1. Sharda_dss11_im_01.doc
Chapter 1:
An Overview of Analytics, and AI
Learning Objectives for Chapter 1
· Understand the need for computerized support of managerial
decision making
· Understand the development of systems for providing
decision-making support
· Recognize the evolution of such computerized support to the
current state of analytics/data science and artificial intelligence
· Describe the business intelligence (BI) methodology and
concepts
· Understand the different types of analytics and review selected
applications
· Understand the basic concepts of artificial intelligence (AI)
and see selected applications
· Understand the analytics ecosystem to identify various key
players and career opportunities
CHAPTER OVERVIEW
The business environment (climate) is constantly changing, and
it is becoming more and more complex. Organizations, both
private and public, are under pressures that force them to

2. respond quickly to changing conditions and to be innovative in
the way they operate. Such activities require organizations to be
agile and to make frequent and quick strategic, tactical, and
operational decisions, some of which are very complex. Making
such decisions may require considerable amounts of relevant
data, information, and knowledge. Processing these in the
framework of the needed decisions must be done quickly,
frequently in real time, and usually requires some computerized
support. As technologies are evolving, many decisions are being
automated, leading to a major impact on knowledge work and
workers in many ways. This book is about using business
analytics and artificial intelligence (AI) as a computerized
support portfolio for managerial decision making. It
concentrates on the theoretical and conceptual foundations of
decision support as well as on the commercial tools and
techniques that are available. The book presents the
fundamentals of the techniques and the manner in which these
systems are constructed and used. We follow an EEE (exposure,
experience, and exploration) approach to introducing these
topics. The book primarily provides exposure to various
analytics/AI techniques and their applications. The idea is that
students will be inspired to learn from how various
organizations have employed these technologies to make
decisions or to gain a competitive edge. We believe that such
exposure to what is being accomplished with analytics and that
how it can be achieved is the key component of learning about
analytics. In describing the techniques, we also give examples
of specific software tools that can be used for developing such
applications. However, the book is not limited to any one
software tool, so students can experience these techniques using
any number of available software tools. We hope that this
exposure and experience enable and motivate readers to explore
the potential of these techniques in their own domain. To
facilitate such exploration, we include exercises that direct the
reader to Teradata University Network (TUN) and other sites
that include team-oriented exercises where appropriate. In our

3. own teaching experience, projects undertaken in the class
facilitate such exploration after students have been exposed to
the myriad of applications and concepts in the book and they
have experienced specific software introduced by the professor.
This chapter has the following sections:
CHAPTER OUTLINE
1.1 Opening Vignette: How Intelligent Systems Work for KONE
Elevators and Escalators Company
1.2 Changing Business Environments and Evolving Needs for
Decision Support and Analytics
1.3 Decision-Making Processes and Computer Decision Support
Framework
1.4 Evolution of Computerized Decision Support to Business
Intelligence/ Analytics/Data Science
1.5 Analytics Overview
1.6 Analytics Examples in Selected Domains
1.7 Artificial Intelligence Overview
1.8 Convergence of Analytics and AI
1.9 Overview of the Analytics Ecosystem
1.10 Plan of the Book
1.11 Resources, Links, and the Teradata University Network
Connection
ANSWERS TO END OF SECTION REVIEW QUESTIONS( ( (
( ( (

4. Opening Vignette Questions
1. It is said that KONE is embedding intelligence across its
supply chain and enables smarter buildings. Explain.
KONE uses a variety of IoT applications to record and
communicate a wide variety of systems status and performance
information that can then be used to identify issues and collect
important data for future applications.
2. Describe the role of IoT in this case.
IoT allows for the collection of multiple discrete points of data
throughout the systems that can be used in a variety of
applications.
3. What makes IBM Watson a necessity in this case?
IBM Watson serves to both collect and analyze the wide variety
of information presented. It can then communicate this
information to other systems and establish patterns based on the
data collected.
4. Check IBM Advanced Analytics. What tools were included
that relate to this case?
The tools available have many possible applications to the case,
specifically the ability to evaluate the data collected across a
large number of systems and different parameters.
5. Check IBM cognitive buildings. How do they relate to this
case?
This solution uses many similar technologies that appears to
focus primarily on the ability to detect issues and potential
issues within the building.

5. Section 1.2 Review Questions
1. Why is it difficult to make organizational decisions?
Organizational decisions may be difficult to make due to a
complex process necessary to both identify and define the
problem as well as evaluate the host of different possible
solutions.
2. Describe the major steps in the decision-making process.
· 1.Define the problem (i.e., a decision situation that may deal
with some difficulty or with an opportunity).
· 2. Construct a model that describes the real-world problem.
· 3. Identify possible solutions to the modeled problem and
evaluate the solutions.
· 4. Compare, choose, and recommend a potential solution to the
problem.
3. Describe the major external environments that can impact
decision making.
· Political factors. Major decisions may be influenced by both
external and internal politics. An example is the 2018 trade war
on tariffs.
· Economic factors. These range from competition to the genera
and state of the economy. These factors, both in the short and
long run, need to be considered.
· Sociological and psychological factors regarding employees
and customers. These need to be considered when changes are
being made.

6. · Environment factors. The impact on the physical environment
must be assessed in many decision-making situations.
4. What are some of the key system-oriented trends that have
fostered IS-supported decision making to a new level?
Computer applications have shifted from merely processing
transaction and monitoring activities to actively analyzing and
seeking solution to problems through cloud-based systems.
5. List some capabilities of information technologies that can
facilitate managerial decision making.
· Group communication and collaboration
· Improved data management.
· Managing giant data warehouses and Big Data
· Analytical support.
· Overcoming cognitive limits in processing and storing
information
· Knowledge management.
· Anywhere, anytime support.
Section 1.3 Review Questions
1. List and briefly describe Simon’s four phases of decision
making.
Simon’s four phases of decision making are intelligence,
design, choice, and implementation.

7. · Intelligence consists of gathering information by examining
reality, then identifying and defining the problem. In this phase
problem ownership should also be established.
· Design consists of determining alternatives and evaluating
them. If the evaluation will require construction of a model, that
is done in this phase as well.
· The choice phase consists of selecting a tentative solution and
testing its validity.
· Implementation of the decision consists of putting the selected
solution into effect.
2. What is the difference between a problem and its symptoms?
Problems arise out of dissatisfaction with the way things are
going. It is the result of a difference or gap between what we
desire and what is or is not happening. A symptom is how a
problem manifests itself. A familiar personal example is a high
temperature (symptom) and an illness (problem). It is necessary
to diagnose and treat the underlying illness. Attempting to
relieve the temperature works if the illness is one which the
body’s defenses can cure, but, can be disastrous in other
situations. A business example: high prices (problem) and high
unsold inventory level (symptom). Another is quality variance
in a product (symptom) and poorly calibrated or worn-out
manufacturing equipment (problem).
3. Why is it important to classify a problem?
Classifying a problem enables decision makers to use tools that
have been developed to deal with problems in that category,
perhaps even including a standard solution approach.

8. 4. Define implementation.
Implementation involves putting a recommended solution to
work, but not necessarily implementing a computer system.
5. What are structured, unstructured, and semistructured
decisions? Provide two examples of each.
· Structured problem, the procedures for obtaining the best (or
at least a good enough) solution are known. Examples would
include commonly and historically addressed issues and
problems within a business or industry.
· Unstructured decisions are fuzzy, complex problems for which
there are no cut-and-dried solution methods. Examples would
include issues or problems within a business or industry that
combined multiple structured problems or problems where the
necessary data or research is not readily available.
· Unstructured problem is one where the articulation of the
problem or the solution approach may be unstructured in itself.
Examples would include problems within the business or
industry where the definition of the problem itself is not agreed
upon where the data is not readily available and there may
currently exist no ability to collect that data.
6. Define operational control, managerial control, and strategic
planning. Provide two examples of each.
· Operational control focuses on the day to day monitoring and
control over plans with existing measures and defined actions.
Examples may include monitoring Accounts Receivable or
controlling inventory.
· Managerial control focuses on short-term control over existing
plans where existing actions and measures may be defined, that
may also require individual or group decision-making to apply

9. or amend to meet the required result. Examples may include
preparing budgets and negotiating contracts.
· Strategic planning focuses on mid and long term planning that
directs the core activities and initiatives of the business.
Examples may include decisions to make major purchases or
conduct research and development.
7. What are the nine cells of the decision framework? Explain
what each is for.
The nine cells of the decision framework (see figure 1.2) aligns
the three types of decisions (structured, semistructured and
unstructured) with the three types of control (operational,
managerial and strategic). Each of these cells can provide
information about the types of decisions that need to be made
based on the availability of information on past decisions or
data for decision-making as well as the level of the decision-
making involved.
8. How can computers provide support for making structured
decisions?
Computers can be instrumental in providing information for
structured decisions because they can be used to collect the
underlying data needed for the decision as well as providing a
known system to abstract analyze and classify possible actions
or results.
9. How can computers provide support for making
semistructured and unstructured decisions?
In these situations, computers can be used to collect the
underlying information needed for decision as well as
potentially applying some of the learnings from past solutions
that may exist. Additionally they may provide the computational
ability to conduct a thorough analysis of the identified problem.

10. Section 1.4 Review Questions
1. List three of the terms that have been predecessors of
analytics.
These terms include decision support systems (DSS), executive
information systems (EIS) and business intelligence (BI).
2. What was the primary difference between the systems called
MIS, DSS, and Executive Information Systems?
The primary differences between the systems are the amount of
information available for analysis as well as the sophistication
of the display and problem solving capabilities of each.
3. Did DSS evolve into BI or vice versa?
Systems and products referred to as DSS transitioned into the
BIA label, although both are content free expressions and mean
different things to different professionals.
4. Define BI.
Business intelligence (BI) is an umbrella term that combines
architectures, tools, databases, analytical tools, applications,
and methodologies.
5. List and describe the major components of BI.
There are three major components to BI:
· the data warehouse environment that organizes summarizes
and standardizes business data
· the business analytic environment which uses the data
warehouse to access and manipulate data to display results

11. · the performance and strategy component that utilizes
information from the analytic environment to create more
detailed analyses and strategy
6. Define OLTP.
Online transaction processing (OLTP) systems handle a
company’s routine ongoing business.
7. Define OLAP.
Online analytical processing (OLAP) systems are used to
process information and research requests.
8. List some of the implementation topics addressed by
Gartner’s report.
The Gartner report proposed splitting planning and executing
into four categories; business organization functionality and
infrastructure components.
9. List some other success factors of BI.
Other success factors may include ease of availability of
software and solutions for self-service, integration of DI into
the corporate culture and appropriate integration between
various BI tools.
Section 1.5 Review Questions
1. Define analytics.
The term replaces terminology referring to individual
components of a decision support system with one broad word
referring to business intelligence. More precisely, analytics is
the process of developing actionable decisions or

12. recommendations for actions based upon insights generated
from historical data. Students may also refer to the eight levels
of analytics and this simpler descriptive language: “looking at
all the data to understand what is happening, what will happen,
and how to make the best of it.”
2. What is descriptive analytics? What various tools are
employed in descriptive analytics?
Descriptive analytics refers to knowing what is happening in the
organization and understanding some underlying trends and
causes of such occurrences. Tools used in descriptive analytics
include data warehouses and visualization applications.
3. How is descriptive analytics different from traditional
reporting?
Descriptive analytics gathers more data, often automatically. It
makes results available in real time and allows reports to be
customized.
4. What is a DW? How can DW technology help in enabling
analytics?
A data warehouse, introduced in Section 1.7, is the component
of a BI system that contains the source data. As described in
this section, developing a data warehouse usually includes
development of the data infrastructure for descriptive
analytics—that is, consolidation of data sources and making
relevant data available in a form that enables appropriate
reporting and analysis. A data warehouse serves as the basis for
developing appropriate reports, queries, alerts, and trends.
5. What is predictive analytics? How can organizations employ
predictive analytics?

13. Predictive analytics is the use of statistical techniques and data
mining to determine what is likely to happen in the future.
Businesses use predictive analytics to forecast whether
customers are likely to switch to a competitor, what customers
are likely to buy, how likely customers are to respond to a
promotion, and whether a customer is creditworthy. Sports
teams have used predictive analytics to identify the players
most likely to contribute to a team’s success.
6. What is prescriptive analytics? What kind of problems can be
solved by prescriptive analytics?
Prescriptive analytics is a set of techniques that use descriptive
data and forecasts to identify the decisions most likely to result
in the best performance. Usually, an organization uses
prescriptive analytics to identify the decisions or actions that
will optimize the performance of a system. Organizations have
used prescriptive analytics to set prices, create production
plans, and identify the best locations for facilities such as bank
branches.
7. Define modeling from the analytics perspective.
As Application Case 1.6 illustrates, analytics uses descriptive
data to create models of how people, equipment, or other
variables operate in the real world. These models can be used in
predictive and prescriptive analytics to develop forecasts,
recommendations, and decisions.
8. Is it a good idea to follow a hierarchy of descriptive and
predictive analytics before applying prescriptive analytics?
As noted in the analysis of Application Case 1.5, it is important

14. in any analytics project to understand the business domain and
current state of the business problem. This requires analysis of
historical data, or descriptive analytics. Although the chapter
does not discuss a hierarchy of analytics, students may observe
that testing a model with predictive analytics could logically
improve prescriptive use of the model.
9. How can analytics aid in objective decision making?
As noted in the analysis of Application Case 1.4, problem
solving in organizations has tended to be subjective, and
decision makers tend to rely on familiar processes. The result is
that future decisions are no better than past decisions. Analytics
builds on historical data and takes into account changing
conditions to arrive at fact-based solutions that decision makers
might not have considered.
10. What is Big Data analytics?
The term Big Data refers to data that cannot be stored in a
single storage unit. Typically, the data is arriving in many
different forms, be they structured, unstructured, or in a stream.
Big Data analytics is analytics on a large enough scale, with
fast enough processing, to handle this kind of data.
11. What are the sources of Big Data?
Major sources include clickstreams from Web sites, postings on
social media, and data from traffic, sensors, and the weather.
12. What are the characteristics of Big Data?
Today Big Data refers to almost any kind of large data that has
the characteristics of volume, velocity, and variety. Examples
include data about Web searches, such as the billions of Web

15. pages searched by Google; data about financial trading, which
operates in the order of microseconds; and data about consumer
opinions measured from postings in social media.
13. What processing technique is applied to process Big Data?
One computer, even a powerful one, could not handle the scale
of Big Data. The solution is to push computation to the data,
using the MapReduce programming paradigm.
Section 1.6 Review Questions
1. What are three factors that might be part of a PM for season
ticket renewals?
Examples might include ticket cost, marketing and team
success.
2. What are two techniques that football teams can use to do
opponent analysis?
Examples might include frequency of running plays and
individual athlete trends and matchups.
3. What other analytics uses can you envision in sports?
Many examples exist including maintenance of facilities and
accuracy of referees.
4. Why would a health insurance company invest in analytics
beyond fraud detection? Why is it in its best interest to predict
the likelihood of falls by patients?
There are many possible applications, for example insurance
companies may want to evaluate causes for conditions so that
those conditions can be avoided. An excellent example of this is

16. patient falls. Having this information allows for preventive
measures to be taken before a fall occurs.
5. What other applications similar to prediction of falls can you
envision?
Student responses will vary that may include prediction of other
conditions such as cancer.
6. How would you convince a new health insurance customer to
adopt healthier lifestyles (Humana Example 3)?
Data can be used to demonstrate to a customer that adoption of
a healthier lifestyle may limit the negative experiences
associated with various conditions or diseases.
7. Identify at least three other opportunities for applying
analytics in the retail value chain beyond those covered in this
section.
Student responses will vary.
8. Which retail stores that you know of employ some of the
analytics applications identified in this section?
Student responses will vary.
9. What is a common thread in the examples discussed in image
analytics?
In each analysis a detailed understanding of both the image data
and other supplementary data sources were used to create
solutions.
10. Can you think of other applications using satellite data
along the lines presented in this section?

17. Student responses will vary.
Section 1.7 Review Questions
1. What are the major characteristics of AI?
•
Technology that can learn to do things better over time.
•
Technology that can understand human language.
•
Technology that can answer questions.
2. List the major benefits of AI.
•
Significant reduction in the cost of performing work. This
reduction continues over time while the cost of doing the same
work manually increases with time.
•
Work can be performed much faster.
•
Work is consistent in general, more consistent than human
work.
•
Increased productivity and profitability as well as a competitive
advantage are the major drivers of AI.
3. What are the major groups in the ecosystem of AI? List the
major contents of each.

18. · Major Technologies include machine learning, deep learning
and intelligent agents.
· Knowledge-based technologies include expert systems,
recommendation engines, chat bots, virtual personal assistants
and robo advisors.
· Biometric related technologies include natural language
processing and other biometric recognition technologies
· support theories, tools and platforms include a variety of
disciplines such as computer science, cognitive science, control
theory, linguistics, mathematics, neuroscience, philosophy,
psychology, and statistics.
· Tools and platforms include the various software applications
and systems available from a wide number of vendors.
4. Why is machine learning so important?
Machine learning presents the promise of creating more
effective and accurate solutions to problems without the direct
intervention of individuals.
5. Differentiate between narrow and general AI.
Narrow AI focuses on a specific, defined domain whereas
general AI may cross multiple domains and become more
powerful as it is refined.
6. Some say that no AI application is strong. Why?
No AI currently performs the full range of human cognitive
capabilities.
7. Define assisted intelligence, augmented intelligence, and

19. autonomous intelligence.
· Assisted intelligence is the equivalent of week AI and works
within narrow domains.
· Augmented intelligence use computer abilities to extend
human cognitive abilities.
· Automated intelligence perform a broad range of functions
without human intervention.
8. What is the difference between traditional AI and augmented
intelligence?
These systems are designed to extend human capabilities as
opposed to replacing them.
9. Relate types of AI to cognitive computing.
Not addressed in this chapter, but students may note that both
can be designed to perform tasks.
10. List five major AI applications for increasing the food
supply.
Examples include increasing productivity of farm equipment,
improved planting and harvesting, improving food nutrition,
reducing the cost of food processing, driverless machines,
picking fruits and vegetables, pest control improvements and
weather monitoring.
11. List five contributions of AI in medical care.
Examples include disease prediction, tracking medication
intake, telepresence, improved diagnostics, more efficient
supply chains, personal diagnoses, providing medical
information and others.

20. Section 1.8 Review Questions
1. What are the major benefits of intelligent systems
convergences?
This convergence allows for a greater number of overall
features and applications to more complex problems as multiple
systems can be combined.
2. Why did analytics initiatives fail at such a high rate in the
past?
Responses will vary but may focus on a lack of availability of
data, lack of processing tools and complexity of the required
analysis.
3. What synergy can be created by combining AI and analytics?
AI may be used to automatically locate, visualize and narrate
important items and can be used to create predictions that can
be compared to actual performance. These activities will free up
time for more analytics.
4. Why is Big Data preparation essential for AI initiatives?
AI works best when it has access to robust data sources.
Properly preparing big data for use in AI allows data to be used
completely and effectively.
5. What are the benefits of adding IoT to intelligent technology
applications?
The primary benefit is the inclusion of additional data that can
be used for various types of analysis.
6. Why it is recommended to use blockchain in support of
intelligent applications?

21. The use of block chain technology can add security to data in a
distributed network.
Section 1.9 Review Questions
(This section has no review questions.)
Section 1.10 Review Questions
(This section has no review questions.)
ANSWERS TO APPLICATION CASE QUESTIONS FOR
DISCUSSION( (
Application Case 1.1: Making Elevators Go Faster!
1. Why this is an example relevant to decision making?
This is an example of how the symptoms may not directly reveal
the problem (perceived versus actual wait time being the issue).
2. Relate this situation to the intelligence phase of decision
making.
This situation demonstrates how the intelligence phase of
decision-making is important because detailed problem
identification is necessary in order to create a satisfactory
solution.
Application Case 1.2: SNAP DSS Helps OneNet Make
Telecommunications Rate Decision
(No questions in this case)
Application Case 1.3: Silvaris Increases Business with Visual
Analysis and Real-Time Reporting Capabilities
1. What was the challenge faced by Silvaris?

22. Material prices changed rapidly and it was necessary to receive
a real-time view of data without moving that data to a separate
reporting format.
2. How did Silvaris solve its problem using data visualization
with Tableau?
Tableau allow the company to easily connect and visualize live
data and create dashboards for reporting purposes.
Application Case 1.4: Siemens Reduces Cost with the Use of
Data Visualization
1. What challenges were faced by Siemens visual analytics
group?
The group needed to provide a wide range of reports for
different organizational needs while maintaining consistency
and self-service ability.
2. How did the data visualization tool Dundas BI help Siemens
in reducing cost?
The system allowed them to create highly interactive
dashboards that enabled early detection of issues.
Application Case 1.5: Analyzing Athletic Injuries
1. What types of analytics are applied in the injury analysis?
In this example both reporting and predictive analysis were
included.
2. How do visualizations aid in understanding the data and
delivering insights into the data?
These visualizations made understanding and depicting the
information easier by displaying healing time based on position,
severity of injury or injuries healing time treatment offered in

23. the associated healing time etc.
3. What is a classification problem?
An issue that occurs in this case when the type of healing
category is incorrectly identified, leading to an incorrect
prediction of healing time.
4. What can be derived by performing sequence analysis?
Student responses may vary, but in this example it may be
possible to predict how one injury may result in other injuries
later.
Application Case 1.6: A Specialty Steel Bar Company Uses
Analytics to Determine Available-to-Promise Date
1. Why would reallocation of inventory from one customer to
another be a major issue for discussion?
This action may require a discount to the first customer or may
result in the delay that may jeopardize the customer
relationship.
2. How could a DSS help make these decisions?
A DSS system would provide greater visibility into actual
inventories, expected inventories and potential customer
implications of reallocation of inventory.
Application Case 1.7: A Specialty Steel Bar Company Uses
Analytics to Determine Available-to-Promise Date
1. What is the purpose of knowing how much ground is covered
by green foliage on a farm? In a forest?
In a farm setting, this may indicate the level of plant growth. In
a forest setting, this may provide details on how the forest is
evolving.
2. Why would image analysis of foliage through an app be

24. better than a visual check?
It will provide a more consistent quantitative estimate than
individual qualitative perceptions of growth.
3. Explore research papers to understand the underlying
algorithmic logic of image analysis. What did you learn?
Student research and responses will vary. Results may indicate
that there are different methods of analysis and that this is a
rapidly changing field.
4. What other applications of image analysis can you think of?
Student responses will vary.
Application Case 1.8: AI Increases Passengers’ Comfort and
Security in Airports and Borders
1. List the benefits of AI devices to travelers.
Benefits will include faster processes such as recognition, more
accurate processes and providing additional services.
2. List the benefits to governments and airline companies.
Benefits will include more accurate, faster and more cost
efficient services being provided.
3. Relate this case to machine vision and other AI tools that
deal with people’s biometrics
This case provides an example of how machine vision and other
AI tools can be used as a part of biometric recognition systems
that more quickly and accurately identify individuals as they
enter an airport.
Application Case 1.9: Robots Took the Job of Camel-Racing
Jockeys for Societal Benefits

25. 1. It is said that the robots eradicated the child slavery. Explain.
This is because robots have replaced children who in the past
may have been kidnapped to act as jockeys.
2. Why do the owners need to drive by their camels while they
are racing?
This is necessary for the camels to react and run. Additionally
owners can vary their interaction with the camel based on how
the camel is performing in comparison to the others in the race.
3. Why not duplicate the technology for horse racing?
Student opinions and responses will vary, but may focus on the
lack of child slavery in Western horseracing.
4. Summarize ethical aspects of this case (Read Boddington,
2017). Do this exercise after you have read about ethics in
Chapter 14.
Student responses will vary.
Application Case 1.10: Amazon Go Is Open for Business
1. Watch the video. What did you like in it, and what did you
dislike?
Student preferences will vary.
2. Compare the process described here to a selfcheck available
today in many supermarkets and “big box” stores (Home Depot,
etc.).
The major difference is that products are scanned as they are
added to a bag, as opposed to using a checkout kiosk.

26. 3. The store was opened in downtown Seattle. Why was the
downtown location selected?
This location was selected because of the proximity of a large
number of potential customers.
4. What are the benefits to customers? To Amazon?
Customers benefit from the ability to quickly purchase items
without a shipping time. Amazon is able to capture additional
sales that may not have been available before due to immediate
needs.
5. Will customers be ready to trade privacy for convenience?
Discuss.
Student responses will vary, but may focus on the lack of
privacy in existing web-based sales.
ANSWERS TO END OF CHAPTER QUESTIONS FOR
DISCUSSION( ( (
1. Survey the literature from the past six months to find one
application each for DSS, BI, and analytics. Summarize the
applications on one page, and submit it with the exact sources.
Student responses and research will vary.
2. Your company is considering opening a branch in China. List
typical activities in each phase of the decision (intelligence,
design, choice, and implementation) regarding whether to open
a branch.
While student responses may vary, typical answers may include:
· Intelligence - data collection on customers and markets,
identification of overall objective, statements of problems to be
solved prior to opening the branch

27. · Design - setting criteria for the decisions to be made, creating
a decision model, identification of alternatives and outcomes
· Choice - sensitivity analysis of choices, selection of solution
to the problems planning for implementation
· Implementation - opening the new branch in China
3. You are about to buy a car. Using Simon’s (1977) four phase
model, describe your activities at each step in making the
decision.
While student responses may vary, typical answers may include:
· Intelligence - understanding needs for a car, collection of
information on different models, definition of the problem
· Design - setting selection criteria for a car, generating a
decision model based on criteria
· Choice - using the model to make a selection
· Implementation - purchasing the car
4. Explain, through an example, the support given to decision
makers by computers in each phase of the decision process.
While student responses may vary, typical answers may include:
· Intelligence - collection and formatting of data
· Design - identification of potential criteria and calculations
required for a model
· Choice - calculation of the model and sensitivity analysis
5. Comment on Simon’s (1977) philosophy that managerial
decision making is synonymous with the whole process of
management. Does this make sense? Explain. Use a real-world
example in your explanation.
Student responses and opinions will vary. Students may note
that much of management is the understanding of challenges

28. and the creation of solutions to those challenges. Some students
may note that managing others may not be approached in this
fashion, although it may be. Student examples will vary based
on their own types of experience in or with management roles.
6. Review the major characteristics and capabilities of DSS.
How does each of them relate to the major components of DSS?
A DSS includes a variety of characteristics with associated
capabilities. Each of these capabilities may be housed in one or
more DSS system components. The arrangement of this
architecture will vary based on system. The characteristics of
the DSS are listed below:
· Provides support for semistructured or unstructured problems
· Supports managers at all levels
· Supports individuals and groups
· Supports interdependent or sequential decisions
· Supports intelligence, design, choice, and implementation
· Support variety of decision processes and styles
· Is adaptable and flexible
· Provides interactivity, ease of use
· Improves effectiveness and efficiency
· Provides complete human control of the process
· Provides ease of development by end users

29. · Provides models and analysis
· Provides data access
· Can be standalone, integrated, and Web-based tool
7. List some internal data and external data that could be found
in a DSS for a university’s admissions office.
Student responses will vary, but may include some of the
following examples:
· internal data - application information, results of application
essays
· external data - high school GPA, results from standardized
tests
8. Distinguish BI from DSS.
A DSS is typically built to support the solution of a certain
problem or to evaluate an opportunity. This is a key difference
between DSS and BI applications. In a very strict sense,
business intelligence (BI) systems monitor situations and
identify problems and/or opportunities using analytic methods.
Business intelligence (BI) is an umbrella term that combines
architectures, tools, databases, analytical tools, applications,
and methodologies. It is, like DSS, a content-free expression, so
it means different things to different people.
9. Compare and contrast predictive analytics with prescriptive
and descriptive analytics. Use examples.
Predictive analytics aims to determine what will likely happen
in the future, whereas descriptive analytics describe what has
happened in the past. Prescriptive analytics seeks to recognize
what is currently going on as well as creating forecasts.

30. Student examples will vary, but may include:
· predictive analytics - using existing data from a DW to create
a forecast of future events
· descriptive analytics - using existing data from DW to
describe what is happened in the past
· prescriptive analytics - using live or current data to understand
current operations and forecast future results to aid in decision-
making
10. Discuss the major issues in implementing BI.
Student responses will vary, but may focus on several issues
that have occurred in implementing BI. These issues may
include:
· availability of data
· ability to format and use data
· ability to use data from multiple sources
· ability to determine root problems
· time required for analysis
· ability to quickly create ongoing analyses
ANSWERS TO END OF CHAPTER EXCERCISES( ( (
Teradata University Network and Other Hands-On Exercises
1. Go to the TUN site teradatauniversitynetwork.com. Using the
site password your instructor provides, register for the site if
you have not already previously registered. Log on and learn the

31. content of the site. You will receive assignments related to this
site. Prepare a list of 20 items on the site that you think could
be beneficial to you.
Student reports will vary based on interest.
2. Go to. Explore the Sports Analytics page, and summarize at
least two applications of analytics in any sport of your choice.
Student reports will vary based on selection of applications.
3. Go to. The TUN site, and select “Cases, Projects, and
Assignments.” Then select the case study “Harrah’s High Payoff
from Customer Information.” Answer the following questions
about this case:
a. What information does the data mining generate?
b. How is this information helpful to management in decision
making? (Be specific.)
c. List the types of data that are mined.
d. Is this a DSS or BI application? Why?
Student reports will vary.
4. Go to teradatauniversitynetwork.com and find the paper titled
“Data Warehousing Supports Corporate Strategy at First
American Corporation” (by Watson, Wixom, and Goodhue).
Read the paper, and answer the following questions:
a. What were the drivers for the DW/BI project in the company?
b. What strategic advantages were realized?

32. c. What operational and tactical advantages were achieved?
d. What were the critical success factors for the
implementation?
e. What data analysis techniques are employed in the project?
Comment on some initiatives that resulted from data analysis.
f. What are the different prediction problems answered by the
models?
g. List some of the actionable decisions taken that were based
on the prediction results.
h. Identify two applications of Big Data analytics that are not
listed in the article.
Student evaluation of the paper will vary.
5. Go to http://analytics-magazine.org/issues/digitaleditions and
find the January/February 2012 edition titled “Special Issue:
The Future of Healthcare.” Read the article “Predictive
Analytics—Saving Lives and Lowering Medical Bills.” Answer
the following questions:
a. What problem is being addressed by applying predictive
analytics?
b. What is the FICO Medication Adherence Score?
c. How is a prediction model trained to predict the FICO
Medication Adherence Score HoH? Did the prediction model
classify the FICO Medication Adherence Score?
d. Zoom in on Figure 4, and explain what technique is applied
to the generated results.

33. e. List some of the actionable decisions that were based on the
prediction results.
Student analysis of the report will vary.
6. Go to http://analytics-magazine.org/issues/digitaleditions,
and find the January/February 2013 edition titled “Work
Social.” Read the article “Big Data, Analytics and Elections,”
and answer the following questions:
a. What kinds of Big Data were analyzed in the article’s Coo?
Comment on some of the sources of Big Data.
b. Explain the term integrated system. What is the other
technical term that suits an integrated system?
c. What data analysis techniques are employed in the project?
Comment on some initiatives that resulted from data analysis.
d. What are the different prediction problems answered by the
models?
e. List some of the actionable decisions taken that were based
on the prediction results.
f. Identify two applications of Big Data analytics that are not
listed in the article.
Student analysis of the report will vary.
6. Search the Internet for material regarding the work of
managers and the role analytics plays in it. What kinds of
references to consulting firms, academic departments, and
programs do you find? What major areas are represented? Select
five sites that cover one area, and report your findings.

34. Student searches and reports will vary
7. Explore the public areas of dssresources.com. Prepare a list
of its major available resources. You might want to refer to this
site as you work through the book.
Student list will vary based on the time the search is conducted.
8. Go to microstrategy.com. Find information on the five styles
of BI. Prepare a summary table for each style.
Student summaries will vary.
9. Go to oracle.com, and click the Hyperion link under
Applications. Determine what the company’s major products
are. Relate these to the support technologies cited in this
chapter.
Student reports will vary based on the time the analysis is
conducted.
10. Go to the TUN questions site. Look for BSI videos. Review
the video of “Case of Retail Tweeters.” Prepare a one-page
summary of the problem, proposed solution, and the reported
results. You can also find associated slides on slideshare.net.
Student papers will vary.
11. Review the Analytics Ecosystem section. Identify at least
two additional companies in at least five of the industry clusters
noted in the discussion.
Student selection of companies will vary.
12. The discussion for the analytics ecosystem also included

35. several typical job titles for graduates of analytics and data
science programs. Research Web sites such as
datasciencecentral.com and tdwi.org to locate at least three
similar job titles that you may find interesting for your career.
Student research and career interests will vary.
13. Go to Brainspace at MIT lab brainspace.com. View the
video about “Augmented Human Intelligence.” Find the
activities that deal with the enabling of meaningful combination
of people and machines. Write a report.
Student reports will vary.
14. Find information about IBM Watson’s activities in the
healthcare field. Write a report.
Student reports will vary based on the date the research is
conducted.
15. Examine Daniel Power’s DSS Resources site at
dssresources.com . Take the Decision Support Systems Web
Tour (dssresources.com/tour/index.html).
Explore other areas of the Web site. List at least three recent
resources related to analytics. What topics do these cover?
Student perceptions of the resources will vary.
1
Copyright © 2014 Pearson Education, Inc.

36. 20
Copyright © 2014 Pearson Education, Inc.
21
Copyright © 2019 Pearson Education, Inc.
Sharda_dss11_im_02.docx
23
Chapter 2:
Artificial Intelligence Concepts, Drivers, Major Technologies,
and Business Applications
Learning Objectives for Chapter 2
1. Understand the concepts of artificial intelligence (AI)
2. Become familiar with the drivers, capabilities, and benefits
of AI
3. Describe human and machine intelligence
4. Describe the major AI technologies and some derivatives
5. Discuss the manner in which AI supports decision making
6. Describe AI applications in accounting
7. Describe AI applications in banking and financial services
8. Describe AI in human resource management
9. Describe AI in marketing
10. Describe AI in production-operation management
CHAPTER OVERVIEW
Artificial intelligence (AI), which was a curiosity for
generations, is rapidly developing into a major applied
technology with many applications in a variety of fields.
OpenAI’s (an AI research institution described in Chapter 14)
mission states that AI will be the most significant technology
ever created by humans. AI appears in several shapes and has
several definitions. In a crude way, it can be said that AI’s aim
is to make machines exhibit intelligence as close as possible to

37. what people exhibit, hopefully for the benefit of humans. The
latest developments in computing technologies drive AI to new
levels and achievements. For example, IDC Spending Guide
(March 22, 2018) forecasted that worldwide spending on AI will
reach $19.1 billion in 2018. It also predicted annual double-
digit spending growth for the near future. According to Sharma
(2017), China expects to be the world leader in AI, with a
spending of $60 billion in 2025. For the business value of AI,
see Greig (2018). In this chapter, we provide the essentials of
AI, its major technologies, its support for decision making, and
a sample of its applications in the major business functional
areas.
CHAPTER OUTLINE
2.1 Opening Vignette: INRIX Solves Transportation Problems
2.2 Introduction to Artificial Intelligence
2.3 Human and Computer Intelligence
2.4 Major AI Technologies and Some Derivatives
2.5 AI Support for Decision Making
2.6 AI Applications in Accounting
2.7 AI Applications in Financial Services
2.8 AI in Human Resource Management (HRM)
2.9 AI in Marketing, Advertising, and CRM
2.10 AI Applications in Production-Operation Management
(POM)
ANSWERS FOR END OF SECTION REVIEW QUESTIONS
Section 2.1 Opening Vignette Review Questions
1. Explain why traffic may be down while congestion is up (see
the London case at inrix.com/uk-highways-agency/).
Congestion may be caused by other reasons such as accidents
and weather.
2. How does this case relate to decision support?

38. Information is provided to various types of decision makers,
some in real time. The system also includes some automated
decision making.
3. Identify the AI elements in this system.
Data is collected, some automatically. AI algorithms process the
data to make predictions and suggest routes. The system makes
inferences based on past drivers’ behavior.
4. Identify developments related to AI by viewing the
company’s press releases from the most recent four months at
inrix.com/press-releases. Write a report.
Open-ended answers.
5. According to INRIX, the new mobile traffic app is a threat to
Waze. Explain why.
It provides similar recommendations but with more accuracy
(more diversified data). It also provides recommendations for
future dates. Waze does not.
6. Go sitezeus.com/data/inrix and describe the relationship
between INRIX and Zeus. View the 2:07 min. video at
sitezeus.com/data/inrix/. Why is the system in the video called a
“decision helper”?
The capabilities of INRIX and Zeus are compatible, so synergy
is created. Note that both are improved with time.

39. Section 2.2 Review Questions
1. Define AI.
Machines that have human-like thought processes. Ability to
immitate human behavior.
2. What are the major aims and goals of AI?
Study of human thought processes and understand what
intelligence is so as to transfer them to machines. Perceive and
properly read environmental changes make machines creative.
3. List some characteristics of AI.
Can facilitate human work, increase productivity, do not get
tired, can work in risky environments. Machines that attempt to
exhibit intelligent behavior.
4. List some AI drivers.
Cost savings, high speed, competition, capable technologies
5. List some benefits of AI applications.
Consistent quality, non-stop production, ever increasing
funcionalities, ability to learn from experience.
6. List some AI limitations.
Lack of human touch and feel, ignoring non-tasks surroundings,
can cause damage.
7. Describe the artificial brain.
Machine that is desired to be intelligent, creative and self-aware
as humans.
8. List the three flavors of AI and describe augmentation.
Assisted, autonomous, and augmented. Augmented refers to
combining different levels and types of AI solutions.
Section 2.3 Review Questions
1. What is intelligence?

40. It is composed of complex concepts such as reasoning, logic,
ability to learn and solve problems.
2. What are the major capabilities of human intelligence? Which
are superior to that of AI machines?
Make sense of ambiguous information, respond quickly to new
situations, prioritize information, and reason. Express emotions
and solve problems.
3. How intelligent is AI?
AI is not yet as intelligent as humans. But it is getting more and
more intelligent and in certain areas is even more successful
(e.g., complex games, diagnosis). AI’s goal is in solving
structured problems.
4. How can we measure AI’s intelligence?
Use Turing Tests. Compare computer generated answers to
those made by humans and to standards.
5. What is the Turing Test and what are its limitations?
Given same tasks to human and computers with knowing which
is which. Try to determine which is which. The test measures
only Q&A. It measures only some parts of intelligence.
6. How can one measure the intelligence level of a vacuum
cleaner?
You need to set criteria of performance (e.g., ability to
recognize objects) and determine the ability of the machine to
make appropriate decisions when the cleaner discovers
obstacles.
Section 2.4 Review Questions
1. Define intelligent agents and list some of their capabilities.
Autonomous small computer programs for conducting routine

41. tasks. For example, spelling checker, price discovery. They are
quick, inexpensive, consistent, and reduce the information
overload burden.
2. Prepare a list of applications of intelligent agents.
Approvs small loans, match people to jobs, assist people with
computer work, match supply and demand.
3. What is machine learning? How can it be used in business?
Ability to identify pattern by learning from experience. Monitor
sense and analyze data in the computing environment. Self
adjust to changes by learning from example. The lessons learned
are used for diagnosis and predictions in business areas,
medicine, etc.
4. Define deep learning.
Ability to learn ‘deeper’ than regular machine learning and thus
solve more complex problems. Uses most powerful learning
algorithms. Supports machine vision, robotics and voice
understanding.
5. Define robotics and explain its importance for manufacturing
and transportation.
Robotics combines several AI technologies (e.g., machine
vision, voice recognition) to make autonomous decisions and
performing mechanical tasks. Thus, they can speed up many
tasks ranging from assembly to welding to transporting things.
Robots also play a role in self driving vehicles.
6. What is NLP? What are its two major formats?
Natural language processing is the capability of a computer to
analyze human language so that the computer can understand its
meaning (voice or speech understanding) and able to generate
human language (speech generation) after data processing by
the computer.

42. 7. Describe machine translation of languages. Why it is
important in business?
Once a human language is understood, it can be translated into
other languages (e.g., use Google Translate). This enables
people to understand messages and websites written in other
languages. This can support global trade and communication
and collaboration.
8. What are knowledge systems?
Knowledge systems are used for autonomous decisions and in
providing answers to queries (e.g., Alexa). They provide advice
based on stored knowledge.
9. What is cognitive computing?
In order to study the human thought process (an AI goal)
scientist uses the knowledge about the human brain to create,
for example, self-learning machines. In addition, such
knowledge is used for teaching machines to reason.
10. What is augmented reality?
Real time integration of digital information and the user’s
environment (e.g., vision voice). Such integration enables to
catch information from the environment (e.g. photos) and then
learn about related characteristics, as well as process it in other
ways.
Section 2.5 Review Questions
1. Distinguish between fully automated and supported decision
making.
Fully automated decisions do not require human colaboration,
the computer does it all. In decision support, the computer
provides help in some steps of the decision making process
(e.g., in generative alternatives, predicting consequences).

43. 2. List the benefits of AI for decision support.
Enable quicker decisions, predict potential results of
alternatives, consolidate relevant data, enable collaboration of
group decision makers.
3. What factors influence the use of AI for decision support?
Type of decision, cost, urgency of getting a solution, possibility
of matching of AI tool to type of problem.
4. Relate AI to the steps in the classical decision-making
process.
1) AI is used in diagnosing problems and in comparing
performance to standards.
2) AI assists in generating alternatives. AI predicts
consequences of alternatives.
3)
Solution
s are compared, and the best one is selected.
4) Finally, AI can assist in implementation.
5. What are the necessary conditions for AI to be able to
automate decision making?
Structured situations, possibility of significant cost and or time
saving, chance of acceptance of the AI solution, fairly routine
situations, lack of human experts on site, and strong
management support.

44. 6. Describe Schrage’s four models.
1) Autonomous advisor provides suggestions on best courses of
action, and strategies which must be approved by humans.
2) Autonomous outsource makes outsourcing decisions. In this
case, all data must be clear and include decision rules (e.g., If-
Then must be provided to the machines).
3) People-machine collaboration requires two partners. The
machine makes the entire decisions. However, humans need to
deal with the constraints. Training of people for the
collaboration is needed.
4) Complete machine autonomy. Here, the entire processes are
fully automated.
Section 2.6 Review Questions
1. What are the major reasons for using AI in accounting?
Increase productivity and speed of routine activities. Reduce
elapsed time and increasing consistency. Total cost reduction.
Provide competitive advantage.
2. List some applications big accounting firms use.
Improving auditing, tax calculation, fraud detection,
verifications, claims verifications, compliance verification,

45. projects’ evaluations, predictions, and quality assessment.
3. Why do big accounting firms lead the use of applied AI?
To attract more business, to increase their productivity and to
gain a competitive edge. Also, they have large R&D budgets.
4. What are some of the advantages of using AI cited by the
ICAEW report?
Solve difficult accounting problems, provide inexpensive and
better data support for decision making, generating insights
from analysis, free time of accountants, detect fraud, task
verifications, checking accuracy of contracts.
5. How may the job of the accountant be impacted by AI?
The accountant will have more time to innovate and perform
complex tasks. Some accountants will lose their jobs (if they do
routine, repetitive tasks).
Section 2.7 Review Questions
1. What are the new ways that banks interact with customers by
using AI?
Interaction via chatbots (e.g., offer real time online

46. conversation). Make real time offers online. Banks offer
machine advisory services. Facial recognition in branches, so
bankers know who the customers are when they see them (they
do not have to ask).
2. It is said that financial services are more personalized with
AI support. Explain.
Computer vision can recognize the customer in the physical
bank. No need to ask. There may be a better match when
replying to customers’ queries.
3. What back-office activities in banks are facilitated by AI?
Processing large amounts of data (e.g., claims), processing
payments, and doing the bookeeping.
4. How can AI contribute to security and safety?
By predicting security breaches and discovering fraud cases
quickly.
5. Wha are the role of chatbots and virtual assistants in
financial services?
Chatbots can provide assistance to customers (e.g., answer
queries, direct where to go next). Personal virtual assistants can
suggest investment activities.

47. 6. How can IBM Watson help banking services?
Watson can analyze big data and provide suggestions for
strategy and for problem solving. Also, it can facilitate
compliance.
7. Relate Salesforce Einstein to CRM in financial services.
Customer relativity is critical in dealing with claims. Salesforce
Einstein is discussed in Section 2.6.
8. How can AI help in processing insurance claims?
AI can expedite claims processing. It also can predict accident-
prone drivers. Computer vision can facilitate the reporting of
accident damages. Also, accuracy increased. Also, accidents can
be simulated and analyzed.
Section 2.8 Review Questions
1. List the activities in recruiting and explain the support
provided by AI to each.
Finding candidates by evaluating resumes is quickly done. Also,
assigning applications to positions and conducting testing.
Screen resumes posted on the Web. Create model resumes that
can be compared to resumes of applicants. Chatbots help with

48. information delivery, save time for recruiters.
2. What are the benefits rewarded to recruiters by AI?
Easier to find talents and do so faster. Better market for jobs
and applicants, identify the best employees internally (in-
house).
3. What are the benefits to job seekers?
Easier to be discovered. A best match of applicants to positions.
Shorter wait time for appointment decisions.
4. How does AI facilitate training?
One way is to use chatbots as tutors. Also, chatbots can be used
for personalized paced learning.
5. How is performance evaluation of employees improved by
AI?
By breaking tasks into small portions, and using AI, it is more
accurate and faster to evaluate perfomance and treat areas that
need improvements.
6. How can companies increase retention and reduce attrition
with AI?
AI can discover what makes employees happier. Also, AI can be
used to figure out why employees are not happy. AI can predict

49. tendencies to leave and find remedies.
7. Describe the role of chatbots in supporting HRM.
Provide information to new and existent employees. Help in
recruiting and training. Some day it may be used to comfort sad
employees.
Section 2.9 Review Questions
1. List 5 of the 15 applications of Davis (2016). Comment on
each.
Product recommendation - using recommenders (Chapter 12) is
popular
Fraud detection - done extensively by credit card issuers
Producer pricing – AI helps in checking and changing prices
based on supply and demand and on competition
Speech recognition – helps to provide customer service and sell
in natural languages
Image recognition – used in market research and in defect
detection
2. Which of the 15 applications relate to sales?

50. Product recommendation
Smart sales engine
Language translation
Sales forecast
Chatbot advisors
3. Which of the 15 applications relate to advertising?
Social semantics for learning about customers’ needs. Target
one-to-one ads. Customer segmentation. Content generation.
4. Which of the 15 applications relate to customer service and
CRM?
Product recommendation
Smart search
Social semantics
Website design
Predictive customer services (the effectiveness of)
5. For what are the prediction capabilities of AI used?
Determine pricing and advertisement stragies. Help in new
product design. Predict the success of certain ads. Predict
consumers’ attitudes towards new products. Predict consumer
behavior (e.g., towards ads, prices). Predict sales volume.
6. What is Salesforce’s Einstein?

51. AI-based personal advisor for customers and vendors. Has
powerful analytical and prediction capabilities. Improves
customer engagement and interaction.
7. How can AI be used to improve CRM?
Predicting the impact of different CRM options. Providing
assistance via chatbots. Enables discussions among customers
and with the vendors. Provides voice communication which is
preferred by customers.
Section 2.10 Review Questions
1. Describe the role of robots in manufacturing.
Robots are used in assembly lines (e,g., cars), for material
handling, can do welding. They also work in toxic environments
and improve the supply chain.
2. Why use AI in manufacturing?
It saves time and permits work to be done in hazardous
environments. Provides competitive edge. Minimizes
interruptions, and people-related problems. Can do certain tasks
much better than humans (e.g., inspection).

52. 3. Describe the Bollard et al. implementation model.
It is a five step model that begins with business process
improvement. Then, certain processes are outsourced. Deploys
AI and analytics to support decision making, automates as much
as possible. Digitizes the customers’ experiences.
4. What is an intelligent factory?
Highly automated factory where machines make most of the
work in an integrated fashion and can make many decisions.
Can produce large volumes quickly.
5. How are a company’s internal and external logistics
supported by AI technologies?
To begin with, robots can do material handling (Amazon’s
internal order fulfillment). Partners’ activities are better
coordinated, and transportation can be better managed and
controlled. External transports are controlled by IoT (e.g., at
DHL). Machine learning helps optimizing shipments. Finally,
logistics may include optimal inventory management and
automatic replenishment.
ANSWERS TO APPLICATION CASES
Answers to Case 2.2
1. Discuss the benefits of combining machine learning with
other AI technologies.

53. They used 100 variables and defined intelligent performance
levels in each. Then, they compared these to the performance of
the machine.
2. How can machine learning improve marketing?
It is able to self-clean floors. Not able to deal with unforseen
obstacles such as a dog.
3. Discuss the opportunities of improving human resource
management.
Deep learning can increase the learning capabilities overtime.
For example, dealing with rarely seen obstacles and dealing
with multifactor environments.
4. Discuss the benefits for customer service.
Open-ended answer.
Answers to Case 2.3
1. Why use machine learning for predictions?
One can get more accurate predictions that can be changed
quickly; predictions are used extensively in decision making in
many areas.
2. Why use machine learning for detections?
Detecting fraud, maintenance problems, health issues, etc. are

54. difficult and must be done quickly. Detecting in real time (e.g.,
computer security breach, illness) can be very useful.
3. What specific decisions were supported in the five cases?
a) Predict which drivers are more likely to be involved in
accidents (insurance issue)
b) Improving satellite image quickly (for several purposes)
c) Detecting illegal overfishing (compliance issue)
d) Deteching fraud in using credit cards (finance issue)
e) Detecting defects in food processing. (manufacturing issue)
Answers to Case 2.4
1. What are the characteristics of the tasks for which AI is
used?
Tasks that require processing of very large amounts of
structured data that take a long time to complete. Also, tasks
that do report generation which is fairly standard, but tedious.
Tasks that require huge amounts of different data (e.g., legal,
tax preparation, and auditing tasks).
2. Why do the big accounting firms use different
implementation strategies?
They may have different clients, tasks, and strategies. Since

55. work is paid by the clients, the firms try to make the clients
happy. Also, they may have different constructs. Finally, all
this is new, so the firms experiment with different
implementation strategies.
Answers to Case 2.5
1. What are Einstein’s advantages to US Bank?
The bank needs superb customer service and one-to-one
advertisement and customer service. The identificaiton of
customers and matching offers of services were provided by
Einstein CRM. The machine also helped in matching customers
and services.
2. What are its advantages to customers?
Customer receive more and better attention. They wait less in
line and they can get quick explanations and answers to queries.
Customers feel more satisfied when bankers understand their
needs.
3. What are the benefits of voice communication?
It is more natural than typed communication and faster to ask
and get a reply.
Answers to Case 2.6

56. 1. What types of decisions are supported?
Screening applicants and their resumes. Creating profiles of
desired jobs and matching them with applicants. So recruitment
decisions can be made faster and better. AI can also help in
performance evaluation and in promotion decisions.
2. Comment on the human–machine collaboration.
Human-machine collaboration, as will be seen in Chapter 13 and
14, can be very beneficial. The HRM employees can use the
machines for decision support and for answering questions made
by employees.
3. What are the benefits to recruiters? To applicants?
Recruiters can save time and be more consistent. Also, they can
do a more accurate and unbiased performance evaluation.
Advanced AI can assist in identifying incorrect information
provided by applicants. Appropriate applicants can be
discovered among the many on the Web. Applicants face an
unbiased evaluation and a usually quicker turnaround.
4. Which tasks in the recruiting process can be fully
automated?
Screening large numbers of resumes online can be fully
automated. Also, providing information by chatbots.

57. 5. What are the benefits of such automation?
Saving time and money. Also, the accuracy of information
provided by chatbots is consistent andt is less subject to legal
cases if innacurate information has been provided.
Answers to Case 2.7
1. Identify all AI technologies used in the Food Assistant.
Chatbots, computer vision, personal assistant, machine learning
recommender.
2. List the benefits to the customers.
Make customers happy, provide immediate answers while
shopping in supermarkets. Get advice about food use.
3. List the benefits to Kraft Foods.
Make customers happy. Can learn about consumers’ behavior
and loyalty. Expand mobile marketing, vendors can better assess
customers’ reaction to promotions. Finally, vendors can better
influence consumers to buy their products via targeted ads and
the personalized advice provided.
4. How is advertising done?
Via targeted ads and the personalized advice provided.
5. What role is “behavioral pattern recognition” playing?

58. AI makes inferences about what specific customers like, and
then recommends promotions. One method of AI is
collaborative filtering.
6. Compare Kraft’s Food Assistant to Amazon.com and Netflix
recommendation systems.
Amazon uses an algorithm to tell shoppers what other shoppers
that bought the same item bought in addition. Netflix suggests
what videos to watch, based also on what smilar customers
watched. Kraft’s Assistant interacts with customers and
evaluates their response. Also, Kraft uses voice communication.
ANSWERS TO TECHNOLOGY INSIGHT CASES
Questions for Discussion: 2.1 Technology Insight
1. What is the basic premise of augmented intelligence?
Improve assisted AI by extending human cognitive capabilities.
2. List the major differences between augmented intelligence
and assisted AI applications.
Assisted AI works only in narrow, well-defined domains
(structured), augmented combine machines, and people
intelligence. Dealing with more complex situations.
3. What are some benefits of augmented intelligence?
Generates better predictions and recommendations, works

59. faster, and is more accurate.
4. How does the technology relate to cognitive computing?
Assists in solving complex problems. Extending human
cognitive capabilities.
Questions for Discussion: 2.2 Technology Insight
1. Differentiate between the autonomous advisor and the
people–machine collaboration models.
The autonomous advisor is based on data-driven management.
The algorithm generates strategies and makes recommendations.
Actions must be approved by humans. In people- machine
collaboration, people not only approve the recommendations of
the algorithms, but are also involved in implementation.
2. In all four models, there are some degrees of people–machine
interaction. Discuss.
While machines can make decisions, humans need to design
them, supervise execution, interpret results, and improve them
over time. The least involvement of humans is in model #4.
3. Why it is easier to use model #4 for investment decisions
than, for example, marketing strategies?
There are less variables in investment decisions and they are
usually more structured. Also, most of the information in

60. investment decisions is quantitative and can decoded.
4. Why is it important for data scientists to work with top
management in autonomous AI machines?
Data scientists provide the analysis whose results managers
view for making decisions. Using autonomous machines
requires full understanding by the scientists of the decision
making process and also the use of the autonomous machines.
ANSWERS TO QUESTION FOR DISCUSSION (End of
Chapter)
1. Discuss the difficulties in measuring the intelligence of
machines.
There are several variables that need to be measured against
standards which may difficult to establish. With many variables,
it is necessary to give each of them a weight, and this may be
difficult. Some of these may be impacted by the physical
enviroment and the skills of the employees that work with the
machines.
2. Discuss the process that generates the power of AI.
The power of AI is provided by the method used and the
technology and algorithms applied. For example, what
knowledge is used and how it is extracted, stored and applied.
Also, in learning-based AI, the process includes the sources of

61. knowledge and the learning mechanisms, algorithms and
procedures.
3. Discuss the differences between machine learning and deep
learning.
Machine learning is done by examining examples by parsing
the data in examples and then learning from the new data and
applying them to make decisions such as pattern recognition.
The machines can adjust their capabilities to changes in the
environment. Deep learning can be viewed as a subset of
machine learning. Deep learning tries to mimic the human brain.
It uses fresh data to learn, so it can use self-direction to solve
difficult problems so it is useful in autonomous vehicles. Its key
motto is continous learning.
4. Describe the difference between machine vision and
computer vision.
Machine vision is based on what cameras “see.” It then provides
images of automated processes (e.g., inspection). It is important
in processes of robotics and autonomous vehicles. It is an
engineering subfield. Computer vision is a computer subfield
that processes digital information from images and videos. It
also deals with 3D images. Analysis of the images is used for
decision making.

62. 5. How can a vacuum cleaner be as intelligent as a six-year-old
child?
The machine can handle certain situations (e.g., deal with
obstacles) as well as the child. Of course the comparison is
related only to limited tasks (such as dealing with obstacles).
6. Why are NLP and machine vision so prevalent in industry?
The knowledge about both technologies is abundant. There are
many applications that are easy to justify (cost benefit). Also,
they are easy to implement. Machine and computer vision
components are fairly simple. Voice recognition is fairly mature
technology which has been in use for decades.
7. Why are chatbots becoming very popular?
Chatbots can look like small people and they use natural
language. When they have a large knowledge base (such as
Alexa and Google Assistant), they can provide fairly accurate
advice at a reasonable cost per usage. Chatbots can be used for
both general purposes (like Alexa) or for specialized knowledge
in a narrow domain (e.g., guide people in airports). Finally,
people like them.
8. Discuss the advantages and disadvantages of the Turing Test.
It is a logical and simple test. Its results can be easily measured
(e.g., in percents, or levels). It is inexpensive.

63. However, it is good only to Q&A dialog and it requires a large
database as well as an intelligent human expert. It does not
cover all aspects of intelligence.
9. Why is augmented reality related to AI?
Augmented reality integrates digital information with the users’
environment in real time (e.g., vision and voice). The
technology uses scene recognition, machine learning, NLP and
even gesture recognition. It is available on some smart phones.
It is used extensively in architectural design of furniture and
building and in their sales.
10. Discuss the support that AI can provide to decision makers.
AI can support the individual steps in decision making as well
as in automating the entire process. Steps such as problem
(task) identification, brainstorming of finding alternative
solutions, and selecting appropriate action to changes in the
environment may be complex. AI can partially or fully automate
these steps. Executing these steps may require expertise or
complex data manipulation and analysis.
11. Discuss the benefits of automatic and autonomous decision
making.

64. The two major benefits are cost reduction and fast execution.
Cost reduction comes from either use of less people, or use of
lower skilled employees. Also, employees working 24/7 are
inexpensive. Finally, the decisions are consistent. For example,
self-driving vehicles cause little or almost no accidents.
12. Why is general (strong) AI considered to be “the most
significant technology ever created by humans”?
Strong AI can result in highly intelligent technologies that will
enable machines to do many tasks that can benefit humans.
Ultimately, people will have to work very little, served by
robots. Also, strong AI will improve medical research, making
people healthier and live longer. Also, more diversed
entertainment will be delivered so quality of life will be
drastically improved.
13. Why is the cost of labor increasing, whereas the cost of AI
is declining?
The cost of labor rises with inflation and in areas of shortage of
skilled labor. Workers demand higher wages. Cost of AI
declines due to innovations, competition among producers,
cheaper designs, and better knowledge.
14. If an artificial brain someday contains as many neurons as
the human brain, will it be as smart as a human brain? (students

65. need to do extra research)
Probably not. While more neurons can improve several machine
activities, it may nor be enough to increase creativity, show
emotions and exhibit other human capabilities. However, in
certain areas, machines will be able to be smart or even smarter
than humans.
15. Distinguish between dumb robots and intelligent ones.
Dumb robots are trained to execute one or a few tasks (e.g.,
move materials, weld a point). They cannot handle complex
tasks or deal with malfunctions in processes which intelligent
robots can do. Intelligent robots can deal with changing
environments by stopping work or providing a solution to fix a
problem (e.g., watch the Bumblebee movie, 2018).
16. Discuss why applications of natural language processing and
computer vision are popular and have many uses.
Refer to question #6. In addition, both technologies have been
around for long time. Machine vision has been extended to
computer vision where even more applications exist. Both
technologies are easy to explain and usually they support
employees by making their job easier. However, recent
applications, especially computer vision, may replace humans.

66. ANSWERS TO EXERCISES (End of Chapter)
1. Go to itunes.apple.com/us/app/public-transit-app-
moovit/id498477945?mt=8. Compare Moovit operations to the
operation of INRIX.
Moovit works for travel by bus or train. It is a geolocation tool
that tells you, for example, when to exit a bus. You must have
cellular data service for your cell phone. It is similar to Waze
and INRIX, but it is not as sophisticated. Yet, it is good for
public transportation. It is a free app. It is also tells you how to
get from where you are to desired locations in many big cities
by using public transportation.
2. Go to sitezeus.com and view the 2:07 min. video. Explain
how the technology works as a decision helper.
The site provides “location intelligence” which is the process of
driving meaningful insights from geospatial data relationships
in order to solve related problems. It assists in making location-
related decisions. You can also create 3D terrain maps of many
locations in the world. The SiteZeus technology works with
machine learning. The technology provides retailers’ and
brands’ capabilities for improving decision making (e.g.,
predictive power).

67. 3. Go to Investopedia and learn about investors’ tolerance.
Then, find out how AI can be used to contain this risk, and
write a report.
Known as risk tolerance, it is the risk investors are willing to
withstand.