Management Information Systems - Managing the Digital Firm 16th Edition 2020

CHAPTER 11 Managing Knowledge and
Artificial Intelligence

LEARNING OBJECTIVES CHAPTER CASES

After reading this chapter, you will be able to Machine Learning Helps Akershus University
answer the following questions: Hospital Make Better Treatment
Decisions
11-1 What is the role of knowledge
management systems in business? Sargent & Lundy Learns to Manage
Employee Knowledge
11-2 What are artificial intelligence (AI) and
machine learning? How do businesses The Reality of Virtual Reality
use AI? Can Cars Drive Themselves—And Should

11-3 What types of systems are used They?
for enterprise-wide knowledge
management, and how do they VIDEO CASES
provide value for businesses?
How IBM’s Watson Became a Jeopardy
11-4 What are the major types of Champion
knowledge work systems, and how
do they provide value for firms? Alfresco: Open Source Document
Management and Collaboration
11-5 How will MIS help my career?

MyLab MIS

Discussion Questions: 11-5, 11-6, 11-7; Hands-on MIS Projects: 11-8, 11-9, 11-10, 11-11;
Writing Assignments: 11-17, 11-18; eText with Conceptual Animations

418

Machine Learning Helps Akershus University
Hospital Make Better Treatment Decisions

The healthcare industry is deluged with big data, including patient histo- © Panchenko Vladimir/Shutterstock
ries, clinical records, charts, and test results. Medical information is now
doubling every 3 years and will be doubling every 73 days by 2020. How
can healthcare professionals keep up with the knowledge in their field, and
how can they use this knowledge to make more informed decisions about treat-
ment options and managing healthcare costs when there is way too much data
for humans to easily analyze and absorb?

One of the many health care organizations struggling with this problem is
Akershus University Hospital (Ahus), a Norwegian public university hospital
serving approximately 500,000 inhabitants around Oslo, Norway, and employ-
ing 9,500 people. Ahus had amassed huge volumes of data on patients and treat-
ments, but much of this information was in unstructured, textual reports that
made it extremely difficult and time-consuming
to extract meaningful information. Combing
through thousands of complex clinical docu-
ments was impossible to complete manually.

Working with Capgemini consultants, Ahus
is trying to solve this problem by using arti-
ficial intelligence technology in IBM Watson
Explorer. IBM Watson Explorer is a cognitive
computing platform that can analyze struc-
tured and unstructured data to uncover trends
and patterns that would be difficult, if not im-
possible, for humans to discern. It uses natural
language processing to search data expressed
in everyday language like ordinary speech
and machine learning algorithms to improve
search results. Natural language processing
technology makes it possible for a machine
to understand, analyze, and derive meaning from human language. Machine
learning software can identify patterns in very large databases without ex-
plicit programming, although with significant human training. IBM Watson
Explorer is able to rapidly mine large volumes of data, interpret speech and
text, pick up on nuances of meaning and context, answer questions, draw
conclusions, and learn from its experience. It can make inferences and cor-
relations about the content it ingests and rank potential responses for a user
to select.

The hospital’s image diagnostic department wanted to improve the use of CT
examinations in emergencies. Ahus used IBM Watson Explorer to analyze when
its CT scans performed on pediatric patients in emergency situations fell within
recommended guidelines. CT scans can be life-saving in critical circumstances,

419

420 Part Three  Key System Applications for the Digital Age

but the radiation can also be potentially harmful, so CT scans should not be
overused. A large amount of Ahus’s CT scan data was in text format. Ahus used
Watson Explorer to gather unstructured data from more than 5,000 anonymous
CT examination reports and apply machine learning and natural language pro-
cessing techniques to learn how often CT scans were undertaken and the find-
ings of those scans.

Ahus and Capgemini implemented the project over a period of seven weeks
during the summer of 2016. Watson had to learn the language used in medi-
cine and understand the context of how that language is used. Capgemini
adapted the technology to the Norwegian language, and Ahus trained Watson
to understand medical words and phrases. The project also created a clas-
sification schema, teaching Watson to distinguish files that reported positive
scan results and those that reported negative results, and categorize the data
accordingly.

After several tests, Watson Explorer attained an accuracy level of 99 percent
for content classification. The final analysis confirmed that frequency of CT
scanning at Ahus was at an acceptable level, and that the hospital was striking
the right balance between the probability of positive gains in relation to the
potential harmful effects. It would have taken a team of people months and
perhaps years to analyze the same amount of data that Watson could process in
minutes.

Sources: IBM Corporation. “Akershus University Hospital,” and “IBM Watson Explorer,”
www.ibm.com, accessed May 17, 2018; and “Akershus University Hospital Optimizes the Use
of CT Examinations,” www.capgemini.com, accessed May 18, 2018.

Akershus University Hospital’s use of artificial intelligence techniques
such as machine learning and natural language processing to determine
whether its CT scans fell within recommended guidelines shows how organiza-
tional performance can benefit by using technology to facilitate the acquisition
and application of knowledge. Facilitating access to knowledge, using knowl-
edge tools to create and utilize new knowledge, and using that knowledge to
improve business processes are vital to success and survival for both private
business firms and public organizations.

The chapter-opening diagram calls attention to important points raised by
this case and this chapter. Like other medical facilities, Akershus University
Hospital was what is termed “data rich but knowledge poor.” It had vast quan-
tities of patient and treatment data, but they were largely unstructured and
very difficult to analyze for information and insights. AI techniques such
as machine learning and natural language processing helped Ahus obtain
new insights and knowledge from thousands of CT scan records so that it
could optimize treatments and ensure doctors and staff were following best
practices.

Here are some questions to think about: How did using IBM Watson Explorer
help Akershus University Hospital improve its knowledge? What was the im-
pact on the hospital’s business processes?

Chapter 11  Managing Knowledge and Artificial Intelligence 421

• Monitor safety and Management Business
costs Challenges
• Unstructured data
• Very large volume of data
• Opportunities from new technology

• Collect patient data Organization Information Business
• Collect procedure Technology System Solutions

and test data CT Scan Analysis System • Optimize treatments
• Implement CT scan
• Mine large databases • Improve safety
process
• Train Watson • Categorize scan results
• Classify content
Explorer • Evaluate conformity with best
practices
• IBM Watson Explorer
• Machine learning
• Natural language

processing

11-1 What is the role of knowledge

management systems in business?

Knowledge management and collaboration systems are among the fastest-
growing areas of corporate and government software investment. The past
decade has shown an explosive growth in research on knowledge and knowl-
edge management in the economics, management, and information systems
fields.

Knowledge management and collaboration are closely related. Knowledge
that cannot be communicated and shared with others is nearly useless.
Knowledge becomes useful and actionable when shared throughout the firm.
We have already described the major tools for collaboration and social business
in Chapter 2. In this chapter, we will focus on knowledge management systems
and be mindful that communicating and sharing knowledge are becoming in-
creasingly important.

We live in an information economy in which the major source of wealth
and prosperity is the production and distribution of information and
­knowledge. At least 20 percent of the total economic output of the United
States, $4 t­rillion, derives from the output of the information and knowledge
sectors of the economy, which employs an estimated minimum of 30 mil-
lion people (U.S. Department of Labor, 2017; Bureau of Economic Analysis,
2018).

Knowledge management has become an important theme at many large
business firms as managers realize that much of their firm’s value depends on
the firm’s ability to create and manage knowledge. Studies have found that a
substantial part of a firm’s stock market value is related to its intangible assets,
of which knowledge is one important component, along with brands, reputa-
tions, and unique business processes. Well-executed knowledge-based projects
have been known to produce extraordinary returns on investment, although
the impacts of knowledge-based investments are difficult to measure (Gu and
Lev, 2001).

422 Part Three  Key System Applications for the Digital Age

Important Dimensions of Knowledge

There is an important distinction between data, information, knowledge, and
wisdom. Chapter 1 defines data as flows of events or transactions captured by
an organization’s systems that are useful for transacting but little else. To turn
data into useful information, a firm must expend resources to organize data into
categories of understanding, such as monthly, daily, regional, or store-based
reports of total sales. To transform information into knowledge, a firm must
expend additional resources to discover patterns, rules, and contexts where the
knowledge works. Finally, wisdom is thought to be the collective and indi-
vidual experience of applying knowledge to the solution of problems. Wisdom
involves where, when, and how to apply knowledge.

Knowledge is both an individual attribute and a collective attribute of the
firm. Knowledge is a cognitive, even a physiological, event that takes place in-
side people’s heads. It is also stored in libraries and records, shared in lectures,
and stored by firms in the form of business processes and employee know-how.
Knowledge residing in the minds of employees that has not been documented
is called tacit knowledge, whereas knowledge that has been documented is
called explicit knowledge. Knowledge can reside in email, voice mail, graph-
ics, and unstructured documents as well as structured documents. Knowledge
is generally believed to have a location, either in the minds of humans or in
specific business processes. Knowledge is “sticky” and not universally appli-
cable or easily moved. Finally, knowledge is thought to be situational and con-
textual. For example, you must know when to perform a procedure as well as
how to perform it. Table 11.1 reviews these dimensions of knowledge.

We can see that knowledge is a different kind of firm asset from, say, build-
ings and financial assets; that knowledge is a complex phenomenon; and that
there are many aspects to the process of managing knowledge. We can also

TABLE 11.1 IMPORTANT DIMENSIONS OF KNOWLEDGE

KNOWLEDGE IS A FIRM ASSET

Knowledge is an intangible asset.

The transformation of data into useful information and knowledge requires organizational
resources.

Knowledge is not subject to the law of diminishing returns as are physical assets but instead
experiences network effects because its value increases as more people share it.

KNOWLEDGE HAS DIFFERENT FORMS

Knowledge can be either tacit or explicit (codified).

Knowledge involves know-how, craft, and skill.

Knowledge involves knowing how to follow procedures.

Knowledge involves knowing why, not simply when, things happen (causality).

KNOWLEDGE HAS A LOCATION

Knowledge is a cognitive event involving mental models and maps of individuals.

There is both a social and an individual basis of knowledge.

Knowledge is “sticky” (hard to move), situated (enmeshed in a firm’s culture), and contextual
(works only in certain situations).

KNOWLEDGE IS SITUATIONAL

Knowledge is conditional; knowing when to apply a procedure is just as important as knowing
the procedure (conditional).

Knowledge is related to context; you must know how to use a certain tool and under what
circumstances.

Chapter 11  Managing Knowledge and Artificial Intelligence 423

recognize that knowledge-based core competencies of firms—the two or three
things that an organization does best—are key organizational assets. Knowing
how to do things effectively and efficiently in ways that other organizations
cannot duplicate is a primary source of profit and competitive advantage that
cannot be purchased easily by competitors in the marketplace.

For instance, having a unique build-to-order production system constitutes
a form of knowledge and perhaps a unique asset that other firms cannot copy
easily. With knowledge, firms become more efficient and effective in their use
of scarce resources. Without knowledge, firms become less efficient and less ef-
fective in their use of resources and ultimately fail.

Organizational Learning and Knowledge Management

Like humans, organizations create and gather knowledge using a variety of
organizational learning mechanisms. Through collection of data, careful mea-
surement of planned activities, trial and error (experimentation), and feedback
from customers and the environment in general, organizations gain experience.
Organizations that learn adjust their behavior to reflect that learning by creat-
ing new business processes and by changing patterns of management decision
making. This process of change is called organizational learning. Arguably,
organizations that can sense and respond to their environments rapidly will
survive longer than organizations that have poor learning mechanisms.

The Knowledge Management Value Chain

Knowledge management refers to the set of business processes developed
in an organization to create, store, transfer, and apply knowledge. Knowledge
management increases the ability of the organization to learn from its envi-
ronment and to incorporate knowledge into its business processes. Figure 11.1
illustrates the value-adding steps in the knowledge management value chain.
Each stage in the value chain adds value to raw data and information as they
are transformed into usable knowledge.

In Figure 11.1, information systems activities are separated from related
management and organizational activities, with information systems activi-
ties on the top of the graphic and organizational and management activities
below. One apt slogan of the knowledge management field is “Effective knowl-
edge management is 80 percent managerial and organizational and 20 percent
technological.”

In Chapter 1, we define organizational and management capital as the set of
business processes, culture, and behavior required to obtain value from invest-
ments in information systems. In the case of knowledge management, as with
other information systems investments, supportive values, structures, and be-
havior patterns must be built to maximize the return on investment in knowl-
edge management projects. In Figure 11.1, the management and organizational
activities in the lower half of the diagram represent the investment in organiza-
tional capital required to obtain substantial returns on the information technol-
ogy (IT) investments and systems shown in the top half of the diagram.

Knowledge Acquisition

Organizations acquire knowledge in a number of ways, depending on the type
of knowledge they seek. The first knowledge management systems sought
to build corporate repositories of documents, reports, presentations, and best
practices. These efforts have been extended to include unstructured documents
(such as email). In other cases, organizations acquire knowledge by developing

424 Part Three  Key System Applications for the Digital Age

FIGURE 11.1 THE KNOWLEDGE MANAGEMENT VALUE CHAIN
Knowledge management today involves both information systems activities and a host of enabling
m­ anagement and organizational activities.

Knowledge Business Value Chain

Knowledge Management Systems

Information System Activities

Data and Acquire Store Disseminate Apply
Information Business analytics Content management Portals Decision support
Acquisition Data mining systems Search engines
Collecting Neural networks Knowledge databases Collaboration and systems
Storing Machine learning Expert systems Enterprise
Disseminating Knowledge workstations social business tools
Feedback applications
Expert knowledge Robotics

networks

Knowledge culture Management and Organizational Activities New IT-based business
Communities of Organizational routines Training processes
Organizational culture Collaboration
practice New products and
Social networks services

New markets

online expert networks so that employees can “find the expert” in the company
who is personally knowledgeable.

In still other cases, firms must acquire new knowledge by discovering pat-
terns in corporate data via machine learning (including neural networks, gene-
netic algorithms, natural language processing, and other AI techniques), or by
using knowledge workstations where engineers can discover new knowledge.
These various efforts are described throughout this chapter. A coherent and or-
ganized knowledge system also requires business analytics using data from the
firm’s transaction processing systems that track sales, payments, inventory,
customers, and other vital areas as well as data from external sources such as
news feeds, industry reports, legal opinions, scientific research, and govern-
ment statistics.

Knowledge Storage

Once they are discovered, documents, patterns, and expert rules must be stored
so they can be retrieved and used by employees. Knowledge storage gener-
ally involves the creation of a database. Document management systems that
digitize, index, and tag documents according to a coherent framework are large
databases adept at storing collections of documents. Expert systems also help
corporations preserve the knowledge that is acquired by incorporating that
knowledge into organizational processes and culture. Each of these is discussed
later in this chapter and in the following chapter.

Management must support the development of planned knowledge storage
systems, encourage the development of corporate-wide schemas for indexing
documents, and reward employees for taking the time to update and store doc-
uments properly. For instance, it would reward the sales force for submitting

Chapter 11  Managing Knowledge and Artificial Intelligence 425

names of prospects to a shared corporate database of prospects where all sales
personnel can identify each prospect and review the stored knowledge.

Knowledge Dissemination

Portals, email, instant messaging, wikis, social business tools, and search en-
gine technology have added to an existing array of collaboration tools for shar-
ing calendars, documents, data, and graphics (see Chapter 2). Contemporary
technology has created a deluge of information and knowledge. How can man-
agers and employees discover, in a sea of information and knowledge, that
which is really important for their decisions and their work? Here, training
programs, informal networks, and shared management experience commu-
nicated through a supportive culture help managers focus their attention on
what is important.

Knowledge Application

Regardless of what type of knowledge management system is involved, knowl-
edge that is not shared and applied to the practical problems facing firms and
managers does not add business value. To provide a return on investment, or-
ganizational knowledge must become a systematic part of management deci-
sion making and become situated in systems for decision support (described
in Chapter 12). Ultimately, new knowledge must be built into a firm’s business
processes and key application systems, including enterprise applications for
managing crucial internal business processes and relationships with customers
and suppliers. Management supports this process by creating—based on new
knowledge—new business practices, new products and services, and new mar-
kets for the firm.

Building Organizational and Management Capital:
Collaboration, Communities of Practice, and Office
Environments

In addition to the activities we have just described, managers can help by
developing new organizational roles and responsibilities for the acquisition of
knowledge, including the creation of chief knowledge officer executive posi-
tions, dedicated staff positions (knowledge managers), and communities of
practice. Communities of practice (COPs) are informal social networks
of professionals and employees within and outside the firm who have simi-
lar work-related activities and interests. The activities of these communities
include self-education and group education, conferences, online newslet-
ters, and day-to-day sharing of experiences and techniques to solve specific
work problems. Many organizations, such as IBM, the U.S. Federal Highway
Administration, and the World Bank, have encouraged the development of
thousands of online communities of practice. These communities of prac-
tice depend greatly on software environments that enable collaboration and
communication.

COPs can make it easier for people to reuse knowledge by pointing com-
munity members to useful documents, creating document repositories, and
filtering information for newcomers. COPs’ members act as facilitators, en-
couraging contributions and discussion. COPs can also reduce the learning
curve for new employees by providing contacts with subject matter experts
and access to a community’s established methods and tools. Finally, COPs
can act as a spawning ground for new ideas, techniques, and decision-making
behavior.

426 Part Three  Key System Applications for the Digital Age

FIGURE 11.2 MAJOR TYPES OF KNOWLEDGE MANAGEMENT SYSTEMS
There are three major categories of knowledge management systems, and each can be broken down further into
more specialized types of knowledge management systems.

Enterprise-Wide Knowledge Work Systems “Intelligent” Techniques
Knowledge Management
Tools for discovering patterns
Systems and applying knowledge to
discrete decisions and knowledge
General-purpose, integrated, Specialized workstations and domains
firmwide e orts to collect, store, systems that enable scientists,
disseminate, and use digital engineers, and other knowledge Data mining
content and knowledge workers to create and discover Neural networks
new knowledge Expert systems
Enterprise content management systems Machine learning
Collaboration and social tools Computer-aided design (CAD) Natural language processing
Learning management systems Virtual reality Computer vision systems
Robotics
Genetic algorithms
Intelligent agents

Types of Knowledge Management Systems

There are essentially three major types of knowledge management systems:
enterprise-wide knowledge management systems, knowledge work systems,
and “intelligent” techniques. Figure 11.2 shows the knowledge management
systems applications for each of these major categories.

Enterprise-wide knowledge management systems are general-purpose
firmwide efforts to collect, store, distribute, and apply digital content and
knowledge. These systems include capabilities for searching for information,
storing both structured and unstructured data, and locating employee exper-
tise within the firm. They also include supporting technologies such as portals,
search engines, collaboration and social business tools, and learning manage-
ment systems.

The development of powerful networked workstations and software for as-
sisting engineers and scientists in the discovery of new knowledge has led to the
creation of knowledge work systems such as computer-aided design (CAD), vi-
sualization, simulation, and virtual reality systems. Knowledge work ­systems
(KWS) are specialized systems built for engineers, scientists, and other knowl-
edge workers charged with discovering and creating new knowledge for a com-
pany. We discuss knowledge work applications in detail in Section 11-4.

Knowledge management also includes a diverse group of “intelligent”
t­ echniques, such as data mining, expert systems, machine learning, neural net-
works, natural language processing, computer vision systems, robotics, genetic
algorithms, and intelligent agents. These techniques have different objectives,
from a focus on discovering knowledge (data mining and neural ­networks) to dis-
tilling knowledge in the form of rules for a computer program (expert systems)
to discovering optimal solutions for problems (genetic algorithms). Section 11-2
provides more detail about these “intelligent” techniques.

Chapter 11  Managing Knowledge and Artificial Intelligence 427

11-2 What are artificial intelligence (AI) and machine

learning? How do businesses use AI?

“Intelligent” techniques are often described as artificial intelligence (AI).
There are many definitions of artificial intelligence. In the most ambitious vi-
sion, AI involves the attempt to build computer systems that think and act like
humans. Humans see, hear, and communicate with natural languages, make
decisions, plan for the future, achieve goals, perceive patterns in their environ-
ments, and learn, among many other capabilities. Humans also love, hate, and
choose what objectives they want to pursue. These are the foundations of what
is called “human intelligence” and what is called “common sense” or general-
ized intelligence.

So far the “Grand Vision” of AI remains a distant dream: there are no com-
puter programs that have demonstrated generalized human intelligence or
common sense. Human intelligence is vastly more complex than the most so-
phisticated computer programs and covers a broader range of activities than is
currently possible with “intelligent” computer systems and devices.

A narrow definition of artificial intelligence is far more realistic and useful.
Stripped of all the hyperbole, artificial intelligence programs are like all com-
puter programs: They take data input from the environment, process that data,
and produce outputs. AI programs differ from traditional software programs in
the techniques and technologies they use to input and process data. AI systems
today can perform many tasks that would be impossible for humans to accom-
plish, and can equal or come close to humans in tasks such as interpreting CT
scans, recognizing faces and voices, playing games like chess or Go, or besting
human experts in certain well-defined tasks. In many industries they are trans-
forming how business is done, where people are employed, and how they do
their jobs.

Evolution of AI

In the last decade, significant progress has been made within this limited vision
of AI. The major forces driving the rapid evolution of AI are the development
of Big Data databases generated by the Internet, e-commerce, the Internet of
Things, and social media. Secondary drivers include the drastic reduction in
the cost of computer processing and the growth in the power of processors. And
finally, the growth of AI has relied on the refinement of algorithms by tens of
thousands of AI software engineers and university AI research centers, along
with significant investment from business and governments. There have been
few fundamental conceptual breakthroughs in AI in this period, or in under-
standing how humans think. Many of the algorithms and statistical techniques
were developed decades earlier but could not be implemented and refined on
such a large scale as is currently possible.

Progress has been significant: Image recognition programs have gone from
25 percent error rates down to less than 3 percent in 2018; natural language
speech recognition errors have dropped from 15 percent to 6 percent; and in
translation among common languages, Google’s Translate program achieves
about 85 percent accuracy compared to humans (Technology Quarterly, 2017;
Hirschberg and Manning, 2016). These advances have made possible personal
assistants like Siri (Apple), Alexa (Amazon), Cortana (Microsoft), and Now
(Google), as well as speech-activated systems in automobiles.

428 Part Three  Key System Applications for the Digital Age

In a famous 1950 paper, computer scientist Alan Turing defined an artifi-
cially intelligent computer program as one that a human could have a conver-
sation with and not be able to tell it was a computer (Turing, 1950). We still
cannot have a genuine conversation with a computer AI system because it has
no genuine understanding of the world, no common sense, and does not truly
understand humans. Nevertheless, AI systems can be enormously helpful to
humans and business firms.

Major Types of AI

Artificial intelligence is a family of programming techniques and technologies,
each of which has advantages in select applications. Table 11.2 describes the
major types of AI: expert systems, machine learning, neural networks, deep
learning, genetic algorithms, natural language processing, computer vision sys-
tems, robotics, and intelligent agents. Let’s take a look at each type of AI and
understand how it is used by businesses and other organizations.

Expert Systems

Expert systems were developed in the 1970s and were the first large-scale
applications of AI in business and other organizations. They account for an es-
timated 20 percent of all AI systems today. Expert systems capture the knowl-
edge of individual experts in an organization through in-depth interviews, and
represent that knowledge as sets of rules. These rules are then converted into
computer code in the form of IF-THEN rules. Such programs are often used to
develop apps that walk users through a process of decision making.

Expert systems provide benefits such as improved decisions, reduced er-
rors, reduced costs, reduced training time, and better quality and service. They
have been used in applications for making decisions about granting credit and
for diagnosing equipment problems, as well as in medical diagnostics, legal
research, civil engineering, building maintenance, drawing up building plans,
and educational technology (personalized learning and responsive testing)

TABLE 11.2 MAJOR TYPES OF AI TECHNIQUES

Expert systems Represent the knowledge of experts as a set of rules that can be
Machine learning programmed so that a computer can assist human decision makers.
Neural networks and deep learning
Software that can identify patterns in very large databases without explicit
Genetic algorithms programming although with significant human training.

Natural language processing Loosely based on human neurons, algorithms that can be trained to classify
Computer vision systems objects into known categories based on data inputs. Deep learning uses
Robotics multiple layers of neural networks to reveal the underlying patterns in data,
Intelligent agents and in some limited cases identify patterns without human training.

Algorithms based loosely on evolutionary natural selection and mutation,
commonly used to generate high-quality solutions to optimization and
search problems.

Algorithms that make it possible for a computer to understand and analyze
natural human language.

Systems that can view and extract information from real-world images.

Use of machines that can substitute for human movements as well as
computer systems for their control and information processing.

Software agents that use built-in or learned knowledge to perform specific
tasks or services for an individual.

Chapter 11  Managing Knowledge and Artificial Intelligence 429

FIGURE 11.3 RULES IN AN EXPERT SYSTEM

An expert system contains a number of rules to be followed. The rules are
­interconnected, the number of outcomes is known in advance and is limited, there
are multiple paths to the same outcome, and the system can consider multiple rules
at a single time. The rules ­illustrated are for simple credit-granting expert systems.

A --> B B --> C
If INC > 50,000 If car payment < 10% of income
Ask about car Ask about mortgage payment
payments Else EXIT
Else EXIT

D --> E C --> D D
If D, ask about If mortgage payment < 20% of Grant credit line
years employed income
Grant credit F
Else EXIT Limit 10,000

E --> F
If years > = 4
Grant 10,000 line
Else do G

G --> H H --> F I
If years < 4 If other debt < 5% of income Limit 3,000
Ask about Do F
other debt Else do I

(Maor, 2003; Mishra, 2016). For instance, if you were the project manager of a
14-story office building and were given the task of configuring the building’s air
conditioning system, which has hundreds of parts and subassemblies, an expert
system could walk you through the process by asking a series of questions,
producing an order to suppliers, and providing an overall cost estimate for the
project, all in a matter of hours rather than weeks. See Figure 11.3 for an expert
system for credit granting.

How Expert Systems Work

Expert systems model human knowledge as a set of rules that collectively are
called the knowledge base. Expert systems can have from a handful to many
thousands of rules, depending on the complexity of the decision-making prob-
lem. The strategy used to search through the collection of rules and formulate
conclusions is called the inference engine. The inference engine works by
searching through the rules and firing those rules that are triggered by facts the
user gathers and enters.

Expert systems have a number of limitations, the most important of which
is that even experts can’t explain how they make decisions: they know more
than they can say. People drive cars, for instance, but are challenged to say
how they do it. The knowledge base can become chaotic as the number of

430 Part Three  Key System Applications for the Digital Age

rules can reach into the thousands. In rapidly changing environments, say
medical diagnosis, the rules change and need to be continually updated.
Expert systems are not useful for dealing with unstructured problems that
managers and employees typically encounter, and do not use real-time data
to guide their decisions. Expert systems do not scale well to the kinds of very
large data sets produced by the Internet and the Internet of Things (IoT), and
they are expensive to build. For these reasons, expert system development
has slowed in the last decade to small domains of expert knowledge such as
automobile diagnosis.

Machine Learning

More than 75 percent of AI development today involves some kind of machine
learning (ML) accomplished by neural networks, genetic algorithms, and
deep learning networks, with the main focus on finding patterns in data, and
classifying data inputs into known (and unknown) outputs. Machine learning
is based on an entirely different AI paradigm than expert systems. In machine
learning there are no experts, and there is no effort to write computer code for
rules reflecting an expert’s understanding. Instead, ML begins with very large
data sets with tens to hundreds of millions of data points and automatically
finds patterns and relationships by analyzing a large set of examples and mak-
ing a statistical inference. Table 11.3 provides some examples of how leading
business firms are using various types of machine learning.

Facebook has over 200 million monthly users in the United States who spend
an average of 35 minutes on site daily. The firm displays an estimated 1 bil-
lion ads monthly to this audience, and it decides which ads to show each per-
son in less than one second. For each person, Facebook bases this decision on
the prior behavior of its users, including information shared (posts, comments,
Likes), the activity of their social network friends, background information
supplied to Facebook (age, gender, location, devices used), information sup-
plied by advertisers (email address, prior purchases), and user activity on apps
and other websites that Facebook can track. Facebook uses ML to identify pat-
terns in the dataset, and to estimate the probability that any specific user will
click on a particular ad based on the patterns of behavior they have identified.

TABLE 11.3 EXAMPLES OF MACHINE LEARNING

WellsFargo Aiera system reads and analyzes a half-million documents daily for 1,600
Allstate Insurance stocks, and produces buy and sell calls for 550 stocks followed by their
Netflix wealth management unit.
Amazon
Schindler Group Amelia system uses deep learning and natural language processing to
PayPal assist call center employees in handling customer queries. Trained on 40
insurance topics, it understands context, and learns from experience.

Recommender system based on video similarity algorithm uses statistical
and machine learning to develop a personalized selection of videos for
each of its 125 million subscribers worldwide.

Alexa uses machine learning and speech recognition for its intelligent
voice-controlled personal assistant.

Monitors over one million elevators and walkways using GE’s Predix
operating system and machine learning to make predictions about
needed maintenance.

Uses machine learning algorithms to identify patterns of fraud for
170 million customers who generate four billion transactions annually.

Chapter 11  Managing Knowledge and Artificial Intelligence 431

Analysts estimate that Facebook uses at least 100,000 servers located in several
very large-scale “hyper datacenters” to perform this task. At the end of this pro-
cess is a simple show ad/no show ad result.

The current response rate (click rate) to Facebook ads is about 0.1 percent,
roughly four times that of an untargeted display ad although not as good as
targeted email campaigns (about 3 percent), or Google Search ads (about 2
percent). All of the very large Internet consumer firms, including Amazon,
Alphabet’s Google, Microsoft, Alibaba, Tencent, Netflix, and Baidu, use similar
ML algorithms. Obviously, no human or group of humans could achieve these
results given the enormous database size, the speed of transactions, or the com-
plexity of working in real time. The benefits of ML illustrated by this brief ex-
ample come down to an extraordinary ability to recognize patterns at the scale
of millions of people in a matter of seconds, and classify objects into discrete
categories.

Supervised and Unsupervised Learning

Nearly all machine learning today involves supervised learning, in which the
system is “trained” by providing specific examples of desired inputs and outputs
identified by humans in advance. A very large database is developed, say ten
million photos posted on the Internet, and then split into two sections, one a
development database and the other a test database. Humans select a target,
let’s say to identify all photos that contain a car image. Humans feed a large
collection of verified pictures that contain a car image into a neural network
(described below) that proceeds iteratively through the development database
in millions of cycles, until eventually the system can identify photos with a car.
The machine learning system is then tested using the test database to ensure
the algorithms can achieve the same results with different photos. In many
cases, but not all, machine learning can come close to or equal human efforts,
but on a very much larger scale. Over time, with tweaking by programmers, and
by making the database even bigger, using ever larger computing systems, the
system will improve its performance, and in that sense, can learn. Supervised
learning is one technique used to develop autonomous vehicles that need to
be able to recognize objects around them, such as people, other cars, buildings,
and lines on the pavement to guide them (see the chapter-ending case study).

In unsupervised learning, the same procedures are followed, but humans
do not feed the system examples. Instead, the system is asked to process the
development database and report whatever patterns it finds. For instance, in a
seminal research effort often referred to “The Cat Paper,” researchers collected
10 million YouTube photos from videos and built an ML system that could
detect human faces without labeling or “teaching” the machine with verified
human face photos (Le et al., 2011). Researchers developed a brute force neu-
ral network computer system composed of 1,000 machines with 16,000 core
processors loaned by Google. The systems processors had a total of 1 billion
connections to one another, creating a very large network that imitated on a
small scale the neurons and synapses (connections) of a human brain. The
result was a system that could detect human faces in photos, as well as cat
faces and human bodies. The system was then tested on 22,000 object images
on ImageNet (a large online visual database), and achieved a 16 percent accu-
racy rate. In principle then, it is possible to create machine learning systems
that can “teach themselves” about the world without human intervention. But
there’s a long way to go: we wouldn’t want to use autonomous cars that were
guided by systems with a 16 percent accuracy rate! Nevertheless, this research
was a 75 percent improvement over previous efforts.

432 Part Three  Key System Applications for the Digital Age

To put this in perspective, a one-year-old human baby can recognize faces,
cats, tables, doors, windows, and hundreds of other objects it has been exposed
to, and continuously catalogs new experiences that it seeks out by itself for rec-
ognition in the future. But babies have a huge computational advantage over our
biggest ML research systems. The human adult brain has an estimated 84 billion
neurons, each with over 10,000 connections to other neurons (synapses), and
over one trillion total connections in its network (brain). Modern homo sapiens
have been programed (by nature) for an estimated 300,000 years, and their pre-
decessors for 2.5 million years. For these reasons, machine learning is applicable
today in a very limited number of situations where there are very large data-
bases and computing facilities, most desired outcomes are already defined by
humans, the output is binary (0,1), and where there is a very talented and large
group of software and system engineers working the problem.

Neural Networks

A neural network is composed of interconnected units called neurons. Each
neuron can take data from other neurons, and transfer data to other neurons
in the system. The artificial neurons are not biological physical entities as in
the human brain, but instead are software programs and mathematical models
that perform the input and output function of neurons. The strength of the con-
nections (weight) can be controlled by researchers using a Learning Rule, an
algorithm that systematically alters the strength of the connections among the
neurons to produce the final desired output that could be identifying a picture
of a cancer tumor, fraudulent credit card transactions, or suspicious telephone
calling patterns.

Neural networks find patterns and relationships in very large amounts of
data that would be too complicated and difficult for a human being to ana-
lyze by using machine learning algorithms and computational models that are
loosely based on how the biological human brain is thought to operate. Neural
networks are pattern detection programs. Neural networks learn patterns
from large quantities of data by sifting through the data, and ultimately finding
pathways through the network of thousands of neurons. Some pathways are
more successful than others in their ability to identify objects like cars, animals,
faces, and voices. There may be millions of pathways through the data. An al-
gorithm (the Learning Rule mentioned above) identifies these successful paths,
and strengthens the connection among neurons in these pathways. This pro-
cess is repeated thousands or millions of times until only the most successful
pathways are identified. The Learning Rule identifies the best or optimal path-
ways through the data. At some point, after millions of pathways are analyzed,
the process stops when an acceptable level of pattern recognition is reached,
for instance, successfully identifying cancer tumors about as well as humans,
or even better than humans.

Figure 11.4 represents one type of neural network comprising an input layer,
a processing layer, and an output layer. Humans train the network by feed-
ing it a set of outcomes they want the machine to learn. For instance, if the
objective is to build a system that can identify patterns in fraudulent credit
card purchases, the system is trained using actual examples of fraudulent trans-
actions.  The data set may be composed of a million examples of fraudulent
­transactions. The data set is divided into two segments: a training data set, and
a test data set. The training data set is used to train the system. After millions of
test runs, the program hopefully will identify the best path through the data. To
verify the accuracy of the system, it is then used on the test data set, which the

Chapter 11  Managing Knowledge and Artificial Intelligence 433

FIGURE 11.4 HOW A NEURAL NETWORK WORKS

A neural network uses rules it “learns” from patterns in data to construct a hidden
layer of logic. The hidden layer then processes inputs, classifying them based on the
­experience of the model. In this example, the neural network has been trained to
­distinguish between valid and fraudulent credit card purchases.

Input Layer Hidden Layer Output Layer

Data Results
• Age
• Income Valid
• Purchase purchase

history Fraudulent
• Frequency of purchase

purchases
• Average

purchase
size

system has not analyzed before. If successful, the system will be tested on new
data sets. The neural network in Figure 11.4 has learned how to identify a likely
fraudulent credit card purchase.

Neural network applications in medicine, science, and business address
problems in pattern classification, prediction, and control and optimization. In
medicine, neural network applications are used for screening patients for coro-
nary artery disease, for diagnosing epilepsy and Alzheimer’s disease, and for
performing pattern recognition of pathology images, including certain cancers.
The financial industry uses neural networks to discern patterns in vast pools of
data that might help investment firms predict the performance of equities, cor-
porate bond ratings, or corporate bankruptcies. Visa International uses a neural
network to help detect credit card fraud by monitoring all Visa transactions for
sudden changes in the buying patterns of cardholders. Table 11.4 provides ex-
amples of neural networks.

TABLE 11.4 EXAMPLES OF NEURAL NETWORKS

FUNCTIONALITY INPUTS PROCESS OUTPUTS/APPLICATION

Computer vision Millions of digital images, Recognize patterns in images, Photo tagging; facial
videos, or sensors and objects recognition; autonomous
Speech recognition vehicles
Digital soundtracks, voices Recognize patterns and Digital assistants, chatbots,
Machine controls, diagnostics meaning in soundtracks and help centers
Language translation Internet of Things: thousands speech
Transaction analysis of sensors Preventive maintenance;
Targeted online ads Millions of sentences in various Identify operational status, quality control
languages patterns of failure Translate sentences from one
Millions of loan applications, language to another
stock trades, phone calls Identify patterns in multiple Fraud control; theft of services;
languages stock market predictions
Programmatic advertising
Identify patterns in financial
and other transactions

Millions of browser histories Identify clusters of consumers;
preferences

434 Part Three  Key System Applications for the Digital Age

FIGURE 11.5 A DEEP LEARNING NETWORK
Deep learning networks consist of many layers of neural networks working in a
h­ ierarchical fashion to detect patterns. Shown here is an expanded look at layer 1. Other
layers have the same structure.

Layer 3

Layer 2

Outputs Medium level feature pattern:
Facial shape
Layer 1 Level 2
Level 1 Feature 2: Circles
Feature 1: Lines

Inputs

“Deep Learning” Neural Networks

“Deep learning” neural networks are more complex, with many layers of
transformation of the input data to produce a target output. Collections of neu-
rons are called nodes or layers. Deep learning networks are in their infancy,
and are used almost exclusively for pattern detection on unlabeled data where
the system is not told what to look for specifically but to simply discover pat-
terns in the data. The system is expected to be self-taught. See Figure 11.5.

For instance, in our earlier example of unsupervised learning involving a
machine learning system that could identify cats (The Cat Paper) and other
objects without training, the system used was a deep learning network. It
consisted of three layers of neural networks (layers 1, 2, and 3). Each of these
layers has two levels of pattern detection (levels 1 and 2). Each level was de-
veloped to identify a low-level feature of the photos: layer 1 identified lines
in the photos, and layer 2 identified circles. The result of the first layer may
be blobs and fuzzy edges. Second and third layers refine the images emerging
from the first layer, until at the end of the process the system can distinguish
cats, dogs, and humans, although in this case not very well, with a 16 percent
accuracy rate.

Many pundits believe deep learning networks come closer to the “Grand Vision”
of AI where ML systems would be capable of learning like a human being. Others
who work in ML and deep learning are more critical (Marcus, 2018; Pearl 2016).

Limitations of Neural Networks and Machine Learning

Neural networks have a number of limitations currently. They require very
large data sets to identify patterns. There are often many patterns in large data
sets that are nonsensical, and it takes humans to choose which patterns “make
sense.” Many patterns in large data sets are ephemeral: there may be a pattern
in the stock market, or the performance of professional sports teams, but they
do not last long. In many important decision situations there are no large data

Chapter 11  Managing Knowledge and Artificial Intelligence 435

sets. Should you apply to College A or College B? Should we merge with an-
other company?

Neural networks, machine learning systems, and the people who work with
them cannot explain how the system arrived at a particular solution. For in-
stance, in the case of the IBM Watson computer playing Jeopardy, researchers
could not say exactly why Watson chose the answers it did, only that they were
either right or wrong. Most real-world ML applications in business involve clas-
sifying digital objects into simple binary categories (yes or no; 0 or 1). But many
of the significant problems facing managers, firms, and organizations do not
have binary solutions. Neural networks may not perform well if their training
covers too little or too much data. AI systems have no sense of ethics: they may
recommend actions that are illegal or immoral. In most current applications, AI
systems are best used as tools for relatively low-level decisions, aiding, but not
substituting for managers.

Genetic Algorithms

Genetic algorithms are another form of machine learning. Genetic algorithms
are useful for finding the optimal solution for a specific problem by examining
a very large number of alternative solutions for that problem. Their method
of solving problems is based on ideas inspired by evolutionary biology such as
inheritance, mutation, selection, and crossover (recombination).

A genetic algorithm works by searching a population of randomly generated
strings of binary digits to identify the right string representing the best possible
solution for the problem. As solutions alter and combine, the worst ones are
discarded and the better ones survive to go on to produce even better solutions.

In Figure 11.6, each string corresponds to one of the variables in the prob-
lem. One applies a test for fitness, ranking the strings in the population accord-
ing to their level of desirability as possible solutions. After the initial population
is evaluated for fitness, the algorithm then produces the next generation of

FIGURE 11.6 THE COMPONENTS OF A GENETIC ALGORITHM

This example illustrates an initial population of “chromosomes,” each representing a
d­ ifferent solution. The genetic algorithm uses an iterative process to refine the initial
­solutions so that the better ones, those with the higher fitness, are more likely to emerge
as the best solution.

Length Width Weight Fitness
1 Long Wide Light 55
2 Short Narrow Heavy 49
3 Long Narrow Heavy 36
4 Short Medium Light 61
5 Long Medium Very light 74

A population of Decoding of Evaluation of
chromosomes chromosomes chromosomes

436 Part Three  Key System Applications for the Digital Age

strings, consisting of strings that survived the fitness test plus offspring strings
produced from mating pairs of strings, and tests their fitness. The process con-
tinues until a solution is reached.

Genetic algorithms are used to solve problems that are very dynamic and
complex, involving hundreds or thousands of variables or formulas. The prob-
lem must be one whose range of possible solutions can be represented geneti-
cally and for which criteria can be established for evaluating fitness. Genetic
algorithms expedite the solution because they can evaluate many solution al-
ternatives quickly to find the best one. For example, General Electric engineers
used genetic algorithms to help optimize the design for jet turbine aircraft en-
gines, in which each design change required changes in up to 100 variables.
The supply chain management software from JDA software uses genetic algo-
rithms to optimize production-scheduling models, incorporating hundreds of
thousands of details about customer orders, material and resource availability,
manufacturing and distribution capability, and delivery dates.

Natural Language Processing, Computer Vision
Systems, and Robotics

Other important AI techniques include natural language processing, computer
vision systems, and robotics.

Natural Language Processing

Human language is not always precise. It is often ambiguous, and meanings of
words can depend on complex variables such as slang, regional dialects, and so-
cial context. Natural language processing (NLP) makes it possible for a com-
puter to understand and analyze natural language—language that human beings
instinctively use, not language specially formatted to be understood by computers.
NLP algorithms are typically based on machine learning, including deep learning,
which can learn how to identify a speaker’s intent from many examples. Akershus
University Hospital, described in the chapter-opening case, used NLP and IBM
Watson Explorer to sift through thousands of medical records with unstructured
textual data expressed in everyday language like natural speech. The algorithms
could read text on a medical record and interpret its meaning. You can also see nat-
ural language processing at work in leading search engines such as Google, spam
filtering systems, and text mining sentiment analysis (discussed in Chapter 6).

Tokyo-based Mizuho Bank employs advanced speech recognition technology,
IBM® Watson™ content analytics software, and a cloud services infrastructure to
improve contact center agents’ interactions with customers. After converting the
customer’s speech to textual data, the solution applies natural language process-
ing algorithms based on machine learning analysis of interactions with thousands
of customers. The system learns more and more from each customer interaction
so that it can eventually infer the customer’s specific needs or goals at each point
of the conversation. It then formulates the optimal response, which is delivered
in real time as a prompt on the agent’s screen. By helping contact center agents
more efficiently sense and respond to customer needs, this solution reduced the
average duration of customer interactions by more than 6 percent (IBM, 2018).

Computer Vision Systems

Computer vision systems deal with how computers can emulate the human
v­ isual system to view and extract information from real-world images. Such sys-
tems incorporate image processing, pattern recognition, and image understanding.

Chapter 11  Managing Knowledge and Artificial Intelligence 437

An example is Facebook’s facial recognition tool called DeepFace, which is nearly
as accurate as the human brain in recognizing a face. DeepFace will help Facebook
improve the accuracy of Facebook’s existing facial recognition capabilities to
e­ nsure that every photo of a Facebook user is connected to that person’s Facebook
account. Computer vision systems are also used in autonomous vehicles such as
drones and self-driving cars (see the c­hapter-ending case), industrial machine
v­ ision systems (e.g., inspecting bottles), military applications, and robotic tools.

In 2017, the National Basketball Association (NBA) decided to allow spon-
sors to place small logo patches representing their brands on player uniforms.
This advertising investment turned out to be worth its multi-million-dollar
cost. According to GumGum, an AI company focusing on computer vision
technology, the image placed by The Goodyear Tire & Rubber Co. on the uni-
forms of the Cleveland Cavaliers generated $3.4 million in value from social
media exposure alone during the first half of the baseball season. GumGum
develops algorithms that enable computers to identify what’s happening in
imagery. GumGum used computer vision technology to thoroughly analyze
broadcast and social media content for placement, exposure, and duration in-
volving Goodyear images that appeared in online or in TV-generated NBA
content. Instead of humans trying to monitor the number of times a logo ap-
peared on a screen, GumGum’s vision technology tracks and reports the data
(Albertson, 2018).

Robotics

Robotics deals with the design, construction, operation, and use of mov-
able machines that can substitute for humans along with computer systems
for their control, sensory feedback, and information processing. Robots can-
not substitute entirely for people but are programmed to perform a spe-
cific series of actions automatically. They are often are used in dangerous
environments (such as bomb detection and deactivation), manufacturing
processes, military operations (drones), and medical procedures (surgical
robots). Many employees now worry whether robots will replace people en-
tirely and take away their jobs (see the Chapter 4 Interactive Session on
Organizations).

The most widespread use of robotic technology has been in manufactur-
ing. For example, automobile assembly lines employ robots to do heavy lifting,
welding, applying glue, and painting. People still do most of the final assembly
of cars, especially when installing small parts or wiring that needs to be guided
into place. A Renault SA plant in Cleon, France, now uses robots from Universal
Robots AS of Denmark to drive screws into engines, especially those that go
into places people find hard to access. The robots verify that parts are properly
fastened and check to make sure the correct part is being used. The Renault
robots are also capable of working in proximity to people and slowing down or
stopping to avoid hurting them.

Intelligent Agents

Intelligent agents are software programs that work in the background without
direct human intervention to carry out specific tasks for an individual user,
business process, or software application. The agent uses a limited built-in or
learned knowledge base to accomplish tasks or make decisions on the user’s
behalf, such as deleting junk email, scheduling appointments, or finding the
cheapest airfare to California.

438 Part Three  Key System Applications for the Digital Age

There are many intelligent agent applications today in operating systems, ap-
plication software, email systems, mobile computing software, and network tools.
Of special interest to business are intelligent agent bots that search for informa-
tion on the Internet. Chapter 7 describes how shopping bots help consumers find
products they want and assist them in comparing prices and other features.

Although some software agents are programmed to follow a simple set of
rules, others are capable of learning from experience and adjusting their behav-
ior using machine learning and natural language processing. Siri, a virtual as-
sistant application on Apple’s iPhone and iPad, is an example. Siri uses natural
language processing to answer questions, make recommendations, and perform
actions. The software adapts to the user’s individual preferences over time and
personalizes results, performing tasks such as getting directions, scheduling
appointments, and sending messages. Similar products include Google Now,
Microsoft’s Cortana, and Amazon’s Alexa.

Chatbots (chatterbots) are software agents designed to simulate a conversa-
tion with one or more human users via textual or auditory methods. They try to
understand what you type or say and respond by answering questions or execut-
ing tasks. They provide automated conversations that allow users to do things like
check the weather, manage personal finances, shop online, and receive help when
they have questions for customer service. Vodafone, a multinational telecommu-
nications company, uses a chatbot to answer 80,000 questions per month, reduc-
ing contact center calls for 75 percent of the customers it chats with. Vodafone
staff use the chatbot to access accurate, up-to-date information on Vodafone prod-
ucts and services. Facebook has integrated chatbots into its Messenger messaging
app so that an outside company with a Facebook brand page can interact with
Facebook users through the chat program. Today’s chatbots perform very basic
functions but will become more technologically advanced in the future.

Procter & Gamble (P&G) used intelligent agent technology to make its sup-
ply chain more efficient (see Figure 11.7). It modeled a complex supply chain
as a group of semiautonomous agents representing individual supply chain
components such as trucks, production facilities, distributors, and retail stores.
The behavior of each agent is programmed to follow rules that mimic actual
behavior, such as “order an item when it is out of stock.” Simulations using the
agents enable the company to perform what-if analyses on inventory levels, in-
store stockouts, and transportation costs.

Using intelligent agent models, P&G discovered that trucks should often be
dispatched before being fully loaded. Although transportation costs would be
higher using partially loaded trucks, the simulation showed that retail store
stockouts would occur less often, thus reducing the number of lost sales, which
would more than make up for the higher distribution costs. Agent-based model-
ing has saved P&G $300 million annually on an investment of less than 1 per-
cent of that amount.

11-3 What types of systems are used for

enterprise-wide knowledge management,
and how do they provide value for businesses?

Firms must deal with at least three kinds of knowledge. Some knowledge
exists within the firm in the form of structured text documents (reports and
presentations). Decision makers also need knowledge that is semistructured,
such as email, voice mail, chat room exchanges, videos, digital pictures,

Chapter 11  Managing Knowledge and Artificial Intelligence 439

FIGURE 11.7 INTELLIGENT AGENTS IN P&G’S SUPPLY CHAIN NETWORK
Intelligent agents are helping Procter & Gamble shorten the replenishment cycles for products such
as a box of Tide.

Supplier Distributor

1 Production 3
facility

Supplier 2 Distributor
1. Software agents 3. Software agents schedule
schedule deliveries Retail Retail shipments from distributors
from suppliers. If a store store to retailers, giving priority to
supplier can’t deliver retailers whose inventories are
on time, agents low. If a shipment to a retailer
negotiate with other is delayed, agents find an
suppliers to create an alternative trucker.
alternative delivery
schedule.

2. Software agents collect real-time
sales data on each P&G product from
multiple retail stores. They relay the data
to P&G production for replenishing
orders and to sales and marketing for
trend analysis.

brochures, or bulletin board postings. In still other cases, there is no formal
or digital information of any kind, and the knowledge resides in the heads of
employees. Much of this knowledge is tacit knowledge that is rarely written
down. Enterprise-wide knowledge management systems deal with all three
types of knowledge.

Enterprise Content Management Systems

Businesses today need to organize and manage both structured and semistruc-
tured knowledge assets. Structured knowledge is explicit knowledge that ex-
ists in formal documents as well as in formal rules that organizations derive
by observing experts and their decision-making behaviors. But according to ex-
perts, at least 80 percent of an organization’s business content is semistructured
or unstructured—information in folders, messages, memos, proposals, emails,
graphics, electronic slide presentations, and even videos created in different
formats and stored in many locations.

Enterprise content management (ECM) systems help organizations
manage both types of information. They have capabilities for knowledge cap-
ture, storage, retrieval, distribution, and preservation to help firms improve
their business processes and decisions. Such systems include corporate re-
positories of documents, reports, presentations, and best practices, as well
as capabilities for collecting and organizing semistructured knowledge such
as email (see Figure 11.8). Major enterprise content management systems
also enable users to access external sources of information, such as news
feeds and research, and to communicate via email, chat/instant messaging,

440 Part Three  Key System Applications for the Digital Age

FIGURE 11.8 AN ENTERPRISE CONTENT MANAGEMENT SYSTEM
An enterprise content management system has capabilities for classifying, ­organizing,
and managing structured and semistructured knowledge and making it available
throughout the enterprise.

Users Create/Capture Unified repository
Tag • Reports/presentations
Store/Retrieve • Best practices
Manage/Review • Memos
Distribute/Publish • Digital slides
• Email
• Graphics
• Video
• News feeds
• Wikis, blogs
• Employee profiles

discussion groups, and videoconferencing. They are starting to incorpo-
rate blogs, wikis, and other enterprise social networking tools. Open Text
Corporation, IBM, and Oracle are leading vendors of enterprise content man-
agement software.

A key problem in managing knowledge is the creation of an appropriate clas-
sification scheme, or taxonomy, to organize information into meaningful cat-
egories so that it can be easily accessed. Once the categories for classifying
knowledge have been created, each knowledge object needs to be “tagged,” or
classified, so that it can be easily retrieved. Enterprise content management
systems have capabilities for tagging, interfacing with corporate databases and
content repositories, and creating enterprise knowledge portals that provide a
single point of access to information resources.

Firms in publishing, advertising, broadcasting, and entertainment have
special needs for storing and managing unstructured digital data such as pho-
tographs, graphic images, video, and audio content. For example, Coca-Cola
must keep track of all the images of the Coca-Cola brand that have been cre-
ated in the past at all of the company’s worldwide offices to prevent both
redundant work and variation from a standard brand image. Digital asset
management systems help companies classify, store, and distribute these
digital objects.

Locating and Sharing Expertise

Some of the knowledge businesses need is not in the form of a digital docu-
ment but instead resides in the memory of individual experts in the firm.
Contemporary enterprise content management systems, along with the sys-
tems for collaboration and social business introduced in Chapter 2, have
capabilities for locating experts and tapping their knowledge. These include
online directories of corporate experts and their profiles with details about
their job experience, projects, publications, and educational degrees, and

Chapter 11  Managing Knowledge and Artificial Intelligence 441

repositories of expert-generated content. Specialized search tools make
it easier for employees to find the appropriate expert in a company. For
knowledge resources outside the firm, social networking and social busi-
ness tools enable users to bookmark web pages of interest, tag these book-
marks with  keywords, and share the tags and web page links with other
people.

Learning Management Systems

Companies need ways to keep track of and manage employee learning and
to integrate it more fully into their knowledge management and other corpo-
rate systems. A learning management system (LMS) provides tools for the
management, delivery, tracking, and assessment of various types of employee
learning and training.

Contemporary LMS support multiple modes of learning, including CD-ROM,
downloadable videos, web-based classes, live instruction in classes or online,
and group learning in online forums and chat sessions. The LMS consolidates
mixed-media training, automates the selection and administration of courses,
assembles and delivers learning content, and measures learning effectiveness.
The Interactive Session on Management shows how Sargent & Lundy used
learning management and enterprise collaboration systems to increase sharing
of employee expertise and employee learning.

Businesses run their own learning management systems, but they are also
turning to publicly available massive open online courses (MOOCs) to edu-
cate their employees. A MOOC is an online course made available via the web
to very large numbers of participants. Companies view MOOCs as a new way
to design and deliver online learning where learners can collaborate with each
other, watch short videos, and participate in threaded discussion groups. Firms
such as Microsoft, AT&T, and Tenaris have developed their own MOOCs, while
others such as Bank of America and Qualcomm are adapting publicly available
MOOCs aligned with their core competencies.

11-4 What are the major types of knowledge

work systems, and how do they provide
value for firms?

The enterprise-wide knowledge systems we have just described provide a wide
range of capabilities that can be used by many if not all the workers and groups
in an organization. Firms also have specialized systems for knowledge workers
to help them create new knowledge and to ensure that this knowledge is prop-
erly integrated into the business.

Knowledge Workers and Knowledge Work

Knowledge workers, which we introduced in Chapter 1, include researchers,
designers, architects, scientists, and engineers who primarily create knowl-
edge and information for the organization. Knowledge workers usually have
high levels of education and memberships in professional organizations and
are often asked to exercise independent judgment as a routine aspect of their
work. For example, knowledge workers create new products or find ways of
improving existing ones. Knowledge workers perform three key roles that

442 Part Three  Key System Applications for the Digital Age

INTERACTIVE SESSION  MANAGEMENT

Sargent & Lundy Learns to Manage Employee Knowledge

Sargent & Lundy is a 125-year-old firm providing of the employees who really needed the instruction
comprehensive engineering, project management, were not getting it. There was no in-house computer-
and consulting services for complex power genera- based training available for new employees.
tion and power transmission projects. During its
125-year history, the company has designed 958 The company decided to transform its learning
power plants all over the world. The headquarters model and implemented a new talent management
are in Chicago, with global offices in Canada and the platform based on SAP SuccessFactors Learning
United Arab Emirates. Sargent & Lundy is noted for Management System. SAP SuccessFactors is a cloud-
its industry knowledge, engineering expertise, and based human capital management (HCM) software
high-quality work. Approximately 87 percent of its suite. It integrates software for orienting and train-
2,500 employees are engineers and designers. ing a new employee, social business and collabora-
tion tools, a learning management system (LMS),
The company takes pride in the depth of knowl- performance management, recruiting software, ap-
edge of its employee experts and their loyalty to the plicant tracking software, succession planning, talent
firm. On average, employees stay with the company management, and HR analytics to enable companies
for 15 years—often much longer. Sargent & Lundy to manage their employees more strategically and
tries to cross-utilize its staff in various types of work maximize their performance. SAP SuccessFactors
because it believes they are the best people for provides detailed capabilities for reporting and track-
the job. Deanna Myers, Sargent & Lundy’s Senior ing individual employee development.
Manager of Learning and Development, works to
ensure that employees have the skills, tools, and re- Working with company experts, Sargent & Lundy’s
sources they need to achieve excellence throughout technical training team used SuccessFactors to de-
their careers. velop a wide range of training programs and learning
plans for specific technologies. Videos and online
In December 2010, Sargent & Lundy’s manage- courses were added to the company’s training arse-
ment learned that around half of the company’s most nal, along with personalized learning plans for people
experienced employees, including engineers, design- working with specific disciplines and technologies.
ers, and power experts, would be eligible to retire by For example, an electrical engineer working on a
2015. When they left the company, they would be tak- transmission project would follow a learning plan with
ing critical business knowledge with them. Although courses and objectives that differed from those of an-
engineers had access to a knowledge database of the other electrical engineer working on a nuclear plant.
firm’s documented processes and procedures, experts’
tacit knowledge was more difficult to capture. Sargent & Lundy’s new recruits wanted more
discussion and feedback on how well they were
The company hired a large group of new recruits performing on a day-to-day basis, so the company
very quickly to replace the retirees. However, with a also implemented SAP Jam, SAP’s cloud-based social
worldwide footprint and ambitious expansion plans, collaboration program. Using SAP Jam made it even
the company needed to find better ways of transfer- easier for employees to share knowledge, often in
ring seasoned employees’ expertise—including their real time.
tacit knowledge—to the new recruits wherever they
were located. Corporate training hand learning facili- Face-to-face meetings are still the primary way
ties needed to be centralized and overhauled to make for staff to share knowledge about industry trends,
it easier for experts to share industry experience and best practices, and innovative solutions. But Sargent
skills as well as tacit knowledge. & Lundy’s employees were too geographically scat-
tered to always meet in person. The company’s
In the past, Sargent & Lundy had used an un- knowledge-sharing model had to change to provide
tracked schedule of instructor-led courses, which more knowledge sharing and employee conversa-
often overlapped with existing training. The moment tions online.
an instructor-led course had finished, the company
might have just hired another two or three people Sargent & Lundy’s new social platform features
who needed that course. This meant that a portion online discussion forums covering everything
from seismic analysis to specific types of valves.

Chapter 11  Managing Knowledge and Artificial Intelligence 443

New employees with questions are able to connect meeting, references and visuals, and which experts
to experts via SAP Jam with just a few mouse clicks. or solutions might be helpful.
The in-house experts respond to the questions and
share their experiences. Before implementing SAP SAP Jam was launched in late 2015, and in the
Jam, only a small percentage of regional staff par- year that followed there was a 125 percent increase
ticipated in discussions for communities of practice. in participation. Many CoP groups are now using SAP
Sargent & Lundy’s Communities of Practice (CoP) Jam, including groups for specific technical topics
program makes it possible for employee experts to such as thermo hydraulics and non-technical groups
collaborate with novice staff around specific topics such as one for women in leadership. Employees of
(see the discussion of CoPs earlier in this chapter). all levels can use Jam to discuss topics of interest,
find answers to questions, and check facts. Besides
The main focus of SAP Jam is on discussion improving employee learning, the tool has increased
groups. When users log in, they are presented with a employee engagement. Conversations in SAP Jam
home feed showing events in the discussion groups have identified areas for process improvement
to which they subscribe. They can drill down to a and problems that need immediate technical staff
specific feed or browse all the CoP groups for topics attention.
of interest. There are no restrictions on who can post
a question, discussion topic, or article. A designer in Sources: www.sargentlundy.com, accessed May 28, 2018; Lauren
the transmission group might look at how designers Bonneau, “Creating a Culture of Collaboration at Sargent & Lundy,”
in other groups solved similar problems by review- SAP Insider Profiles, March 24, 2017; and SAP SE, “Sargent & Lundy:
ing what these groups discussed in their last CoP Powering the Next Generation on Blended Learning with SAP
SuccessFactors Solutions,” 2016.

CASE STUDY QUESTIONS

1. How is knowledge management related to Sargent did it change the firm’s operations and decision
& Lundy’s business model? making?

2. Identify the knowledge management problem 4. What management, organization, and technology
faced by Sargent & Lundy. What management, or- issues had to be addressed in selecting and imple-
ganization, and technology factors contributed to menting the solution?
this problem?

3. Describe the solution selected for this problem.
Was it effective? Why or why not? How much

are critical to the organization and to the managers who work within the
organization:

• Keeping the organization current in knowledge as it develops in the external
world—in technology, science, social thought, and the arts

• Serving as internal consultants regarding the areas of their knowledge, the
changes taking place, and opportunities

• Acting as change agents, evaluating, initiating, and promoting change
projects

Requirements of Knowledge Work Systems

Most knowledge workers rely on systems such as word processors, email, vid-
eoconferencing, collaboration, and scheduling systems, which are designed
to increase worker productivity in the office. However, knowledge work-
ers also require highly specialized knowledge work systems with powerful

444 Part Three  Key System Applications for the Digital Age

FIGURE 11.9 REQUIREMENTS OF KNOWLEDGE WORK SYSTEMS
Knowledge work systems require strong links to external knowledge bases in addition to
specialized hardware and software.

External SOFTWARE
knowledge Graphics -> Visualization
Modeling -> Simulation
base Document management

Communications

User
interface

Hardware platform: knowledge workstation

graphics, analytical tools, and communications and document management
capabilities.

These systems require sufficient computing power to handle the sophisti-
cated graphics or complex calculations necessary for such knowledge workers
as scientific researchers, engineers, and product designers. Because knowledge
workers need knowledge from the external world, these systems also must give
the worker quick and easy access to external databases. They typically feature
user-friendly interfaces that enable users to perform needed tasks without hav-
ing to spend a great deal of time learning how to use the system. Knowledge
workers are highly paid—wasting a knowledge worker’s time is simply too ex-
pensive. Figure 11.9 summarizes the requirements of knowledge work systems.

Examples of Knowledge Work Systems

Major knowledge work applications include CAD systems and virtual reality
systems for simulation and modeling. Computer-aided design (CAD) auto-
mates the creation and revision of designs, using computers and sophisticated
graphics software. Using a more traditional physical design methodology, each
design modification requires a mold to be made and a prototype to be tested
physically. That process must be repeated many times, which is very expensive
and time-consuming. Using a CAD workstation, the designer need only make a
physical prototype toward the end of the design process because the design can
be easily tested and changed on the computer. The ability of CAD software to
provide design specifications for the tooling and manufacturing processes also
saves a great deal of time and money while producing a manufacturing process
with far fewer problems.

For example, Ford Motor Company used a computer simulation to cre-
ate an engine cylinder to come up with the most efficient design possible.

Chapter 11  Managing Knowledge and Artificial Intelligence 445

Engineers altered that design to account for manufacturing constraints and
tested the revised design virtually in models that used decades of data on
material properties and engine performance. Ford then created the mold to
make a real part that could be bolted onto an engine for further testing. The
entire process took days instead of months and cost thousands of dollars in-
stead of millions.

CAD systems can supply data for 3-D printing, also known as additive man-
ufacturing, which uses machines to make solid objects, layer by layer, from
specifications in a digital file. Unlike traditional techniques, by which objects
are cut or drilled from molds, resulting in wasted materials, 3-D ­printing lets
workers model an object on a computer and print it out with plastic, metal,
or composite materials. 3-D printing is currently used for p­ rototyping, custom
manufacturing, and fashioning items with small production runs. Today’s 3-D
printers can handle materials including plastic, titanium, and human carti-
lage and produce fully functional components including batteries, transistors,
prosthetic devices, LEDs, and other complex mechanisms, and there are now
3-D printing services that run over the cloud, such as that offered by Staples.

Virtual reality (VR) systems have visualization, rendering, and simula-
tion capabilities that go far beyond those of conventional CAD systems. They
use interactive graphics software to create computer-generated simulations that
are so close to reality that users almost believe they are participating in a real-
world situation. In many virtual reality systems, the user dons special clothing,
headgear, and equipment, depending on the application. The clothing contains
sensors that record the user’s movements and immediately transmit that in-
formation back to the computer. For instance, to walk through a virtual real-
ity simulation of a house, you would need garb that monitors the movement
of your feet, hands, and head. You also would need goggles containing video
screens and sometimes audio attachments and feeling gloves so that you can be
immersed in the computer feedback.

At NYU Langone Medical Center in New York City, students wearing 3-D
glasses are able to “dissect” a virtual cadaver projected on a screen. With the
help of a computer, they can move through the virtual body, scrutinizing lay-
ers of muscles or watching a close-up of a pumping heart along with bright red
arteries and deep blue veins. The 3-D virtual cadaver is a valuable complemen-
tary teaching tool. The Interactive Session on Technology describes some of the
issues raised by applications of VR technology.

Augmented reality (AR) is a related technology for enhancing visual-
ization by overlaying digital data and images onto a physical real-world envi-
ronment. The digital technology provides additional information to enhance
the perception of reality, making the surrounding real world of the user
more interactive and meaningful. The yellow first-down markers shown on
televised football games are examples of augmented reality as are medical
procedures like image-guided surgery, where data acquired from comput-
erized tomography (CT) and magnetic resonance imaging (MRI) scans or
from ultrasound imaging are superimposed on the patient in the operating
room. Other industries where AR has caught on include military training,
engineering design, robotics, and consumer design. For example, Newport
News Shipbuilding, which designs and builds U.S. Navy aircraft carriers,
uses AR to inspect a ship near the end of the manufacturing process. By
seeing the final design superimposed on the ship, engineers have reduced
inspection time by 96 percent—from 36 hours to only 90 minutes (Porter
and Heppelmann, 2017).

446 Part Three  Key System Applications for the Digital Age

INTERACTIVE SESSION  TECHNOLOGY

The Reality of Virtual Reality

In the past, your best chance of experiencing vir- to grow, and expects one billion people to use this
tual reality (VR) was to view Hollywood films. medium.
Today, this technology has become more sophisti-
cated and immersive, and it is finding more busi- In March 2014 Facebook purchased Oculus VR
ness uses. for $2 billion to start working with virtual reality.
Oculus produces the Rift high-end VR headset and
Besides entertainment, the most popular vir- also Oculus Go, a standalone headset costing only
tual reality applications are currently in retail and $199 that is supposed to be easier to use than the
manufacturing, where an immersive experience Rift. A beta version of Spaces, Facebook’s virtual
can help customers visualize products or teach reality app for the Oculus Rift, was launched in
factory workers how to use complex equipment. April 2018. Spaces is intended as a virtual hangout
Audi has used virtual reality technology in its where you can interact with up to four friends,
“dealership in a briefcase” program. By donning each represented by self-created digital avatars.
an Oculus Rift virtual reality headset, prospec- Inside Spaces you can also video chat with friends
tive buyers can feel as if they are sitting behind via Facebook Messenger, do a Facebook Live broad-
the wheel of a car or opening up the trunk. The cast, or take “selfies” in VR to share with Facebook
VR headset displays in 3-D exactly what you’d see friends.
if you were looking over a real-life Audi. Bang &
Olufsen headphones simulate the sounds of doors In Oculus Rooms for the Oculus Go, people can
slamming shut and music from the stereo system spend time together in a virtual world, chatting,
of the cars being browsed. This VR experience is watching a movie, or playing cards. Oculus Go has
available for the entire Audi model range and cus- a virtual reality TV viewing app called Oculus TV
tomization options. where users can “sit” in a 3-D environment with
a massive screen and watch video with a friend.
Volkswagen Group is experimenting with virtual Content from Netflix, Hulu Showtime, Redbull,
reality to speed up vehicle design and development Pluto TV, Facebook video, and ESPN is available.
and to identify potentially costly design problems Oculus Venues makes it possible to watch sports or
earlier in the development cycle. Volkswagen has live concerts with friends or other people in virtual
been able to cut out costly physical prototypes and reality.
replace them with immersive, 360-degree views of
digitally constructed interior and exterior compo- Facebook is trying to create photorealistic ava-
nents of a vehicle using virtual reality HTC Vive tars, with technology to help users personalize
headsets. Virtual components of a car, including their avatars with more lifelike features and ges-
interior and exterior parts such as buttons, lights, or tures so that they more closely resemble the people
consoles, can be switched out and replaced easily they represent. Facebook’s avatars are still very
with a few lines of software code during the design cartoon-like. Creating the virtual reality experi-
process. ence Facebook wants carries a hefty price tag—an
i­nvestment of around $3 billion during the next
A major drawback of virtual reality is how it decade. It is too early to tell whether Facebook can
isolates people from others and from real-world make a virtual reality breakthrough for widespread
surroundings because they are immersed in a vir- use.
tual world encased in a headset. Facebook wants to
change that by using virtual reality to create a new Not all organizations will benefit from VR. Greg
type of shared social experience. Facebook envi- Meyers, CIO at Motorola Solutions, a data com-
sions a virtual world with an imaginary social space munications and telecommunications equipment
where avatars that look like real people “hang out” provider, doesn’t see any obvious applications
with other avatars representing friends and fam- where virtual reality can help drive growth for his
ily. CEO Mark Zuckerberg believes virtual reality company. Virtual reality may be useful for stag-
provides a new and powerful way for his company ing more effective meetings, but Meyers feels the
company would derive more benefit from using

Chapter 11  Managing Knowledge and Artificial Intelligence 447

AI technology to help it make better and faster the organization’s databases and major corporate
decisions. systems.

There is a gap between the hype and the re- According to research analyst Ian Hughes, for
ality of what VR can actually do for business. most workplace applications, shutting people off
Management needs to ensure that the company from other people and the real world to engage in
will receive concrete returns on investment. Virtual a virtual experience does not fit well with the way
reality shouldn’t be viewed as the next platform people behave. Rather than a fully simulated work
everyone’s going to use, because it’s unclear that environment, for most tasks, Hughes recommends
will be the case, according to Forrester Research blending real and virtual worlds. It’s better for tech-
analyst J. P. Gownder. A business needs to be able to nology to be extending the real world rather than
demonstrate that VR will solve a very specific busi- replacing it.
ness problem, and must determine exactly how VR
technology can improve its operations, engage cus- Sources: Kurt Wagner, “Oculus Go, the Virtual Reality Headset
tomers for highly configurable goods, or deliver new Facebook Hopes Will Bring VR to the Mainstream, Is Finally
consumer experiences. Here,” Recode, May 1, 2018; Sara Castellanos, “Volkswagen Brings
Sense of Touch to Virtual Reality,” Wall Street Journal, April 25,
Many enterprise applications of immersive tech- 2018; Chuong Nguyen, “Facebook Wants to Make Your Virtual
nology are still in the piloting and troubleshoot- Self Appear as Real as Possible in VR,” Digital Trends, May 3, 2018;
ing phases. Today’s virtual reality technology also “Virtual and Augmented Reality—Reshaping Business Futures,”
lacks strong security and management features ETCIO.com, March 12, 2018; Matt Kapko, “Making Waves with
appropriate for deployment in the enterprise, Immersive Technologies,” CIO, May–June 2017; and Lisa Eadiccio,
where VR applications might need to be linked to “Inside Facebook’s Plan to Take Virtual Reality Mainstream,” Time,
August 2, 2017.

CASE STUDY QUESTIONS

1. If your company wanted to implement a virtual 3. Do you think Facebook’s virtual reality strategy
reality application, what management, organiza- will be successful? Explain your answer.
tion, and technology factors should it consider?

2. Should all businesses use virtual reality? Why or
why not? What kinds of organizations will benefit
most from this technology?

11-5 How will MIS help my career?

Here is how Chapter 11 and this book will help you find an entry-level job as a
sales assistant for an AI company.

The Company

RazzleDazzle Technology, an artificial intelligence company based in San Jose,
California, is looking for an entry-level sales assistant. RazzleDazzle specializes
in computer vision technology, seeking to unlock the value of visual content
produced daily across diverse data sets to solve problems for a variety of indus-
tries, including advertising and professional sports.

448 Part Three  Key System Applications for the Digital Age

Position Description

The sales assistant will work closely with the sales team on planning and
staging events, database management, administrative tasks, and account
­research to support sales and marketing objectives. Job responsibilities
include:

• Using Salesforce.com for lead generation collection, data entry, and
maintenance

• Using Excel to update sales team resources
• Scheduling meetings and taking meeting notes
• Assisting with research on sales accounts and new event ideas and

locations
• Assisting sales with client meeting preparation
• Assembling promotional materials

Job Requirements

• Recent college graduate.
• Bachelor’s degree in marketing, MIS, finance, or liberal arts.
• Strong interest in learning the business and industry.
• Knowledge of Microsoft Office essential.
• Attention to detail, effective communication skills, enthusiastic attitude, and

the ability to thrive in a fast-paced environment.

Interview Questions

1. What do you know about our company and about computer vision systems?
Have you ever done any work with AI technology?

2. Have you ever worked with Salesforce.com? How have you used the soft-
ware?

3. What is your proficiency level with Microsoft Office tools? What work have
you done with Excel spreadsheets?

4. Can you provide samples of your writing to demonstrate your communica-
tion skills and sense of detail?

Author Tips

1. Review the section of this chapter on AI and use the web to find out more
about computer vision systems.

2. Use the web and LinkedIn to find out more about this company, its
­products, services, and competitors and the way it operates. Think about
what it needs to support its sales team and how you could specifically
contribute.

3. Learn what you can about Salesforce.com, with attention to how it handles
lead generation, data entry, and maintenance.

4. Inquire exactly how you would be using Excel in this job. Describe some of
the Excel work you have done and perhaps bring samples with you to the
interview.

Chapter 11  Managing Knowledge and Artificial Intelligence 449

REVIEW SUMMARY

11-1 What is the role of knowledge management systems in business?

Knowledge management is a set of processes to create, store, transfer, and apply knowledge in the
organization. Much of a firm’s value depends on its ability to create and manage knowledge. Knowl-
edge management promotes organizational learning by increasing the ability of the organization to
learn from its environment and to incorporate knowledge into its business processes. There are three
major types of knowledge management systems: enterprise-wide knowledge management systems,
knowledge work systems, and “intelligent” techniques.

11-2 What are artificial intelligence (AI) and machine learning? How do businesses use AI?

AI involves the attempt to build computer systems that try to think and act like humans. At pres-
ent, artificial intelligence lacks the flexibility, breadth, and generality of human intelligence, but it
can be used to capture, codify, and extend organizational knowledge.

Expert systems capture tacit knowledge from a limited domain of human expertise and express
that knowledge in the form of rules. Machine learning software can learn from previous data and ex-
amples. It can identify patterns in very large databases without explicit programming, although with
significant human training

Neural networks consist of hardware and software that attempt to mimic the thought processes
of the human brain. Neural networks are notable for their ability to learn on their own with some
training, and to recognize patterns that cannot be easily identified by humans. Deep learning neural
networks use multiple layers of neural networks to reveal the underlying patterns in data, and in
some limited cases identify patterns without human training

Genetic algorithms develop solutions to particular problems using genetically based processes
such as fitness, crossover, and mutation. Genetic algorithms are useful for solving problems involving
optimization where many alternatives or variables must be evaluated to generate an optimal solution.

Intelligent agents are software programs with built-in or learned knowledge bases that carry out
specific tasks for an individual user, business process, or software application. Intelligent agents can
be programmed to navigate through large amounts of data to locate useful information and in some
cases act on that information on behalf of the user. Chatbots are software agents designed to simulate
a conversation with one or more human users via textual or auditory methods.

Natural language processing technology makes it possible for a machine to understand human lan-
guage and to process that information. Computer vision systems deal with how computers can emulate
the human visual system to view and extract information from real-world images. Robotics deals with the
design, construction, operation, and use of movable machines that can substitute for some human actions.

11-3 What types of systems are used for enterprise-wide knowledge management, and how do they
provide value for businesses?

Enterprise-wide knowledge management systems are firmwide efforts to collect, store, distribute,
and apply digital content and knowledge. Enterprise content management systems provide databases
and tools for organizing and storing structured documents and tools for organizing and storing semi-
structured knowledge, such as email or rich media. Often these systems include group collaboration
tools, portals to simplify information access, search tools, tools for locating experts, and tools for clas-
sifying information based on a taxonomy that is appropriate for the organization. Learning manage-
ment systems provide tools for the management, delivery, tracking, and assessment of various types
of employee learning and training.

11-4 What are the major types of knowledge work systems, and how do they provide value for firms?

Knowledge work systems (KWS) support the creation of new knowledge and its integration into
the organization. KWS require easy access to an external knowledge base; powerful computer hard-
ware that can support software with intensive graphics, analysis, document management, and com-
munications capabilities; and a user-friendly interface. KWS include computer-aided design (CAD)
systems, augmented reality applications, and virtual reality systems, which create interactive simu-
lations that behave like the real world, with intensive graphics and powerful modeling capabilities.

450 Part Three  Key System Applications for the Digital Age

Key Terms Knowledge, 422
Knowledge base, 429
3-D printing, 445 Knowledge management, 423
Artificial intelligence (AI), 427 Knowledge work systems (KWS), 426
Augmented reality (AR), 445 Learning management system (LMS), 441
Chatbot, 438 Machine learning, 430
Communities of practice (COPs), 425 Massive open online course (MOOC), 441
Computer-aided design (CAD), 444 Natural language processing (NLP), 436
Computer vision systems, 436 Neural networks, 432
Data, 422 Organizational learning, 423
“Deep learning,” 434 Pattern detection program, 432
Digital asset management systems, 440 Robotics, 437
Enterprise content management (ECM), 439 Structured knowledge, 439
Enterprise-wide knowledge management Supervised learning, 431
Tacit knowledge, 422
systems, 426 Taxonomy, 440
Expert systems, 428 Unsupervised learning, 431
Explicit knowledge, 422 Virtual reality (VR) systems, 445
Genetic algorithms, 435 Wisdom, 422
Inference engine, 429
Intelligent agents, 437
“Intelligent” techniques, 426

MyLab MIS

To complete the problems with the MyLab MIS, go to the EOC Discussion Questions in MyLab MIS.

Review Questions

11-1 What is the role of knowledge management 11-3 • Define and describe computer ­vision
11-2 systems in business?  11-4 systems, natural language ­processing
­systems, and robotics and give
• Define knowledge management and ex- ­examples of their applications in
plain its value to businesses. organizations.

• Describe the important dimensions of What types of systems are used for enterprise-
knowledge. wide knowledge management, and how do
they provide value for businesses? 
• Distinguish between data, knowledge, and
wisdom and between tacit knowledge and • Define and describe the various types of
explicit knowledge. enterprise-wide knowledge management
systems and explain how they provide
• Describe the stages in the knowledge value for businesses.
management value chain.
• Describe the role of the following in facili-
What are artificial intelligence (AI) and machine tating knowledge management: taxono-
learning? How do businesses use AI?  mies, MOOCs, and learning management
systems.
• Define artificial intelligence (AI) and the
major AI techniques. What are the major types of knowledge work
systems, and how do they provide value for
• Define an expert system, describe how it firms? 
works, and explain its value to business.
• Define knowledge work systems and de-
• Define machine learning, explain how scribe the generic requirements of knowl-
it works, and give some examples of the edge work systems.
kinds of problems it can solve.
• Describe how the following systems sup-
• Define neural networks and deep ­learning port knowledge work: CAD, virtual reality,
neural networks, describing how they and augmented reality.
work and how they benefit organizations.

• Define and describe genetic algorithms,
and intelligent agents. Explain how each
works and the kinds of problems for
which each is suited.

Chapter 11  Managing Knowledge and Artificial Intelligence 451

Discussion Questions 11-7 Compare artificial intelligence to human
MyLab MIS intelligence. How “intelligent” is artificial
11-5 Knowledge management is a business
MyLab MIS process, not a technology. Discuss. intelligence today?

11-6 Describe various ways that knowledge
MyLab MIS management systems could help firms with

sales and marketing or with manufacturing
and production.

Hands-On MIS Projects

The projects in this section give you hands-on experience designing a knowledge portal, identifying opportunities
for knowledge management, creating a simple expert system, and using intelligent agents to research products
for sale on the web. Visit MyLab MIS to access this chapter’s Hands-On MIS Projects.

Management Decision Problems

11-8 U.S. Pharma Corporation is headquartered in New Jersey but has research sites in Germany, France,
11-9 the United Kingdom, Switzerland, and Australia. Research and development of new pharmaceuticals
is key to ongoing profits, and U.S. Pharma researches and tests thousands of possible drugs. The
company’s researchers need to share information with others within and outside the company,
including the U.S. Food and Drug Administration, the World Health Organization, and the International
Federation of Pharmaceutical Manufacturers & Associations. Also critical is access to health information
sites, such as the U.S. National Library of Medicine, and to industry conferences and professional
journals. Design a knowledge portal for U.S. Pharma’s researchers. Include in your design specifications
relevant internal systems and databases, external sources of information, and internal and external
communication and collaboration tools. Design a home page for your portal.

Canadian Tire is one of Canada’s largest companies, with 50,000 employees and 1,100 stores and gas
bars (gas stations) across Canada selling sports, leisure, home products, apparel, and financial services as
well as automotive and petroleum products. The retail outlets are independently owned and operated.
Canadian Tire has been using daily mailings and thick product catalogs to inform its dealers about new
products, merchandise setups, best practices, product ordering, and problem resolution, and it is looking
for a better way to provide employees with human resources and administrative documents. Describe the
problems created by this way of doing business and how knowledge management systems might help.

Improving Decision Making: Building a Simple Expert System for Retirement Planning

Software skills: Spreadsheet formulas and IF function or expert system tool
Business skills: Benefits eligibility determination

11-10 Expert systems typically use a large number of rules. This project has been simplified to reduce
the number of rules, but it will give you experience working with a series of rules to develop an
application.
When employees at your company retire, they are given cash bonuses. These cash bonuses are based

on the length of employment and the retiree’s age. To receive a bonus, an employee must be at least 50 years of
age and have worked for the company for more than five years. The following table summarizes the criteria for
determining bonuses.

LENGTH OF EMPLOYMENT BONUS

<5 years No bonus
5–10 years   20 percent of current annual salary
11–15 years   30 percent of current annual salary
16–20 years   40 percent of current annual salary
21–25 years   50 percent of current annual salary
26 or more years 100 percent of current annual salary

452 Part Three  Key System Applications for the Digital Age

Using the information provided, build a simple expert system. Find a demonstration copy of an expert
system software tool on the web that you can download. Alternatively, use your spreadsheet software to build
the expert system. (If you are using spreadsheet software, we suggest using the IF function so you can see how
rules are created.)

Improving Decision Making: Using Intelligent Agents for Comparison Shopping

Software skills: Web browser and shopping bot software
Business skills: Product evaluation and selection

11-11 This project will give you experience using shopping bots to search online for products, find product infor-
mation, and find the best prices and vendors. Select a digital camera you might want to purchase, such as
the Canon P­ owerShot SX540 or the Olympus Tough TG-5. Visit MySimon (www.m­ ysimon.com), BizRate.
com (www.bizrate.com), and Google Shopping to do price comparisons for you. Evaluate these shopping
sites in terms of their ease of use, number of offerings, speed in obtaining information, thoroughness of in-
formation offered about the product and seller, and price selection. Which site or sites would you use, and
why? Which camera would you select, and why? How helpful were these sites for making your decision?

Collaboration and Teamwork Project

Rating Enterprise Content Management Systems

11-12 With a group of classmates, select two enterprise content management (ECM) products, such as those from
Oracle, OpenText, and IBM. Compare their features and capabilities. To prepare your analysis, use articles
from computer magazines and the websites of the ECM software vendors. If possible, use Google Docs and
Google Drive or Google Sites to brainstorm, organize, and develop a presentation of your findings for the
class.

Chapter 11  Managing Knowledge and Artificial Intelligence 453

Can Cars Drive Themselves—And Should They?

CASE STUDY

Will cars really be able to drive themselves A lidar fires out millions of laser beams every sec-
without human operators? Should they? ond, measuring how long they take to bounce back.
And are they good business investments? The lidar takes in a 360-degree view of a car’s sur-
Everyone is searching for answers. roundings, identifying nearby objects with an accu-
racy up to 2 centimeters. Lidars are very expensive
Autonomous vehicle technology has reached a and not yet robust enough for a life of potholes, ex-
point where no automaker can ignore it. Every major treme temperatures, rain, or snow.
auto maker is racing to develop and perfect autono-
mous vehicles, believing that the market for them GPS:  A global positioning system (GPS) pinpoints
could one day reach trillions of dollars. Companies the car’s macro location, and is accurate to within
such as Ford, General Motors, Nissan, Mercedes, 1.9 meters. Combined with reading from tachom-
Tesla, and others have invested billions in autono- eters, gyroscopes, and altimeters, it provides initial
mous technology research and development. Ford in- positioning.
vested $1 billion in AI firm Argo AI, and GM bought
a self-driving car startup called Cruise. Ford has set Radar:  Radar bounces radio waves off of objects to
a goal of producing a self-driving car with no pedals help see a car’s surroundings, including blind spots,
by 2021. Ride-hailing companies like Uber and Lyft and is especially helpful for spotting big metallic ob-
believe driverless cars that eliminate labor costs are jects, such as other vehicles.
key to their long-term profitability. Cars that drive
themselves have been on the road in select loca- Computer:  All the data generated by these tech-
tions in California, Arizona, Michigan, Paris, London, nologies needs to be combined, analyzed, and turned
Singapore, and Beijing. Waymo, the company that into a robot-friendly picture of the world, with in-
emerged from Google’s self-driving car project, pre- structions on how to move through it, requiring
dicts that by 2020 its fleet of self-driving Jaguars will almost supercomputer-like processing power. Its soft-
make as many as one million trips per day. ware features obstacle avoidance algorithms, predic-
tive modeling, and “smart” object discrimination (for
A car that is supposed to take over driving from a example, knowing the difference between a bicycle
human requires a very powerful computer system and a motorcycle) to help the vehicle follow traffic
that must process and analyze large amounts of data rules and navigate obstacles.
generated by myriad sensors, cameras, and other
devices to control and adjust steering, accelerating, Machine Learning, Deep Learning, and
and braking in response to real-time conditions. Key Computer Vision Technology:  The car’s
technologies include: c­ omputer system has to be “trained” using machine
intelligence and deep learning to do things like
Sensors:  Self-driving cars are loaded with sensors of detect lane lines and identify cyclists, by show-
many different types. Sensors on car wheels measure ing it millions of examples of the subject at hand.
car velocity as it drives and moves through traffic. Because the world is too complex to write a rule
Ultrasonic sensors measure and track positions of line for every possible scenario, cars must be able to
curbs, sidewalks, and objects very close to the car. “learn” from experience and figure out how to
­navigate on their own.
Cameras:  Cameras are needed for spotting things
like lane lines on the highway, speed signs, and Maps:  Before an autonomous car takes to the
t­raffic lights. Windshield-mounted cameras create streets, its developers use cameras and lidars to map
a 3-D image of the road ahead. Cameras behind the its territory in extreme detail. That information helps
rear-view mirror focus on lane markings. Infrared the car verify its sensor readings, and it is key for
cameras pick up infrared beams emitted from head- any vehicle to know its own location.
lamps to extend vision for night driving.
Self-driving car companies are notorious for over-
Lidars:  Lidars are light detection and ranging de- hyping their progress. Should we believe them? At
vices which sit on top of most self-driving cars. this point, the outlook for them is clouded.

454 Part Three  Key System Applications for the Digital Age

In March 2018, a self-driving Uber Volvo XC90 whether a bag on the road is empty or is hiding bricks
operating in autonomous mode struck and killed a or heavy objects inside. Although autonomous vehicle
woman in Tempe, Arizona. Since the crash, Arizona vision systems are now capable of picking out traffic
has suspended autonomous vehicle testing in the lights reliably, they are not always able to make correct
state, and Uber is not renewing its permit to test decisions if traffic lights are not working. This requires
self-driving cars in California. The company has also experience, intuition, and knowing how to cooperate
stopped testing autonomous cars in Pittsburgh and among multiple vehicles. Autonomous vehicles must
Toronto and it’s unclear when it will be revived. Even also be able to recognize a person moving alongside a
before the accident, Uber’s self-driving cars were road, determine whether that person is riding a bicycle,
having trouble driving through construction zones and how that person is likely to respond and behave.
and next to tall vehicles like big truck rigs. Uber’s All of that is still difficult for an autonomous vehicle to
drivers had to intervene far more frequently than do right now. Chaotic environments such as congested
drivers in other autonomous car projects. streets teeming with cars, pedestrians, and cyclists are
especially difficult for self-driving cars to navigate.
The Uber accident raised questions about whether
autonomous vehicles were even ready to be tested Driving a car to merge into rapidly flowing lanes
on public roads and how regulators should deal with of traffic is an intricate task that often requires eye
this. Autonomous vehicle technology’s defenders contact with oncoming drivers. How can autono-
pointed out that nearly 40,000 people die on U.S. mous vehicles communicate with humans and other
roads every year, and human error causes more than machines to let them know what they want to do?
90 percent of crashes. But no matter how quickly Researchers are investigating whether electronic
self-driving proliferates, it will be a very long time signs and car-to-car communication systems would
before the robots can put a serious dent in those solve this problem. There’s also what’s called the
numbers and convince everyday folks that they’re “trolley problem”: In a situation where a crash is
better off letting the cars do the driving. unavoidable, how does a robot car decide whom or
what to hit? Should it hit the car coming up on its left
While proponents of self-driving cars like Tesla’s or a tree on the side of the road?
Elon Musk envision a self-driving world where al-
most all traffic accidents would be eliminated, and A less advanced version of autonomous vehicle
the elderly and disabled could travel freely, most technology is already on the market. Cadillac Super
Americans think otherwise. A Pew Research Center Cruise, Nissan ProPilot Assist, and Tesla Autopilot
survey found that most people did not want to ride in are capable of keeping a car in its lane and a safe dis-
self-driving cars and were unsure if they would make tance from other cars, allowing the “driver” behind
roads more dangerous or safer. Eighty-seven percent the wheel to take hands off the wheel, provided that
wanted a person always behind the wheel, ready to person keeps paying attention and is ready to take
take over if something went wrong. control if needed. These less-advanced systems can’t
see things like stopped fire trucks or traffic lights.
There’s still plenty that needs to be improved But humans haven’t made good driving backups be-
before self-driving vehicles could safely take to the cause their attention tends to wander. At least two
road. Autonomous vehicles are not yet able to op- Tesla drivers in the U.S. have died using the system.
erate safely in all weather conditions. Heavy rain (One hit a truck in 2016, another hit a highway
or snow can confuse current car radar and lidar ­barrier in 2018.) There is what is called a “handoff
s­ ystems—autonomous vehicles can’t operate on their problem.” A semi-autonomous car needs to be able to
own in such weather conditions. These vehicles also determine what its human “driver” is doing and how
have trouble when tree branches hang too low or to get that person to take the wheel when needed.
bridges and roads have faint lane markings. On some
roads, self-driving vehicles will have to make guid- And let’s not forget security. A self-driving car is es-
ance decisions without the benefit of white lines or sentially a collection of networked computers and sen-
clear demarcations at the edge of the road, including sors linked wirelessly to the outside world, and it is no
Botts’ Dots (small plastic markers that define lanes). more secure than other networked systems. Keeping
Botts’ Dots are not believed to be effective lane-­ systems safe from intruders who want to crash or wea-
marking for autonomous vehicles. ponize cars may prove to be the greatest challenge con-
fronting autonomous vehicles in the future.
Computer vision systems are able to reliably rec-
ognize objects. What remains challenging is “scene Self-driving cars require new ecosystems to sup-
understanding”—for example, the ability to determine port them, much as today’s cars are dependent on

Chapter 11  Managing Knowledge and Artificial Intelligence 455

garages, gasoline stations, and highway systems. while commuting. Some people will prosper. Most
New roads, highways, and automotive supply chains will probably benefit, but many will be left behind.
will have to be rebuilt for self-driving cars. The big Driverless technology is estimated to change one in
auto makers that build millions of cars a year rely every nine U.S. jobs, although it will also create new
on complex, precise interaction among hundreds of jobs. Another consideration is that the tremendous
companies, including automotive component suppli- investment in autonomous vehicles, estimated to be
ers and the services to keep cars running. They need around $32 billion annually, might be better spent
dealers to sell the cars, gas pumps or charging sta- on improving public transportation systems like
tions to fuel them, body shops to fix them, and park- trains and subways. Does America need more cars in
ing lots to store them. Manufacturers of autonomous sprawling urban areas where highways are already
vehicles need to rethink interactions and processes jammed?
built up over a century. The highway infrastructure
will need to change over time to support autonomous The accidents self-driving cars have experienced
vehicles. Waymo has partnered with Avis to take care so far point to the need to create a dependable stan-
of its fleet of driverless minivans in Arizona, and it’s dard for measuring reliability and safety. In 2018,
working with a startup called Trov to insure their pas- twenty-nine states have enacted legislation regulat-
sengers. GM is retooling one of its plants to produce ing autonomous vehicles, with a few states requiring
Chevrolet Bolts without steering wheels or pedals. a safety driver always be in the car ready to take con-
trol. U.S. federal regulators have delayed formulat-
A computer-driven car that can handle any situ- ing an overarching set of self-driving car standards,
ation as well as a human under all conditions is leaving a gap for the states to fill. The federal gov-
decades away at best. Many analysts expect the first ernment is only now poised to create its first law for
deployment of self-driving technology will be robot autonomous vehicles. This law is similar to Arizona’s
taxi services operating in limited conditions and and would allow hundreds of thousands of driverless
areas, so their operators can avoid particularly tricky cars to be deployed within a few years and would re-
intersections and make sure everything is mapped strict states from putting up hurdles for the industry.
in fine detail. The Boston Consulting Group predicts
that 25 percent of all miles driven in the U.S. by 2030 Sources: Christopher Mims, “Driverless Hype Collides with
may be by shared self-driving vehicles. To take a ride, Merciless Reality,” Wall Street Journal, September 13, 2018; National
you’d probably have to use predetermined pickup Conference of State Legislatures, “Autonomous Vehicles—Self
and drop-off points, so your car can always pull over Driving Vehicles Enacted Legislation,” June 25, 2018; Jack Karsten
safely and legally. The makers of self-driving cars and Darrell West, “The State of Self-Driving Car Laws Across the
will be figuring out how much to charge so they can U.S.,” Brookings Institute, May 1, 2018; Alex Davies, “The WIRED
recoup their research and development costs, but not Guide to Self-Driving Cars,” WIRED, May 17, 2018; Daisuke
so much as to dissuade potential riders. They’ll strug- Wakabashai, “Uber’s Self-Driving Cars Were Struggling Before
gle with regulators and insurance companies over Arizona Crash,” New York Times, March 23, 2018; Kevin Roose,
what to do in the inevitable event of a crash. “The Self-Driving Car Industry’s Biggest Turning Point Yet,” New
York Times, March 29, 2018; Tim Higgins, “VW, Hyundai Turn
Some pundits predict that in the next few decades, to Driverless-Car Startup in Silicon Valley,” Wall Street Journal,
driverless technology will add $7 trillion to the global January 4, 2018; John Markoff, “A Guide to Challenges Facing Self-
economy and save hundreds of thousands of lives. Driving Car Technologists,” New York Times, June 7, 2017; and The
At the same time, it could devastate the auto indus- Editorial Board, “Would You Buy a Self-Driving Future from These
try along with gas stations, taxi drivers, and truck- Guys?” New York Times, October 14, 2017.
ers. People might stop buying cars because services
like Uber using self-driving cars would be cheaper. CASE STUDY QUESTIONS
This could cause mass unemployment of taxi drivers
and large reductions in auto sales. It would also cut 11-13 What are the management, organizational,
down the need for many parking garages and parking and technology challenges posed by self-
spaces, freeing up valuable real estate for other pur- driving car technology?
poses. More people might decide to live further from
their workplaces because autonomous vehicles linked 11-14 Are self-driving cars good business invest-
to traffic systems would make traffic flow more ments? Explain your answer.
smoothly and free riders to work, nap, or watch video
11-15 What ethical and social issues are raised by
self-driving car technology?

11-16 Will cars really be able to drive themselves
without human operators? Should they?

456 Part Three  Key System Applications for the Digital Age

MyLab MIS

Go to the Assignments section of MyLab MIS to complete these writing exercises.
11-17 How do each of the following types of systems acquire and model knowledge: expert system, genetic

algorithms, neural network?
11-18 How do enterprise content management systems help organizations manage structured and semistruc-

tured knowledge? What are two examples of each type of knowledge handled by these systems?

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CHAPTER 12 Enhancing Decision Making

LEARNING OBJECTIVES CHAPTER CASES

After reading this chapter, you will be able to Big Data and the Internet of Things Drive
answer the following questions: Precision Agriculture

12-1 What are the different types of Siemens Makes Business Processes More
decisions, and how does the decision- Visible
making process work?
Anthem Benefits from More Business
12-2 How do information systems support Intelligence
the activities of managers and
management decision making? Is Predix GE’s Future?

12-3 How do business intelligence and VIDEO CASES
business analytics support decision
making? PSEG Leverages Big Data and Business
Analytics Using GE’s Predix Platform
12-4 How do different decision-making
constituencies in an organization use FreshDirect Uses Business Intelligence to
business intelligence, and what is the Manage Its Online Grocery
role of information systems in helping
people working in a group make Business Intelligence Helps the Cincinnati
decisions more efficiently? Zoo Work Smarter

12-5 How will MIS help my career?

MyLab MIS

Discussion Questions: 12-5, 12-6, 12-7; Hands-on MIS Projects: 12-8, 12-9, 12-10, 12-11;
Writing Assignments: 12-17, 12-18; eText with Conceptual Animations

458

Big Data and the Internet of Things Drive
Precision Agriculture

By 2050, the world will be populated with an estimated 9 million people, © Ekkasit keatsirikul/123RF
and in order to feed all of them, agricultural output will need to double.
Information technology, in the form of the Internet of Things (IoT), wire-
less and mobile technologies, and automated data collection and analysis is
likely to provide part of the solution to this problem.

Purdue University’s College of Agriculture is one of the organizations lead-
ing the way toward more data-driven farming. The College has developed an
a­ griculture-oriented network with advanced IoT sensors and devices that will
allow researchers to study and improve plant growth and food production pro-
cesses. According to Pat Smoker, di-
rector of Purdue Agriculture IT, in
West Lafayette, Indiana, every pro-
cess from farm to table has poten-
tial for improvement through bet-
ter use of information technology.

Purdue College of Agriculture
partnered with Hewlett Packard
Enterprise (HPE) on a digital ag-
riculture initiative. In fall 2016,
the university began installing
an Internet of Things (IoT) net-
work on its 1,408-acre research
farm, the Agronomy Center for
Research and Education (ACRE).
The system captures terabytes of
data daily from sensors, cameras,
and human inputs. To collect, ag-
gregate, process, and transmit
such large volumes of data back to Purdue’s HPE supercomputer, the university
is deploying a combination of wireless and edge computing technologies (see
Chapters 5 and 7). They include solar-powered mobile Wi-Fi hotspots, an adap-
tive weather tower providing high-speed connectivity across the entire ACRE
facility, and the PhenoRover, a semi-automated mobile vehicle that roams
throughout ACRE research plots capturing real-time data from plant-based
­sensors. Purdue is also experimenting with drones for plant-growth data collec-
tion. ACRE researchers can enter data into a mobile device on-site and transmit
them via the wireless network to an HPE data center for analysis.

Previously, Purdue’s faculty had to figure out how to transmit data from the
sensors back to the lab, and assign someone to write the software for analyzing
the data. The new system is faster and responsive. For example, researchers

459

460 Part Three  Key System Applications for the Digital Age

using mobile devices in the field can transmit data about seed growth back to
ACRE labs to analyze the impact of water levels, fertilizer quantities, and soil
types. The labs can then communicate the results of their analysis back to the
field to allow quick adjustments. Computerized instructions control how plant-
ing and spraying machines apply seed and nutrients to a field.

The Purdue project is an example of “precision agriculture,” in which data
collected and analyzed with digital tools drive decisions about fertilizer levels,
planting depth, and irrigation requirements for small sections of fields or in-
dividual plants, and automated equipment can apply the ideal treatment for
specific weeds.

Large agricultural companies like Monsanto and DuPont are big precision
agriculture players, providing computerized data analysis and planting recom-
mendations to farmers who use their seeds, fertilizers, and herbicides. The
farmer provides data on his or her farm’s field boundaries, historic crop yields,
and soil conditions to these companies or another agricultural data analysis
company, which analyzes the data along with other data it has collected about
seed performance weather conditions, and soil types in different areas. The
company doing the data analysis then sends a computer file with recommen-
dations back to the farmer, who uploads the data into computerized planting
equipment and follows the recommendations as it plants fields. For example,
the recommendations might tell an Iowa corn farmer to lower the number of
seeds planted per acre or to plant more seeds per acre in specified portions of
the field capable of growing more corn. The farmer might also receive advice
on the exact type of seed to plant in different areas and how much fertilizer
to apply. In addition to producing higher crop yields, farmers using fertilizer,
water, and energy to run equipment more precisely are less wasteful, and this
also promotes the health of the planet.

Sources: “Envision: The Big Idea,” https://ag.purdue.edu, accessed April 26, 2018; “Precision
Agriculture,” www.farms.com, accessed April 26, 2018; www.monsanto.com, accessed May 1,
2018; and Eileen McCooey, “Purdue Uses IoT to Reinvent Farming, Boost Output,” Baseline,
December 6, 2017.

Precision agriculture is a powerful illustration of how information systems
can dramatically improve decision making. In the past, deciding what to
plant, how, where, and when was based on farmers’ historical experience with
their land and best guesses. Wireless networks, myriad sensors in the field, mo-
bile devices, powerful computers, and big data analytics tools have created sys-
tems that can make many of these decisions much more rapidly and accurately.

The chapter-opening diagram calls attention to important points raised by
this case and this chapter. There is a worldwide need to increase food produc-
tion, both to feed a rapidly growing global population and to make farms more
profitable. Wireless technology and big data analytics create new opportunities
for managing crops almost on a plant-by-plant basis. Managing fields with this
level of computerized precision means farmers need to use less fertilizer and
less seed per unit of land, potentially saving an individual farmer tens of thou-
sands of dollars while increasing crop yields. Precision agriculture may also
help solve the world food crisis.

Chapter 12  Enhancing Decision Making 461

• Identify technologies Management Business
• Identify decisions for Organization Challenges
Technology • Explosive population growth
improvement • Opportunities from new technology

• Collect agri- Information Business
cultural data System Solutions

• Revise farm Precision Agriculture • Increase crop yield
production process Systems • Reduce costs
• Determine optimal
• Wireless sensors
• Wireless network water/fertilizer/seed amounts
• Supercomputer • Adjust planting patterns
• BI analytic

software
• Computerized

planting/spraying
machines
• PhenoRover
• Mobile devices

Here are some questions to think about: How is information technology
changing the way farmers run their business? How is precision agriculture
changing decision making? Give examples of two decisions that can be im-
proved by using precision agriculture.

12-1 What are the different types of decisions, and

how does the decision-making process work?

Decision making in businesses used to be limited to management. Today, lower-
level employees are responsible for some of these decisions, as information sys-
tems make information available to lower levels of the business. But what do
we mean by better decision making? How does decision making take place in
businesses and other organizations? Let’s take a closer look.

Business Value of Improved Decision Making

What does it mean to the business to make better decisions? What is the mon-
etary value of improved decision making? Table 12.1 attempts to measure the
monetary value of improved decision making for a small U.S. manufacturing
firm with $280 million in annual revenue and 140 employees. The firm has
identified a number of key decisions where new system investments might im-
prove the quality of decision making. The table provides selected estimates of
annual value (in the form of cost savings or increased revenue) from improved
decision making in selected areas of the business.

We can see from Table 12.1 that decisions are made at all levels of the
firm and that some of these decisions are common, routine, and numerous.
Although the value of improving any single decision may be small, improving
hundreds of thousands of “small” decisions adds up to a large annual value for
the business.

462 Part Three  Key System Applications for the Digital Age

TABLE 12.1 BUSINESS VALUE OF ENHANCED DECISION MAKING

NUMBER ESTIMATED VALUE

OF ANNUAL TO FIRM OF A SINGLE

EXAMPLE DECISION DECISION MAKER DECISIONS IMPROVED DECISION ANNUAL VALUE

Allocate support to most valuable customers Accounts manager 12 $100,000 $1,200,000
4 $150,000 $600,000
Predict call center daily demand Call center
management 365 $5,000 $1,825,000
1 $2,000,000 $2,000,000
Decide parts inventory levels daily Inventory manager
150 $10,000 $1,500,000
Identify competitive bids from major Senior management
suppliers 100 $4,000 $400,000

Schedule production to fill orders Manufacturing
manager

Allocate labor to complete a job Production floor
manager

Types of Decisions

Chapters 1 and 2 showed that there are different levels in an organization. Each
of these levels has different information requirements for decision support and
responsibility for different types of decisions (see Figure 12.1). Decisions are
classified as structured, semi-structured, and unstructured.

Unstructured decisions are those in which the decision maker must pro-
vide judgment, evaluation, and insight to solve the problem. Each of these deci-
sions is novel, important, and nonroutine, and there is no well-understood or
agreed-on procedure for making them.

Structured decisions, by contrast, are repetitive and routine, and they in-
volve a definite procedure for handling them so that they do not have to be
treated each time as if they were new. Many decisions have elements of both

FIGURE 12.1 INFORMATION REQUIREMENTS OF KEY DECISION-MAKING
GROUPS IN A FIRM

Senior managers, middle managers, operational managers, and employees have
d­ ifferent types of decisions and information requirements.

Decision Characteristics Examples of Decisions

Unstructured Senior Decide entrance or exit
Management from markets

Semistructured Middle Management Approve capital budget
Decide long-term goals
Structured Operational Management
Individual Employees and Teams Design a marketing plan
Develop a departmental

budget
Design a new corporate

Web site
Determine overtime

eligibility
Restock inventory
O er credit to customers
Determine special o ers

to customers

Chapter 12  Enhancing Decision Making 463

types of decisions and are semi-structured, where only part of the problem
has a clear-cut answer provided by an accepted procedure. In general, struc-
tured decisions are more prevalent at lower organizational levels, whereas un-
structured problems are more common at higher levels of the firm.

Senior executives face many unstructured decision situations, such as estab-
lishing the firm’s 5- or 10-year goals or deciding new markets to enter. Answering
the question “Should we enter a new market?” would require access to news,
government reports, and industry views as well as high-level summaries of firm
performance. However, the answer would also require senior managers to use
their own best judgment and poll other managers for their opinions.

Middle managers face more structured decision scenarios, but their deci-
sions may include unstructured components. A typical middle-level manage-
ment decision might be “Why is the reported order fulfillment report showing a
decline over the past six months at a distribution center in Minneapolis?” This
middle manager will obtain a report from the firm’s enterprise system or distri-
bution management system on order activity and operational efficiency at the
Minneapolis distribution center. This is the structured part of the decision. But
before arriving at an answer, this middle manager will have to interview em-
ployees and gather more unstructured information from external sources about
local economic conditions or sales trends.

Operational management and rank-and-file employees tend to make more
structured decisions. For example, a supervisor on an assembly line has to de-
cide whether an hourly paid worker is entitled to overtime pay. If the employee
worked more than eight hours on a particular day, the supervisor would rou-
tinely grant overtime pay for any time beyond eight hours that was clocked on
that day.

A sales account representative often has to make decisions about extending
credit to customers by consulting the firm’s customer database that contains
credit information. If the customer met the firm’s pre-specified criteria for grant-
ing credit, the account representative would grant that customer credit to make
a purchase. In both instances, the decisions are highly structured and are rou-
tinely made thousands of times each day in most large firms. The answer has
been preprogrammed into the firm’s payroll and accounts receivable systems.

The Decision-Making Process

Making a decision is a multistep process. Simon (1960) described four differ-
ent stages in decision making: intelligence, design, choice, and implementation
(see Figure 12.2).

Intelligence consists of discovering, identifying, and understanding the prob-
lems occurring in the organization—why a problem exists, where, and what
effects it is having on the firm.

Design involves identifying and exploring various solutions to the problem.

Choice consists of choosing among solution alternatives.

Implementation involves making the chosen alternative work and continuing
to monitor how well the solution is working.

What happens if the solution you have chosen doesn’t work? Figure 12.2
shows that you can return to an earlier stage in the decision-making process
and repeat it if necessary. For instance, in the face of declining sales, a sales
management team may decide to pay the sales force a higher commission for
making more sales to spur on the sales effort. If this does not produce sales in-
creases, managers would need to investigate whether the problem stems from

464 Part Three  Key System Applications for the Digital Age

FIGURE 12.2 STAGES IN DECISION MAKING
The decision-making process is broken down into four stages.

Problem discovery: Intelligence
What is the problem?

Solution discovery: Design
What are the possible solutions?

Choosing solutions: Choice
What is the best solution?

Solution testing: Implementation
Is the solution working?
Can we make it work better?

poor product design, inadequate customer support, or a host of other causes
that call for a different solution.

12-2 How do information systems support

the activities of managers and
management decision making?

The premise of this book and this chapter is that systems to support decision
making produce better decision making by managers and employees, above-
average returns on investment for the firm, and ultimately higher profitability.
However, information systems cannot improve every decision taking place in
an organization. Let’s examine the role of managers and decision making in
organizations to see why this is so.

Managerial Roles

Managers play key roles in organizations. Their responsibilities range from
making decisions, to writing reports, to attending meetings, to arranging birth-
day parties. We are able to better understand managerial functions and roles by
­examining classical and contemporary models of managerial behavior.

Chapter 12  Enhancing Decision Making 465

The classical model of management, which describes what managers do,
was largely unquestioned for more than 70 years after the 1920s. Henri Fayol
and other early writers first described the five classical functions of managers
as planning, organizing, coordinating, deciding, and controlling. This descrip-
tion of management activities dominated management thought for a long time,
and it is still popular today.

The classical model describes formal managerial functions but does not ad-
dress exactly what managers do when they plan, decide things, and control
the work of others. For this, we must turn to the work of contemporary behav-
ioral scientists who have studied managers in daily action. Behavioral models
argue that the actual behavior of managers appears to be less systematic, more
informal, less reflective, more reactive, and less well organized than the classi-
cal model would have us believe.

Observers find that managerial behavior actually has five attributes that dif-
fer greatly from the classical description. First, managers perform a great deal
of work at an unrelenting pace — studies have found that managers engage in
more than 600 different activities each day, with no break in their pace. There
is never enough time to do everything for which a CEO is responsible (Porter
and Nohria, 2018). Second, managerial activities are fragmented; most activi-
ties last for less than nine minutes, and only 10 percent of the activities exceed
one hour in duration. Third, managers prefer current, specific, and ad hoc in-
formation (printed information often will be too old). Fourth, they prefer oral
forms of communication to written forms because oral media provide greater
flexibility, require less effort, and bring a faster response. Fifth, managers give
high priority to maintaining a diverse and complex web of contacts that acts as
an informal information system and helps them execute their personal agendas
and short- and long-term goals.

Analyzing managers’ day-to-day behavior, Henry Mintzberg found that it
could be classified into 10 managerial roles (Mintzberg, 1971). Managerial
roles are expectations of the activities that managers should perform in an or-
ganization. Mintzberg found that these managerial roles fell into three catego-
ries: interpersonal, informational, and decisional.

Interpersonal Roles

Managers act as figureheads for the organization when they represent their
companies to the outside world and perform symbolic duties, such as giving
out employee awards, in their interpersonal role. Managers act as leaders,
attempting to motivate, counsel, and support subordinates. Managers also act
as liaisons between various organizational levels; within each of these levels,
they serve as liaisons among the members of the management team. Managers
provide time and favors, which they expect to be returned.

Informational Roles

In their informational role, managers act as the nerve centers of their organi-
zations, receiving the most concrete, up-to-date information and redistributing
it to those who need to be aware of it. Managers are therefore information dis-
seminators and spokespersons for their organizations.

Decisional Roles

Managers make decisions. In their decisional role, they act as entrepreneurs
by initiating new kinds of activities, they handle disturbances arising in the or-
ganization, they allocate resources to staff members who need them, and they
negotiate conflicts and mediate between conflicting groups.

466 Part Three  Key System Applications for the Digital Age

TABLE 12.2 MANAGERIAL ROLES AND SUPPORTING INFORMATION SYSTEMS

ROLE BEHAVIOR SUPPORT SYSTEMS

INTERPERSONAL ROLES

Figurehead Telepresence systems

Leader Interpersonal Telepresence, social networks, Twitter

Liaison Smartphones, social networks

INFORMATIONAL ROLES

Nerve center Management information systems, executive support system

Disseminator Information Texting, email, social networks

Spokesperson processing Webinars, telepresence

DECISIONAL ROLES

Entrepreneur Decision None exist

Disturbance handler making None exist

Resource allocator Business intelligence, decision-support system

Negotiator None exist

Sources: Authors and Mintzberg, Henry. “Managerial Work: Analysis from Observation.” Management Science 18 (October 1971).

Table 12.2, based on Mintzberg’s role classifications, is one look at where sys-
tems can and cannot help managers. The table shows that information systems
are now capable of supporting most, but not all, areas of managerial life.

Real-World Decision Making

We now see that information systems are not helpful for all managerial roles.
And in those managerial roles where information systems might improve deci-
sions, investments in information technology do not always produce positive
results. There are three main reasons: information quality, management filters,
and organizational culture (see Chapter 3).

Information Quality

High-quality decisions require high-quality information. Table 12.3 describes
information quality dimensions that affect the quality of decisions.

If the output of information systems does not meet these quality criteria,
decision making will suffer. Chapter 6 describes how corporate databases and
files have varying levels of inaccuracy and incompleteness, which in turn will
degrade the quality of decision making.

TABLE 12.3 INFORMATION QUALITY DIMENSIONS

QUALITY DIMENSION DESCRIPTION

Accuracy Do the data represent reality?
Integrity Are the structure of data and relationships among the entities and attributes consistent?
Consistency Are data elements consistently defined?
Completeness Are all the necessary data present?
Validity Do data values fall within defined ranges?
Timeliness Are data available when needed?
Accessibility Are the data accessible, comprehensible, and usable?

Chapter 12  Enhancing Decision Making 467

Management Filters

Even with timely, accurate information, managers often make bad decisions.
Managers (like all human beings) absorb information through a series of filters
to make sense of the world around them. Cognitive scientists, behavioral econ-
omists, and recently neuro-economists have found that managers, like other
humans, are poor at assessing risk, and are risk averse; perceive patterns where
none exist; and make decisions based on intuition, feelings, and the framing
of the problem as opposed to empirical data (Kahneman, 2011; Tversky and
Kahneman, 1986).

For instance, Wall Street firms such as Bear Stearns and Lehman Brothers
imploded in 2008 because they underestimated the risk of their investments
in complex mortgage securities, many of which were based on subprime loans
that were more likely to default. The computer models they and other financial
institutions used to manage risk were based on overly optimistic assumptions
and overly simplistic data about what might go wrong. Management wanted
to make sure that their firms’ capital was not all tied up as a cushion against
defaults from risky investments, preventing them from investing it to generate
profits. So the designers of these risk management systems were encouraged to
measure risks in a way that minimized their risk.

Organizational Inertia and Politics

Organizations are bureaucracies with limited capabilities and competencies for
acting decisively. When environments change and businesses need to adopt
new business models to survive, strong forces within organizations r­esist
m­ aking decisions calling for major change. Decisions taken by a firm often rep-
resent a balancing of the firm’s various interest groups rather than the best
solution to the problem.

Studies of business restructuring find that firms tend to ignore poor perfor-
mance until threatened by outside takeovers, and they systematically blame
poor performance on external forces beyond their control—such as economic
conditions (the economy), foreign competition, and rising prices—rather than
blaming senior or middle management for poor business judgment. When the
external business environment is positive and firm performance improves,
managers typically credit themselves for the improved performance rather
than the positive environment.

High-Velocity Automated Decision Making

Today, many decisions made by organizations are not made by managers—or
any humans. For instance, when you enter a query into Google’s search engine,
Google has to decide which URLs to display in about half a second on aver-
age (500 milliseconds). High-frequency traders at electronic stock exchanges
execute their trades in under 30 milliseconds.

In high-velocity decision environments, the intelligence, design, choice, and
implementation parts of the decision-making process are captured by the soft-
ware’s algorithms. The humans who wrote the software have already identi-
fied the problem, designed a method for finding a solution, defined a range of
a­ cceptable solutions, and implemented a solution. Obviously, with humans out
of the loop, great care needs to be taken to ensure the proper operation of these
systems to prevent significant harm.

Organizations in these areas are making decisions faster than what manag-
ers can monitor or control. The past few years have seen a series of break-
downs in computerized trading systems, including one on August 1, 2012, when