Making sense of Knowledge Management (KM) , Information Technology (IT) and Artificial Intelligence (AI): an Integrative Approach

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Making sen
se of

Knowledge Management (KM)
,

Information Technology
(IT)
and Artificial Intelligence (AI)
: an Integrative Approach

Hala AbdulQader Sabri
, PhD

Associate Professor of Management

Faculty of Administrative and Financial Sciences,
Petra University

Amman
-
Jordan

habdulqader@uop.edu.jo
Abstract


Knowledge management

(KM)

has
become well established in organization
and management

theory
.
In their
enthusiastic quest in the field of
knowledge management,
o
r
ganizati
on
theorists and
information system
deve
lopers however,

mostly discussed
knowledge management f
rom one single
perspective, overlooking the need to

treating it from a multidisciplinary
perspective, by means of relating it to
different factors
such a
s information
technology (IT) and artificial intelligence
(AI)
.
T
his study

aims to fill this gap. It
seeks to examine

the interrelationship
between
knowledge management,
information technology
and artificial

intelligence.

It argues that knowledge
managemen
t is not just computer and
information systems; it embodies
organizational processes that seek to
augment the creative, intelligent, and
innovative capacity of human
beings.
Therefore,

t
he
re are

moral and ethical
implications
of artificial intelligences

th
at
need to be raised and discussed.

Keywords
:
Artificial Intelligence,
Information Technology, Knowledge
Management.


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1.

INTRODUCTION

T
he business literature has witnessed, in
recent years, a booming interest in
knowledge manage
ment

(KM)

and
artificial inte
lligence

from different
disciplines.
A
s k
now
ledge work is
increasingly
immersed in a compu
ting
environment, together with other existing
realities of

globalization, information
overload,

rapid technological changes and
increased concern in artificial intel
ligence
,
organizations started to
learn to place
increasing values on managing knowledge
and maintain continuous learning.
O
rganization theorists, information system
deve
lopers, and economists
, however,

mostly discussed knowledge management
f
rom only one s
ingle perspective,
overlooking the need to

treating it from a
multidisciplinary perspective, by means of
relating it to different factors
such as
information technology

(IT)

and artificial
intelligence

(AI)
.
Therefore, addressing
the link between

knowledge

management
,
information technology

and artificial
intelligence is
a critical challenge for
organizations to enhance their competitive
advantage in the global economy.

T
his study

examines
the interrelationship
between
knowledge management,
information tech
nology
and artificial
intelligence.

It argues that knowledge
management is not just c
omputer and
information systems;

it embodies
organizational processes that seek to
augment the creative, intelligent, and
innovative capacity of human
beings.
Therefore,

c
reating the right corporate
culture is essential for act
ivating a
knowledge base organization

th
at sustains
the application of information technology
and artificial i
ntelligence techniques in
modern organizations.

2.

THEORETICAL BACKGROU
ND

2.1
Defining
Infor
mation
,
Knowledge
,

and Intelligence

Information

is

data

that

has been verified
to be
accurate

and timely,

is s
pecific and
organized

for a purpose,

is presented
within a
context

that gives i
t meaning and
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relevance, an
d

that can
lead

to an
increase

in
understanding

and
decrease

in
uncertainty
.
The
value of information

lies
solely in its
ability

to
affect

a
behavior
,
decision
, or outcome. A piece of
information is considered valueless if,
after
receiving

it, things remain
unchanged
.

Information is any kind of
event

that affects the
state

of a
dynamic
system
. Moreover, the concept of
information is closely related to notions of
constraint
,
communication
,
control
,
data
,
form
,
instruction
,
knowledge
,
meaning
,
mental stimulus
,
pattern
,
perception
,
representation
, and even
entropy

(Businessdictionary.com)
.

Knowledge

a
s defined by the
Oxford
English Dictionary

are

e
xpertise, and
skills acquired by a person through
experience or education; the theoretical or
practical understanding of a subject, what
is known in a particular field or in total;
facts and information or awareness or
familiarity gained by exper
ience of a

fact
or situation.
The term
knowledge

is also
used to mean the confident
understanding

of a subject with the ability to use it for a
specific purpose if appropriate
(WikePedia, Knowledge).

However
,
Drucker (2003)

argues that in the new
information economy „knowledge‟ is not
only another resource, it is the most
powerful resource, and organizations are
more than information processors, they
are knowledge creat
ors. Drucker (2003:
287) states

"
Knowled
ge is not impersonal,
like money. Knowledge does not reside in
a book, a database, or a software program;
these contain only information.
Knowledge is always embodied in a
person; applied by a person; taught and
passed on by a person; used or misused by
a
person
."

Drucker (1997) maintains
also
that k
nowledge is not the same as data or
information, although it uses both
.
Knowledge is information that changes
something or somebody, either by
becoming grounds for actions, or by
making an individual capable of
different
effective action. This suggests that
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knowledge is personal and intangible in
nature, whereas information is tangible
and available to anyone who cares to seek
it out. Stenmark (2001) argues that
knowledge and information affect one
another. He al
so maintains that all
knowledge is tacit and what can be
articulated and made tangible is merely
information. Nonaka (1994) argues also
that knowledge and information are
similar in some aspects, but different in
others. While information is more factual,
knowledge is about beliefs and
commitments. He asserts that knowledge
can be tacit and explicit. Tacit knowledge
is understood and applied, difficult to
articulate, developed from direct
experience and action and usually shared
through highly interactive c
onversation
and shared experience. Explicit
knowledge, in contrast, is more easily
codified, documented, transferred or
shared. Davenport (1997) argues that

knowledge

is part of a hierarchy made up
of da
ta, information, and knowledge that
is
based on the m
eaningful organized
information from the human mind through
experience and communication with
guidance for action and is a much more
implicit entity. Knowledge, as opposed to
data and information always has a human
factor

as

it is stored in the individual'
s
brain or encoded in the organizational
processes, documents, products, services,
facilities and systems.

March (1997),
therefore, argues that the management of
intellectual capitals has become a central
theme

in today'
s business competitive
advantage.

I
ntelligence

is an
umbrella term

used to
describe a property of the mind that
encompasses many related abilities,
such
as the capacities

to plan, to solve
problems, to think abstractly, to
comprehend ideas, to use language, and to
learn. There are several w
ays to define
intelligence. In some cases, intelligence
may include traits such as creativity,
personality, character, knowledge, or
wisdom. However, some psychologists
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prefer not to
include these traits in the
definition of
intelligence (WikePedia,
Intell
igence).

Although
Intelligence is
most widely studied in hu
mans, it

is also
observed in animals and plants.
Artificial
intelligence

is the intelligence of machines
or the simulation of in
telligence in
machines.

2.2
Knowledge Management

(KM)

Some researchers feel that knowledge is
based on individual and organizational
competencies such as skills, know
-
how
and know
-
what (Nonaka and Tekeuchi
1995; Davenport and Prusak, 1998).
O‟Dell and Gray
son (1998) defines
Knowledge

management as a conscious
strategy of getting the right knowledge to
the right people at the right time and
helping people share and put information
into action in ways that will strive to
improve
organizational performance
.
Ac
cording
to Newman

(1991) intellectual
assets are the valuable knowledge
avai
lable to be used to exploitation



must
be

encouraged
, preserved and used to the
largest extent possible by both individuals
and organizations. Skyrme (1998)

has also
identified se
veral critical factors to KM,
which include knowledge leadership,
knowledge creating and sharing culture,
continuous learning, well developed ICT
infrastructure and systematic
organizational knowledge processes.

Knowledge management is not new, but
only re
cently have organization executives
begun thinking about deliberate,
systematic ways to create, capture,
organize, and transfer knowledge
. Hansen
et. al
.,

(1990) emphasize that several
driving forces have surged the interest in
knowledge management. First
the rapid
advances in information technology have
made it possible to share explicit
knowledge more quickly and easily as
well as to connect people in networks for
the sharing of
knowledge. Second the
companies'

efforts to become learning
organizations, in

which managers strive to
create a culture and a system for creating
new knowledge and for capturing both
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explicit and tacit knowledge and getting it
to the right place at the right time.

Managers
started to

look for ways for
managing

knowledge by using th
e
techniques and methods that were
developed as part of the information
technology (IT) to support the human
resources through training and developing
knowledge based systems that allow the
organization to stay competitive
(Davenport,
et. al.
,

1996).


Ther
efore, n
owadays, one of the
main functions of management is to
achieve the synergy between data,
information processing capacity of
information technology and the creative
and innovative capacity of their human
members.
The existence of a supportive
cultur
e in an organization is vital in
developing the association

between the
knowledge capabilities and the business
strategy. Putting KM into action, cultural
renovation
is required as the
organization'
s ability to
announce

KM
concept and its advantages to the

members
are critical for effective KM
.

2.3
Artificial Intelligence

(AI)

Artificial intelligence

(AI) (
or
Manufactured Minds)

is the science and
engineering
,

of making intelligent
machines

and
intelligent computer
programs

(McCarthy, 2007
). Most of the
re
search in artificial intelligence, however,
is highly technical and specialized,
Today

AI has become an essential part of the
technology industry, providing the heavy
lifting for many of the most difficult
problems in computer science

(Russel &
Norvig, 200
3).

Artificial Intelligence is the area of
computer science focusing on creating
machines that can engage on behaviors
that humans consider intelligent. The
ability

to create intelligent machines
, the
dream of smart machines is becoming a
reality. Researc
hers are creating systems
which can mimic human thought,
understand speech, beat the best human
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chess player
, and countless other
achievements

never before possible.

Artificial intelligence has undoubtedly
seen its share of breakthroughs throughout
its

hi
story, many of which have been widely
reported by the media

According to a
recent report, the manager of the Adaptive
Systems gro
up at Microsoft estimated that
"
about a quarter of all Microsoft re
search
is focused on AI efforts"
.

Google
has also

arti
culate
d their goal of creating
"
ob
viously artificial intelligence"
, in
the
sense of a truly "smart" program that
"understands"

user queries and the
universe of potential results to the point
that searches as well as a human

with
immediate access to most of the I
nternet.

Clearly, artificial intelligence has led to
important medical, commercial, and other

benefits, and
promises many more.
Artificial intelligence is already used to
automate and replace some human
functions with computer
-
driven machines.
These machin
es can see and hear, respond
to questions, learn, draw inferences and
solve problems.

Some of the areas
artificial intelligence is us
ed in are:
crime
computers that recog
nize patterns of serial
killers
, optical character recognition,
handwriting recognitio
n, speech
recognition, face recognition and a
rtificial
creativity
;

in addition to g
ame playin
g,
chess,

understanding natural language,
computer
vision, and expert systems
.

And
o
ne of the most

famous
uses

of artificial
intelligence is the email spam filteri
ng

(WikePedia)
. However, r
esearch in
artificial intelligence is mainly driven by a
desire to make machines that automate
tasks an
d require intelligent behavior

that
could include things like planning and
scheduling, handwriting, natural language
and speech
, among other things.

For this
reason, many people think artificial
intelligence is supposed to imitate human
intelligence
.


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2.4
Knowledge Management and
Information Technology

The terms „Information Management‟ and
„Information Value Chain‟ came about
whe
n people realized that information is a
resource that can
,

and needs to
,

be
managed for the benefit of the
organization
. Stenmark (2001)

indicates
that both terms consider technological
systems as key components guiding the
organization‟s processes, while
treating
humans as relatively passive workers who
implement best practices archived in
information database towards fulfillment
of organizational goals and objectives.
Consequently, knowledge management is
often referred to as an application of
information

systems. However, many
questions
remain on
how can the
knowledge that resides within the
organizational members be made visible
using the information technology?

Zack (1994)

maintains that information
technology provides an infrastructure for
the flow of

explicit knowledge through
capturing, defining, storing, categorizing,
indexing and linking digital objects
corresponding to knowledge units. Thus,
knowledge management involves the
understanding of and commitment to the
information technology. However, u
se of
the information and control systems, and
compliance with pre
-
defined goals and
objectives and best practices may not
necessarily achieve long
-
term
organizational competence. All
organizations should take up a strategic
view that considers its organiz
ational
context that upholds a synergistic
combination of information technology
and the creative, innovative capacity of
human beings, as necessary,
for survival
in the

uncertain environments.

Mishra (2010)

confirms that a
s knowledge
has become a key suc
cess factor in the
global economy, IT has been generally
accepted as a critical enabler for the
successful KM implementation. It
becomes quite important to ensure that
knowledge in the minds of resources is
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safeguarded. It is found that, while 26 per
cent
of knowledge in the average
organization is stored on paper and 20
percent digitally, an amazing 42 per cent
is stored in employees' heads. There have
been many instances where the learning
and knowledge is lost when resources
move to newer roles, or leave

the
organization.
Moreover, knowledge
m
anagement helps library a
nd information
professionals in

improving the services
being rendered to their users. Information
professionals have to recast their roles as
knowle
dge professional.
Information
technology an
d systems can provide
effective support in implementing
knowledge management in organizations.

3
.
KNOWLEDGE MANAGEMENT
,
INFORMATION TECHNOLO
GY
AND ARTIFICIAL INTEL
LIGENCE
:
AN INTEGRATIVE APPRO
ACH

There are many opinions of KM, whereas
some refer to it as an
emerging discipline
(Harris, Bair and Stear, 1998) others argue
that it evolves from expert systems and
artificial intelligence (O‟Dell & Grayson
,

1998). Liebowitz and Beckman (1998)
believe that KM has evolved out of an
amalgamation of concepts borrowed f
rom
Artificial Intelligence (AI), Business
Process Reengineering (BRP), Human
Resource Management (HRM) and
Organizational Behavior (OB) fields.
They add also that i
n order for knowledge
management to survive as a critical

and
strategic concept in organiza
tions, the
knowledge
management fi
eld needs
also
to borrow from other established

disciplines, like artifi
cial intelligence (AI),
in order to learn

and apply what others
have already accomplished towards

adva
ncing the knowledge management
fi
eld.
They add t
hat more recently,
computer science, cognitive psychology,
information technology, social research
and brain research have also contributed
to this field.
However,
Newman (1996)
takes an
other point of view and
differentiates between knowledge
management an
d re
-
engineering. He
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argues that meanwhile reengineering
implies one
-
time radical shifts in
organizational processes from one stage of
mechanization to a more efficient phase of
mechanization to achieve maximum
increase of efficiency; knowledge
management
implies continuous and
ongoing organic renewal of organizational
processes to anticipate future
opportunities and threats surrounding the
organization.

Some researchers claim that Artificial
Intelligence research is
deeply divided
into subfields that ofte
n fail to
communicate with each other
.
Hendriks
and Vriens (1999) argue that the
knowledge engineering methodologies

for
building expert systems have applied
knowledge acquisition techniques
(interviewing, protocol analysis,
simulation, personal construct
theory, card
sorting, etc.) for obtaining the tacit
knowledge from experts.
Within this
context,
Bradshaw
et al.,

(1998)
maintain
that i
n looking at ways for sharing
knowledge, transforming individual
knowledge into colle
ctive, organizational
knowledge

and

restoring, the field of
artificial intelligence (AI) can help push
these views of knowledge management.


Bradshaw
et al.,

(1998) pertain that
k
nowledge

representation in
rules, cases,
scripts, frames/objects, semantic
networks, a
re typically created in th
e AI
fi
eld for building expert

and other
intelligent systems. Bradshaw
et al.,

(
1998)
also add that t
he knowledge
management

fi
eld can apply these AI
techniques to help codify the knowledge
.
Other A
rtificial
I
ntelligence

techniques

like intelligent agents
(software agents)
can

also

be

used to help in the search and
retrieval methods of knowledge

and
information

in th
e knowledge
management systems and they

can be

used
to help in combining knowledge which
would ultimately

lead to the creation of
new knowledge
.
Mishra (2010) maintains
also that intelligent agents

are a class of
software that operates autonomously,
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intelligently, and knowledgeably. They
are technologies that use a built
-
in or
learned knowledge base to carry out
specific, recurring and predictabl
e tasks
on the behalf of users. For example,
intelligent agent software can travel over
the internet and capture the most
appropriate
information to the user'
s
preference.

These technologies are
like

expert systems, which are used to manage
narrow domains
of knowledge.
Organizational knowledge can be captured
and stored using case
-
based reasoning
systems.

In addition, k
nowledge distribution, one
of the basic functions of

knowledge
management, involves sending knowledge

internally and ext
ernally to those wh
o
could benefi
t from

the use and application
of the knowledge. Typically, there

is an
infrastructure within the organization
whose

respo
nsibility is to disseminate
knowledge to pertinent

individuals or
groups. Instead of simply having a passive

distributio
n mode where it is up to the
individual workers to

access the
org
anization's knowledge
and information
base
, it may be

preferable to have
i
ntelligent agents
who
could be applied to
analyze

the knowledge, email, web pages,
and the like and to disseminate

ap
propriate summaries or individual
pieces of information

and knowledge to
those who should best make use of

it.
Data mining and knowledge discovery
techniques could

also be employed to look
for trends, relationships,

and possibly new
knowledge and informati
on from

the
organ
ization's knowledge bases
. Online
communities,
such as
Centers of Expertise

or Knowledge Centers or Communities of
Practice in organizations
,
which share a
common interest with

knowledge
management, are also ways of sharing and
distributin
g

knowledge.

Members of these
communities share their experiences,

thoughts, information,
questions

/answers,
and

knowledge over the web. Knowledge
is distributed

via the web to members of
these online communities.

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3.

CONCLUSION

A
rtificial intelligence

is tur
ning the
spotlight back on the question of whether
compute
r intelligence will surpass the
human
, and how quickly.

However,
d
ebates about the social impact of c
reating
intelligent machines have

occupied many
organizations and individuals over the
past decad
es.
Mark
off (2009) believes

th
at
artificial intelligence
today is getting
serious attention from
NASA

and from
Silicon Valley companies like
Microsoft,
Google

as well as a new round of start
-
ups
that are designing everything from next
-
generation search engines to machines
that listen or that are capable of walking
around in the world.

Advances

in artificial intelligence

will
have
slight

effects on individuals and on
culture.

They may create new knowledge; make
certain types of knowledge m
ore
accessible
and change the

value of some
types of knowledge and ways of thinking.
Even though the

exact form of t
hese
effects is unpredictable, arti
ficial
intelligence

researchers have an ethical
responsibility to evaluate their work from
this perspective.

That is o
nce it is decided
to have
machines with an artificially
created intelligence
adequately
sophisticated to challenge

the human
brain,

ethica
l and moral values do we
instill them? Looking at human
civilization with its diverse cultural,
religious, ethical and moral values, what
exactly are we trying to create here and to
what purpose?

The moral and ethical implications of
artificial intelligen
ces are obvious
.
Meanwhile some argue

that
there are
already too many people

living in poverty
without work there is little or no reason to
create mechanical

laborers.

O
thers argue

that society cannot develop or take
advantage of resources without the help

of
machines that can think fo
r themselves
.

Opinions
also differ about th
e extent to
which

machines
should be made
intelligent and what
they

should look like.

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Research is widespread and diverse,
covering all of the aspects of artifici
al
intelligence.
Altho
ugh a
gree
ment among
theorist and practitioners,

on what exactly
defines intelligence
, is not yet reached,

artificial ones

are still created
.
Difficulties
in programming an artificial brain are
apparent as t
he human

brain has

evolved
through millions of yea
rs o
f survival and
social behavior
.
Therefore, imitating the
human's brain

is a
great

challenge and
will
take at least several decades more to reach
even the most
basic

levels.

Yet,
Scientist
need to

make sure intelligent

machines
have
no
control

over huma
ns or human
society and
aren't capable of making any
decision beyond m
echanical, programmed
movement.


Limitation

T
his study is theoretically oriented one. It
requires further empirical research to
overcome its limitations. But the analysis
in this study h
as raised important issues
for future research. For example, the
researcher made an attempt to present
the

interdependent relationships between
knowledge management, information
technology and artificial intelligence.
Therefore, statistical tests are natur
ally
needed to assess the proposition and to
operationalize the variables in the
proposed theoretical model. Better
approach would be to study organizations
in a particular sector of industry and to
establish empirical data about the
interrelationships bet
ween the three
constructs considered in the study.

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