Top Application Modernization Trends for 2025: What IT Leaders Must Know
Top 10 app modernization trends for 2025 explained in simple terms. Perfect for IT leaders and business teams planning ahead
Read moreA Perspective on AI-Driven ITSM: What Vendors Often Miss (And Users Need Most)
Accelerate IT operations with AI-driven Automation
Automation in IT operations enable agility, resilience, and operational excellence, paving the way for organizations to adapt swiftly to changing environments, deliver superior services, and achieve sustainable success in today's dynamic digital landscape.
Driving Innovation with Next-gen Application Management
Next-generation application management fueled by AIOps is revolutionizing how organizations monitor performance, modernize applications, and manage the entire application lifecycle.
AI-powered Analytics: Transforming Data into Actionable Insights
AIOps and analytics foster a culture of continuous improvement by providing organizations with actionable intelligence to optimize workflows, enhance service quality, and align IT operations with business goals.
|
When tech giants automate, what happens to humans? A global tech giant rolls out AI across its IT service management function, automating 80% of support queries. Tickets that once took hours are resolved in seconds. Human error drops. Operational costs are reduced by up to 40%. On paper, it’s a resounding success.
But fast-forward six months, there’s a different story unfolding. Morale within the support team is nosediving. Attrition has increased by 20%. AI adoption has plateaued. Why?
This is the paradox of AI in IT Service Management (ITSM). The technology works—but the people it’s meant to support feel lost. This scenario is not just hypothetical—it reflects a growing pattern across enterprises implementing AI-led ITSM platforms. AI-led automation may deliver speed and scale—but without buy-in, the transformation is incomplete.
The truth is that automation without a human strategy is short-sighted.
In this blog, we explore why most AI-for-ITSM initiatives fail to go beyond automation, and how platforms like Qinfinite can be positioned to fill this gap—not just with intelligent automation, but with intelligent engagement.
The Blind Spot in AI-Driven ITSM
Before We Dive In: AI-driven automation is revolutionizing IT Service Management by delivering speed, efficiency, and cost savings. Yet, amid all the hype, there’s a critical blind spot that vendors often overlook: the people behind the screens.
1. The Missing Link: AI That Makes People Stronger, Not Redundant
AI in ITSM is largely marketed as a tool to eliminate L1 tickets, automate triage, and deliver faster response times. While those benefits are important, they only scratch the surface of what’s required to drive adoption.
But the biggest mistake vendors make is failing to show users how their own roles evolve once AI takes over routine tasks.
“It’s not enough to say, ‘AI will make your job easier.’ You must show how the job changes—and why that’s a good thing.”
Frame automation as an enabler, not a replacement:
- Explain how it frees time for higher-value tasks.
- Show what those tasks are.
- Design workflows where AI and humans work together, not in silos.
Why this matters is without a clear vision of their evolving roles, users disengage. AI feels like a threat, not a tool.
2. The Career Path Nobody Talks About (But Everyone Needs to See)
Imagine you’re a support agent handling password resets and simple provisioning tasks. Tomorrow, those are automated by an AI-powered virtual assistant. What next?
Most IT professionals don’t have an answer—and that’s the problem.
What’s needed is a clear AI-driven career narrative – create new role trajectories, such as:
- L1 Support Agent → AI Agent Manager
- AI Agent Manager → Virtual Agent Product Owner
- Subject Matter Expert → Automation Strategist
This journey reframes AI from being a job killer to being a career enabler.
3. Build Incentive Loops That Reward Human Contribution
One of the main reasons AI adoption struggles is because vendors assume that better tools alone will change behavior. In reality, users need motivation — especially when new systems shift how they work.
Best practice: Introduce gamification and performance visibility.
- Track time saved through automation collaboration.
- Create leaderboards for knowledge-based article contributions.
- Offer skill badges for AI training and oversight.
Incentives reinforce behavior. They help teams internalize AI as part of their professional identity.
4. Build Education into the Platform, Not Just Training Sessions
Despite the hype, most IT employees still don’t fully understand AI. According to Gartner, about 70% of ITSM professionals are unclear on how AI impacts their work. That’s the dangerous gap.
What users really need is ongoing, in-the-moment education that explains how AI decisions are made and how to interact with them.
Implementation checklist:
- In-platform tips and walkthroughs: When a bot handles a ticket, show the logic behind it in real time.
- AI simulations for new joiners: Use sandbox environments to demonstrate how hybrid workflows (human + AI) operate.
- Monthly microlearning refreshers: Regular 10-minute updates on new AI features or changes to agent responsibilities.
This demystifies faster ramp-up and onboarding, reduced reliance on IT admins to explain AI workflows and more confident decision-making from frontline teams.
5. Use Human-in-the-Loop Design for Greater Trust and Performance
Fully autonomous AI may sound exciting, but for ITSM, the real-world sweet spot is AI working with humans, not without them.
This is called Human-in-the-Loop (HITL) model, where AI handles initial triage or suggestions, and humans review, approve, or escalate based on context. This keeps decision-making grounded and allows for real-time learning and error correction.
Build this HITL model into your ITSM workflows:
- Initial triage by AI, with escalation control in human hands
- Agent review of flagged anomalies or low-confidence predictions
- Feedback loops that improve AI accuracy based on agent corrections
This approach builds trust, reduces errors, and allows continuous AI improvement while keeping humans in control.
6. Update KPIs and Performance Metrics to Reflect AI Outcomes
Traditional metrics like ticket volume or average resolution time don’t capture the value users bring in an AI-enhanced environment. Yet many vendors fail to update performance frameworks accordingly.
Modernize KPIs with these:
- Agent-AI interaction success: How many cases were resolved with AI assistance?
- Automation coverage: What percentage of L1 issues were handled without human involvement?
- Time saved per agent: How much agent capacity was freed for strategic work?
Impact: Users feel recognized for their new responsibilities, and management gains clearer insights into where AI is delivering value.
7. Empower I&O Teams with AI-First Transformation Playbooks
CIOs may have a bold vision for AI in ITSM — but at the ground level, many I&O leaders are still running pilots with no real transformation blueprint. Many vendors focus on leadership buy-in but neglect the Infrastructure & Operations (I&O) teams who drive day-to-day implementation.
Middle managers and team leads need transformation playbook including:
- What new roles will look like
- How to train and coach their teams
- What “good performance” means in an AI-first model
This kind of clear framework accelerates less resistance from frontline supervisors, stronger middle management alignment and provides faster, smoother organizational AI adoption.
8. Help Partners (SIs & MSPs) Reframe Their Value with AI
AI in ITSM can shrink ticket volumes — a revenue concern for traditional Managed Service Providers (MSPs) and System Integrators (SIs). Some resist. While some vendors overlook this, progressive platforms view it as an opportunity.
Instead of resisting AI, partners should be enabled to create new services such as:
- AI-powered observability and dashboards across client environments
- Root cause and pattern analysis using automation data
- Consultative support roles (e.g., Automation Strategist, Experience Analyst)
This helps partners create new revenue streams and strengthens the overall AI-first ITSM ecosystem.
Be the Practical AI Alternative Enterprises Are Looking For
Enterprises want platforms that are trustworthy, usable, and built with real people in mind. Too often, vendors over-engineer features while overlooking the end-user experience. That’s where Quinnox’s intelligent application management platform, Qinfinite stands apart.
Qinfinite in Action: Bridging Gaps with AI-Powered ITSM Capabilities
Qinfinite doesn’t just promise transformation—it delivers it with intelligent features that solve what most vendors miss and what ITSM users need most:
- AI-Powered Ticket Triage & Assignment: Resolves the fatigue of repetitive L1 tasks, letting agents focus on high-value work and innovation.
- Self-Learning Knowledge Base & Smart Resource Allocation: Motivates agents by capturing and reusing their expertise—converting their inputs into enterprise-wide improvements.
- Predictive Incident Prevention & Root Cause Analysis: Equips I&O teams with proactive insights and clear pathways to reduce ticket volumes and enhance resolution speed.
- Automated Escalation, SLA Monitoring & End-to-End Workflow Automation: Aligns performance with modern KPIs like time saved, SLA compliance, and AI-assisted resolution rates.
- Integrated Observability & Ticket Cluster Analysis Dashboards: Empower MSPs, partners, and IT leaders with the data they need to move from reactive fixes to strategic service excellence.
Before vs. After AI in ITSM: A Side-by-Side Reality Check
Before AI Adoption |
After AI Integration with Qinfinite-style AI Platforms |
| Repetitive Tasks Dominate: L1 agents manually resolve password resets, basic outages, and ticket routing all day long. | Cognitive Load Reduced: AI agents handle FAQs, routing, and resolution instantly freeing human agents for high-value tasks. |
| Static Career Paths: Little to no clarity on how support staff grow beyond tickets or shift into strategic roles. | Evolving Career Journeys: Agents move from ticket-handlers to trainers, bot managers, and AI product owners. |
| Low Morale & Burnout: Work is reactive, thankless, and rarely recognized. | Recognition & Incentives Built-In: AI dashboards highlight agent contributions to knowledge bases and saved effort. |
| One-Size-Fits-All Metrics: Performance is measured in volume, not value. | New KPIs for a New Era: Measure collaboration with AI, improvement of response quality, and successful automation interventions. |
| Fear of Obsolescence: AI is viewed as a threat. | Confidence in the Future: Clear role design makes AI a growth enabler—not a job killer. |
| Pilot Paralysis: AI adoption is stuck in safe, small experiments. | Human-in-the-Loop at Scale: Full-blown AI-human collaboration models power efficient and resilient support operations. |
Closing Perspective
“The success of AI in ITSM doesn’t depend on how much you automate—it depends on how well you explain what’s next for the humans.”
By focusing on intelligent engagement alongside intelligent automation, Qinfinite helps organizations unlock the full potential of AI-driven ITSM—transforming it from a technology upgrade into a strategic enabler of innovation and growth. The future of ITSM lies not just in smarter machines, but in platforms that truly understand and support the people behind the technology.
So, are you ready to move beyond automation and create an ITSM experience that drives real value?
Connect with our experts today to start your transformation journey!
With iAM, every application becomes a node within a larger, interconnected system. The “intelligent” part isn’t merely about using AI to automate processes but about leveraging data insights to understand, predict, and improve the entire ecosystem’s functionality.
Consider the practical applications:
In the Infinite Game of application management, you can’t rely on tools designed for finite goals. You need a platform that understands the ongoing nature of application management and compounds value over time. Qinfinite is that platform that has helped businesses achieve some great success numbers as listed below:
1. Auto Discovery and Topology Mapping:
Qinfinite’s Auto Discovery continuously scans and maps your entire enterprise IT landscape, building a real-time topology of systems, applications, and their dependencies across business and IT domains. This rich understanding of the environment is captured in a Knowledge Graph, which serves as the foundation for making sense of observability data by providing vital context about upstream and downstream impacts.
2. Deep Data Analysis for Actionable Insights:
Qinfinite’s Deep Data Analysis goes beyond simply aggregating observability data. Using sophisticated AI/ML algorithms, it analyzes metrics, logs, traces, and events to detect patterns, anomalies, and correlations. By correlating this telemetry data with the Knowledge Graph, Qinfinite provides actionable insights into how incidents affect not only individual systems but also business outcomes. For example, it can pinpoint how an issue in one microservice may ripple through to other systems or impact critical business services.
3. Intelligent Incident Management: Turning Insights into Actions:
Qinfinite’s Intelligent Incident Management takes observability a step further by converting these actionable insights into automated actions. Once Deep Data Analysis surfaces insights and potential root causes, the platform offers AI-driven recommendations for remediation. But it doesn’t stop there, Qinfinite can automate the entire remediation process. From restarting services to adjusting resource allocations or reconfiguring infrastructure, the platform acts on insights autonomously, reducing the need for manual intervention and significantly speeding up recovery times.
By automating routine incident responses, Qinfinite not only shortens Mean Time to Resolution (MTTR) but also frees up IT teams to focus on strategic tasks, moving from reactive firefighting to proactive system optimization.
Did you know? According to a report by Forrester, companies using cloud-based testing environments have reduced their testing costs by up to 45% while improving test coverage by 30%.
FAQs Related to AI in ITSM
AI automates repetitive tasks like ticket triage, password resets, and routing, which speeds up resolution times and reduces human error. It also provides predictive insights and root cause analysis, enabling proactive incident management.
Many implementations focus solely on automation technology without considering user adoption and evolving human roles. Without clear communication, training, and incentives, employees may resist change, leading to stalled AI adoption.
Qinfinite combines intelligent automation with intelligent engagement. It not only automates tasks but also empowers IT teams through career pathing, incentive loops, and human-in-the-loop workflows, ensuring smoother adoption and better long-term outcomes.
HITL is a collaborative model where AI handles initial triage or suggestions, but humans review, approve, or escalate issues based on context. This approach maintains decision accuracy and builds trust between AI and human agents.
Related Blogs
Blogs
Qinfinite
Blogs
Qinfinite
Why Chaos Engineering Matters in SRE and DevOps
Learn how Chaos Engineering helps SRE and DevOps teams prevent outages, improve system reliability, and reduce downtime with real-world strategies and tools.
Read more
Blogs
AI
Why Agentic AI Is the Future of IT Operations
Agentic AI is reshaping IT ops—from automation to autonomy. Our expert explains why thinking agents, not bots, are the future beyond the automation plateau.
Read moreContact Us
Get in touch with Quinnox Inc to understand how we can accelerate success for you.