The document discusses the evolution and increasing use of automated decision making systems in organizations. It provides an overview of different types of automated decision making technologies used in industries like banking, insurance, transportation and healthcare. Some benefits highlighted are reducing costs, leveraging expertise, improving quality and ensuring compliance. Challenges discussed include needing structured data and decisions, impacts on staffing, and potential legal issues. Effective solutions proposed are providing training to existing staff, recruiting and training new experts, and properly delegating work as automation increases.

1. Submitted To: Ms. Ramandeep Kaur

2. Name Registration
No.
Diksha Kumari 11607795
Shashank Thakur 11601730
Anushka Sharma 11606126

3.  Overview of the Case
 Identification and Analysis of Main Issues
 Comments on Effective Solutions
 Future Expects

4.  Many early artificial intelligence applications were just
solutions looking for problems, contributing little for
organizational performance.
 During the 1970s, managers began to address this need by
employing intelligence augmentation tools that provided
managers and analysts with “decision support”.
 Although some decision support tools offered the potential
for sophisticated statistical insight into business problems,
they generally required skilled users to direct their use.
 The tools were usually not integrated with business
applications.

5.  Another reason why managers resisted making these
systems a part of their organizations was that they would
be too complex for most users to understand.
 Despite these earlier obstacles, automated decision making
is finally comes of age .
 An Automated Decision Making is a rule based system that
provide a solution to a repetitive managerial problems in a
specific areas. It is a relatively new approach to support
decision making system and usually applies to highly
structured system.

6. Merits:
 Require little or no human intervention
 Reduces labor costs
 Leverage scare expertise
 Improve quality
 Enforce policies and respond more quickly to customers.

7.  Rule Engines: process a series of business rules that use
conditional statements to address logical questions . Key
Vendors- Ilog,Pegasystems
 Industry Specific Packages: make automated decisions
for questions faced by companies in a particular Industry.
Key Vendors- CSE Continuum in Insurance and Lending
Tree
 Statically or Numerical Algorithm : process quantitative
data to arrive at an optimal target, such as a price or a loan
amount. Key Vendors- SAS and SPSS(Statistical Package
for the Social Sciences )

8.  Work Flow Applications: After a decision is made by
a rules engine, the workflow system moves the rest of
the case or file through the required steps, which may
include routing it for approvals, printing it and
notifying all stakeholders. Key Vendors-Documentum
and FileNet Corp.
 Enterprise Systems: software applications that
automate, connect and manage information flows and
transaction processes in complex organizations. They may
use automated decision technology for specific functions.
Key Vendors- Sap and Oracle.

9.  Best suited for decisions that must be made frequently and
rapidly, using information that is available electronically.
 The knowledge and decision criteria used in these systems
need to be highly structured, and the factors that must be
taken into account must be well understood.
 Bank credit decisions are a good example, they are
repetitive, susceptible to uniform criteria and can be made
by drawing on the vast supply of consumer credit data that
are available.
 A decision about whom to hire as CEO, by contrast, would
be a poor choice. It occurs only rarely, and different
observers are apt to apply their own criteria, such as
personal chemistry, which cannot be easily captured in a
computer model.

10.  To analyze the success implementation of this
systems, researchers interviewed people from
more than 19 organizations including banking,
insurance, travel and transportation, health care,
utilities and agriculture who have implemented
such systems, also interviewed managers from
different software vendors along with Accenture
Consultants who provide or work with these
systems..

11.  Solution Configuration: Increase Profitability and Customer
Satisfaction
 Yield Optimizations: Optimize Financial Performance
 Routing or Segmentation Decision: increased the “no
touch” rate (cases it can resolve without person-to-person
contact)
 Corporate or Regulatory Compliance: save on cost
 Fraud Detection: improved systems for detecting possible
fraud
 Dynamic Forecasting: customers’ forecasts more closely
with their own manufacturing and sales plans.
 Operational Control: respond rapidly on the basis of rules
or algorithms

12.  The transportation industry was one of the first to employ
automated decision making on a large scale.
 After being used initially by airlines to optimize seat
pricing, decision making technologies has since been
applied to a variety of areas including flight scheduling and
crew and airport staff scheduling.
 An Automated system can also help insurance companies
to increase consistency and leverage abilities of their best
underwriters.
 Harrah Entertainment Incorporations(makes several
million dollar a month): Used Yield Optimization

13.  Banking and Insurance Industry
 Deep green Financial in Cleveland, Ohio
 Healthcare Industry
 Accenture Consultants

14. Management Problems
After the Introduction of Automated Decision
Making
 Managers still need to involved in reviewing and confirming decisions
and, in exceptional cases, in making the actual decisions.
 The knowledge and decision criteria used in these systems need to be
highly structured and the information must be available electronically.
 One of the greatest challenges with automated decision system is
finding the experts who are capable of running them.
 New systems can also have significant impacts on staffing
requirements, particularly for less skilled and less experienced
employees.
 It is difficult to find out experts to oversee some of the new systems and
companies still not clear from where they find tomorrow’s experts.
 There is a significant likelihood that more and more companies will be
affected by lawsuit challenging automated decision methods.

15.  Providing training to the Existing Staff
 Recruitment of Employees and provides training to
them.
 Proper Delegation of Work

16.  The more data that exist, the greater the potential
there is for automation.
 Organizations will need to think carefully about the
number and types of human decision makers they
need and begin to develop them now.
 Businesses need to find ways to incorporate it into
their strategies and processes or they may be left
behind and lose their competitive advantage.
 This brave new world of automated decision making
has been a long time in coming, but it is now upon us.