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.
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As AI continues to evolve within IT Service Management (ITSM), many organizations focus narrowly on automation to cut costs and improve resolution speed. However, a myopic focus on replacing roles often sparks resistance, fear of redundancy, and underutilization of AI capabilities. Gartner insightfully pointed out that vendors should move beyond job displacement narratives and project a clear career path evolution—where Level 1 agents don’t just get replaced but transition into roles like Agent Managers or AI Product Owners.
Also Read: AI in ITSM: Revolutionizing Service Management (and Your IT Experience)
This vision aligns perfectly with the philosophy behind Qinfinite, Quinnox’s AI-powered intelligent application management (iAM) platform. Rather than viewing AI as a disruptor of careers, Qinfinite positions AI as a catalyst for professional growth, enabling a new wave of AI-literate ITSM professionals who manage, train, and optimize AI agents.
Stage 1: From L1 Support Agent to AI Agent Operator
In traditional models, L1 agents often handle repetitive tickets, such as password resets or application access. With Qinfinite, these are handled by micro AI agents. However, L1 support staff are not displaced—they are trained to supervise and fine-tune these agents.
Key Skills Developed:
- Understanding AI behavior models
- Annotating data to improve ML accuracy
- Supervising conversational flows using Qinfinite’s RAG-powered interfaces
Qinfinite Features that Support This Transition:
- Conversational Agent Studio for training dialogues
- Talk2KG (Knowledge Graph) for structured learning
- Feedback Loops to rate AI outputs and supervise agent decisions
Stage 2: AI Agent Operator to AI Agent Manager
Once proficient, operators evolve into managers of AI agents, taking responsibility for the performance of the virtual team—just like they would a team of human agents.
Key Responsibilities:
- Overseeing the productivity of AI agents across ITSM processes
- Managing multiple types of root cause analyzers and anomaly detectors
- Conducting A/B tests and performance tuning for AI workflows
Qinfinite Features that Enable This Role:
- Agent Performance Dashboards
- Root Cause Analyzer (Config, Infra, and General)
- Change Impact Analyzer with simulation capabilities
Stage 3: AI Agent Manager to AI Product Owner
With deeper understanding, the next natural step is becoming Product Owner for AI agents, focused on aligning business needs with technical AI functionality.
Key Outcomes at This Stage:
- Creating agent roadmaps for incident resolution, knowledge generation, and process automation
- Managing integration with third-party ITSM platforms like ServiceNow, BMC, Atlassian
- Championing ethical AI, transparency, and continuous learning systems
Qinfinite Capabilities Used:
- API integration layer with platforms like ServiceNow
- Knowledge Graph Insights for contextual decision-making
- Autonomic Feedback Collection
Stage 4: AI Product Owner to Intelligent Ops Strategist
At the peak, we envision a role that bridges ITSM with business strategy—Intelligent Ops Strategist. These professionals don’t manage incidents—they predict and prevent them.
Strategic Focus Areas:
- Leading cross-functional initiatives around autonomous ITSM
- Integrating Qinfinite into broader FinOps, DevOps, and SRE strategies
- Evangelizing intelligent quality and operational excellence via chaos engineering and feedback analytics
Qinfinite Enablers:
- Intelligent Decisioning Engine
- Qinfinite’s Chaos Engineering-as-a-Service
- Closed-loop automation across L1–L3
Why This Matters: Tangible Business and Talent Outcomes
By reimagining career paths in ITSM, organizations can not only create a workforce that embraces AI but also drive tangible business results. Here’s how:
Career Path Level |
Outcome for Organization | Outcome for Employee |
| L1 to Operator | Reduced ticket load & improved TTR | Upskilled in AI operations |
| Operator to Manager | Performance benchmarking of AI agents | Entry into AI-enabled management roles |
| Manager to Product Owner | Scalable AI roadmap alignment | Strategic role in IT transformation |
| Product Owner to Strategist | Autonomic ITSM & business foresight | Executive-level visibility & growth |
A Culture Shift, Not Just a Tech Shift
The future of ITSM is not just about technology but about transforming the people who drive it. By adopting Qinfinite, organizations can create a culture where AI is seen not as a threat, but as a tool for career advancement and professional growth. This vision enables companies to maximize AI adoption while cultivating a workforce that’s skilled, adaptable, and future-ready.
At Quinnox, we believe that AI should not only transform systems but also elevate people. With Qinfinite, we are enabling ITSM professionals to unlock their full potential. Ready to build your future-ready ITSM workforce?
Partner with Quinnox and let us help you drive AI career progression within your IT service organization.
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.