AI Is Changing Service Management. Are You Ready?

Introduction: A Tipping Point for Service Management

For the last decade, improvements in service management have come from better platforms, stronger integrations, and process refinements. Tools like Atlassian’s Jira Service Management (JSM) helped organizations modernize IT service delivery and connect teams.

2025 marks a different kind of shift. Artificial Intelligence is no longer an add-on; it is becoming the operating layer that shapes every request, workflow, and decision in service management. Adoption is accelerating quickly, and leaders are moving from pilots to measurable outcomes. (1)

Why it matters: organizations that adapt will see gains in efficiency, agent productivity, and customer experience. Those that delay will fall behind peers who are already scaling AI. (3)

What Is AI in Service Management?

AI in service management integrates machine learning (ML), natural language processing (NLP), and generative AI into service processes to:

• Automate repetitive tasks like ticket triage, categorization, and routing
• Enhance knowledge management with summarization and semantic search.
  
• Predict incidents and risks using pattern recognition and historical trends.
• Enable conversational self-service through virtual agents.
  

In JSM specifically, Atlassian has introduced agentic AI experiences and a virtual service agent designed to deflect routine requests and coach agents with next-best actions. (5)

Key AI Technologies in ITSM and Enterprise Service Management

• Natural Language Processing (NLP): Understanding and categorizing user requests from plain language.
• Generative AI: Producing knowledge base articles, resolution steps, or user responses on the fly.
• Machine Learning Models: Predicting ticket priority, resolution time, and change success rates.
• Conversational AI: Providing 24/7 virtual agent support for common issues.

The AI-Driven Shift in Service Management

Proof points:

• Adoption is surging: organizations reporting AI use jumped to 78% in 2024, up from 55% in 2023. Generative AI use in at least one function rose to 71%. (1)
• Leaders plan to move fast: 85% of customer service leaders plan to explore or pilot customer-facing conversational GenAI in 2025. (2)
• Maturity remains low: almost all companies invest in AI, but only 1% consider themselves mature; 92% plan to increase AI investment over the next three years. (3)

5 High-Impact AI Use Cases in Jira Service Management

1) AI-Powered Request Routing

Challenge: Misrouted tickets waste time and frustrate users.
Solution: AI classifies requests from natural language and assigns to the right team the first time.
Impact: Forrester finds AI in JSM improves ticket handling efficiency by up to 30%. (4)

2) Predictive Incident Management

Challenge: Outages and spikes catch teams off guard.
Solution: JSM’s AI and AIOps group alerts, surface similar incidents and change risks, and suggest responders.
Impact: IT operations teams save ~55 minutes per incident when fully leveraging JSM AI capabilities. (4)

3) Knowledge Base Enhancement

Challenge: KB content decays or is hard to navigate.
Solution: AI-generated summaries and recommendations help both agents and end-users find answers faster.
Impact: Employees save ~25 minutes per request with AI-assisted self-service and automation. (4)

4) Change Risk Prediction

Challenge: Change approvals rely on limited context.
Solution: AI scores change requests using historical success and failure patterns.
Impact: JSM customers see 35% faster change approvals in the TEI analysis. (5)

5) Conversational Interfaces

Challenge: Users prefer natural, real-time help over forms.
Solution: JSM’s virtual service agent handles common requests across Slack, Teams, email, web widget, and Help Center.
Impact: Atlassian reports the virtual agent can handle ~75% of internal requests with high satisfaction when deployed at scale. (5)

The Risks of Standing Still

If you wait, you risk:

• Data overload without intelligent filtering.
• Operational delays due to manual handoffs.
• Falling behind expectations as employees and customers experience AI-enhanced service elsewhere.
  Industry bodies (6, 7) emphasize that AI augments, not replaces, sound service management practices and human expertise.

E7 Solutions: AI at the Intersection of Atlassian and Service Management

As an Atlassian Platinum Solution Partner, E7 helps organizations:

• Integrate AI into JSM workflows without disrupting what works.
• Modernize knowledge so it is current, structured, and AI-ready.
• Deploy predictive and preventative AI for incident and change.
• Provide managed services to continuously tune models, governance, and outcomes.

Our differentiator: we design AI-powered service ecosystems that unify data, workflows, and human expertise, not just tools.

How to Get AI-Ready for Service Management: E7’s 4-Step Framework

1. Assessment: map current processes, data sources, and tool integrations.
2. Readiness Gap Analysis: prioritize AI opportunities with fast ROI.
3. Pilot Implementation: start where value is highest (for example, routing, virtual agent, KB enrichment).
4. Scale and Optimize: expand across the service lifecycle with clear governance and continuous tuning.

These steps align with ITIL guidance to optimize and automate while maintaining strong practices. (6)

Case Example: Real-World ROI from AI in JSM

Findings from a Forrester Total Economic Impact™ study on Atlassian JSM show a composite organization achieved:

• Up to 30% ticket deflection with virtual agent and self-service. (4)
• ~55 minutes saved per incident for IT operations with AI and automation. (4)
• ~25 minutes saved per service request for end users with AI-assisted self-service. (4)

Atlassian also reports customers where the virtual service agent handles ~75% of internal requests, improving productivity and experience. (5)

Call to Action

AI in service management is now the baseline. If you want faster resolution times, higher self-service, and a future-ready service desk, the path is clear.

Book an AI-Readiness Assessment with E7 Solutions to pinpoint where AI in JSM will drive immediate ROI and how to scale it responsibly.

References

1. Stanford HAI — AI Index 2025: Economy Chapter. https://hai.stanford.edu/ai-index/2025-ai-index-report/economy
2. Gartner — Customer Service AI: Hone in on High-ROI Use Cases, Apr 2, 2025. https://www.gartner.com/en/articles/customer-service-ai
3. McKinsey Global Institute — Superagency in the workplace: Empowering people to unlock AI’s full potential, Jan 2025 https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work
4. Forrester — The Value of AI in Jira Service Management (TEI Spotlight PDF), Dec 2024. https://tei.forrester.com/go/atlassian/JSMAISpotlight//docs/Atlassian_Jira_Spotlight.pdf
5. Atlassian — AI in action: the next chapter for Jira Service Management, Feb 28, 2025. https://www.atlassian.com/blog/announcements/jira-service-management-agentic-ai
6. AXELOS (ITIL) — ITIL 4 and Artificial Intelligence (White Paper, Julie L. Mohr). PDF copy: https://peoplecert.jp/doc/ITIL_WP_ITIL4-and-AI.pdf
7. Pink Elephant — AI-Augmented ITSM (Thought-Leadership e-book), 2024. https://www.pinkelephant.com/uploadedfiles/Content/ResourceCenter/Thought-Leadership/AI-Augmented-ITSM.pdf

About the author

Edmond Delude is the Founder and CEO of E7 Solutions, where he guides organizations through transformation by aligning technology, conscious leadership, and strategy. With over 25 years of experience as an entrepreneur and leader, Edmond has helped businesses across industries modernize operations, innovate at scale, and create sustainable growth. His work combines deep technical expertise with a human-centered approach to leadership, enabling teams to thrive while delivering measurable business outcomes.