As a product manager at Salto, I've had the opportunity to witness firsthand how AI is transforming various aspects of our work. One area where this transformation is particularly exciting is in Jira and Jira Service Management (JSM) administration.

Let’s dive into how AI is changing the game for Jira admins and how we at Salto are helping to drive this change!

The No-Code Limitation

Let’s start with a simple observation: Jira's no-code nature has made it incredibly accessible, allowing users without a programming background to configure and manage their environments. However, this ease of use also brings challenges:

• Limited AI Integration: AI tools like ChatGPT can’t effectively assist because they cannot access Jira’s internal configurations and workflows.
• Visibility and Auditing: Tracking changes and understanding the impact of modifications often requires manual navigation through the UI, which is time-consuming and prone to errors.
• Version Control and Rollback: Without a code-based approach, rolling back changes or implementing version control can be challenging and unreliable.

To address these challenges, we at Salto have worked on a shift towards a pro-code and DevOps methodology for Jira administration. This doesn’t mean that Jira admins need to become developers. Instead, it involves translating the Jira configuration into code, enabling a more efficient, version-controlled, and auditable management process.

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Turning Jira into Code with Salto and Practical AI Applications

At Salto, we’ve developed a solution that transforms Jira’s entire configuration—workflows, custom fields, automations, boards, dashboards, and more—into code.

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This process not only gives admins the superpowers they’ve been dreaming of, allowing them to search through their entire configuration using basic text search commands, understand the dependencies and potential impacts of changes before implementing them, quickly revert to previous configurations and more, but it also bridges the gap between Jira's no-code interface and AI's powerful capabilities.

With the Jira configuration as code, several AI-driven functionalities become possible:

• Configuration Explanation: Use AI to explain automations, workflows, and other configurations.
• Summarize Changes: Generate summaries of changes made within the Jira environment.
• Generate Customizations: Describe the desired setup in natural language and let AI build the project, workflows, and automations.

Example Scenarios

Imagine inheriting a Jira instance with hundreds of complex automations. Instead of manually deciphering each one, you can use AI to understand what each automation does quickly. For example, exporting an automation rule to a JSON format and asking an AI tool like ChatGPT for an explanation can provide a detailed overview, saving time and reducing errors.

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In Salto, we’ve taken this a step further. You can select an automation within the Salto platform and ask for an explanation. The result is a concise, accurate description of the automation’s purpose, triggers, and actions. This functionality extends to any configuration item within Jira, providing instant insights that would otherwise take hours to obtain.

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Another practical application is change management. Understanding the ripple effects is crucial before making changes to your Jira configuration. With Salto, you can identify all dependencies related to a specific field or workflow. For instance, if you plan to delete or modify a custom field, Salto can pinpoint every automation, screen, and project where this field is used, helping you avoid unintended disruptions.

AI and the Future of Jira Administration

The potential of AI in Jira administration extends beyond simple explanations and summaries. As AI technology advances, we envision a future where AI can generate entire Jira projects, workflows, and automations based on natural language descriptions provided by admins. This would revolutionize how Jira is configured and managed, making it faster, more efficient, and less error-prone.

Consider a scenario where an admin can describe a new project's requirements, and AI sets up everything—from project creation, configuring workflows, and setting up automations to defining custom fields and permissions. This not only saves time but also ensures consistency and adherence to best practices.

Moreover, AI can assist in monitoring and maintaining Jira instances. Predictive analytics could alert admins to potential issues before they become critical, such as identifying configurations that may lead to performance bottlenecks or security vulnerabilities. AI could also optimize workflows, suggesting improvements based on usage patterns and historical data.

Conclusion

Integrating AI and a pro-code approach into Jira and JSM administration represents a significant leap forward. By leveraging tools like Salto, Jira admins can overcome the limitations of the no-code approach, streamline their workflows, and fully harness the power of AI and DevOps principles. If you want to explore these capabilities, try Salto today at salto.io.

AI is not just a future possibility—it’s a current reality, ready to transform how we manage our Jira environments. Embracing these innovations will improve efficiency and ensure that our Jira instances are robust, scalable, and prepared for the challenges of tomorrow.

Are you curious to learn more about Jira administration and AI? You can watch the full recording of the webinar I led on this topic.

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