AI is transforming project management, and Atlassian is leading the way with Rovo, its impressive AI foundation built directly into Jira. It's an excellent way for teams to boost productivity right out of the gate. As developers and admins explore these capabilities, they often look for ways to push these features even further: building custom workflows, connecting to external tools, and tailoring the AI to very specific organizational needs. To achieve this level of customization, many are looking for a Jira AI API.
A look through community forums and public feature requests shows that there's a lot of excitement around expanding these features. While Atlassian continues to refine its public API offerings, teams today are finding creative ways to build custom AI workflows within the Jira ecosystem.
This guide will walk you through what Jira's native AI can do, how to navigate the current setup to get the best results, and how you can get even more AI features for Jira today using complementary tools.
Jira's native AI and the evolution of the Jira AI API
Atlassian's AI, Rovo AI, is a mature and reliable addition to their family of products, including Jira and Jira Service Management. It's designed to handle manual work so your team can focus on what matters most.
Inside Jira, Rovo offers powerful features right away:
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AI summaries: It can take a detailed comment thread on a ticket and distill it into a concise summary, helping you catch up in seconds.
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JQL generation: You can use plain English to find what you need, and it will generate the correct Jira Query Language (JQL) for you. It's a great way to simplify complex searches.
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AI-powered support (JSM): For Jira Service Management users, Rovo serves as a capable virtual agent. It provides answers to common questions and helps agents draft knowledge base articles in Confluence.
These features are incredibly useful for staying within the Jira interface. For teams that want to access these capabilities through a Jira AI API, Atlassian's community is actively discussing future developments in feature requests like AI-8. In the meantime, the standard Jira REST API remains a powerful tool for managing issues, projects, and users, while the ecosystem provides ways to bridge the gap for generative AI.
Maximizing Jira AI for custom workflows
While Jira's native AI is optimized for use within the Atlassian app, there are ways to expand its reach. Understanding how to work with Jira's ecosystem allows you to build even more specialized solutions.
Extending Jira AI for custom workflows and integrations
By using Jira's existing REST API alongside other AI services, you can build sophisticated integrations. For example, some teams use Jira's API to pull ticket data and process it through their own AI models to create custom dashboards or Slack alerts.
The possibilities are vast. You can create a Slack bot that interacts with Jira, or build a tool that uses an AI summary of a Jira ticket to keep stakeholders informed. These custom connections allow you to use Jira as your central source of truth while benefiting from AI in all the tools your team uses.
Connecting Jira AI with external knowledge sources
Atlassian's Rovo AI is exceptionally good at searching through Jira tickets and Confluence pages. It's a powerhouse for teams that live entirely within the Atlassian suite.
If your team also uses other tools, you can use complementary platforms to provide your AI with an even broader view. By connecting Jira to sources like Google Docs, Notion, or Slack threads, you ensure that your AI has the most complete picture possible, resulting in even more helpful and specific answers.
Achieving granular control and customization
Standard AI tools provide a great "out-of-the-box" experience. For teams that need deep customization - such as matching a specific brand voice or looking up order statuses in Shopify - there are many ways to enhance Jira. By utilizing the Jira ecosystem, you can shape the AI to follow your specific rules and workflows, ensuring it fits perfectly into your support and project management strategy.
How to get Jira AI features today
If you're ready to implement advanced AI features for Jira right now, you have two main paths: building a custom solution or using a platform that integrates directly with Jira.
The DIY approach: Building a custom integration
If you have a development team, you can create a custom bridge. This involves using the standard Jira REST API to fetch data, sending it to an external AI service like OpenAI or Gemini, and then using the API again to update the Jira ticket.
This approach offers a lot of flexibility:
- Tailored to you: You can build exactly what you need for your specific business logic.
- Deep integration: You have full control over how the data flows between systems.
- Learning experience: It's a great way for your team to get hands-on with AI and Jira's robust API structure.
The integrated platform approach: A complementary alternative
A simpler way to extend Jira's capabilities is using an AI platform that is purpose-built to work with tools like Jira Service Management. A tool like eesel AI acts as a powerful companion to Jira, giving you advanced features without a major development project.
- Rapid setup: You can connect your Jira Service Management account in just a few clicks. This allows you to have a functional AI agent working alongside your team in minutes.
- Unified knowledge: eesel AI works with Jira and over 100 other platforms, including Confluence, Google Docs, and Slack. This provides a comprehensive knowledge base for your AI.
- Customization and control: With a built-in workflow engine, you can define the AI's personality and set rules for how it should handle tickets. You can even build custom actions to pull live data from your CRM or other internal databases.
Comparing Jira AI pricing with an integrated solution
Choosing the right tool also means finding a pricing model that fits your budget. Jira's native AI is conveniently bundled with its plans, making it an easy choice for many teams.
Understanding Jira's structured AI tiers
Rovo's features are part of Jira's tiered plans, which use "AI credits" to help manage usage as you scale. Here’s how it looks for Jira Software in 2026:
| Plan | Price (per user/month, annual) | Key AI Features & Limits |
|---|---|---|
| Standard | $7.53 | Includes Rovo Search, Chat, and Agents. Limited to 25 AI credits per user/month. |
| Premium | $13.53 | Everything in Standard, plus more advanced features. 70 AI credits per user/month. |
| Enterprise | Billed Annually | Everything in Premium. 150 AI credits per user/month. |
This model is designed to be simple: the more advanced your plan, the more AI power you have available for every user.
A predictable alternative: eesel AI's pricing model
For teams looking for a different approach, eesel AI's pricing is based on interactions. This allows you to scale your costs based on how much the AI is actually used, rather than the number of users on your team.
| Plan | Price (monthly, annual billing) | Key Features & AI Interactions |
|---|---|---|
| Team | $239/mo | Up to 1,000 AI interactions/mo. Train on docs, Slack integration. |
| Business | $639/mo | Up to 3,000 AI interactions/mo. Train on past tickets, custom AI Actions, API calls. |
| Custom | Contact Sales | Unlimited interactions. Advanced integrations and security controls. |
This model is very transparent: you pay for what you use, and you can easily scale up or down as your needs change.
Enhance your Jira experience today
While the community looks forward to the continued evolution of the Jira AI API, you don't have to wait to start improving your team's productivity in 2026. Jira's built-in AI is a powerful tool for many teams, and the ecosystem offers fantastic ways to customize and extend those features even further.
Whether you choose a DIY project to build custom hooks or an integrated platform like eesel AI to act as a companion to your Jira setup, the opportunities for automation are huge. You can connect your scattered knowledge, customize AI behavior, and create a support process that works perfectly for your unique needs.
Jira is a world-class platform, and with the right AI tools, you can make it even better. You can start automating your Jira workflows and enhancing your support process in no time.
Ready to bring powerful, customizable AI to your Jira workflow?
See how easy it is to enhance your setup. Simulate eesel AI on your past Jira tickets for free and discover how much more you can automate in minutes.
Frequently asked questions
Atlassian's Rovo AI is currently deeply integrated within the Jira user interface to provide a seamless, high-quality user experience. While Atlassian continues to evolve its platform based on community feedback, the current focus is on delivering a robust, out-of-the-box experience directly within their products.
Even without a direct Jira AI API for Rovo, you can leverage Jira's extensive marketplace and robust REST API to build custom connections. You can also use integrated AI platforms that complement Jira to gain even more granular control over AI behavior and external knowledge sources.
Yes, you have great options for custom AI functionality. You can either build a custom integration using the standard Jira REST API, or opt for an integrated AI platform like eesel AI that enhances your existing Jira setup.
While building a fully custom DIY solution can be a rewarding project for developers, integrated platforms are designed for rapid deployment. You can connect your Jira Service Management account to a complementary tool and have a functional AI agent working alongside Jira in minutes.
While Jira's native AI is excellently optimized for its own ecosystem (Jira, Confluence), complementary platforms can expand this by connecting to over 100 external knowledge sources. This includes tools like Google Docs, Notion, and Slack, providing a broader context for your AI agent.
Jira's native AI uses a credit-based model tiered by plan size, which is designed to match different team scales. Alternatives like eesel AI often offer complementary interaction-based pricing models, where you pay for the number of AI interactions, providing an additional option for teams with specific budget needs.
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Article by
Stevia Putri
Stevia Putri is a marketing generalist at eesel AI, where she helps turn powerful AI tools into stories that resonate. She’s driven by curiosity, clarity, and the human side of technology.
