
Transforming the IT Services Lifecycle with AI Technologies
Description
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As more and more industries are experiencing digital disruption, using information technology to enable a competitive advantage becomes a critical success factor for all enterprises. This book covers the authors' insights on how AI technologies can fundamentally reshape the IT services delivery lifecycle to deliver better business outcomes through a data-driven and knowledge-based approach. Three main challenges and the technologies to address them are discussed in detail:
· Gaining actionable insight from operational data for service management automation and improved human decision making
· Capturing and enhancing expert knowledge throughout the lifecycle from solution design to ongoing service improvement
· Enabling self-service for service requests and problem resolution, through intuitive natural language interfaces
The authors are top researchers and practitioners with deep experience in the fields of artificial intelligence and IT service management and are discussing both practical advice for IT teams and advanced research results. The topics appeal to CIOs and CTOs as well as researchers who want to understand the state of the art of applying artificial intelligence to a very complex problem space.
Although the book is concise, it comprehensively discuss topics like gaining insight from operational data for automatic problem diagnosis and resolution as well as continuous service optimization, AI for solution design and conversational self-service systems.
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Content
- Intro
- Abstract
- Contents
- Introduction
- The Main Areas for Applying AI to the IT Service Lifecycle
- Transforming the IT Services Lifecycle into a System of AI-Supported Feedback Loops
- Core Elements of an AI Platform for the Services Lifecycle
- Consumable Services
- Common Content and Data
- Common Services
- Establishing an AI-based Innovation Eco-System
- Overview of the Content of the Book
- Gaining Insight from Operational Data for Automated Responses
- Gaining Insight from Operational Data for Services Optimization
- AI for IT Solution Design
- AI for Conversational Self-service Systems
- Conclusion
- References
- Gaining Insight from Operational Data for Automated Responses
- Background
- Eliminating Non-actionable Tickets
- Challenges
- Solution Overview
- Finding Predictive Rules for Non-actionable Alerts
- Predictive Rules
- Predictive Rule Generation
- Predictive Rule Selection
- Why Choose a Rule-based Predictor?
- Calculating Waiting Time for Each Rule
- Differentiation
- Ticket Analysis and Resolution
- Challenges and Proposed Solutions
- Ticket Resolution Quality Quantification
- Feature Description
- Findings
- Deep Neural Ranking Model
- Differentiation
- Auto-resolving Actionable Tickets
- Challenges and Solution
- Differentiation
- Dataset Description
- Conclusion and Future Work
- References
- Gaining Insight from Operational Data for Service Optimization
- Background
- Best of Breed and Opportunity Identification
- Challenges
- Solution
- Ticket Vectorization
- Removing Tickets with Generic Resolutions
- Differentiation
- Cognitive Analytics for Change
- Challenges
- Solution
- Change Data
- Configuration Data
- Data Discovery and Clean-Up
- Structured Fields
- Unstructured Fields
- Risk Prediction Algorithm
- Differentiation
- Change Action Identification
- Dataset Description
- Conclusion and Future Work
- References
- AI for Solution Design
- Background
- Extraction and Topical Classification of Requirement Statements from Client Documents
- Challenge
- Solution Overview
- Differentiation
- Matching Client Requirements to Service Capabilities
- Challenge
- Solution Overview
- Differentiation
- Social Curation and Continuous Learning
- Challenge
- Solution Overview
- Differentiation
- Architecture
- Architectural considerations
- Conclusion
- References
- Conversational IT Service Management
- Background
- Architecture
- Ontology Driven Conversation
- Ontology Driven Knowledge Graph
- Ontology Driven Question Analysis
- Ontology Driven Context Resolution
- Troubleshooting Questions
- Guided Troubleshooting
- Long Tail Search Through Orchestrator
- Natural Language Interface to Structured Data
- Natural Language Interface to Service Requests
- Empirical Evaluation
- Ontology Driven Question Analysis
- Troubleshooting Questions
- Conclusion and Future Work
- References
- Practical Advice for Introducing AI into Service Management
- Establishing a Holistic Strategy for AI Applying Agile Transformation Principles
- Building a Data-Driven Culture
- Establishing a Knowledge Lifecycle Strategy
- Conclusion
- Reference
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