Organizations are increasingly seeking to leverage ServiceNow Gen AI capabilities to enhance service delivery and automate routine tasks within IT operations.
Implementing GenAI capabilities can significantly improve incident resolution times, provide more accurate solutions for problem management, streamline change processes, and enhance knowledge base utilization through AI-driven content suggestions.
However, many companies struggle to evaluate their current readiness to adopt AI capabilities effectively. This solution addresses the gap by providing a structured approach to assess and prepare organizations for AI adoption.
The purpose of this solution is to assess the readiness of implementing ServiceNow GenAI capabilities within a customer’s ServiceNow instance, specifically for the following modules:
· Incident Management
· Problem Management
· Change Management
· Knowledge Management
This solution targets IT leaders, such as CIOs, IT Service Managers, and ServiceDesk/HelpDesk Managers, who are responsible for improving operational efficiency and customer satisfaction. Additionally, it benefits IT Operations Managers and Change Managers who aim to leverage AI to automate processes and drive data-driven decision-making.
Evaluate GenAI Readiness Accurately
The solution aims to provide a comprehensive assessment of the organization’s readiness to implement GenAI in ServiceNow across Incident, Problem, Change, and Knowledge Management.
Prioritize Areas for Improvement
The analysis will highlight the specific data quality issues (e.g., completeness, consistency) that must be addressed to optimize GenAI outcomes.
Enable Informed Decision-Making
By providing readiness scores for each module based on benchmarks, organizations can understand their current state and what actions are needed for successful GenAI adoption.
Reduce Implementation Risks
The solution aims to minimize risks associated with poor data quality and inconsistent data, ensuring a smoother GenAI implementation.
Features
Readiness Assessment Tool
· Analyzes data for the last 2 years across multiple parameters, including data volume, completeness, text quality, consistency, duplicates, and freshness.
· Supports the outcome of evaluating GenAI readiness accurately.
Scoring and Benchmarking
· Provides readiness scores based on defined benchmarks and weightage, helping organizations understand their position relative to GenAI requirements.
· Supports prioritization of improvement areas and informed decision-making outcomes.
Gap Analysis and Recommendations
· Identifies specific gaps in data quality and provides tailored recommendations for improving data completeness, quality, and consistency.
Supports all outcomes by offering clear action plans to optimize GenAI readiness.
Key Features:
Data Readiness Score Calculation Application for ServiceNow to evaluate the quality of data for Generative AI skills
GenAi Readiness: Focus on ITSM integration with GenAi for resolution notes, knowledge articles, and summarization.
Data Parameters: Key parameters include data volume, completeness, quality, consistency, and freshness, evaluated over the last 2 years.
Rating Criteria: Data readiness is rated as Excellent, Good, Fair, or Poor based on benchmarks for various parameters.
DRS Calculation: Data Readiness Score (DRS) is calculated by summing weighted scores of all parameters
- Resolution Notes Generation checking
- Knowledge Article Generation
- Summarization
Data Parameters:
- Data Volume
- Data Completeness
- Text Quality
- Data Consistency
- Duplicate/Irrelevant Data
- Data Freshness
Rating Criteria:
- Data readiness is rated as Excellent, Good, Fair, or Poor based on benchmarks for various parameters.
- The Data Readiness Score (DRS) is calculated by summing weighted scores of all parameters. A score of 83.5% indicates good readiness.
Release Notes: GenAi Readiness – ITSM
Version: 1.0
Key Features:
Data Readiness Score Calculation Application for ServiceNow to evaluate the quality of data for Generative AI skills
- Resolution Notes Generation checking
- Knowledge Article Generation
- Summarization
Data Parameters:
- Data Volume
- Data Completeness
- Text Quality
- Data Consistency
- Duplicate/Irrelevant Data
- Data Freshness
Rating Criteria:
- Data readiness is rated as Excellent, Good, Fair, or Poor based on benchmarks for various parameters.
- The Data Readiness Score (DRS) is calculated by summing weighted scores of all parameters. A score of 83.5% indicates good readiness.
- ServiceNow active subscription
- ITSM Application