ServiceNow Otto for CSM - Agent Productivity

[FernandoCastro]

SERVICENOW OTTO for CSM — Agent Productivity

Help human agents resolve cases faster with
embedded generative AI

Agent Productivity in ServiceNow Otto for CSM enhances how human agents engage with cases, chats, calls, and knowledge by embedding generative AI directly into the agent's CSM configurable workspace. With summarization, intelligent response recommendations, sentiment analysis, multi-turn Q&A in the Otto Panel and automated content generation, agents spend less time gathering information or drafting content and more time resolving issues.

These capabilities reduce manual effort and improve consistency and case quality in documentation and communication. Paired with agentic orchestration, agents can also receive suggested actions or execute background workflows in real time, all within the case context.

Virtuous Content Loop Skills Measured Success

Virtuous Content LoopHow agent productivity feeds continuous improvement

Agent productivity features contribute directly to a continuous improvement cycle for content quality and resolution accuracy. By capturing structured and high quality outputs like AI-generated summaries, recommended steps, and knowledge and resolution notes generation with guidance from generated suggested steps, the system feeds insights back into both the knowledge base and automation design, increasing the health of the transactional data.

This loop ensures that:

1

Resolution notes and summaries: are indexed for future AI Search and suggested answers.

2

Suggested steps and article gaps, now resolved: are flagged to content authors via feedback loops.

3

Agent usage signals: inform which knowledge and templates are most useful.

4

Both customers and agents reduce the usage of tribal knowledge and start using better content.

5

Usage data and resolution patterns help identify automation opportunities through Process Mining, clustering and trends, enabling admins to define agentic workflows over time.

This dynamic reuse of agent-generated content reduces future handle time, improves deflection rates, and enhances both self-service and agent assist experiences over time.

SkillsDiscover more by clicking on each of the skills

Summarization

1

Case Summarization

2

Chat Summarization and Wrap-Up Generation

3

Call Summarization

4

Sidebar Summarization

Recommendations

1

Chat Reply Recommendations

2

Email Reply Recommendations

3

Sentiment Analysis

4

Multi-Turn Q and A in Otto Panel

5

Automated Quality Assurance

Content Generation

1

Resolution Notes Generation

2

Knowledge Generation

3

Suggested Steps Generation

4

Activity Response Generation

Measured SuccessHow do we usually measure success in this set of purpose-driven skills?

Outcome	Explanation (with applicable use case)	Success Metric
Reduced average handle time	Empower agents with auto-generated summaries and suggested responses to reduce time spent on manual documentation and content switching. Use case: Case and Chat Summarization	Avg. handle time per interaction (minutes)
Improved documentation consistency	Standardize output across agents using LLM-generated resolution notes, suggested steps, and wrap-up content. Use case: Resolution Notes, Suggested Steps	% of cases with AI-authored summaries or resolutions
Increased agent satisfaction	Eliminate repetitive tasks and reduce cognitive load by surfacing contextually relevant knowledge and actions. Use case: Sidebar, Otto Panel Q&A, Recommendations	Agent satisfaction score (ASAT)
Faster onboarding and ramp-up	Accelerate new agent effectiveness by embedding AI guidance and reducing reliance on tribal knowledge. Use case: Recommendations and Sidebar Assist	Avg. time to proficiency for new agents (weeks)
Improved customer satisfaction	Deliver accurate, personalized responses faster using generative email and chat replies. Use case: Email Reply, Chat Reply with Sentiment Awareness	CSAT / customer feedback per interaction
Reduced escalations and rework	Present relevant steps and recommendations early, reducing back-and-forth or case reopening. Use case: Suggested Steps, Otto Panel Q&A, Sentiment Analysis	% of cases escalated / reopened
Increased CS agent retention	Transform the agent experience by automating mundane activities to increase job satisfaction and mitigate service agent attrition.	Customer service agent turnover (%)
Reduced effort to resolve customer cases (transfer / ramp up time)	Reduce transfer portion of resolution time per case via auto-generated summarization of case history to date, applicable to live agent escalation and virtual to live agent transfers. Use case: Auto summarize case for transfer	Time spent on transfer per case (in hours)
Reduced number of customer cases worked	Deliver actionable AI answers and conversational virtual agent interactions to customers, leading to fewer cases to the contact center. Use case: Promoted, narrative search results	Customer case volume (#)
Reduced effort to resolve customer cases (wrap-up time)	Reduce wrap up portion of resolution time per case via auto-generated resolution notes. Use case: Auto draft resolution notes	Time spent on wrap up per case (in hours)

Ready to go deeper?

Head back to the Implementation Library Home for the full guided path across ServiceNow Otto for CSM.

Back to Implementation Library Home

© 2026 ServiceNow, Inc. All rights reserved.