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on 02-08-2026 01:34 PM - edited Monday
Here's the irony of my career shift: I spent years as a data scientist finding and organizing good data to build mathematical models. Now? We have incredible models—but organizations are starving them of the data they need to deliver value.
The models are ready. Your data practices need to catch up. Let me show you how in this deeper dive into ITSM.
Your organization has invested in Now Assist and AI Agents. The technology works brilliantly. But adoption remains low because AI needs rich data to learn from, and most organizations are still capturing data like it's 2015—sparse descriptions, missing resolution notes, minimal context.
Here's a typical conversation I have: "Kelli, we enabled Now Assist for ITSM six months ago. Our agents love the technology, but adoption is lower than expected." I ask to see their incidents.
| Short description: | "Printer broken" |
| Description: | "Printer not working, submitted ticket" |
| Work notes: | "Fixed it" |
| Resolution notes: | [empty] |
Your AI is ready to learn. Your data just needs to catch up.
ServiceNow's AI has this powerful learning loop that multiplies human expertise:
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1
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Agent documents incident with detailed context (200+ characters) |
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2
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Incident Summarization can generate a useful summary for stakeholders and incoming agents |
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3
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AI-assisted resolution notes can capture the solution approach (when enabled and reviewed) |
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4
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AI-assisted knowledge article generation can create searchable, reusable content from groups of similar incidents (when enabled and reviewed) |
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5
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Future agents can receive AI-suggested steps based on collective resolution patterns from similar past incidents |
Each step amplifies the next. One well-documented incident doesn't just help that agent—it creates learning opportunities for every future agent who encounters similar issues.
| What: | UI Policy requiring 200+ characters in the incident description field |
| Where: | UI Policy on the Incident form |
| Why: | Incident Summarization needs substantial context to generate useful content. Brief descriptions produce generic AI output that agents ignore. |
| Value: | Organizations see significant increases in usable AI-generated knowledge articles. Supervisors get better visibility without reading every incident detail. |
| What: | Business Rule or UI Policy blocking incident closure without resolution notes (sometimes labeled Close notes or Work notes, depending on your instance configuration) |
| Where: | Business Rule or UI Policy on the Incident form |
| Why: | AI-assisted resolution notes generation uses documented solutions as the foundation for building collective knowledge. Without resolution notes, future agents get less AI guidance on similar incidents. |
| Value: | Resolution time for common issues can drop significantly. Junior agents benefit from senior agents' documented expertise without constant interruptions. |
| What: | Mandatory Category, Subcategory, and Assignment Group before closure |
| Where: | Data Policy or UI Policy on the Incident form |
| Why: | AI-driven triage and classification capabilities depend on consistent categorization to learn routing patterns and identify recurring problems. |
| Value: | Incidents route correctly on first assignment, reducing frustrating reassignments. AI identifies recurring issues and recommends knowledge creation. |
| What: | Require Description, Resolution notes, and Close notes fields |
| Where: | Data Policy or UI Policy on the Incident form |
| Why: | Complete problem-solution context enables AI-assisted knowledge article generation (which works best from groups of similar, well-documented incidents). Partial data produces incomplete knowledge that agents don't trust. |
| Value: | Duplicate incidents can decrease in the first quarter. AI-generated knowledge becomes actually useful instead of generic filler. |
One organization launched a campaign that changed everything. Instead of mandating documentation, they celebrated examples where detailed notes helped other agents resolve incidents quickly. They showed agents their documented solutions becoming AI-powered suggestions. Through showing value—not issuing mandates—they drove a meaningful jump in adoption over just a matter of weeks.
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Monday
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Pull 50 recent incidents. Calculate average description length. That's your baseline. |
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Tuesday
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Share the Now Assist learning loop with your team. Ask: "What if our documentation became AI help for everyone?" |
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Wednesday
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Implement the 200-character minimum with positive messaging. |
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Thursday
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Monitor impact. How many incidents meet the threshold? |
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Friday
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Sample 10 new incidents. Compare to Monday. Celebrate improvement. |
Post your before/after stats in the comments.
AI handles pattern recognition, knowledge retrieval, and routine troubleshooting. Humans focus on complex problem-solving, creative solutions, and situations requiring empathy and judgment. You're not replacing expertise—you're amplifying it. Your experienced agents tackle challenging incidents while AI helps junior agents using the team's collective knowledge.
Organizations implementing these four configurations see meaningful improvements across key metrics:
| ●Knowledge article generation increases significantly |
| ●Similar incident resolution time drops |
| ●Agent productivity improves |
| ●Now Assist adoption climbs |
| ●First-time resolution rates improve |
Results vary by implementation maturity, data quality, and change adoption; ranges reflect outcomes observed across multiple enterprise customers.
Your ITSM AI is ready. Start feeding the brain today.
What's your average incident description length? Comment below. Let's compare notes and build momentum together.
Which workflow should we cover next? CSM? HRSD? ITOM? Your input shapes this series.
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