Seeking Real-World ROI and Readiness Assessment Approaches for ServiceNow Otto / Now Assist / AI Age
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2 hours ago
Seeking Real-World ROI and Readiness Assessment Approaches for ServiceNow Otto / Now Assist / AI Agent Advisor
Hello everyone,
We are beginning to evaluate ServiceNow Otto (formerly Now Assist), AI Agent Advisor, and related ITSM AI capabilities and are looking to learn from organizations that have already gone through the process.
Rather than starting with technology features, we're trying to build a business case based on measurable operational outcomes and would appreciate hearing about your experience.
Questions
1. How did you identify the highest-value use cases?
Did you start with:
- Incident Management?
- Intelligent Incident Routing?
- Agent Assist?
- Knowledge Recommendations?
- Virtual Agent?
- Major Incident Management?
What method did you use to prioritize among competing opportunities?
2. How did you establish an ROI baseline?
Before enabling Otto/AI Agent Advisor, did you measure metrics such as:
- Average Handle Time (AHT)
- MTTR
- Reassignment rates
- Time spent searching for knowledge
- Time spent documenting incidents
- First Contact Resolution
- Ticket throughput
Were there particular KPIs that proved most useful?
3. How did you assess AI readiness?
What readiness factors mattered most?
For example:
- Knowledge article quality and coverage
- Incident documentation quality
- CMDB maturity
- Historical incident volume
- Change management processes
- User adoption/culture
Did you perform a formal readiness assessment before deployment?
4. What implementation effort was required?
For the use cases that delivered value:
- How many FTEs were involved?
- What roles participated?
- How much effort was platform configuration versus data preparation, knowledge cleanup, governance, and user adoption?
Where did you underestimate effort?
5. Did you perform any controlled testing?
We're considering treating each use case as a hypothesis and validating results against a baseline.
Examples:
- Did Agent Assist reduce handling time?
- Did Intelligent Routing reduce reassignments?
- Did Knowledge Recommendations improve resolution times?
Did anyone use a control group, pilot group, A/B test, or before/after analysis?
6. What surprised you?
Looking back:
- Which use cases generated the most value?
- Which generated less value than expected?
- What prerequisites turned out to be critical?
- If you were starting again, what would you do first?
Additional Area of Interest
We're also exploring whether operational telemetry sources (e.g., ServiceNow history, Splunk logs, monitoring data, ticket activity patterns, assignment group metrics, etc.) can be used to identify operational drag before selecting AI use cases.
If you've used data-driven approaches to identify where support organizations spend the most time, I'd be especially interested in hearing about your methodology.
Appreciate any lessons learned, metrics, pitfalls, or success stories you're willing to share.
Thank you!
