
Problem classification
Problem decomposition
Problem ownership

Application Case 1.1
Making Elevators Go Faster!
Questions for Discussion:
Why this is an example relevant to decision making?
Relate this situation to the intelligence phase of decision making.

Decision-Making Processes (2 of 2)
Phase 2 – The Design Phase
Models
Phase 3 – The Choice Phase
Evaluating alternatives
Phase 4 – The Implementation Phase
Implementing the solution
Phase 5 – Monitoring
Phase 4 and 5 were not part of Simons’ original model

The Classical Decision Support System Framework
Degree of structuredness
Structured, unstructured, semistructured problems
Type of control
Operational, managerial, strategic
The decision Support matrix
Computer support for …
Structured decisions
Unstructured decisions
Semistructured problems

Decision Support Framework

Key Characteristics and Capabilities of Decision Support System (D S S)

Components of a D S S (1 of 2)
The Data Management System
D S S database
Database management system (D B M S)
Data directory
Query facility

Components of a D S S (2 of 2)
The Model Management Subsystem
Model base
M B M S
Modeling language
Model directory
Model execution, integration, and command processor
The User Interface Subsystem
The Knowledge-Based Subsystem

Evolution of Computerized Decision Support to Business Intelligence, Analytics, Data Science
Figure 1.5 Evolution of Decision Support, Business Intelligence, Analytics, and A I.

A Framework for Business Intelligence
Definitions of business intelligence (B I)
A brief history of B I
The architecture of B I
Data warehousing (D W) [as a foundation of B I]
Business performance management (B P M)
User interface (dashboard)
Transaction processing versus analytics processing
Appropriate planning and alignment of B I with the business strategy

Evolution of Business Intelligence (B I)

The Origins and Drivers of B I
Figure 1.7 A High-Level Architecture of B I.

Source: Based on W. Eckerson. (2003). Smart Companies in the 21st Century: The Secrets of Creating Successful Business Intelligent Solutions Seattle, W A: The Data Warehousing Institute, p. 32, Illustration 5.

Data Warehouse Framework

A Multimedia Exercise in B I
Teradata University Network (T U N)
B S I (Business Scenario Investigations) [like C S I]
Go to
https://www.teradatauniversitynetwork.com/Library/Items/BSI-The-Case-of-the-Misconnecting-Passengers/ or
www.youtube.com/watch?v=NXEL5F4_aKA

Watch the video
Analyze the video –
www.slideshare.net/teradata/bsi-how-we-did-itthe-case-of-the-misconnecting-passengers

Analytics Overview (1 of 2)

Analytics Overview (2 of 2)
Three types of analytics
Descriptive (or reporting) analytics …
Predictive analytics …
Prescriptive analytics …
Analytics applied to different domains
Analytics or data science?
What is Big Data?

Application Case 1.3
Silvaris Increases Business with Visual Analysis and Real-Time Reporting Capabilities
Questions for Discussion:
What was the challenge faced by Silvaris?
How did Silvaris solve its problem using data visualization with Tableau?

Application Case 1.4
Siemens Reduces Cost with the Use of Data Visualization
Questions for Discussion:
What challenges were faced by Siemens visual analytics group?
How did the data visualization tool Dundas B I help Siemens in reducing cost?

Application Case 1.5
Analyzing Athletic Injuries
Questions for Discussion:
What types of analytics are applied in the injury analysis?
How do visualizations aid in understanding the data and delivering insights into the data?
What is a classification problem?
What can be derived by performing sequence analysis?

Application Case 1.6
A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise Dates
Questions for Discussion:
Why would reallocation of inventory from one customer to another be a major issue for discussion?
How could a D S S help make these decisions?

Analytics Examples in Selected Domains (1 of 2)
Sports Analytics—An Exciting Frontier for Learning and Understanding Applications of Analytics
Example 1: Business office
Example 2: The Coach
Healthcare—Humana Examples
Example 1: Preventing Falls in a Senior Population
Example 2: Define the Right Metrics
Example 3: Predictive Models to Identify the Highest Risk Membership in a Health Insurer
Retail—Retail Value Chain …
Image Analytics

Analytics Examples in Selected Domains (2 of 2)
Retail …
Figure 1.15 Example of Analytics Applications in a Retail Value Chain.

Source: Contributed by Abhishek Rathi, C E O,
vCreaTek.com.

Application Case 1.7
Image Analysis Helps Estimate Plant Cover
Questions for Discussion:
What is the purpose of knowing how much ground is covered by green foliage on a farm? In a forest?
Why would image analysis of foliage through an app be better than a visual check?
Explore research papers to understand the underlying algorithmic logic of image analysis. What did you learn?
What other applications of image analysis can you think of?

Artificial Intelligence Overview
What Is artificial intelligence (A I)?
Technology that can learn to do things better over time.
Technology that can understand human language.
Technology that can answer questions.
The major benefits of A I
Reduction in the cost of performing work.
Work can be performed much faster.
Work is more consistent than human work.
Increased productivity, profitability, …

The Landscape of A I
Major technologies
Knowledge-based technologies
Biometric related technologies
Tools and platforms …
A I applications …
Narrow (weak) versus general (strong) A I
The three flavors of A I decisions
Assisted intelligence
Autonomous A I
Augmented Intelligence

Application Case 1.8
A I Increases Passengers’ Comfort and Security in Airports and Borders
Questions for Discussion:
List the benefits of A I devices to travelers.
List the benefits to governments and airline companies.
Relate this case to machine vision and other A I tools that deal with people’s biometrics.

Societal Impacts of A I
Impact on agriculture
Contribution to health and medical care
Other societal applications
Transportation
Utilities
Education
Social services
Also see Chapter 13 for smart cities

Application Case 1.9
Robots Took the Job of Camel-Racing Jockeys for Societal Benefits
Questions for Discussion:
It is said that the robots eradicated the child slavery. Explain.
Why do the owners need to drive by their camels while they are racing?
Why not duplicate the technology for horse racing?
Summarize ethical aspects of this case (Read Boddington, 2017). Do this exercise after you have read about ethics in Chapter 14.

Convergence of Analytics and A I
Major differences between analytics and a i
Why combine intelligent systems?
How convergence can help?
Big Data Is empowering A I technologies
The convergence of A I and the IoT
The convergence with blockchain and other technologies
I B M and Microsoft support for intelligent systems convergence

Application Case 1.10
Amazon Go Is Open for Business
Questions for Discussion:
Watch the video. What did you like and/or dislike?
Compare the process described here to a selfcheck available today in many supermarkets and “big box” stores (Home Depot, etc.).
The store was opened in downtown Seattle. Why was the downtown location selected?
What are the benefits to customers? To Amazon?
Will customers be ready to trade privacy for convenience? Discuss.

Overview of Analytics Ecosystem

Plan of the Book

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Analytics, Data Science and A I: Systems for Decision Support

Eleventh Edition

Chapter 2

Artificial Intelligence Concepts, Drivers, Major Technologies, and Business Applications

Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved
Slide in this Presentation Contain Hyperlinks. JAWS users should be able to get a list of links by using INSERT+F7

Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved
If this PowerPoint presentation contains mathematical equations, you may need to check that your computer has the following installed:
1) Math Type Plugin
2) Math Player (free versions available)
3) NVDA Reader (free versions available)
1

Learning Objectives (1 of 2)
2.1 Understand the concepts of artificial intelligence (A I).
2.2 Become familiar with the drivers, capabilities, and benefits of A I.
2.3 Describe human and machine intelligence.
2.4 Describe the major A I technologies and some derivatives.
2.5 Discuss the manner in which A I supports decision making.

Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved
Slide 2 is list of textbook LO numbers and statements
2

Learning Objectives (2 of 2)
2.6 Describe A I applications in accounting.
2.7 Describe A I applications in banking and financial services.
2.8 Describe A I in human resource management.
2.9 Describe A I in marketing.
2.10 Describe A I in production-operation management.

Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved
Slide 2 is a list of textbook LO numbers and statements
3

Opening Vignette (1 of 3)
I N R I X Solves Transportation Problems

Home

The problem…
The solution…
The results…

Opening Vignette (2 of 3)
I N R I X Solves Transportation Problems
Questions for the Opening Vignette:
Explain why traffic may be down while congestion is up (see the London case at inrix.com/
uk
-highways-agency
/).
How does this case relate to decision support?
Identify the A I elements in this system.
Identify developments related to A I by viewing the company’s press releases from the most recent four months at inrix.com/press-releases. Write a report.

Opening Vignette (3 of 3)
I N R I X Solves Transportation Problems
Questions for the opening vignette (cont.):
According to Gitlin (2016), I N R I X’s new mobile traffic app is a threat to Waze. Explain why.
Go to sitezeus.com/data/
inrix and describe the relationship between I N R I X and Zeus. View the 2:07 min. video at sitezeus.com/data/
inrix
/. Why is the system in the video called a “decision helper”?

Introduction to Artificial Intelligence
Definitions for artificial intelligence (A I)
Many definitions of A I
Relationship between A I and logic
plato.stanford.edu/entries/logic-ai
Major characteristics of A I machines
Smarter computers/machines
Major elements of A I …

The Functionalities and Applications of A I

Artificial Intelligence (A I) (1 of 8)
Many application of A I exists
Example: Pitney Bowes Is Getting Smarter with A I
Major goals of A I
Perceive and properly react to changes in the environment that influence specific business processes and operations.
Introduce creativity in business processes and decision making.

Artificial Intelligence (A I) (2 of 8)
Drivers of A I
Interest in smart machines and artificial brains
The low cost of A I applications
The desire of large tech companies
The pressure on management to increase productivity
The availability of quality data
The increasing functionalities and reduced cost of computers in general
The development of new information technologies, particularly the cloud computing

Artificial Intelligence (A I) (3 of 8)
Benefits of A I
A I has the ability to complete certain tasks much faster
The consistency of the work
A I machines do not make arbitrary mistakes
A I systems allow for continuous improvement projects
A I can be used for predictive analysis via its capability of pattern recognition
A I can manage delays and blockages in business processes
A I machines do not stop to rest or sleep
Many more…

Artificial Intelligence (A I) (4 of 8)
Figure 2.2 Cost of Human Work versus the Cost of A I Work.

Artificial Intelligence (A I) (5 of 8)
Examples of A I Benefits
I S D A uses A I to eliminate tedious activities
A I revolutionizing business recruitment
A I is redefining management
Help blind people experience the world around them
Identify overlooked borrowers
Predict customer expectation
Startup A I companies are emerging in large numbers
Most impactful: customer experience and enjoyment.

Artificial Intelligence (A I) (6 of 8)
Some limitations of A I Machines
Lack human touch and feel
Lack attention to non-task surroundings
Can lead people to rely on A I 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
Hypothetically … no evidence of that!
These limitations are diminishing over time

Artificial Intelligence (A I) (7 of 8)
What A I can and cannot do?
Three flavors of A I decisions
Assisted intelligence
Autonomous intelligence
Augmented intelligence
Artificial brain
A people made machine “as intelligent, creative, and self-aware as humans”
To date, no one has created such a machine

Artificial Intelligence (A I) (8 of 8)
Technology Insight – Augmented Intelligence
Combining the performance of people and machines [combining  augmenting]
Augmented machines extend human abilities
Examples
Cybercrime fighting
E-commerce decisions
High-frequency stock market trading

Human and Computer Intelligence (1 of 4)
What is intelligence?
Types of intelligence:
Linguistic and verbal, logical, spatial, body/movement, musical, interpersonal, intrapersonal, naturalist
Intelligence is not a simple concept!
Content of intelligence
Reasoning, learning, logic, problem-solving, perception, and linguistic ability

Human and Computer Intelligence (2 of 4)
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

Human and Computer Intelligence (3 of 4)
How intelligent is A I?
Comparing human intelligence with A I
Table 2.1 Artificial Intelligence versus Human Intelligence.
Area AI Human
Execution Very fast Can be slow
Emotions Not yet Can be positive or negative
Computation speed Very fast Slow, may have trouble
Imagination Only what is programmed for Can expand existing knowledge
Answers to questions What is in the program Can be innovative
Flexibility Rigid Large, flexible

Many more, in the book…

Human and Computer Intelligence (4 of 4)
Measuring A I: The Turing Test

Application Case 2.1
How Smart Can a Vacuum Cleaner Be?
Questions for Discussion:
How did the Korean researchers determine the performance of the vacuum cleaners?
If you own (or have seen) the Roomba, how intelligent do you think it is?
What capability can be generated by the deep learning feature? (You need to do some research.)
Find recent information about L G’s Roboking. Specifically, what are the newest improvements to the product?

Major A I Technologies & Drivers (1 of 3)
Intelligent agents
Intelligent? Autonomous? Mobile? …
Machine learning
“Human learning embedded into machines”
Deep learning
A part of machine learning (see Chapter 6)
Computer vision (machine vision)
Video analytics

Major A I Technologies

Application Case 2.2
How Machine Learning Is Improving Work in Business
Questions for Discussion:
Discuss the benefits of combining machine learning with other A I technologies.
How can machine learning improve marketing?
Discuss the opportunities of improving human resource management.
Discuss the benefits for customer service.

Major A I Technologies & Drivers (2 of 3)
Robotic systems
Industrial robots [for manufacturing]
Service robots
Example: Walmart is using robots to properly stock shelves
Use of robots (or bots) in eComemrce
Many are being used at Amazon.com
Online shopping robots (shopbots)
SoftBank – a cellphone store in Tokyo entirely staffed by robots

Major A I Technologies & Drivers (3 of 3)
Natural language processing
Natural language understanding
Natural language generation
Speech (voice) understanding
An interesting application cs.cmu.edu/~./listen
Machine translation of human languages
Balel fish (babelfish.com)
Google translator (translate.google.com)
Example: Sogou’s travel translator

Knowledge and Expert Systems (1 of 2)
Knowledge sourced intelligent systems
Knowledge acquisition
Identifying experts
Knowledge representation
Reasoning from knowledge
Chatbots
Emerging A I technologies
Effective computing
Biometric analysis

Knowledge and Expert Systems (2 of 2)
Cognitive computing
Knowledge derived from cognitive science
Self learning algorithms
I B M Watson
More on this is covered in Chapter 6
Augmented reality
Augmentation: integration of digital information within the user environment in real time
Real + virtual combined
Virtual reality

Automated Decision Making Process

A I Support for Decision Making
Jeff Bezos, the C E O of Amazon.com, said in May 2017 that A I is in a golden age …
A I 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.
Watch “A I Will Be Making Decisions for You” at https://www.youtube.com/watch?v=Dr9jeRy9whQ

Using A I in Decision Making
Issues & factors:
The nature of the decision [routine vs non-routine]
The method of support / technologies used
Expert systems, recommender systems
Deep learning, pattern recognition, biometrics recognition
Cos-benefit and risk analysis
Using business rules
A I algorithms
Speed

A I Support for Decision-Making Process
As it relates to Simon’s decision making process (see Chapter 1 for the background information)
A I support in problem identification
A I support in generating or finding alternative solutions
A I support in selecting a solution
A I support in implementing the solution
A I can (and should) play a role in each and every step in the decision making process

Application Case 2.3
How Companies Solve Real-World Problems Using Google’s Machine-Learning Tools
Questions for Discussion:
Why use machine learning for predictions?
Why use machine learning for detections?
What specific decisions were supported in the five cases?

Intelligent & Automated Decision Support
Automated decision making (since 1970s)
Common examples:
Small loan approvals
Initial screening of job applicants
Simple restocking
Prices of products and services (when and how to change them)
Product recommendation (e.g., at Amazon.com)
Example: Supporting Nurses Diagnosis Decisions
An experiment conducted in a Taiwanese hospital (in 2015)
87% agreement between A I and human experts

Technology Insight 2.2
Schrage’s Models for Using A I to Make Decisions
The autonomous advisor
The autonomous outsource
People-machine collaboration
Complete machine autonomy
Implementing these four models require appropriate management leadership and collaboration with data scientists.

A I Applications in Accounting
A I in big accounting firms (see application case 2.4)
A I in small accounting firms
Solve complex billing problems (especially in healthcare)
Claim processing and reimbursement
Real estate contracts, risk analysis …
A I 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
A I will improve and automate accounting tasks but at the same time will take away some accounting jobs.

Application Case 2.4
How E Y, Deloitte, and P w C Are Using A I
Questions for Discussion:
What are the characteristics of the tasks for which A I is used?
Why do the big accounting firms use different implementation strategies?

A I Applications in Financial Services (1 of 2)
Diverse use of A I, in banking and insurance.
Examples of A I use in general 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
Banking can also uses A I for …
Face recognition (safer online banking), help customer with smart investment decisions, prevent money laundering, …
Insurance – mostly in issuing policies and handling claims

A I Applications in Financial Services (2 of 2)
Application of A I uses in Banking
Employee surveillance (A I machines, e.g., I B M Watson).
Tax preparation/filing (H&R block uses I B M Watson).
Automated customer service; answering customer inquiries in real-time.
See Rainbird Co. ar rainbirf.ai as a company that provides such services (using I B M Watson).
Automated online support for paying bills and account inquiries using Amazon Alexa (e.g., Capital One).
Fraud detection and anti–money-laundering activities; also improving customer experience (Bank Danamon).
Victual banking assistant, Olivia at H S B C, learn from experience and helps customer better.

Application Case 2.5
U S Bank Customer Recognition and Services
Questions for Discussion:
What are Einstein’s advantages to U S Bank?
What are its advantages to customers?
What are the benefits of voice communication?

A I in Human Resource Management (1 of 2)
Recruitment – talent acquisition
See Application Case 2.6 for an example
Training – A I facilitates training
Performance assessment (evaluation)
Retention –eliminating attrition
Predicting attrition way ahead of time to eliminate loss of talent
Using chatbots for supporting H R M
See olivia.paradox.ai.

A I in Human Resource Management (2 of 2)
Introducing A I to H R M operations:
Experiment with a variety of chatbots
Develop a team approach involving other functional areas
Properly plan a technology roadmap for both the short and long term, including shared vision with other functional areas
Identify new job roles and modifications in existing job roles in the transformed environment
Train and educate the H R M team to understand A I and gain expertise in it.

Application Case 2.6
How Alexander Mann Solutions (A M S) Is Using A I to Support the Recruiting Process
Questions for Discussion:
What types of decisions are supported?
Comment on the human–machine collaboration.
What are the benefits to recruiters? To applicants?
Which tasks in the recruiting process are fully automated?
What are the benefits of such automation?

A I in Marketing, Advertising, & C R M (1 of 2)
One of the richest area for A I applications:
Product and personal recommendations
Smart search engines
Fraud and data breaches detection
Social semantics
Web site design
Producer pricing
Predictive customer service
… many more in the book …

A I in Marketing, Advertising, & C R M (2 of 2)
Improving customer experience and C R M
Use N L P for generating user documentation. This capability also improves the customer–machine dialogue.
Use visual categorization to organize images (for example, see I B M’s Visual Recognition and Clarifai)
Provide personalized and segmented services by analyzing customer data. This includes
A I in C R M Example: Salesforce’s A I Einstein

Application Case 2.7
Kraft Foods Uses A I for Marketing and C R M
Questions for Discussion:
Identify all A I technologies used in the Food Assistant.
List the benefits to the customers.
List the benefits to Kraft Foods.
How is advertising done?
What role is “behavioral pattern recognition” playing?
Compare Kraft’s Food Assistant to Amazon.com and Netflix recommendation systems.

A I in Production-Operation Management
A I in manufacturing
Automation for compliance and cost reduction
React quicker and more effectively (agility)
Implementation model
Streamlining processes, smart outsourcing, work automation, improving customer experience
Intelligent factories
Logistic and transportation
Example: D H L supply-chain

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