The AI tidal wave has landed on the shores of system integrations. This wave brings a mixed bag of complications and benefits for teams looking to connect systems like Jira and ServiceNow and sync data between them. That’s why we thought we should focus on AI-powered Jira ServiceNow integration separately.
Companies and teams are now considering how to use artificial intelligence to improve the accuracy and convenience of data integration and mapping.
This applies to both no-code integrations as well as script-based solutions. Using AI in no-code integrations can help you map the right fields and projects faster and in an accurate manner. With script-based integrations, AI can help with the coding and scripting for setting up advanced integrations.
This option is the game-changer: using AI script builders to speed up script generation and optimize existing code.
In this article, we’ll discuss the current reality of AI-powered Jira ServiceNow integration for script-based solutions, focusing on use cases, benefits, and practical implementation.
What is AI-powered Jira SNOW Integration?
AI-powered Jira ServiceNow integration refers to the process of relying partially or fully on an AI scripting assistant to map fields and generate configuration scripts for your connection. As already implied, this applies to script-based solutions, where a lot of coding is necessary.
Say a team of support staff wants progress updates on a specific user-reported ServiceNow incident from a team of developers using Jira Software.
The administrator can ask the AI to map incident fields to the Jira issue (default and custom) fields. By doing so, they can reference the documentation to generate the script faster with limited human input.
This involves choosing a software tool that relies on artificial intelligence and machine learning algorithms to assess databases and suggest context-relevant scripts.
In essence, the AI helps inexperienced integration administrators determine how the native APIs of both platforms interpret, interact with, and transform data.
Advanced AI-powered Jira ServiceNow Integration Use Cases
Here are some use cases for implementing Jira to ServiceNow integration:
- You can sync urgency and priority between Jira and ServiceNow.
- The AI can help you sync ServiceNow change requests and Jira issues.
- You can also sync different entities, including incidents, CMDB, requests, problems, and custom fields between Jira and ServiceNow.
- To sync Jira issue fields as work notes in ServiceNow, you’d need to prompt the AI-powered solution with the description and requirements.
- Your admins can also sync SLA records and maintain state (status) updates.
- You can also sync time-related information between ServiceNow and Jira by breaking down the epic into stories.
Let’s explore a sample use case for the syncing of internal comments in Jira as work notes in ServiceNow.
The AI prompt could look something like this:
“I have some comments in Jira, and I want to sync them with ServiceNow. These comments are external, but I want them to appear as work notes on the ServiceNow side.”
You can continue refining the prompt to get the right configuration for syncing internal and external comments.
Benefits of AI in Script-Based ServiceNow Jira Integration Solutions
Jira and ServiceNow users rely on AI-enabled integration solutions for the following reasons:
Faster Data Analysis
AI-powered solutions can speed up sync configurations by reviewing multiple data sources in a few seconds to fetch a script that meets your requirements.
The alternative is that the system admin will have to scour through the documentation and rely on their expertise to get the connection to work as intended.
Accurate Data Mapping
As the AI takes over the manual aspects of sync configuration, it also increases the accuracy of the generated integration logic.
Despite not being flawless, AI-powered integration for ServiceNow and Jira is more reliable than having engineers without knowledge of the programming language tinkering with the system.
Efficient Data Extraction
Apart from scouring and fetching data from multiple sources and databases, the AI can extract data from relevant custom and default fields.
By following a self-reinforcing algorithm, the AI-enabled solution can figure out how Jira and ServiceNow APIs interact in order to fetch information.
The customer service team can use the AI-powered integration to sync ServiceNow incident priority. Depending on the priority and urgency of the incident, the accurate priority is always assigned in the Jira issue.
Productive Collaboration
When working with other teams or companies, an AI-powered Jira to ServiceNow integration can automate syncs to guarantee real-time bidirectional data updates and automatic restarts.
It also gives both ends of the connection ultimate control. End users and admins can experiment with infinite configuration possibilities without spending all day.
Context-Aware Error Handling
Another application of AI-powered integration is in error handling. If you’ve handled mapping scripts, you’d know it can be a nightmare to crush bugs, especially if you don’t understand the programming language or API.
When the code snippet throws errors or does not work as expected, the AI can analyze it to determine the mistake and suggest improvements.
For instance, if your custom field mappings for a Jira to ServiceNow Sync are not working, the AI can fetch the right API name or value for you.
How can AI Integration Fail Between Jira and ServiceNow
The common misconception is that AI-powered connection is the ultimate solution to all integration problems.
The reality is that it comes with its own issues, which we’ll go through in detail.
- AI is prone to errors. As with every other AI-powered solution, you should always approach its suggestions with skepticism. So, whenever you receive a suggestion, review and test it before implementing it.
- The AI can misinterpret the intention of the prompt–which means you need an experienced prompt engineer, or you’d have to spend time refining the prompts to get the desired output.
- The AI model might not work offline. If the integration solution goes offline or experiences network issues, you won’t be able to use the AI chatbot or virtual assistant.
- Adding AI features can cost you extra licensing fees and introduce privacy and compliance issues. Some of your customers might be skeptical about interacting with it due to security concerns.
Considering these challenges will help you understand how to implement AI-powered Jira ServiceNow integration without causing more problems for your team.
How Exalate Handles Jira and ServiceNow Integrations
Exalate is an AI-powered integration solution that connects Jira and ServiceNow instances. It also supports integrations with systems such as Azure DevOps, GitHub, Zendesk, Salesforce, etc.
Exalate has two AI features:
- Aida is a standalone AI documentation assistant that you can use to fetch vital information from the Exalate documentation.
- AI Assist is a dedicated AI script builder intrinsically embedded in the configuration console.
Both systems accept user prompts and process them in order to generate code snippets for scripting connections and mappings, but the expected output from each can be different.
For example, Aida cannot embed the script in the UI or generate sync scripts for you, but AI Assist is directly embedded in the scripting console and takes into consideration your existing sync rules while suggesting scripts. Use Aida when you have any questions related to Exalate and use AI Assist when you want to generate sync scripts for your Jira ServiceNow integration use case.
How to Set Up a Jira ServiceNow Integration Using AI Assist
Here are the steps to follow according to our documentation:
- Install Exalate on both Jira and ServiceNow.
- Set up a Script mode connection between both instances.
- Click Edit Connection and go to the Rules tab.
- Under both the Incoming sync and Outgoing sync boxes, you’d see an AI chat feature.
- Enter a detailed prompt describing the sync requirements, such as “I want to map the select list custom field called Org to a text custom field in ServiceNow called Company.”
Or “Create a status mapping that sets New to Open, Working to In Progress, and Closed to Done in the Jira Incoming configuration.” - The AI will generate an output in the textbox.
- The green highlights are suggested additions, while the red highlights are the suggested deletions.
- If the output is to your satisfaction, click Insert Changes.
- If the output is incorrect, click Discard.
- Once done, Publish the changes.
Now, the established connection is ready to start syncing data between Jira and ServiceNow.
If you have a specific use case in mind and are wondering how AI Assist can help you set it up faster and easier, reach out to our solutions engineers for a free discovery session.
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