As organizations modernize their IT operations and customer support systems, Jira Service Management (JSM) has emerged as one of the most powerful and flexible platforms for managing service requests, incidents, problems, and changes. Built on the Jira platform and designed to support ITIL practices, Jira ITSM enables IT and business teams to collaborate effectively, deliver value faster, and improve service reliability.
In this guide, we’ll walk through the key elements of setting up Jira Service Management, best practices for success, and how to optimize your JSM implementation for scale and efficiency.
Chapter 1: Getting Started with Jira Service Management
What is Jira Service Management?
Jira Service Management is Atlassian’s service desk platform designed for ITSM (IT Service Management). It supports a wide range of use cases across IT support, HR service delivery, facilities management, and customer service.
Core Features Include:
Request and incident management
SLA management
Knowledge base integration with Confluence
Automation and approval workflows
Change and problem management
Asset and configuration management (Assets/Insight)
Setup Basics:
Choose a JSM Template: Select ITSM, General Service, or HR template based on your use case.
Define Request Types: Customize based on services offered (e.g., password reset, hardware request).
Configure Queues: Set up queues for agents based on priority, issue type, or SLA.
Customize Forms and Fields: Add required fields to streamline ticket data collection.
Set SLAs: Define target response and resolution times with escalation paths.
Connect Knowledge Base: Integrate Confluence to support self-service.
Chapter 2: Best Practices for Implementing Jira ITSM
1. Start with ITIL-Aligned Processes
Even if you don’t adopt ITIL fully, aligning to its core practices (incident, problem, change, request) helps ensure process clarity and system scalability.
2. Don’t Over-Engineer Workflows
Keep workflows as simple as possible. Use clear status names and transitions to avoid confusing agents and requesters. Complexity leads to friction.
3. Use Request Types and Issue Types Strategically
Request types define how end-users see the form; issue types define internal tracking. Don’t duplicate unnecessarily. Map them carefully.
4. Automate Low-Value Tasks
Use Jira automation to eliminate manual triaging, categorization, or status updates. Examples:
Auto-assign tickets based on category
Send reminders if SLAs are at risk
Notify approvers automatically
5. Leverage the Knowledge Base for Deflection
Use Confluence to surface relevant articles in the portal—reducing ticket volume and improving first-contact resolution with guidance from an ITSM consultant.
6. Set Up Smart SLAs and Notifications
Go beyond basic timers. Set up condition-based SLAs and build escalation workflows. Notify managers or team leads before breaches.
7. Standardize Change Management Workflows
Use templates for standard, normal, and emergency changes. Include risk assessments, approvals, and implementation steps.
8. Use Assets (formerly Insight) for CMDB
Track and relate configuration items (hardware, software, licenses) to tickets. Helps in root cause analysis and incident impact assessment.
9. Train Users and Agents Thoroughly
Offer onboarding, cheat sheets, and video demos. This reduces confusion and support errors.
10. Monitor Metrics and Feedback
Use dashboards and surveys to track resolution time, SLA compliance, and customer satisfaction.
How many issues are resolved in the first response.
3. Customer Satisfaction (CSAT)
User rating after ticket closure.
4. Ticket Reopen Rate
Indicates quality of resolution.
5. Mean Time to Resolution (MTTR)
Average time it takes to close a ticket.
6. Backlog Age
How long unresolved tickets have been sitting in the queue.
Conclusion
Jira Service Management empowers IT and business teams with ITIL-compliant, agile service delivery. MicroGenesis, a top IT company offering expert Jira consulting services, helps configure, automate, and optimize the platform for scalable, unified ITSM.
By following these best practices and optimization strategies, your organization can reduce service delivery costs, improve customer satisfaction, and scale service operations with confidence.
Robotic Process Automation (RPA) has become a transformative technology for businesses seeking to streamline operations, reduce manual work, and boost efficiency. By automating repetitive, rule-based tasks, RPA allows employees to focus on higher-value activities, driving productivity and cost savings. However, to fully realize the benefits of RPA, organizations must implement it strategically and follow proven best practices to maximize RPA efficiency.
In this article, we explore the top 7 RPA best practices that help businesses improve efficiency, enhance productivity, and maximize their automation investments.
What is RPA and Why is it Important?
RPA, or Robotic Process Automation, refers to the use of software robots, or “bots,” to automate repetitive, rule-based tasks that were previously performed by humans. These bots interact with digital systems and applications in the same way that a person would, but with greater speed, accuracy, and consistency.
Implementing RPA helps businesses:
Reduce human errors
Improve process efficiency
Lower operational costs
Enhance compliance and auditability
Free up employees for more strategic work
Achieve higher RPA ROI (Return on Investment)
Industries like banking, insurance, healthcare, manufacturing, and retail are increasingly adopting RPA to drive digital transformation, optimize business processes, and improve competitiveness.
1. Identify High-Impact Automation Opportunities
The success of an RPA initiative depends on selecting the right processes for automation. Not all tasks are suitable for RPA. Focus on processes that are:
Repetitive and rule-based
High-volume and time-consuming
Prone to human error
Stable with minimal process exceptions
Examples include data entry, invoice processing, report generation, and system integration tasks. Conduct a thorough process assessment to prioritize automation opportunities that deliver the highest ROI and support business process optimization.
2. Standardize and Optimize Processes Before Automation
Automating inefficient or inconsistent processes can lead to suboptimal results. Before implementing RPA, take time to standardize and optimize processes:
Eliminate redundant steps
Document process flows clearly
Resolve process exceptions where possible
Engage stakeholders to ensure process alignment
Standardized processes create a strong foundation for successful RPA implementation and prevent bots from amplifying existing inefficiencies.
RPA success is best achieved through a phased approach. Begin with a small, well-defined pilot project that demonstrates quick wins. This allows your organization to:
Validate RPA tools and technology
Gain experience with development and deployment
Identify potential challenges early
Build internal support and confidence
Once the pilot succeeds, gradually scale RPA across departments and business units, using lessons learned to refine your approach and enhance RPA scalability.
4. Establish Strong RPA Governance and Compliance
Without proper governance, RPA initiatives can become fragmented and expose the business to risks. Implement governance frameworks to:
Define roles and responsibilities for RPA management
Ensure compliance with data security and privacy regulations
Maintain version control and audit trails
Monitor bot performance and behavior
Strong governance ensures RPA operates effectively, securely, and in alignment with organizational policies and automation best practices.
5. Collaborate with IT and Business Teams for Successful RPA Implementation
RPA is most successful when IT and business teams work together. Collaboration helps to:
Ensure bots integrate smoothly with existing systems
Address technical challenges proactively
Align automation goals with business objectives
Maintain system security and stability
An RPA Center of Excellence (CoE) that brings together technical and business expertise can accelerate implementation and drive sustainable success.
6. Invest in Scalable and User-Friendly RPA Tools
Selecting the right RPA software is crucial for long-term success. Look for solutions that offer:
Scalability to support business growth
Intuitive, low-code interfaces for ease of use
Integration with legacy and modern systems
Advanced capabilities like AI and machine learning (for intelligent automation)
Robust security and compliance features
Popular RPA platforms include UiPath, Automation Anywhere, Blue Prism, and Microsoft Power Automate—all designed to boost process automation efficiency. Choosing the right platform with guidance from an experienced RPA solution provider ensures a tailored, scalable automation strategy that aligns with your business goals.
7. Monitor, Measure, and Continuously Improve RPA Efficiency
RPA is not a “set it and forget it” solution. To maximize efficiency gains and achieve high RPA ROI, businesses must:
Continuously monitor bot performance
Track key performance indicators (KPIs) such as processing time, accuracy, and error rates
Solicit feedback from users and stakeholders
Regularly review processes for new automation opportunities
A culture of continuous improvement ensures RPA delivers sustained value, adapts to changing business needs, and supports enterprise automation initiatives.
Common RPA Implementation Challenges to Avoid
While RPA offers significant benefits, organizations often face challenges that can hinder success if not addressed proactively:
Over-automation: Trying to automate overly complex processes that require human judgment.
Lack of Change Management: Failing to prepare employees for automation can lead to resistance and low adoption.
Inadequate Testing: Insufficient testing of bots before deployment can result in errors and process disruptions.
Underestimating Maintenance Needs: Bots require ongoing maintenance to adapt to system changes and evolving processes.
Recognizing and addressing these challenges early increases the chances of a smooth, efficient RPA implementation.
The Role of Intelligent Automation (IA) in Boosting RPA Efficiency
As businesses mature in their RPA journey, they can enhance efficiency further by incorporating Intelligent Automation (IA). IA combines RPA with technologies like Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) to automate more complex, decision-based tasks. With the support of expert RPA implementation services, organizations can seamlessly integrate these advanced capabilities into their workflows, driving smarter automation, greater scalability, and long-term digital transformation.
Examples of IA applications include:
Automated document processing with AI-powered data extraction
Chatbots handling customer service inquiries
Predictive analytics for proactive process optimization
Intelligent Automation unlocks new levels of efficiency and enables businesses to automate a broader range of processes, maximizing process automation ROI.
Real-World Example: RPA in Action for Business Process Efficiency
A large financial services company implemented RPA to automate its loan application process. By applying the best practices outlined above, they achieved:
80% reduction in processing time
Significant decrease in manual errors
Enhanced regulatory compliance through automated audit trails
Increased employee satisfaction by reducing repetitive work
The result was improved operational efficiency, faster customer service, reduced costs, and significant RPA ROI.
Conclusion: Maximize RPA Efficiency with Best Practices
Robotic Process Automation (RPA) offers significant efficiency gains, but success depends on a strategic, well-managed approach. By following these 7 RPA best practices—from selecting the right processes and tools to fostering collaboration and continuous improvement—businesses can unlock the full potential of automation.
For organizations seeking to reduce manual work, improve accuracy, and scale operations efficiently, RPA is a powerful solution when implemented thoughtfully. When guided by the expertise of a digital transformation company and a trusted RPA solution provider like MicroGenesis, the path to automation becomes more structured and results-driven..
Looking to improve efficiency with RPA? Contact our automation experts today to discover how we can help your business implement scalable, secure, and high-impact RPA solutions tailored to your unique needs and designed to maximize RPA efficiency and process automation success.
1. Jira is built on an issue‑type hierarchy designed to help teams manage work more effectively. But confusion often arises when teams misuse Epics, Stories, or Tasks, which can lead to bloated backlogs, poor traceability, and execution bottlenecks.
This guide explores each issue type—Epic, Story, Task—clarifying their purpose, differences, and best practices, so you can align Jira structure with Agile principles and achieve better delivery outcomes.
2. Jira Issue Hierarchy Overview
In standard Jira configurations, the issue hierarchy is structured as follows:
Epic → large cross‑cutting initiative or theme spanning multiple sprints or teams
Story → a user‑centric feature or requirement, typically completed within a sprint
Task → a technical or administrative piece of work that may or may not align with a Story
Sub‑task → a smaller unit of work, child to a Story or Task, for detailed tracking
Understanding this hierarchy is essential for governance, reporting, and scaling Agile practices across multiple teams.
3. What Is an Epic?
Definition: An Epic represents a significant business objective or theme, often made up of multiple Stories and spanning several sprints or teams.
Key Characteristics:
Large scope – Encompasses many smaller work items
Time-spanning – May take weeks or months to complete
Cross-functional – Involves multiple teams or disciplines
Strategic alignment – Maps to product roadmap, OKRs, or business goals
Examples:
“Implement Mobile App Version 2.0”
“Launch New E-commerce Checkout Flow”
“Upgrade Infrastructure for GDPR Compliance”
Best Use Cases:
Breaking down long-term strategic initiatives
Planning across multiple teams or releases
Organising backlog around outcomes or goals
4. What Is a Story?
Definition: A Story (or User Story) defines a user-facing feature or requirement intended to deliver value within a single sprint.
Sub‑tasks: micro-work tied directly to a parent Story or Task for fine-grained progress. Not visible in backlog by default.
Tasks: standalone work units, sometimes levels above Sub‑tasks. Use when work does not directly belong to a Story or when tracking independently across sprints.
c. Epics Across Teams and Projects
Epics can span multiple Jira projects or cross-team initiatives.
Use tools like Advanced Roadmaps, Portfolio, or filters to visualize Epic progress across the organization.
Allocate Stories in respective team boards while tracking parent Epic centrally.
7. Best Practices in Using Epics, Stories, and Tasks
1. Use Epics for Outcomes
Epics should describe what you want to achieve, not how you’ll do it. Focus on user value or business goals. Instead of naming an Epic “Refactor Backend,” a better example would be “Enable Faster Checkout Experience.” This outcome-driven approach helps align teams with strategic initiatives and allows stakeholders to better understand the value of ongoing work.
✅ Tip: An Epic should represent a deliverable that takes multiple sprints to complete and has a measurable impact.
2. Craft INVEST Stories
Each Story should adhere to the INVEST model:
Independent – Can be delivered separately from others
Negotiable – Collaborative and not overly detailed
Valuable – Provides clear value to users or customers
Estimable – Can be roughly estimated
Small – Can be completed in a sprint
Testable – Has clear acceptance criteria
This method ensures that your Stories are actionable and effective. A well-written Story like, “As a user, I want to save my payment method so I can check out faster next time” provides value, clarity, and direction for developers and testers.
3. Don’t Overuse Sub‑tasks
Sub-tasks are best used to break down a Story into specific, dependent work that must be done by different team members (e.g., development, design, QA). Overusing them can create clutter and micromanagement issues. Use Sub-tasks only when clarity or division of labor requires it.
🚫 Avoid creating Sub-tasks like “Work on it,” “Review it,” “Test it” unless they represent distinct phases with a clear owner or purpose.
4. Minimize Task Overloading
Tasks should be execution-focused and represent a meaningful chunk of work—not too vague and not too large. Assigning Tasks that cover multiple days or skills makes progress hard to track. Overloaded Tasks can slow velocity tracking, lead to missed deadlines, and dilute accountability.
✅ Keep Tasks manageable—ideally within 1–2 days of effort—and avoid creating a “mega task” that could be broken into Stories instead.
5. Keep the Hierarchy Flat
Avoid overcomplicating the structure. The ideal Jira hierarchy should be:
Epic → Story → Task → Sub-task Adding more levels increases complexity and makes it harder to manage, especially when working with dependencies, reporting tools, or portfolio views. A flat structure boosts visibility and supports faster planning cycles.
💡 Remember: Agile is about adaptability and speed. Deep nesting introduces rigidity.
6. Link Dependencies Properly
Dependencies should never live in a spreadsheet or someone’s head. Jira, including Jira Service Management, provides built-in link types like:
“Blocks” – This issue blocks progress on another
“Relates to” – Issues are associated but not dependent
“Is required by” – This issue must be completed first
Using these relationships makes it easy to see and manage cross-team impact, prioritize work correctly, and minimize surprises during sprint planning.
📈 Use dependency links in Roadmaps and Advanced Roadmaps for program-level planning.
7. Archive Done Epics
Once an Epic is fully delivered and its child Stories are closed, archive or mark the Epic as “Done.” Keeping old, completed Epics in your backlog can clutter views, confuse teams, and reduce reporting clarity.
🎯 Closed Epics also improve performance in Jira, declutter the Epic panel in backlog views, and help in filtering only active initiatives—something a Jira expert consultant can help you optimize.
✅ Tip: Create a Kanban board or filter view to display only open Epics to keep your product and program management dashboard clean and focused.
8. Common Pitfalls & How to Avoid Them
Pitfall
Why It Happens
How to Fix
Using Tasks for User Stories
Tech-centric teams writing low-context tasks
Reframe as INVEST Stories
Epics Too Detailed
Teams confuse Story for Epic
Educate on scope and timespan
Excessive Backlog
Garbage-in, garbage-out from granularity
Periodic backlog grooming
Misaligned Sprint Planning
Epics pulled into sprints
Limit to Stories and Tasks
Poor Traceability
Unlinked work causing confusion
Enforce clear links and hierarchy
9. Jira Configuration & Customization
Hierarchy Enhancements: Use Portfolio/Advanced Roadmaps to add Initiative level above Epics.
Custom Issue Types: Introduce “Feature,” “Capability,” etc., as needed.
Workflows by Issue Type: Add approvals for Epics, testing gates for Stories, etc.
Epic/Story Templates: Use marketplace add-ons or blueprints for standardization.
Q: Can a Task belong directly to an Epic? Yes—Tasks can link to Epics using the Epic Link field, but this should be reserved for technical work outside Stories.
Q: Should every Story have Sub‑tasks? Not necessarily; only use Sub‑tasks for complex Stories with distinct steps or handoffs.
Q: Where do Bugs fit? Bugs are typically a type of Story or Task depending on your workflow. Use depending on scope and impact.
Q: Can Epics span multiple sprints? Definitely. Epics are by definition larger than a sprint—they serve as containers for delivery across several sprints.
Q: Is it okay to convert a Story into an Epic? Yes, Jira allows converting item types if scope changes—but consider backlog impact.
Conclusion:
Understanding the differences between Epics, Stories, and Tasks in Jira is key to managing projects effectively and delivering value consistently. When used correctly, this hierarchy brings clarity, improves team alignment, and boosts productivity. As a leading digital transformation company, MicroGenesis leverages Jira services to help organizations streamline workflows and drive agile success.
At MicroGenesis, our Jira consulting services help teams structure and optimize their workflows using best practices tailored to their needs. Whether you’re just getting started or scaling your Agile practices, we’re here to guide you every step of the way.
As businesses continue to scale and adapt to modern digital requirements, migrating from an on-premises setup to the cloud has become a strategic necessity. Moving to the cloud offers numerous benefits, including scalability, cost efficiency, security, and improved collaboration. However, a successful migration requires careful planning and execution to avoid risks like data loss, downtime, or compliance issues.
In this guide, we’ll cover the step-by-step process of migrating from on-prem to cloud, best practices, challenges to watch out for, and how to ensure a smooth transition for your organization with Jira migration experts.
Step 1: Assess Your Current On-Prem Infrastructure
Before beginning the migration, conduct a thorough assessment of your existing infrastructure.
1. Identify Your IT Assets
List all applications, databases, and services running on-prem.
Categorize workloads based on business criticality and dependencies.
Identify outdated applications that need to be upgraded or replaced.
2. Evaluate Cloud Readiness
Determine if your applications are cloud-compatible.
Identify network and security requirements.
Ensure compliance with industry standards (e.g., GDPR, HIPAA).
3. Define Migration Goals
Reduce operational costs.
Improve performance and accessibility.
Enhance security and disaster recovery.
Enable remote workforce collaboration.
Step 2: Choose the Right Cloud Provider
Selecting the right cloud provider is crucial for a successful migration. Some of the top cloud providers offer seamless integration with Atlassian tools. With expert Atlassian migration services, ensure a smooth transition, optimized performance, and enhanced scalability.
Amazon Web Services (AWS) – Scalable infrastructure, security, and global presence.
Microsoft Azure – Seamless integration with Microsoft products and enterprise solutions.
Google Cloud Platform (GCP) – Advanced AI/ML capabilities and high-performance computing.
IBM Cloud, Oracle Cloud – Industry-specific solutions for complex enterprise needs.
Factors to Consider When Choosing a Cloud Provider:
Performance & Scalability – Can the provider handle your workloads?
Security & Compliance – Does it meet regulatory standards?
Cost & Pricing Model – Pay-as-you-go vs. fixed pricing.
Integration & Compatibility – Does it support your existing applications?
Step 3: Plan Your Cloud Migration Strategy
There are several cloud migration strategies depending on your business needs.
1. Lift-and-Shift (Rehosting)
Migrate applications as-is to the cloud.
Faster and less complex but may not be optimized for cloud performance.
2. Refactoring (Re-architecting)
Modify applications to take advantage of cloud-native features.
Best for long-term cost savings and improved efficiency.
3. Replatforming (Lift, Tinker, and Shift)
Make minor modifications to optimize applications for cloud use.
Reduces performance issues post-migration.
4. Retiring & Replacing
Decommission obsolete applications and replace them with cloud-native services.
Choose a hybrid approach if some workloads need to remain on-prem while others move to the cloud.
Step 4: Develop a Migration Plan
1. Create a Timeline & Roadmap
Define migration phases (e.g., pilot migration, full-scale migration).
Assign roles and responsibilities to IT teams.
2. Backup Data Before Migration
Ensure full data backup and disaster recovery planning.
Use replication tools to mirror on-prem data to the cloud.
3. Test in a Staging Environment
Deploy workloads in a sandbox environment first.
Identify performance issues before the final migration.
4. Establish Security Measures
Set up identity and access management (IAM).
Implement multi-factor authentication (MFA) and encryption.
Ensure DDoS protection and firewall configurations.
Once planning is complete, begin the migration in phases.
1. Perform Pilot Migration
Migrate non-critical workloads first to test stability.
Monitor latency, performance, and integration with cloud services.
2. Full Migration Deployment
Move business-critical workloads in a controlled manner.
Monitor network traffic, database synchronization, and security logs.
3. Validate and Optimize
Test application functionality, performance, and connectivity.
Fine-tune configurations for cost and performance optimization.
Step 6: Post-Migration Optimization & Maintenance
1. Monitor & Troubleshoot
Use cloud monitoring tools (e.g., AWS CloudWatch, Azure Monitor).
Identify bottlenecks and performance gaps.
2. Optimize Costs
Right-size instances and optimize resource allocation.
Implement auto-scaling to reduce costs during non-peak hours.
3. Train IT & Employees
Provide cloud training for IT administrators.
Educate end-users on new workflows and security best practices.
4. Maintain Security & Compliance
Regular security audits and penetration testing.
Ensure compliance with data regulations.
Challenges in Cloud Migration & How to Overcome Them
Challenge
Solution
Downtime & Data Loss
Use migration tools & backup data.
Security & Compliance Risks
Implement strong IAM & encryption.
Performance Issues
Optimize workloads & use monitoring.
Compatibility Issues
Choose hybrid cloud if needed.
Conclusion
Migrating from on-prem to cloud enhances scalability, cost savings, and security. With expert digital transformation consultants, plan and execute a seamless transition by assessing infrastructure, choosing the right cloud provider, and implementing the best migration strategies.
At MicroGenesis, we specialize in cloud migration services tailored to your business needs. Contact us today for expert guidance on your journey to the cloud!
Before diving into customization, it’s essential to understand the basics of Jira workflows. A workflow in Jira represents the series of steps that an issue moves through during its lifecycle, from creation to resolution. Workflows typically consist of statuses (e.g., Open, In Progress, Resolved) and transitions (e.g., To Do → In Progress, In Progress → Done) that define how issues progress through the workflow.
Best Practices for Customization of Workflows in Jira
Define Your Process:
Before customizing your workflow, clearly define your team’s process and identify the stages through which issues need to progress. Consider factors such as team structure, project requirements, and stakeholder expectations. Engaging with a Jira implementation consultant can help in accurately defining these processes.
Keep It Simple:
Strive for simplicity when designing your workflow. Avoid creating overly complex workflows with unnecessary statuses or transitions. A streamlined workflow is easier to understand, maintain, and navigate for team members. Jira consulting services can assist in creating effective yet simple workflows.
Use Standard Jira Workflow Features:
Leverage standard Jira workflow features such as conditions, validators, and post-functions to enforce rules, validate transitions, and automate tasks. These features allow you to customize workflows without the need for custom scripting or plugins. Expert advice from Jira consulting can maximize the use of these features.
Involve Stakeholders:
Collaborate with key stakeholders, including team members, project managers, and product owners, when designing and customizing workflows. Solicit feedback and incorporate input to ensure that the workflow meets the needs of all stakeholders. A Jira implementation consultant can facilitate this collaboration.
Test Iteratively:
Test your customized workflows iteratively in a staging or test environment before rolling them out to production. Solicit feedback from users and make adjustments as needed to optimize the workflow for usability and effectiveness. Jira consulting services can provide valuable insights during the testing phase.
Customize workflows to align with agile methodologies such as Scrum or Kanban. Define stages for backlog grooming, sprint planning, development, testing, and deployment to support iterative development cycles.
IT Service Management (ITSM):
Design workflows tailored to IT service management processes such as incident management, change management, and service request fulfillment. Customize statuses and transitions to reflect the stages of each process and enforce ITIL best practices.
Bug Tracking and Resolution:
Create workflows specifically for bug tracking and resolution, with statuses representing bug triage, assignment, fixing, testing, and verification. Use automation to expedite the resolution process and ensure timely bug fixes.
Document Review and Approval:
Customize workflows for document review and approval processes, incorporating statuses for drafting, review, approval, and publishing. Implement automation to notify stakeholders of pending reviews and track document revisions.
Conclusion
Unlocking efficiency in Jira involves customizing workflows to meet your team’s unique needs. By implementing best practices, you can streamline processes, improve productivity, and achieve project success. For organizations looking to maximize their Jira capabilities, MicroGenesis, an Atlassian partner, offers expert guidance as part of their enterprise digital transformation services. With their support, you can fully leverage Jira to enhance your workflow efficiency and drive your projects to succeed
