We just wrapped a Configuring request types: Building the foundation AI depends on live learning session! We covered how to configure request types for your service desk, and why well-structured request types in Jira Service Management matter not just for help seekers, but for the AI features that rely on them.
Here’s a recap of the key takeaways, plus resources to help you put them into practice. Bookmark this page, try a few changes in your service space, and join the discussion to share what worked for your team!
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Step 1: Define your request types
Request types are the options customers select in your portal. Each one defines what’s being requested, what information to collect, and how it’s handled. Each request type maps to a single work type, which determines its workflow and available fields.
Why it matters for AI: Nearly every AI feature in Jira Service Management reads from, writes to, or acts on your request types, so how you name (and describe) them shapes how well AI works.
Tips
- Start with what customers actually ask for. Define your services first, then build request types around real requests, not internal team categories.
- Be clear and specific. Avoid vague catch-alls and technical jargon customers won’t recognize.
- Choose work types intentionally. The work type locks in the workflow and fields. Loop in your Jira admin if none of the available options fit.
Step 2: Build request types
Before creating anything new, customize the default request types that came with your space. Space admins can create request types from scratch, and can use Rovo Chat to create and update request types and portal groups using natural language.
Why it matters for AI: AI triage helps your team sort incoming requests faster by using Atlassian Intelligence to classify work items and suggest the right request type, so tickets end up in the right queue without manual sorting. It relies on your request type names and descriptions to decide where each ticket belongs. The clearer and more distinct they are, the more accurate the results.
Tips for starting out
- Customize defaults first. Rename, update descriptions, and adjust fields before adding new request types.
- Write clear, specific descriptions with keywords. Say what each request type covers, and include keywords customers might use. AI tools rely heavily on descriptions to match requests to the right request type.
- Be specific when using Rovo Chat. Open Rovo Chat and enter /manage-request-type. Describe what you want the request type to be (its name, purpose, and portal group) and always include your space key or name so Rovo makes changes in the right space. Then review what it created in Space settings.
- Check for templates. Ask your Jira admin whether a request type template already exists that meets your need. This can save you a lot of time.
Step 3: Configure request type fields
Space admins choose which fields appear on the request form (customer view) and the work item view (agent view). You can rename, add, remove, hide, preset, reorder, and set fields as required or optional.
Why it matters for AI: A Rovo self-service agent helps customers get answers through a natural conversation in your help portal. When Rovo can’t resolve the issue and the customer needs human help, the Rovo agent raises a request for them. It uses your request type fields to gather the right information during the conversation, so you team gets a complete ticket. Focused fields mean a smoother handoff for customers and fewer follow-up questions for agents.
Tips for starting out
- Ask your agents what information they need to work a request without back-and-forth.
- Only require what’s truly needed. Too many required fields lead to abandoned forms and unhelpful data.
- Hide and preset internal fields (like routing values) so they’re set automatically without customers seeing them.
- Start with standard fields. Use Forms when you need conditional logic, sections, or validation.
Step 4: Organize the request experience
Use portal groups to help customers quickly find the right request type, especially when you have many to choose from.
Why it matters for AI: Request Resolver is an AI feature that helps your team resolve requests faster by working tickets for you, using your connected knowledge sources. Space admins turn it on per request type and chose how it works: autonomously (Rovo resolves the ticket on its own) or with supervision (an agent reviews Rovo’s plan first). The more clearly your request types are organized, the easier it is to spot the right ones to hand off.
Tips for starting out
- Keep the portal focused. Aim for 3–5 portal groups with 5–7 request types each.
- Group for discoverability. A request type can belong to more than one portal group if that helps customers find it. For example, a broken laptop request could appear under both ‘Hardware’ and ‘Report an issue.'
- Start Request Resolver small. Choose 1-2 simple, repeatable request types (like access requests or password resets) and begin in supervised mode before expanding.
Try it: Pick one high-volume request type and review its name, description, fields, and portal group using the tips above. Then submit a test request from the customer portal.
NOTE: You must be a space admin of a company-managed Jira Service Management space, and Rovo must be enabled on your site to use AI features.
👉️ Check out these resources when you’re ready to get started!
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Try something new this week
Pick one of your most common request types and give it a quick checkup:
- Name and description: Would a customer recognize it right away? It is clear enough for AI triage and self-service agents to know when to suggest it?
- Fields: Is every required field truly needed? Could a Rovo self-service agent collect them in a quick conversation?
- Portal experience: Is it in the right portal group (or groups) so customers can easily find it?
- Request Resolver: Is it simple and repeatable enough to try Request Resolver in supervised mode?
Then submit a test request through the customer portal to see the experience from your help seeker’s point of view.
🌟 Share your experience below: Which request type did you check first, and what did you change? Which Jira Service Management AI feature are you keen to try out?
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