Discussion - Weeks 1–2COLLAPSETop of FormShared Practice—Rol.docx

Discussion - Weeks 1–2
COLLAPSE
Top of Form
Shared Practice—Role of Business Information Systems
Note: This Discussion has slightly different due dates than what
is typical for this program. Be mindful of this as you post and
respond in the Discussion. Your post is due on Day 7 and your
Response is due on Day 3 of Week 2.
As a manager, it is critical for you to understand the types of
business information systems available to support business
operations, management, and strategy. As of 2013, these
include, but are certainly not limited to the following:
· Supply Chain Management (SCM)
· Accounting Information System
· Customer Relationship Management (CRM)
· Decision Support Systems (DSS)
· Enterprise Resource Planning (ERP)
· Human Resource Management
These types of systems support critical business functions and
operations that every organization must manage. The effective
manager understands the purpose of these types of systems and
how they can be best used to manage the organization's data and
information.
In this Discussion, you will share your knowledge and findings
related to business information systems and the role they play in
your organization. You will also consider your colleagues'
experiences to explore additional ways business information
systems might be applied in your colleagues' organizations, or
an organization with which you are familiar.
By Day 7
· Describe two or three of the more important technologies or
business information systems used in your organization, or in
one with which you are familiar.
· Discuss two examples of how these business information

systems are affecting the organization you selected. Be sure to
discuss how individual behaviors and organizational or
individual processes are changing and what you can learn from
the issues encountered.
· Summarize what you have learned about the importance of
business information systems and why managers need to
understand how systems can be used to the organization's
advantage.
You should find and use at least one additional current article
from a credible resource, either from the Walden Library or the
Internet. Please be specific, and remember to use citations and
references as necessary.
General Guidance: Your initial Discussion post, due by Day 7,
will typically be 3–4 paragraphs in length as a general
expectation/estimate. Refer to the rubric for the Week 1
Discussion for grading elements and criteria. Your Instructor
will use the rubric to assess your work.
Week 2
By Day 3
In your Week 1 Discussion you described how business
information systems have been applied in an organization with
which you are familiar. Read through your colleagues' posts and
by Day 3 (Week 2), respond to two of your colleagues in one or
more of the following ways:
· Examine how the business information systems described by
your colleague could be or are being used by your organization.
Offer additional ways either organization might take advantage
of these systems.
· Examine how the business information systems identified by
your colleague would affect your professional or personal
practices or your organization. What opportunities might there
be?
· Share your insights about the business information systems
identified by your colleague, and offer additional ideas about
the impact they might have and challenges that could occur.
· Expand upon the role of the business information systems

presented by your colleague and offer other opportunities,
solutions, or implications that your colleague may not have
shared.
· Explore other ways in which the technologies shared by your
colleague could be used.
General Guidance: Your Discussion responses, due by Day 3,
will each typically be 1–2 paragraphs in length as a general
expectation/estimate. Refer to the rubric for the Week 1
Discussion for grading elements and criteria. Your Instructor
will use the rubric to assess your work.
Bottom of Form
Running Head: ARTIFICIAL INTELLIGENCE AND EXPERT
SYSTEMS
Artificial Intelligence and Expert Systems
Artificial Intelligence and Expert systems
ABSTRACT:
· What is artificial intelligence?
· Characteristics of AI
· The field of AI
· How the AI field evolved
· What is Expert System?
KEYWORDS:
· Artificial intelligence Technology
· International Technology Transfers
· Expert Systems
· Applied Artificial Intelligence Human Experts
INTRODUCTION:
· AI to Predict and Adapt
· Making Decisions

· Continuous Learning
· Capability of Motion and Perception
OVERVIEW OF APPLIED ARTIFICIAL INTELLIGENCE:
· Natural language processing
· Robotics
· Computer vision
· Speech Recognition
· Machine learning
EXPERIMENT:
APPLICATIONS OF EXPERT SYSTEMS:
· Troubleshooting and Diagnosis
· Financial decision making
· Knowledge Publishing
· Design and Manufacturing
EXPERT SYSTEMS IN ORGANISATION:
BENEFITS AND LIMITATIONS OF AI ALONG WITH
EXPERT SYSTEMS:
NEURAL NETWORKING:
Artificial Intelligence and Expert systems
ABSTRACT:
· What is artificial intelligence?
· Characteristics of AI
· The field of AI
· How the AI field evolved
· What is Expert System?
KEYWORDS:
· Artificial intelligence Technology

· International Technology Transfers
· Expert Systems
· Applied Artificial Intelligence Human Experts
INTRODUCTION:
· AI to Predict and Adapt
· Making Decisions
· Continuous Learning
· Capability of Motion and Perception
OVERVIEW OF APPLIED ARTIFICIAL INTELLIGENCE:
· Natural language processing
· Robotics
· Computer vision
· Speech Recognition
· Machine learning
EXPERIMENT:
APPLICATIONS OF EXPERT SYSTEMS:
· Troubleshooting and Diagnosis
· Financial decision making
· Knowledge Publishing
· Design and Manufacturing
EXPERT SYSTEMS IN ORGANISATION:
BENEFITS AND LIMITATIONS OF AI ALONG WITH
EXPERT SYSTEMS:
NEURAL NETWORKING:

Artificial Intelligence and Expert Systems
Sapana Dahal
CSCI 303
Artificial Intelligence (AI) in the current technological crazed
world has been on the center stage. AI has commendable gained
notable popularity and visibility that is very string in the public
domain, business community, Educations and various other
special fields. At the moment the most successful business in
the world have not hesitated to fully take advantage of AI to
better their products, through presented AI to the consumer in a

cheaper state. This is the example Amazon Alexa AI, Apples
Siri AI and Microsoft’s Cortana (Kaplan & Haenlein 2019).
These are a perfect example of the use of AI to make things
easier for the consumer, all these considered to be “personal
assistants. Expert Systems (ES) on the other hand are a
dominant filed in AI, basically the largest field in AI currently,
this is because it offers scientific, commercial and military
application of AI. This paper is aimed at looking at and
explaining the AI concepts and ES applications that have been
able to make the life of every individual in different field
easier. Like ROSS the Ai attorney, or the Dendral expert system
in medicine. The Implementation of AI technology cannot be
ignored in our daily lives cannot be ignored because of the
impact that such a technology is bringing to our world. Never
before have machines been able to mimic the human brain and
be able to tackles decision making or problem-solving instance
as better or just as the human brain. These aspects have caused
a major quagmire of mixed feelings. Mainly because the AI and
ESs will make our lives easier bhut at the same time it will step
in the place of human experts that will mean they will be
replaced. The paper clearly discusses on this fact on how the
ESs and Ai are not here to replace the Human experts,
especially the white-collar jobs but only to make their task
much easier.
KEYWORDS
Artificial Intelligence Technology, International Technology
Transfers, Experts Systems, Applied Artificial Intelligence
Human Experts.

INTRODUCTION
Artificial Intelligence (AI) can be described as the creation or
the simulation of human intelligence into machines with the aim
of mimicking the actions and thinking like human beings. The
term is broad as it can eb associated with any machine that is
able to display various human thinking traits like problem-
solving and learning. In the current world or the world to come
artificial intelligence will be the most used or preferred
technology as it will make the work easier or rather replace
human beings in tasks that are hard to be completed by human
beings the ideal characteristic of artificial intelligence is taking
actions and at the same rationalization with the aim of
completing a specific goal (Upadhyay & Khandelwal, 2019).
The most perceived idea about artificial intelligence is robots,
the immediate thing that comes to mind when someone mentions
the term artificial intelligence. This is through the novel and the
movies that weave stories depicting AI machines that bring
havoc to the world. But it might not be far from the truth, the
most important aspect to understand is that AI is continuously
developing into new and better versions on a daily basis; with
machines build withing various fields like; psychology,
linguistics, mathematics, computer science and more with the
aim of solving issues in these fields better than human beings.
There are many merits of AI, it is because as a simulation of the
human thinking it is developing and growing every day. These
merits clearly represent the AI and the include: AI has the
ability to adapt and predict. The use of algorithms has enabled
AI to predict and adapt, whereby patterns of past data are used
for future prediction and decision making. These patterns give
AI the ability to learn like human beings and put through
software systems that can be used to correct errors or predicting
what is going to be typed and estimation shortest routes to take
(Kietzmann & Pitt, 2019). These are some of the abilities under
this characteristic. AI simulates human intelligence, and

therefore every aspect the human intelligence has is mimicked
like for instance the ability to analyze data, make decisions and
gain new insights to make the best decisions, faster than human
counterparts. Which makes it preferred to human labor by most
companies. AI is also known as machine learning, meaning it
has the ability to continually learning through of the most
notable method know as deep learning used by Alexa, Siri,
Netflix and Google. Whereby analytical models are built to
perform tasks through countless trial and error. AI is reactive
meaning that atypical AI will perceive a problem and develop a
better solution for it; like the application on our gadgets predict
on our actions and provide recommendations that suit the user.
Finally, the rother major characteristic is the capability of
motion and perception since the development of AI in the last
50 years ago, AI has evolved into this phenomenon that is able
to have auditory, visual, speech perception and information
learning and processing that is perceptual. Whereby the goof
examples include: Amazon Alexa, Apple’s Siri and the Tesla’s
Self driving Cars.
The first practical application of AI was developed by the
British Mathematician Alan Turing, who was responsible for
conceptualizing machines that had the ability to think in the
1950s. at this point the seeds of AI were set and it picked up
from this moments, whereby followed by an AI lab that was
setup in Massachusetts in the year 1959 by Marvin Minsky
(Garnham 2017). Through this lab their collaboration with
Stanley Kubrick who received radical ideas from Marvin who
developed various literature and movies relating to AI the AI
excitement was further catapulted by the Invention of the
computer in the 1980s (Garnham 2017). whereby this opened
way for the tech corporations who have explored various fields
of AI that are used to bring technology close to the consumer
through affordability and through the various applications.
Today AI is considered to be everyway, in smartphones, traffic
routes, Netflix, Amazon and Apples and the other various
corporations or fields embracing AI. AI has transformed and is

continuing to transform how things work in the world, through
the basics of machine learning that are instrumental in various
instances: such as advertisement that are based on machine
learning focusing on the data inputs that are received from
different channels. But this can be considered as the beginning
of AI take over, the future is envisioned as an economy and
industry that is robot driven. A lot of companies are looking
into self-driven cars, which basically will change the basic
characteristics of private and public transport. Additionally, AI
has played an important part in the management and security
sector, through the analysis of anomalous data patterns. A good
example is the chatbot which is an AI application that is
increasingly used by companies replacing regular customer
service agents, which are not as effective as human. Although
creates an all-round customer engagement through machine
learning. Whereby a good example is the trials of AI genoming,
proving that humans and machines will come to works side by
side, and it will be considered normal as it is growing form the
various efforts put in place at the moment. The future of AI is
very clear, looking at how it has evolved to this moment;
although there are various challenges in the widespread
adoption, the world has overlooked over the advantages that AI
brings to the world and making work much easier, but also
ensuring that AI is managed effectively. There is an increased
risk which will affect the development of AI as it majorly
depends on Data, and it will be challenging as it will cause
social unrest and ensure human jobs are redundant. Whereby it
is the responsibility of people pushing AI to makes sure there is
a good balance between the positives and the negatives of AI
which will not destroy but bring a Positive AI revolution in the
Future (Pauschunder 2019).
Experts systems can be described as programs that rely on AI,
in other words these two are tied. There is no way to discuss AI
and without mentioning Expert systems, and vice versa. Experts
systems can use AI to simulate behavior and judgement of an
organization or human that has expert knowledge of a particular

field. Basically, an expert system brings together an interface of
rules known as engine and accumulated experience known as
knowledge base (Andikos et al. 2016). Whereby the rules engine
applies knowledge based to each situation that is spelled out in
the program. The capabilities of the system can be improved
with various addition to the set of rules or the knowledge base.
Current expert systems are typically equipped with capabilities
of machine learning which makes it easy for them to perform
better as humans through learning from experience. Developed
in the 1970s the expert systems have been significant in many
industries like telecommunications, financial, customer
services, healthcare, video games, transportation,
manufacturing, written communication and aviation. A good
example of an expert system was the groundbreaking medical
system which helped in the diagnosis. Deandra Expert System
assisted chemists to understand organic molecules that led to
the easy identification of various bacteria such as the meningitis
and bacteremia and recommend the nest dosage and antibiotics
(Tan 2017).
APPLICATIONS
Overview of Applied Artificial Intelligence
AI is rapidly growing and soon enough it will be covering
almost all areas of the world, whereby it is quickly moving from
the lad to the consumer and business applications (Bravo et al.
2016). The result creates a significant on how software is built
now as compared to previous versions. Now applied AI is one of
the powerful technologies powering the most successful
business currently, Apple, Google, Facebook and Amazon.
There are a few areas where AI has been applied which are:
Natural Language Processing (NLP) a branch of AI that mainly
deals with interactions between humans and computers using the
natural language, with the aim of reading, deciphering,
understanding and making sense of human language in a way
that is valuable. Robotics, AI in this field helps in saving
certain motions in the systems of robotics, these motions are
constantly refined that makes moving and installing robotic

systems easy to achieve; the result is robots being used in the
customer service sector globally. Machine Learning, as
mentioned before it is the way in machines learn new concepts
on its own without explicit programming, which is an applied
AI that enables automatic improvement and learning form
experience. These are some of the Application of AI in addition
to more others like Speech recognition and computer vision
(Hengstler et al. 2016).
Capabilities of Expert Systems
In AI, expert systems emulate the decision-making ability of
humans to solve various issues which is just a simple
description. Experts systems are built to have knowledge to one
domain like, engineering, science and medicine. This knowledge
as mentioned before is known as a knowledge base and has
gathered experiences that are tested after being loaded in the
system. The rules and adds-on to the knowledge base maybe add
to give the expert systems to create a better system. The system
is described as reliable, highly responsive, understandable and
high performance. Expert system typically will come into the
world to replace the individuals in the white collar and
analytical jobs. Because expert systems are excellent in pattern
matching, configuration, classification, reasoning, planning and
diagnosis (Bogdanova et al. 2016).
The Inference engine is the basis for the capabilities of ESs. To
have a particular solution the Inference engine manipulates the
knowledge that as acquired. For instance; when an Expert
system is rule-based in AI, there is the need to add knowledge if
require, the rules obtained from earlier application need to be
applied to the facts and this will resolve rules conflict,
especially when multiple rules are presented in an instance. The
following strategies are used to recommend a solution, the
forward chaining and the backwards chaining.
Forward chaining: this is a strategy used by an Experts System.
This strategy helps to answer the question what happens next.
Basically, the chain of derivations and conditions are followed
in this strategy and at the same time reduce the outcome. All the

rules and facts are considered and are sorted before the
conclusion is made to recommend a particular solution. A good
example is predicting the share market status, based on the
change in interest rates.
Image Source: Data Flair Team
Backward Chaining: This strategy is sued by the Expert System
to answer the question “why this happened?” normally in this
case what had already happened will be very significant matter
to the organization or the individual. Therefore, this strategy
will focus on finding out the conditions that led to the particular
outcome in place in the pat to have this result. Therefore, is a
strategy that finds reason or cause. For example, human blood
cancer.
Image source: Data Flair Team
Applications of Expert Systems
There are various application of Expert Systems, but the most
important things to consider when choosing and Expert System
ES is that the tool that has been chosen needs to match the
qualification the project team possess, and the tool selected for
the projected has to go hand in hand with the sophistication and
capability of the projected ES. The first application is the
troubleshooting and diagnosis of Devices of all kinds, basically
discovering faults and suggest remedies for these faults for a
device or process that is malfunctioning. A good example is
medical diagnosis the first ever application of ES. Planning and
Scheduling, systems in this case analyses interacting and
potentially complex goals and determine set of actions that can
be sued to achieve the same goals. At the same temporal
ordering of the actions can be initiated with the ES considering
material, account personnel and various constraints. Financial
Decisions Making, the financial service industry has a common
use of ES techniques some programs have been created for the
sole reason of advising bankers to whether give loans to
individuals and businesses (Wagner 2017). Knowledge

Publishing, this is a new application that has not been there for
some years but potentially an area that is rapidly growing. The
main function of the ES in this case is deliver relevant
knowledge to the problems the user presented. A good example
of ES in this sector is the advisor that guides the user on the use
of grammar in a text and an ES that guides the user on
individual tax policy and tax strategy, whereby the ES is known
as the tax advisor. Process Monitoring and Control, ES in this
application analyze real-time data obtained from physical
devices with the objective of predicting trends, establishing
anomalies, controlling failure correction and optimality. Design
and Manufacturing, Expert Systems in falling in particular field
assist in the design of physical processes and devices. Which
can be factory floor configuration of processes in manufacturing
or can be level conceptual design of entities considered to be
abstract.
Development of AI and Expert Systems
Experts Systems ES have been identified to be systems that
utilize AI to provide decisions making and problem techniques
that as close as to human intelligence working. Which means
that ES really needs Ai to function but at the same time it is an
independent entity on its own. As mentioned before expert
systems are hand in hand, AI as one grows the other follows the
pattern. As AI is taking over the world, ES follows the same
path are they are being deployed and developed in a various part
of the world in applications considered to be myriad. This is
because of the explanation capabilities and symbolic reasoning
of the ES. A good example is the recently developed Expert
System known as ROSS, which is an attorney AI that is built
under the principles of pattern recognition, deep learning, self-
learning systems and natural language processing that utilizes
data mining and at the same copy the working of the human
brain (Semmler & Rose, 2017). Whereby this qualifies to be a
particular exciting development yet controversial. It may not be
in every enterprise, but the development of AI and Experts
Systems has taken over the world.

EXPERT SYSTEMS (ESs) IN ORGANIZATION
ES provide both important and tangible value to various
organizations. As mentioned before, the most successful
business in the world have benefitted from the use AI and
Experts systems majorly through introducing them to their
consumers. The benefits should be measured against the
exploitation and development costs of an ES, that are basically
very expensive especially for ESs that are considered to
organizationally important and large (Masuch 2018). At this
moment it seems that Experts systems are the substitution for an
individual’s work but that is not the case as the ES can
substitute overall performance for a knowledge worker or the
white-collar jobs, mainly in analytics and problem-solving
tasks. But this is not true, yes, the systems can significantly
reduce the individual’s work dramatically required for the
knowledge worker to solve the problem but leave the innovative
and creative aspect of problem solving to the people. Basically,
ESs are tools that are developed to make work easier. The issue
is that it will reduce the work that two people would have
performed to be performed by one person, which is good for the
company, but it will create societal unrest of unemployment.
Therefore, looking at the benefits and limitation will help in
establishing where the ESs stand in organizations.
Benefits of AI along with Expert Systems
· The benefits of ESs are straight forward and are used to
reduce the tine taken, workload or resources used to complete a
task. This is only possible when AI is incorporated to the ESs.
The benefits include:
· Es are designed to be able to complete task presented to them
faster than how long a human expert would take in the same
task.
· The systems that are successful presents a low error rate,
whereby at times the error rate is much lower than when a
human expert is performing the same task.
· At the same when the ESs are given task, or problems to solve
they will come up with various recommendations, which is only

possible to a human expert who has experience in the particular
field.
· At some point there is the need for difficult-to-use sources of
knowledge in an application, ESs are used and turn out to be
convenient to make sure bring9ng the application to this point.
· ESs are instrumental because through machine they can eb
able to capture scarce expertise, maybe there is only a few
experts in a particular field, let’s say finance who are the only
ones who can perform the specific task. Through Experience the
uniquely qualified expert can be accurately mimicked by ESs.
· Currently ES are known used differently, basically
organizations know use ESs to build up organizational
knowledge, as compared to the organization’s knowledge of
individuals.
· For novices, wen training vehicles are used ESs produce
higher and faster learning curves.
· Finally, a point that will change the current narrative that
taints ESs existence is that, organizations can utilize AI and
ESs in environments that are hazardous for regular Human
Experts. A good example can be the exploration of Mars; the
planet is not safe for human habitation but AI and ESs can be
used to come up a better environment that will pave way for
human experts.
Limitation of Expert Systems
Given the various advantages or benefits that the ESs present,
the human expert remains the most intelligent and efficient
system on the planet. This is because there is no total and easy
solution that technology can offer. ESs are large, take a lot of
time to develop, need a lot of computer resources and overall,
very costly. This makes ESs have their own limitations which
are:
· Technological limitations, such as the dependence on data.
· There are also issues with the acquisition of knowledge.
· The principle area of ES application is operational domains.
· Affect the maintenance of Human Expertise in an
organization, whereby it is important as ESs are not 100%

reliable at some point they will need upgrading or break down.
NEURAL NETWORKING
Neural Networks are described as computer systems that are
modelled on the brain of a human being. These systems look
like interconnected mesh network of processing elements known
as neurons. The neural network has around 100 billion neuron
brain cells making them simpler that the brain (Shakya et al.
2018). However, just as the brain, these networks have the
ability to process various information pieces at the same time
and learn how to understand patterns and program to resolve
problems independently. A neural network “is an array of
interconnected processing elements, each of which can accept
inputs, process them, and produce a single output with the
objective of imitating the operation of the human brain.”
Information or knowledge that is given out in a neural network
in form of pattern connections that is within the elements of
processing and through the adjustment of the weights of the
same connections. Therefore, the neural networks get strength
from applications that need sophisticated recognition of
patterns. The weakness of neural networks is that they do not
sugar coat an explanation to the conclusion or answers they give
out.

REFERENCES
Andikos, A. F., Ali, G., & Purnomo, W. A. (2016). Expert
System for Decision Support Division of Inheritance According
to Islamic Law. IAES International Journal of Artificial
Intelligent (IJAI), 5(3), 89-94.
Bravo Serrano, À., Li, T. S., Su, A. I., Good, B. M., & Furlong,
L. I. (2016). Combining machine learning, crowdsourcing and
expert knowledge to detect chemical-induced diseases in text.
Bogdanova, Y. A., Zaripova, G. R., Kataev, V. A., & Galimov,
O. V. (2016). CAPABILITIES OF EXPERT SYSTEMS IN
FORECASTING OPERATIONAL RISK FOR THE MOST
COMMON INTERVENTIONS OF ABDOMINAL
SURGERY. YAKUT MEDICAL JOURNAL, (4), 54-58.
Garnham, A. (2017). Artificial intelligence: An introduction.
Routledge.
Puaschunder, J. M. (2019). Artificial Intelligence evolution: On
the virtue of killing in the artificial age. Scientia Moralitas-
International Journal of Multidisciplinary Research, 4(1), 51-72.
Hengstler, M., Enkel, E., & Duelli, S. (2016). Applied artificial
intelligence and trust—The case of autonomous vehicles and
medical assistance devices. Technological Forecasting and
Social Change, 105, 105-120.
Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand:
Who’s the fairest in the land? On the interpretations,
illustrations, and implications of artificial intelligence. Business
Horizons, 62(1), 15-25.
Kietzmann, J., & Pitt, L. F. (2019). Artificial intelligence and
machine learning: What managers need to know.
Masuch, M. (Ed.). (2018). Organization, management, and
expert systems: models of automated reasoning (Vol. 23).
Walter de Gruyter GmbH & Co KG.
Mettleq, A., Soliman, A., & Abu-Naser, S. S. (2019). A Rule

Based System for the Diagnosis of Coffee
Diseases. International Journal of Academic Information
Systems Research (IJAISR), 3(3), 1-8.
Shakya, A., Bhattarai, L., Shahi, S., Kumar, S., & Rao, S.
(2018). Application of Artificial Neural Networking Technique
for the Lifecycle Assessment of Recycled Aggregate Concrete.
Semmler, S., & Rose, Z. (2017). Artificial intelligence:
Application today and implications tomorrow. Duke L. & Tech.
Rev., 16, 85.
Tan, H. (2017, September). A brief history and technical review
of the expert system research. In IOP Conference Series:
Materials Science and Engineering (Vol. 242, No. 1, p. 012111).
IOP Publishing.
Upadhyay, A. K., & Khandelwal, K. (2019). Artificial
intelligence-based training learning from
application. Development and Learning in Organizations: An
International Journal.
Wagner, W. P. (2017). Trends in expert system development: A
longitudinal content analysis of over thirty years of expert
system case studies. Expert systems with applications, 76, 85-
96.
What is Expert System in Artificial Intelligence – How it Solve
Problems by Data flair team, November 15th 2018. Retrieved
from https://data-flair.training/blogs/expert-system/
Discussion 1 - Week 1
Top of Form
Shared Practice—How Technology Changes How We Live and

Work
Digital technology seems ubiquitous, touching nearly every
aspect of our personal and professional lives. Its rapid evolution
continues to significantly affect how people live and work, and
how they communicate with one another, within increasingly
diverse, complex social networks. And the information lifecycle
today moves much faster than it did 30, 20, even 10 years ago.
Just as we adopt a new device or learn a new piece of software,
the next best and greatest innovation comes along that renders
our new tool or toy obsolete.
By Day 3
Post your insights about how information and information
technology have changed your daily life, both professionally
and personally. Focus on the technologies that have helped you
increase your effectiveness at work and in business, and how
you might apply these technologies as a business manager.
General Guidance: Your initial Shared Practice Discussion post,
due by Day 3, will typically be 2–3paragraphs in length as a
general expectation/estimate. Refer to the rubric for the Week 1
Shared Practice Discussion for grading elements and criteria.
Your Instructor will use the rubric to assess your work.
Respond to two of your colleagues in one or more of the
following ways:
· Explore additional ways that the technology experiences of
your colleagues might impact you or change your practices.
· Share with your colleague ideas for how they might adopt
other technologies to enable them to further improve their
effectiveness as business managers.
· Compare your colleague's experience with your own, and share
additional insights you gained.
General Guidance: Your Shared Practice Discussion responses,
due by Day 5, will each typically be 1–2 paragraphs in length as
a general expectation/estimate. Refer to the rubric for the Week
1 Shared Practice Discussion for grading elements and criteria.
Your Instructor will use the rubric to assess your work.

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