The promise of AI in the workplace is everywhere. Boost productivity. Automate the mundane. Work smarter, not harder. But here's a sobering statistic from Atlassian's 2025 AI Collaboration Report: 96% of organizations aren't seeing true AI ROI. Not incremental gains. Not marginal improvements. True organizational efficiency and innovation gains.
That gap between promise and reality is costing Fortune 500 companies an estimated $98 billion annually in lost returns. But here's what makes this statistic interesting: 4% of companies are seeing returns. They're not using different tools. They're using the same AI capabilities differently.
This guide breaks down how to measure and maximize returns from Jira Service Management's AI features. Whether you're already using Atlassian Intelligence or evaluating whether the investment makes sense, you'll find a practical framework for moving from the 96% to the 4%.
Why 96% of companies struggle with AI ROI
The fundamental disconnect comes down to this: most organizations focus on personal productivity when they should be focusing on organizational transformation.
Think about how most companies approach AI adoption. They give employees AI writing tools and expect magic. Individuals get faster at drafting emails. They summarize documents quicker. But the organization as a whole? Nothing really changes. Workflows stay the same. Handoffs remain manual. Knowledge stays siloed.
Dr. Molly Sands, Head of Atlassian's Teamwork Lab, put it clearly: "While a lot of companies are using AI to make individuals more productive, the real transformation happens when teams use AI to work better together."
The 4% who succeed don't just deploy AI tools. They reimagine how work gets done. They use AI to coordinate across teams, not just accelerate individual tasks. They connect knowledge bases, automate handoffs, and create feedback loops where AI learns from every interaction.
At eesel AI, we see this pattern constantly. Teams that treat AI as a tool you configure struggle. Teams that treat AI as a teammate you hire and level up succeed. The difference isn't the technology. It's the mental model.
Understanding Jira AI capabilities
Jira Service Management includes two primary AI platforms: Atlassian Intelligence (embedded AI features) and Rovo (standalone AI assistant with specialized agents).
Here's what you get:
Virtual Service Agent. This is the flagship AI feature, available in Premium and Enterprise plans. It provides 24/7 conversational support across Slack, Microsoft Teams, email, and embedded widgets. According to Forrester research, it deflects approximately 30% of tier-1 requests (password resets, software access, basic troubleshooting). The agent uses two approaches: intent flows for guided troubleshooting and AI answers for knowledge base queries.
AI-powered issue summaries. Instead of reading through dozens of comments to understand a ticket's history, agents click a button and get a bulleted summary. This is particularly valuable when tickets escalate or transition between team members.
Natural language to JQL. Write "show me high priority bugs assigned to the backend team last week" and get the query automatically. No memorizing JQL syntax.
AIOps capabilities. AI alert grouping reduces noise by identifying patterns across monitoring tools. AI incident creation automatically populates incident records from alert groups. The platform also generates post-incident reviews (PIRs) automatically, saving operations teams significant time after outages.
Knowledge management AI. The system suggests knowledge base topics based on recent customer requests, drafts articles from resolved tickets, and recommends relevant articles to agents during ticket resolution.
Sentiment analysis and draft replies. AI analyzes customer tone in real-time and drafts recommended responses based on how agents resolved similar requests in the past.
These capabilities map closely to what we offer at eesel AI. Our AI Agent handles frontline support autonomously, our AI Copilot drafts replies for human review, and our AI Triage automatically tags, routes, and prioritizes tickets. The difference is often more about ecosystem (Atlassian's deep Jira integration versus our broader help desk support including Zendesk, Freshdesk, and Gorgias) than fundamental capability.
The Forrester TEI framework for measuring ROI
In 2024, Atlassian commissioned Forrester Consulting to conduct a Total Economic Impact study on Jira Service Management. The results provide a concrete framework for measuring AI ROI.
The study analyzed a composite organization based on interviewed customers. Here's what they found:
275% ROI over three years. That's $9.5 million in total benefits against $3.5 million in costs.
Payback period of less than six months. Most organizations recover their investment quickly.
$2.3 million in savings from retiring previous ITSM solutions over three years.
The benefit breakdown tells an interesting story about where value actually comes from:
| Benefit Category | 3-Year Value | Source |
|---|---|---|
| End-user productivity gains | $3.0M | Time saved submitting and tracking requests |
| Service desk productivity | $2.9M | Faster resolution, reduced manual work |
| IT operations productivity | $866K | Faster incident detection and response |
| Engineer and decision-maker productivity | $362K | Reduced context-switching, faster access to information |
| Retired solution savings | $2.3M | Elimination of legacy ITSM tool costs |
Notice where the biggest gains come from: end-user productivity, not agent efficiency. When employees can self-serve instead of opening tickets, everyone wins. The employee gets immediate help. The service desk handles fewer tickets. The organization moves faster.
Here's how a Director of IT Operations at a home services company described it in the Forrester study: "Previously, our help desk chat relied on human responses, which was inefficient. Now, with the virtual service agent, we have 24/7 availability, responding to any question at any time."
Specific metrics to track for Jira AI ROI
The Forrester study provides benchmark numbers you can use to estimate your own potential returns. Let's break down the specific metrics to track.
Time savings per interaction:
| Role | Time Saved | Source |
|---|---|---|
| End users | 25 minutes per service request | Forrester TEI study |
| IT operations | 55 minutes per incident | Forrester TEI study |
| Software engineers | 12 minutes per incident | Forrester TEI study |
| Service desk agents | 30% improved efficiency | Forrester TEI study |
Operational metrics to track:
- Ticket deflection rate. Target 30% based on Forrester benchmarks. Measure what percentage of requests are resolved by the virtual service agent without human involvement.
- First response time. AI should dramatically reduce this by providing immediate responses to common queries.
- Mean time to resolution (MTTR). AI summaries and suggested solutions help agents resolve tickets faster.
- Change request approval speed. Forrester found AI risk assessment speeds approvals by 35%.
Cost metrics:
- License cost savings versus legacy solutions. The Forrester study found $2.3M in savings over three years from retiring previous tools.
- Agent cost per ticket. As deflection improves and resolution speeds up, this should decrease.
- Administrative overhead. Track time spent on manual triage, routing, and ticket hygiene.
At eesel AI, we provide similar measurement capabilities. Our dashboard tracks resolution rates, response times, and cost per interaction. We also offer simulation tools that let you run AI against historical tickets to estimate ROI before going live. This test before you invest approach helps teams build confidence and accurate projections.
Moving from the 96% to the 4%: implementation best practices
Getting AI ROI isn't about buying the right tool. It's about implementing it the right way. Here are the practices that separate the 4% from the 96%.
Set up a connected company-wide knowledge base. AI is only as good as the knowledge it can access. The virtual service agent's AI answers feature requires a well-structured, up-to-date knowledge base. This means connecting Confluence spaces, organizing articles by topic, and ensuring permissions are set correctly ("All logged-in users" for viewing).
Make AI part of the team, not just a tool. This sounds like semantics, but it's crucial. When you treat AI as a teammate, you start with oversight and gradually increase autonomy, provide feedback when it makes mistakes, define escalation paths for when it needs help, and measure its performance like you would any team member.
Start with guided workflows before full automation. The virtual service agent lets you begin with intent flows that guide users through troubleshooting. You can test these thoroughly before activating them for customers. This start supervised, level up approach builds confidence.
Define clear outcomes beyond productivity. What does success look like? Is it 30% ticket deflection? Sub-5-minute first response times? 90% customer satisfaction? Specific targets keep implementation focused.
Capture and share knowledge as part of daily work. The AI drafts feature suggests knowledge base articles based on resolved tickets. Make creating these articles part of your resolution workflow. The more you feed the system, the better it gets.
Experiment to find where AI makes the most difference. Not every use case delivers equal value. Test different intents, monitor resolution rates, and double down on what works.
Common pitfalls to avoid:
- Activating AI answers without reviewing knowledge base quality
- Setting expectations too high initially (start with 10-15% deflection, build from there)
- Ignoring the change management aspect (agents need to trust and understand AI suggestions)
- Treating AI as set it and forget it (it requires ongoing tuning and feedback)
At eesel AI, our teammate model is built around these principles. You don't configure our AI. You hire it, train it on your knowledge, start with guidance, and level up based on performance. The approach mirrors how you'd onboard any new team member.
Calculating your potential Jira AI ROI
Ready to build your business case? Here's a simple framework.
The basic ROI formula: (Value - Cost) ÷ Cost
Step 1: Calculate costs.
| Cost Component | Calculation |
|---|---|
| License costs | Number of agents × monthly price × 12 |
| Implementation | Internal hours + any professional services |
| Training | Time to onboard agents and administrators |
| Consumption overages | Estimated extra virtual agent conversations × $0.30 |
Jira Service Management Premium costs $51.42 per agent per month (or $42.51 on annual billing). The virtual service agent includes 1,000 assisted conversations monthly; beyond that, it's $0.30 per conversation with volume discounts.
Step 2: Estimate value.
Use the Forrester benchmarks as a starting point, adjusted for your organization size:
- End-user time savings: 25 minutes × number of self-served requests × average hourly rate
- Agent efficiency gains: 30% productivity improvement × number of agents × average loaded cost
- Legacy tool savings: Current ITSM tool costs you'll eliminate
Step 3: Build scenarios.
Create conservative, moderate, and optimistic projections. The Forrester study showed 275% ROI, but your results will depend on implementation quality and starting maturity.
Step 4: Set expectations with stakeholders.
The Forrester data shows payback in under six months for organizations that implement well. But emphasize that this requires commitment to the best practices outlined above. AI ROI doesn't happen automatically. It happens intentionally.
At eesel AI, we offer an ROI calculator and simulation tools that let you model these scenarios specifically for your ticket volume and team structure. You can run our AI against historical tickets to see exactly how it would have performed, building confidence (and accurate projections) before making any commitment.
