Optimizing your service catalog with AI classification
AI classification optimizes your service catalog by automatically mapping incoming tickets to the correct catalog item based on the ticket description, regardless of what the user selected. This eliminates the misrouting caused by users picking the wrong item, which happens in 30 to 50 percent of tickets according to service desk benchmarks. Organizations using AI classification for service catalog mapping typically reduce ticket rerouting by 60 to 80 percent and cut average resolution time by 20 to 35 percent.
Why do service catalogs cause so many problems?
A well-designed service catalog should make it easy for users to request what they need. In practice, catalogs grow organically over years and become confusing mazes of overlapping items, unclear naming, and inconsistent categorization.
Consider a typical enterprise catalog. It might have 150 to 300 items spread across multiple categories. "Software Request" and "Application Installation" might be separate items that lead to different teams. "Hardware Request" could mean a new laptop, a monitor, a keyboard, or a docking station, each with different fulfillment processes. "Access Request" splits into network access, application access, shared drive access, and VPN access.
Users don't know your internal categorization. They have a problem and they want to submit it. When faced with a dropdown menu of 200 options, they pick the one that seems closest and hope for the best. The result: 30 to 50 percent of tickets land in the wrong queue.
Each misrouted ticket costs time. The receiving team reads it, determines it's not theirs, and reroutes it. That adds 30 minutes to several hours per ticket. Multiply that across hundreds of monthly tickets, and you're looking at a massive amount of wasted time. Better ticket triage automation starts with fixing this misrouting problem.
How does AI classification work for service catalogs?
AI takes a fundamentally different approach. Instead of relying on the user to pick the right catalog item, it reads the ticket description and determines the correct item based on the content.
Understanding intent, not keywords
When a user writes "I need Teams installed on my new laptop," the AI understands this is a software installation request for Microsoft Teams, involving a recently provisioned device. It doesn't matter whether the user selected "Hardware Request" or "Software Request" or "New Employee Setup" from the catalog. The AI maps it to the correct catalog item and routes accordingly.
This works because AI matches on meaning. "My VPN keeps disconnecting" and "remote access drops every few minutes" both map to the VPN troubleshooting category, even though they use completely different words.
Handling ambiguity
Some tickets genuinely could belong to multiple categories. "I can't open the finance report" might be an access issue, a software issue, or a data issue. AI handles this by assessing probability. If the knowledge base shows that 80 percent of similar tickets were access-related, AI routes there first. If the confidence is low, it can ask a clarifying question or flag the ticket for manual triage.
Correcting user selections
When a user selects a catalog item that doesn't match their description, AI can either silently correct the routing (overriding the selection) or flag the mismatch for an agent to review. Most organizations start with flagging and move to automatic correction once they trust the AI's accuracy.
What does AI classification reveal about your catalog?
One of the most valuable side effects of AI classification is the insight it provides into your catalog's effectiveness.
Identifying misused items
AI tracks how often the user's selection matches the AI's classification. If users consistently pick "General IT Support" for what are actually software access requests, your catalog has a gap. Either the access request item is hard to find, poorly named, or missing entirely.
Finding redundant items
When AI maps tickets to catalog items, it reveals which items overlap. If "Software Request" and "Application Installation" consistently receive the same types of tickets, you probably only need one of them.
Spotting missing items
When tickets don't clearly match any catalog item, it often means a category is missing. If AI struggles to classify a cluster of tickets about conference room equipment, your catalog might need a dedicated item for AV support.
These insights help you continuously improve your catalog. A cleaner catalog means better user experience, fewer misroutes, and more accurate reporting.
How does this reduce rerouting in practice?
Here's what typical improvement looks like:
| Metric | Before AI classification | After AI classification |
|---|---|---|
| Tickets with wrong catalog item | 30-50% | Under 5% |
| Average reroutes per ticket | 0.4-0.8 | Under 0.1 |
| Time lost to rerouting per month | 40-80 hours | Under 10 hours |
| First-touch resolution rate | 45-55% | 70-85% |
How does AI classification connect to the rest of your ITSM workflow?
Service catalog classification doesn't exist in isolation. It's the starting point for the entire ticket lifecycle.
Priority assignment. Once the correct catalog item is identified, AI can assign the right priority based on your rules. A password reset gets P3. A complete email outage gets P1. No more manually adjusting priorities that were set based on the wrong category.
Team routing. Each catalog item maps to a resolver group. Correct classification means correct routing, which means faster mean time to resolve.
SLA assignment. Different catalog items have different SLA targets. When the item is correct from the start, the right SLA clock starts immediately.
Knowledge matching. AI searches for solutions specific to the correct catalog item. This means agents (or the AI) get the most relevant knowledge immediately. First call resolution improves because the right information is paired with the right ticket from the beginning.
ITSM Autopilot handles this entire chain automatically, from classification through routing to knowledge matching, across Freshservice, ServiceNow, TOPdesk, Zendesk, Jira SM, and HaloITSM.
How do you get started?
A practical rollout:
- Analyze your current misrouting rate. Pull a report on ticket rerouting for the past three months. Identify which catalog items are most frequently misrouted.
- Connect and observe. Set up ITSM Autopilot in shadow mode. The AI classifies every incoming ticket and you can compare its classifications against what users selected and what agents ultimately determined.
- Enable AI classification. Start with the catalog items that have the highest misrouting rates. Let AI override incorrect user selections for those categories.
- Review catalog insights. After two to four weeks, review the AI's data on misused, redundant, and missing catalog items. Use these insights to simplify and improve your catalog.
- Expand and automate. Once classification accuracy is validated, enable it across all catalog items. Combine with level 1 support automation for ticket types that can be resolved without human involvement.