Implementing and Integrating AI Systems

What Is Implementation? Implementation can be defined as getting a newly developed or significantly changed system to be used by those for whom it was intended.

What Is Institutionalization? It is a process through which the AI system becomes incorporated as an ongoing part of organizational activities.

Measuring Implementation Success Intended or actual use of the system User’s satisfaction Attainment of original objectives Documented benefits ($): cost savings, time savings

Determinants of Successful Implementation Technical Factors Behavioral Factors Change Management Process and Structure User Involvement Ethics Organizational Support External Environment

Technical Factors Level of complexity (must be low) Response time Reliability Availability Accessibility Lack of equipment Mismatched hardware/software

Behavioral Factors Decision styles: analytically-oriented, autocratic Organizational climate: supporting innovations vs. lagging with changes Resistance change: unknown entity, strange technology, self-preservation Organizational expectations: overexpectations

Process Factors Top management support User involvement

Organizational Factors Adequate resources Relationship with the IS department Organizational politics

Values and Ethics Goals of the project Implementation process: Consider an example to achieve a sales goal through violation of an antitrust law. Possible impact on other systems

External Environment Legal issues: The French government requires that all Web sites based in France be in French. Social issues Economic issues Political issues

Implementation Strategies Divide the project into manageable pieces –Prototypes – Evolutionary approach Keep the solution simple –Be simple –Hide complexity –Avoid change Get user participation

System Integration Integration of computer-based systems means that the systems are merged into one facility rather than having separate hardware, software, and communications for each independent system.

AI Attached to DSS Database intelligent component Intelligent agent for model management Improving the user interface Consultant to DSS builders Consultant to users

AI Contributions to DBMS Helps on data warehouse Helps on access to large databases Symbolic representation of data

AI Contributions to Model Management Systems Helps in selecting models Provides judgmental elements to models Improves sensitivity analysis Generates alternative solutions Provides heuristics Speeds up trial-and-error simulation

AI Contributions to Interface Provides explanations Provides terms familiar to user Acts as a tutor

Examples ES rules on investment can be analyzed by a DSS via a simulation. ES identification of a problem can be referred to a DSS for a solution. A DSS decision on acquisition can be directed to an ES for qualitative evaluation. A DSS that schedules employees can direct its output to an ES.

Technical Issues of Integration Technical feasibility Connectivity Architecture Data structure