How we saved 6 hours per week by automating project management using AI

Managing projects manually can be a boring task. Before automation, we spent an average of 6 hours per week creating project roadmaps, defining Jira tickets, and planning dependencies. This process was not only tedious but also prone to errors, leading to inefficiencies and delays in project execution.

As our projects scaled, we realized we needed a more streamlined and intelligent system. That’s when we turned to AI-driven automation using LangChain and ChatGPT. The result? A faster, more accurate, and scalable way to handle project management with minimal manual effort.

In this article, we’ll walk you through our automation journey—from the challenges we faced to the step-by-step process of building our AI-powered project management system. By the end, you’ll have a clear understanding of how you can implement similar automation in your own workflow.

‍

The challenge

Before automation, our project managers had to manually:

• Define and document project roadmaps
• Create and assign Jira Epics and Tickets
• Track dependencies between tasks
• Gather design information from Figma
• Ensure consistent documentation across teams

This process was time-consuming and prone to human errors, causing inefficiencies in execution. We knew there had to be a better way.

‍

Why we chose LangChain and ChatGPT

We needed a solution that could handle multiple aspects of project management with minimal human intervention. After extensive research, we decided to integrate LangChain and ChatGPT into our workflow.

‍

LangChain

LangChain is an AI framework that enables seamless integration of large language models (LLMs) with existing systems. It helps in:

• Retrieving structured information from various sources (e.g., Figma, Jira, APIs).
• Automating decision-making based on predefined rules.
• Processing and organizing complex data to generate meaningful outputs.
• ‍

ChatGPT

We leveraged ChatGPT for:

• Generating detailed Jira tickets with descriptions and dependencies.
• Extracting and interpreting information from design documents.
• Creating structured and consistent project roadmaps.

‍

With these two technologies combined, we were able to automate our project management workflow end-to-end.

‍

‍

How We Automated Our Project Management

Step 1: Extracting project data from Figma

The first step in our automation was extracting project structure from Figma. We needed to convert UI designs into actionable development tasks.

We built an integration that:

• Extracts all pages from Figma related to a project.
• Identifies node-ids and UI components from the design files.
• Generates names and metadata for each element.

This gave us a clear breakdown of all screens and UI components that needed development, without requiring manual tracking.

‍

Step 2: Automating the treation of Jira epics

With structured design data in place, we automated the creation of Jira Epics:

• The system analyzed extracted data and mapped it to relevant project phases.
• ChatGPT generated meaningful Epic descriptions based on component functionalities.
• The AI then created these Epics in Jira automatically.

Now, instead of spending hours writing Epics, we could generate them in seconds with AI.

‍

Step 3: Auto-generating Jira tickets

Once Epics were in place, we moved to automating Jira ticket generation.

This involved:

• Extracting Figma screens associated with each Epic.
• Using an API to generate images for reference.
• Feeding this data into ChatGPT, which produced detailed ticket descriptions.

Each ticket included:✔️ A clear title✔️ A well-defined objective✔️ Reference images from Figma✔️ Expected functionality and acceptance criteria✔️ Task dependencies

The result? A fully structured Jira backlog ready for development.

‍

Step 4: Automating dependency planning

One of the most challenging aspects of project management is ensuring that tasks are properly sequenced. Before automation, project managers had to manually review each ticket and establish dependencies.

Our AI-driven solution now:

• Analyzes ticket descriptions to detect relationships between tasks.
• Automatically assigns dependencies based on logical execution order.
• Links related tickets together in Jira to ensure seamless task progression.

With this automation in place, developers always have a clear sequence of tasks, eliminating confusion and bottlenecks.

‍

Step 5: Review and refinement

After ticket creation, our AI performs a final quality check:

• Ensures clarity and completeness of ticket descriptions.
• Assigns tasks to the right team members based on workload.
• Provides a summary report for project managers to review.

This automated review process saves additional time, ensuring that all tickets meet quality standards before development starts.

‍

The Results

The impact of AI automation on our project management process was remarkable:

- Saved 6 hours per week previously spent on manual planning.
- Reduced human error, ensuring greater accuracy in project structuring.
- Streamlined collaboration, allowing teams to focus on execution.
- Improved documentation consistency, ensuring all tickets had AI-generated descriptions.

We transformed our workflow from manual and error-prone to automated and efficient. This has not only boosted our productivity but also allowed us to scale effortlessly as project complexity grows.

‍

Conclusion

Automating project management with LangChain and ChatGPT has revolutionized how we work. What once took six hours per week now happens automatically with AI, allowing our team to focus on what truly matters—building great products.

If your team is spending too much time on manual planning and ticket creation, consider implementing AI-driven automation. With the right tools, you can save time, reduce errors, and improve overall efficiency.

Are you ready to transform your project management workflow?

Let’s talk and make it happen!

‍

‍