How Jira service desk AI can improve ticket management

If you’re using Jira Service Management, you know it’s a fantastic tool for keeping track of all sorts of requests, from IT help to HR questions. It’s super capable and lots of teams rely on it. But as your company grows and more and more tickets flood in, things can start to feel a bit overwhelming. Requests get trickier, your support agents get stuck doing the same things over and over, and sometimes, it takes longer to get back to people.

This is where bringing AI into your Jira Service Management setup can really help. It’s not just a trendy buzzword; it’s a practical way to handle these growing pains. In this guide, we’ll chat about how AI for your Jira service desk can change the game for how you manage tickets. We’ll cover everything from what AI can actually do to figuring out which solution is right for you and getting it all set up. You’ll see how AI can take care of the boring stuff, give your agents a hand, and ultimately help you provide quicker, smoother support.

While Jira Service Management does have some AI features built in, sometimes looking at specialized AI tools can give you even more flexibility and power, especially when you need something just right for your team.

What you’ll need to add AI to Jira service management

Adding AI to your Jira Service Management setup isn’t quite as simple as flipping a light switch. It takes a little groundwork.

Here’s a general idea of what you’ll want to have sorted before you jump in:

• Your Jira Service Management is up and running. This is your starting point.
• You have a good idea of the specific headaches you want AI to fix. Maybe you want to cut down on easy Tier 1 tickets or get requests routed faster.
• The AI needs access to your team’s brain. This means things like your knowledge base articles, past support tickets, or any internal guides you use.
• A rough budget in mind. Whether you’re thinking about paying for Atlassian’s AI extras or checking out other tools, knowing what you can spend helps.
• A clear picture of how your team currently handles tickets in Jira Service Management. Understanding the current process helps you see where AI can fit in best.

Step 1: Understand the potential of AI in Jira service management

Okay, so what can AI actually do within your Jira Service Management workflows? It’s much more than just basic chatbots. It’s about making your whole ticket handling process smarter and quicker. Getting a handle on these possibilities is the first step to seeing how AI can really lend a hand.

AI in Jira Service Management matters because it helps teams move past those manual, time-consuming tasks. It can look at incoming requests in ways a person just can’t, instantly spotting key info and knowing what to do next.

Here are some key areas where AI can make a real difference:

• Sorting and sending tickets to the right place: AI can instantly detect what the request is about, what language it’s in, and even the tone. Tools like Atlassian Intelligence use this to route tickets to the right teams faster, without manual sorting.
• Automated answers and helping customers help themselves: AI can pull up knowledge base articles or give quick responses to common questions. Atlassian’s virtual agent handles basic requests like password resets, helping reduce simple ticket volume.
• Giving agents a helping hand: Features like issue summaries and draft replies save agents time and help keep responses consistent, especially on high-volume days.
• Making your knowledge easier to find: AI connects Jira with tools like Confluence to surface relevant info as tickets come in. This helps both customers and agents find answers faster.

The big wins here are pretty clear: things run more smoothly, agents aren’t as swamped, tickets get solved faster, and customers end up happier. It’s all about making your support operations more flexible and quick to respond.

Step 2: Identify specific AI use cases for your JSM workflows

Now that you’ve got an idea of what AI could do, the next step is figuring out what it should do for you. Where are things getting stuck in your current Jira Service Management setup? Where do your agents spend too much time doing the same things? Nailing down specific examples will help you pick the best AI tool and see if it’s actually helping.

Think about the requests your team handles most often. Are you constantly helping people reset passwords or get access to software? Those are perfect jobs for AI to take over. Atlassian’s virtual service agent, for instance, has been used to automate these kinds of common, repeatable Tier 1 questions, freeing up human agents for more complex stuff.

Other common ways teams use AI in Jira Service Management include:

• Automatically adding tags to tickets and assigning them based on what they say.
• Suggesting helpful knowledge base articles or past tickets to agents or customers based on the ticket details.
• Creating summaries of long ticket conversations so agents can get up to speed fast.
• Automatically grabbing data, like checking an order status, by connecting with other systems.

Finding these exact scenarios is super important. It helps you understand exactly what you need an AI tool to be able to do. This makes it way easier to look at different solutions and see if they can actually fix the problems you have.

Step 3: Evaluate AI solutions for Jira service management

Alright, you know what AI can do and where you want to use it in Jira Service Management. Now it’s time to check out the tools available. You generally have two main paths: using the AI features already built into Jira Service Management or adding a separate AI solution.

Option 1: Atlassian’s native AI tools

Atlassian Intelligence and the Virtual Agent are built right into the Jira ecosystem. They’re convenient if you’re already using Confluence or sticking to standard workflows. These tools can:

• Predict intent, language, and sentiment
• Suggest articles or auto-reply to common questions
• Help agents with summaries and draft responses

Downsides?

You might need a higher-tier plan or pay add-on fees. Customization is limited, and you’re mostly tied to Atlassian tools. Costs can also grow fast if you’re billed per agent or per resolution.

Option 2: Third-party tools like eesel AI

Let’s talk about eesel AI as a strong option here. eesel AI is built to connect with your current Jira Service Management setup. It adds extra power without making you completely switch platforms.

Here’s a quick look at how eesel AI compares, especially when it comes to some common tricky spots with native or other tools:

• • More flexible training sources: Unlike tools often limited to just one or two places for information, eesel AI can learn from all sorts of knowledge. This includes past tickets, different kinds of documents, and connections with over 100 other tools. It’s not just stuck with your Jira Service Management or Confluence knowledge base.
  • You can customize it more: You get really detailed control over how your AI sounds and acts. This lets you fine-tune how it talks to match your company’s voice perfectly and fit your specific workflow needs.
  • Solid testing: eesel AI lets you test and simulate things really well. You can see exactly how your AI would handle past tickets before it ever talks to a live customer. This is a big plus compared to solutions that don’t let you test much before going live.

• • AI that can actually do things: eesel AI can do more than just answer questions. It can actually perform tasks, like grabbing data using custom connections (APIs). This makes it truly helpful and active within your workflows.
  • Clear pricing: eesel AI uses a straightforward pay-per-interaction pricing model. This can be much easier to predict and manage as you grow compared to paying per agent or dealing with confusing fees per resolution that you sometimes see with native add-ons.

Here’s a little table showing the differences:

Capability	Atlassian Native AI (Atlassian Intelligence, Virtual Agent)	eesel AI for Jira Service Management
Training Sources	Primarily JSM/Confluence KB	Past tickets, KB, docs, 100+ integrations
Customization	Limited preset options	Deep customization of tone, behavior, actions
Testing	Limited pre-launch testing	Robust simulation on past tickets, selective rollout
Actionability	Article suggestions, basic automation	Suggestions, routing, tagging, custom API actions (e.g., data retrieval)
Pricing Model	Often per-agent, potentially resolution fees	Transparent pay-per-interaction
Integration	Built-in	Seamless integration with JSM via API/app

Picking the right solution really depends on what you need most. But checking out options like eesel AI can give you the flexibility and power you might need to really make your Jira Service Management ticket handling work smoothly.

Step 4: Integrate your chosen AI solution with Jira service management

Once you’ve decided on the AI solution that seems like the best fit, the next practical step is getting it hooked up to Jira Service Management. How you do this will be a bit different depending on whether you’re using features built into Jira or adding a separate tool.

If you’re going with native Atlassian AI, you’ll usually turn on and set up the features right within your Jira Service Management admin settings. It’s typically a pretty simple process inside the platform you already know.

If you’re adding a third-party AI tool like eesel AI, the steps are generally designed to be quick and easy:

• You’ll connect the AI platform to your Jira Service Management account. This often happens through a connection using APIs or by adding an app from the marketplace.
• You’ll set up the necessary permissions so the AI tool can access the information it needs (like reading tickets or knowledge base articles) and do the things you want it to do (like adding comments or changing statuses).

eesel AI has a smooth integration process with Jira Service Management. It’s specifically set up to get you going fast, often within just 1-2 weeks. This means you spend less time wrestling with complicated technical setups and more time seeing the benefits of AI taking care of tasks.

Step 5: Train and customize your AI agent or assistant

Getting the tool connected is just the start. To make your Jira service desk AI truly useful, you need to teach it using your specific information and tweak how it acts. This is where things get really cool – turning a standard AI into a super helpful assistant made just for your team and customers.

Training is super important because your AI agent is only as smart as the information you give it. It needs access to the right stuff, like your knowledge base, past tickets, and how-to guides (SOPs), so it can give accurate and helpful answers. eesel AI is great here because it can learn from all sorts of places, including your past Jira Service Management tickets.

The process involves:

• Pulling those relevant tickets.
• Using AI to turn them into organized knowledge.
• Using that knowledge to train your bot.
• Letting you check and edit the knowledge before it goes live.

Plus, with automatic syncing, you don’t have to worry about your AI using old information.

But training isn’t the whole story; making it your own is key to making the AI fit your brand and how you work. This means more than just picking a “friendly” or “professional” tone. It’s about telling the AI exactly how it should handle different types of requests, when it should pass things to a human, and what specific actions it should take.

eesel AI has a special tab for customizing prompts and actions. This gives you detailed control over how your bot behaves. For example, you can tell the AI to automatically close tickets that contain certain things (like spam) or set rules for sending urgent requests to a human agent.

Finally, testing and trying things out are absolutely necessary before you let your AI loose on live customers. You want to feel confident it will work the way you expect.

eesel AI offers robust testing methods:

• Simulation: Test how your bot would respond to past tickets to spot and fix issues.
• Selective Rollout: Roll it out slowly to just a few agents first to watch how it does in a controlled setting before everyone starts using it.

This kind of testing upfront helps avoid problems, especially since many native tools don’t give you much, if any, testing before you go live.

Step 6: Measure impact and refine AI performance

Putting AI into Jira Service Management isn’t something you just set up and forget about. To make sure you’re getting the most out of it and always making your support better, you need to see how it’s doing and fine-tune it over time.

Keeping an eye on the right numbers will show you exactly how AI is changing things for your team. Some important things to track include:

• How many tickets is the AI solving on its own? This is called the ticket deflection rate. Atlassian’s virtual agent has shown it can handle a good number of tickets, and eesel AI tracks this too.
• How quickly are tickets getting solved on average? Is AI helping you speed things up?
• Are your agents getting more done? Are your human agents spending less time on simple tasks and more time on requests that need their expertise?
• Are customers happier? Are customers liking the faster answers and being able to help themselves? People using Atlassian’s virtual agent have reported being quite happy.
• Where are the gaps in your knowledge? Where is your AI having trouble finding answers? This tells you where your knowledge base needs some work. eesel AI gives you insights specifically for this.

What you learn from these numbers is super valuable. If the AI isn’t solving as many tickets as you hoped, maybe you need to give it more information to learn from or tweak its answers for common questions. If agents are still spending too much time sorting tickets, maybe you need to adjust the rules for sending tickets automatically.

eesel AI comes with built-in tools for reporting and insights. This includes looking at knowledge gaps and even an ROI calculator. These tools make it easy to track how things are going and see the value your AI is bringing.

Pro Tip: Use the insights on knowledge gaps from your AI tool. They can help you find articles that are missing or information in your knowledge base that’s out of date. Making your knowledge base better directly helps your AI do a better job.

Ready to enhance your Jira service management with smarter AI?

While Jira Service Management has its own AI features, eesel AI offers a flexible, capable, and cost-effective way to bring advanced AI into your existing JSM workflows. It learns from all sorts of places, including your past tickets, lets you really customize how the bot acts, and provides solid testing before you launch. With clear, pay-per-interaction pricing and helpful insights, eesel AI helps you grow your support efficiently without unexpected costs.

See how eesel AI can change how you manage tickets in Jira Service Management. You can start a free trial here (no credit card needed!) or book a demo to see how eesel AI could work for your specific needs.