CASE STUDY
Underutilized Artificial Intelligence Investment.
The majority of AI functionality in ServiceNow was uninstalled or unconfigured, and much of the automation potential had not been exploited throughout the organization.
Slow, Decentralized ITSM Processes.
The management of Incidents was based on manual processes and fragmented workflows, which directly added to increased Mean Time to Resolution (MTTR).
High Agent Administrative Burden
Instead of resolving technical problems, agents used to spend too much time in manual notetaking, call summarization, and drafting of knowledge articles.
No AI Governance or Roadmap
The absence of a planned adoption strategy or governance framework meant that AI initiatives were directionless and would not become valuable in the first place.
Complex User Onboarding
On boarding of new employees involved several manual touchpoints in various teams which resulted in friction and delays.
Inconsistent Data Quality
Close notes and descriptions of incidents had diverse quality, which constrained the quality of any artificial intelligence or machine learning skills.
20–30%
15–25%
25–40%
Improved
Improved
Hands-on AI Capability Analysis
Assessed Generative AI, Now Assist, Predictive Intelligence, Virtual Agent, AI Search and AIOps throughout the ServiceNow environment to determine maturity and gaps of current-state.
High Impact Use Case Identification
The first area of AI enablement is selected user onboarding, which focuses on one of the processes that have a high volume of manual work and user friction.
Now Assist for ITSM Implementation
Enabled Now Assist capabilities for incident summarization and resolution notes, reducing agent documentation effort and improving knowledge capture.
AI Search Assist Enablement
Deployed AI Search Assist to improve search relevance and enhance the self-service experience for end users.
Virtual Agent Expansion
Developed and deployed additional Virtual Agent topics for high-volume employee requests, increasing automation coverage and deflection rates.
Predictive Intelligence Configuration I
ntroduced Predictive Intelligence models for automated incident categorization and assignment, reducing manual triage effort.
Platform
ServiceNow ITSM
AI Capabilities Assessed
Now Assist for ITSM, Virtual Agent, AI Search Assist, Predictive Intelligence, Generative AI, AIOps
Assessment Framework
Royal Cyber AI Readiness Methodology
Governance Model
AI Control Tower with Phased Roadmap
Data Foundation
Incident Descriptions, Close Notes, Knowledge Articles
Monitoring & KPIs
MTTR Tracking, Deflection Rates, Agent Effort Metrics
Executive Summary
- By bringing the incident summarization and triage intelligence via AI, the MTTR will also speed up by 2030 faster solving problems with fewer manual resources.
- The AI Search and Virtual Agent automation of self-service will help prevent 1525% of the tickets and decrease the number of agents and enhance the user experience.
- The cost of the automated notes, computerized drafting of knowledge, and the simplification of workflows will ease the work of agents by 25-40 percent, leaving experienced personnel to handle challenging issues.
- Formal data hygiene programs will enhance the quality of predictions and actionable insights, and AI will make better predictions.
- Manual touchpoints will be removed through conversational onboarding redesigned with
- Virtual Agent to provide a seamless experience to new employees.
- An approved, scaled 90-day roadmap with AI governance can be used to guarantee controlled adoption that will not cause disruptions to current activities.
80%
Increase in Customer Activity
Audience
- Executives, CTOs, Director
- IT Leaders & Service Management Executives
- Digital Transformation Teams
- Project Managers
- IT Project Coordinators
- AI & Automation Strategists
