What is happening to it analysts
- Impact
AI tools are being used to analyze system logs for troubleshooting, monitor network and system performance, automate initial diagnostics, assist in gathering and documenting user requirements, and provide insights for IT strategy.
- Risk
Significant workflow augmentation; focus on complex problem-solving, strategic IT alignment, and user advocacy.
The IT Analyst role will be significantly augmented by AI. AI will automate many routine monitoring, diagnostic, and documentation tasks. This requires analysts to become proficient in using AI-driven tools, interpreting their outputs, and focusing more on complex system troubleshooting, aligning IT solutions with business strategy, managing user expectations, and ensuring the effective implementation and use of technology.
- Sector readiness
Progressive Integration, Especially in ITSM & AIOps
AI is being increasingly embedded into IT Service Management (ITSM) platforms, network monitoring tools, and AIOps (AI for IT Operations) solutions to improve efficiency, reduce downtime, and provide proactive insights.
Where you stand
The IT Analyst role is being significantly augmented by AI, particularly in monitoring, diagnostics, and automating routine support tasks.
AI provides powerful tools for sifting through vast amounts of IT data, identifying issues proactively, and speeding up resolution times.
The future IT Analyst will focus more on complex problem-solving, strategic alignment of IT with business needs, managing new technology implementations (including AI), and ensuring a positive user experience with IT systems.
What this means for you
Concrete changes to how the work gets done, in the order you are likely to meet them.
- 01
AI-Powered System Monitoring & Anomaly Detection. Utilize AIOps platforms that analyze system logs, network traffic, and performance metrics to proactively identify issues, predict outages, and detect security anomalies.
- 02
Automated Diagnostics & Root Cause Analysis. Employ AI tools that can perform initial troubleshooting steps, analyze patterns leading to incidents, and suggest potential root causes for IT problems.
- 03
Intelligent Requirements Gathering & Documentation. Leverage AI to help analyze user feedback, transcribe interviews, identify patterns in user requests, and assist in drafting initial requirements documents or user stories.
- 04
AI-Assisted Solution Design & Vendor Selection. Use AI tools to research potential IT solutions, compare vendor offerings based on requirements, or model the impact of different technology choices.
- 05
Focus on Strategic IT Alignment with Business Goals. With AI handling some technical analysis, more time can be dedicated to understanding business needs deeply and ensuring IT solutions deliver strategic value.
- 06
Enhanced User Support & Self-Service Portals. AI-powered chatbots and knowledge bases can provide first-line support and self-service options, allowing IT Analysts to focus on more complex user issues.
- 07
Change Management & User Training for New Systems. Guiding users through the adoption of new AI-driven or AI-augmented IT systems, and developing training materials.
- 08
Data Analysis for IT Optimization. Using AI-generated analytics to identify opportunities for process improvement, cost optimization, or enhanced system performance within the IT landscape.
- 09
Security Incident Analysis & Response Support. AI tools can help analyze security alerts and provide context for incident response, augmenting the work of security-focused IT analysts.
- 10
Vendor Management & SLA Monitoring. AI might assist in tracking vendor performance against Service Level Agreements (SLAs) and flagging potential issues.
- 11
IT Asset Management Optimization. AI could help in tracking IT assets, predicting end-of-life for hardware, and optimizing software licensing.
- 12
Capacity Planning & Forecasting. Using AI to analyze usage trends and predict future IT infrastructure needs (storage, compute, network).
- 13
Knowledge Base Creation & Maintenance. AI can help identify gaps in IT support knowledge bases or suggest updates based on recurring user issues.
- 14
Translating Technical Findings for Business Stakeholders. Clearly communicating insights from AI-driven IT analytics to non-technical business leaders.
- 15
Ethical Considerations in AI for IT Operations. Ensuring that AI tools used for monitoring or decision-making in IT are fair, transparent, and respect user privacy.
What is pushing this change
- 01
Increasing Complexity of IT Environments & Systems. Modern IT infrastructures are complex, involving cloud, on-premise, and hybrid systems; AI helps manage this complexity.
- 02
Need for Proactive IT Operations & Problem Prevention (AIOps). AIOps aims to automate IT operations, identify issues before they impact users, and provide predictive insights.
- 03
Volume & Velocity of IT Data (Logs, Metrics, Alerts). AI is essential for processing and analyzing the massive amounts of data generated by IT systems to find meaningful patterns.
- 04
Demand for Faster IT Support & Issue Resolution. Users expect quick resolution of IT problems; AI can speed up diagnostics and provide self-service options.
- 05
Integration of AI into ITSM & Monitoring Platforms. Leading IT Service Management, network monitoring, and observability platforms are embedding AI capabilities.
- 06
Cybersecurity Threats & Need for AI-Driven Detection. AI can detect anomalous activity and potential security breaches much faster than manual analysis alone.
- 07
Cloud Computing Adoption & Hybrid Environments. Managing and optimizing resources across diverse cloud and on-premise environments benefits from AI-driven analytics.
- 08
Desire for Data-Driven IT Decision Making & Strategy. AI provides the analytical tools to support strategic decisions about IT investments, architecture, and process improvements.
- 09
Automation of Routine IT Support & Maintenance Tasks. AI can handle tasks like password resets, basic troubleshooting, and system health checks, freeing up analysts.
- 10
Focus on Improving User Experience with IT Systems. AI can help identify user pain points with IT systems and provide insights for improving usability and satisfaction.
Impact by sector
The headline figure is an average. Where you work changes the picture.
- Systems Analysts (Infrastructure & Architecture)
AI for monitoring system performance, predicting hardware failures, capacity planning, and optimizing infrastructure configurations. Human focus on strategic architecture design.
- Business Systems Analysts (Bridging IT & Business Units)
AI for analyzing business processes, gathering user requirements (e.g., from interview transcripts), and identifying opportunities for tech solutions. Human focus on stakeholder management and translating business needs to technical teams.
- IT Support Analysts / Help Desk Analysts
High augmentation with AI chatbots for first-level support, AI-powered knowledge bases, and automated diagnostics. Human focus on complex troubleshooting and user hand-holding.
- Network Analysts
AIOps for network traffic analysis, anomaly detection, predictive maintenance of network equipment, and optimizing network performance.
- IT Security Analysts
AI for threat detection, analyzing security logs, vulnerability scanning, and assisting in incident response. Human focus on strategic security policy and complex investigations.
Skills to build
The skills that keep the human part of this work valuable as the routine part is automated.
- 01
Analytical & Problem-Solving Skills. Diagnosing complex IT issues, identifying root causes, and developing effective solutions, often using AI-provided data.
- 02
Technical Proficiency (Systems, Networks, Databases, Cloud). Deep understanding of the IT infrastructure, applications, and systems being analyzed and supported.
- 03
AI Tool Literacy & Data Interpretation (AIOps, ITSM AI). Ability to use AI-powered monitoring, diagnostic, and analytical tools, and to critically interpret their outputs.
- 04
Business Acumen & Requirements Elicitation. Understanding business processes and needs, and translating them into clear technical requirements for IT solutions.
- 05
Communication & Stakeholder Management. Effectively communicating technical information to non-technical users and business stakeholders, and managing expectations.
- 06
Documentation & Technical Writing. Creating clear and concise documentation for systems, processes, and user guides, potentially assisted by AI drafting tools.
- 07
Adaptability & Continuous Learning (of new technologies). Staying updated with rapidly evolving IT technologies, AI tools, and security threats.
- 08
Understanding of ITIL/ITSM Frameworks (augmented by AI). Applying service management best practices, with AI tools helping to automate and optimize these processes.
Tools in use
Kinds of tool worth knowing
- 01
AIOps (AI for IT Operations) Platforms. Platforms that use AI/ML to automate IT operations, detect anomalies, predict outages, and perform root cause analysis.
- 02
IT Service Management (ITSM) Software with AI. Help desk and service management tools that embed AI for ticket routing, chatbot support, knowledge base suggestions, and workflow automation.
- 03
Network Monitoring Tools with AI Analytics. Software that uses AI to analyze network traffic, predict performance issues, and identify security threats.
- 04
AI-Powered Log Analysis Tools. Tools that use AI/ML to parse, analyze, and find patterns in large volumes of system and application logs for troubleshooting.
- 05
Generative AI for Documentation & Troubleshooting. LLMs used to help draft technical documentation, user guides, or provide natural language interfaces for querying IT knowledge bases.
- 06
Security Information and Event Management (SIEM) with AI. Systems that use AI to correlate security events, detect advanced threats, and support incident response.
Named tools already in use
Dynatrace / Datadog / Splunk (with AI/ML capabilities)
Observability and AIOps platforms that use AI to monitor complex IT environments, detect anomalies, and assist in root cause analysis.
ServiceNow (with Now Assist / AI features) / Jira Service Management (with AI)
Leading ITSM platforms that are heavily investing in AI for chatbot support, automated ticket categorization/routing, and predictive insights.
SolarWinds Network Performance Monitor / Nagios (with AI plugins)
Network monitoring solutions that are incorporating AI/ML for more intelligent alerting, traffic analysis, and performance optimization.
Elastic Stack (ELK) with ML features / Sumo Logic
Log management and analytics platforms that use machine learning to identify patterns, anomalies, and operational issues from log data.
Microsoft Copilot / ChatGPT (for drafting documentation, scripts)
Generative AI tools that can assist IT Analysts in drafting technical documentation, troubleshooting scripts, or summarizing incident reports.
In practice
Ways people in this role are already using AI, and what they get from it.
- Use AIOps for Proactive System Outage PreventionExample 1
- How
Monitor an AIOps dashboard that uses machine learning to analyze system performance and predict potential failures, allowing you to address issues before users are impacted.
GainReduces system downtime, improves IT reliability, and shifts focus from reactive firefighting to proactive problem management.
- Automate Initial Troubleshooting with AI Diagnostic ToolsExample 2
- How
When a user reports an issue, first run it through an AI diagnostic tool that can check common configurations, analyze logs, and suggest initial solutions or next steps.
GainSpeeds up issue resolution for common problems, reduces the workload on human analysts, and can provide users with faster initial support.
- Leverage AI for Analyzing User RequirementsExample 3
- How
Feed transcripts of user interviews or collected requirements into an AI tool that can help identify common themes, potential conflicts, or generate initial user story drafts.
GainAccelerates the requirements gathering phase, helps ensure all user needs are captured, and can improve the quality of software/system specifications.
- Draft Technical Documentation with Generative AIExample 4
- How
Use an LLM to help write first drafts of user manuals, system guides, or knowledge base articles based on technical specifications or existing notes.
GainSaves significant time in creating documentation, ensures consistency, and allows you to focus on validating and refining the content.
- Implement AI Chatbots for First-Line IT SupportExample 5
- How
Set up and manage an AI-powered chatbot that can handle common IT support requests like password resets, software access, or basic troubleshooting, escalating to you when needed.
GainProvides 24/7 support for basic issues, reduces the number of routine tickets handled by human analysts, and empowers users with self-service options.
How this role compares
Three neighbouring roles chosen to show the direction of travel, then the roles either side of yours on the exposure scale.
- Level 1 Help Desk Technicians (Basic, Scripted Support)More exposed
- AI impact
Very High (AI chatbots and automated knowledge bases can handle a large percentage of common L1 support queries and password resets)
Work moves toRole may contract or shift to managing/training AI support tools, handling chatbot escalations, or more complex L1 tasks.
- AIOps Engineers / SREs (Site Reliability Engineers)Different skills, growing
- AI impact
Foundational/Enabling (They build, implement, and manage the AI-driven IT operations and observability platforms)
Work moves toDeep skills in automation, cloud infrastructure, software engineering, data analysis, and AI/ML for IT operations.
- IT Strategists / Enterprise ArchitectsComplementary, less exposed · exposure 45
- AI impact
High Augmentation (Use AI-driven insights for planning, trend analysis, solution evaluation), but core strategic decision-making, technology roadmapping, and architectural design remain human-led.
Work moves toBroad understanding of business and technology, strategic thinking, long-term planning, and complex system design.
- 552–5 yrs
- 552–5 yrs
- 551–6 yrs
IT Analysts · this report
552–6 yrs- 601–4 yrs
- 602–5 yrs
Corporate Development Managers
602–5 yrs
Closing judgement
For IT Analysts, AI is a powerful ally, automating the detection and initial diagnosis of many IT issues, and streamlining documentation. This allows analysts to focus on more complex problem-solving, strategic IT initiatives, user advocacy, and ensuring that technology truly serves the business's evolving needs.
Evidence and revisions
What the exposure figure rests on, what changed when it was last revised, and the published work cited for this role.
45 → 55
Window3-7 years → 2-6 years
The 4 October 2026 review moved the score up by 10 points.
Microsoft's AI applicability score for the matching occupation is 0.31, in the top decile of 785 US occupations; Anthropic's observed exposure (the share of the occupation's tasks already being done with Claude) is 0.28, which is substantial by the standards of the 756 occupations measured; the US Bureau of Labor Statistics places it in the 'very high' AI-exposure tier; BLS projects employment to grow 7.9% over 2025–35. Blending our 2025 editorial figure (60%) with the 2026 evidence composite (40%) moves the score from 45 to 55 and shortens the window from 3-7 years to 2-6 years.
US Bureau of Labor Statistics · Employment Projections 2025–35 and AI Exposure Categories
Official statistics · 27 August 2026AI-exposure tier: Very high. Projected employment change 2025–35: +7.9%. Matched to Computer systems analysts.
Microsoft Research · Working with AI: Measuring the Applicability of Generative AI to Occupations
Working paper · 10 July 2025AI applicability score 0.31 (percentile 90 of 785 occupations) for SOC 15-1211.
Anthropic · Anthropic Economic Index report: Cadences
Report · 26 June 2026Observed exposure 0.28 for SOC 15-1211 (percentile 90 of 756 occupations).
UK Department for Science, Innovation and Technology · Assessment of AI capabilities and the impact on the UK labour market
Report · 28 January 2026UK context: around 70% of UK workers are in occupations with tasks AI could perform or enhance, above the US average; a one-standard-deviation rise in exposure was associated with a 3.9% fall in UK job postings.
Archived copies are served only where the licence permits; otherwise the link goes to the publisher. Full research library →
Readers' view
What people who do this work make of our reading: their own scores, their reasons, and the notes they left on each section.
Our report is one reading of the evidence. This section is the other dataset: what people who do or know this work make of it. Nobody has scored this role yet. Sign in to add yours.
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55
No one has explained their score yet. A line or two about what you see in your own work is the most useful thing on this page.
No notes yet. Every section above has a “Readers' notes” line at the bottom; open one and say what you know.
Method and sources
Each report was written from a large body of published research and then, in October 2026, re-scored against occupation-level evidence: the US Bureau of Labor Statistics AI-exposure classification and 2025–35 projections, Microsoft Research’s AI applicability scores and Anthropic’s observed-exposure data, cross-checked against the reports listed in the Evidence section above. The organisations and publications below are the standing literature behind the narrative sections. Every source, with dates, licences and archived copies where we are permitted to hold them, is catalogued in the research library.
Scores are revised by blending the previous editorial figure (60%) with a composite of the three occupation-level measures (40%), capped at fifteen points per revision and rounded to the nearest five. The window shifts one notch when a score moves ten points or more. Hand adjustments are recorded with their reason in the revision log.
Research library: every source, with dates, licences and archived copies →
- World Economic Forum
- The Future of Jobs Report series — Employer survey of expected job growth and decline, skill shifts and technology adoption (2020, 2023 and 2025 editions); Artificial Intelligence and the Future of Entry-Level Work (2026).
- AI governance and transformation reports — Frameworks on ethical AI, talent strategy and industry transformation.
- McKinsey Global Institute
- AI, Automation and the Future of Work series — Research quantifying automation potential by task, sector and demographic, from "Jobs Lost, Jobs Gained" to "Agents, robots, and us" (2025).
- Industry-specific reports — Financial services, healthcare, manufacturing and others.
- PwC
- Global AI Jobs Barometer — Annual analysis of job postings and productivity by AI exposure (2024–2026 editions).
- Upskilling Hopes and Fears survey — Employee perceptions and readiness.
- Microsoft Research and Anthropic
- Working with AI (2025); Anthropic Economic Index (2025–2026) — Occupation-level usage data from Copilot and Claude conversations, the two observed-usage measures behind the 2026 revision.
- Stanford Digital Economy Lab and Stanford HAI
- Canaries in the Coal Mine? (2025–2026); AI Index Report (annual) — Payroll evidence on early-career employment in exposed occupations; annual measurement of AI capability, investment and adoption.
- Deloitte
- Human Capital Trends series — Workforce, talent and HR technology trends.
- Tech Trends series — Emerging technologies and their business implications.
- Accenture
- Technology Vision series — Forward-looking analysis of emerging technology, with emphasis on AI.
- Fjord Trends — Design, innovation and human experience in a digital world.
- Boston Consulting Group
- AI/ML insights and industry solutions — "The AI Revolution in the Workplace" and related research.
- EY
- AI and workforce reports — Adoption, talent strategy and ethics.
- IBM Institute for Business Value
- AI and automation studies — Business models, workforce evolution and leadership.
- OECD
- AI Policy Observatory — International data and policy on AI, labour markets and skills.
- Employment Outlook — Labour-market trends including technological impact (2023–2026 editions).
- International Labour Organization
- Generative AI and Jobs: A Refined Global Index of Occupational Exposure (2025) — Task-level exposure gradients for every ISCO occupation; successor to the 2023 global index.
- International Monetary Fund
- Staff Discussion Notes on AI and work (2024, 2026) — Complementarity framing: where AI augments and where it substitutes.
- UK Department for Science, Innovation and Technology
- Assessment of AI capabilities and the impact on the UK labour market (2026) — UK occupational exposure and job-posting evidence.
- Brookings Institution
- AI and automation research — Economic and social implications, displacement and skills.
- Yale Budget Lab and Goldman Sachs Research
- Tracking the Impact of AI on the Labor Market; AI and the US labour market (2026) — Aggregate labour-market monitoring; macro displacement estimates.
- Oxford University (Oxford Martin School)
- The Future of Employment — Frey & Osborne and subsequent research on susceptibility to automation.
- MIT Technology Review
- AI & Work — Reporting on AI research and its implications for industries and jobs.
- Gartner
- Hype Cycle for Artificial Intelligence — Maturity and adoption of AI technologies.
- Future of Work reports — Workplace models and talent strategy.
- U.S. Bureau of Labor Statistics
- Employment Projections 2025–35; AI Exposure Categories; Occupational Outlook Handbook — Ten-year employment projections and, from the 2025 cycle, an AI-exposure tier for every detailed occupation.
- Indeed Hiring Lab
- AI at Work Report (2025) and posting-market updates — Skill-level transformation estimates and job-posting trends by occupation.
- OpenAI
- Research papers, blog and API documentation — Large language models, generative AI, safety and societal impact.
- Google DeepMind
- Research papers and blog — Reinforcement learning, AI for science, AGI and ethics.
- Meta AI
- Research papers and blog — Large language models, computer vision, AI for social good.
- Hugging Face
- Transformers library and model hub — Open-source state-of-the-art NLP models.
- TensorFlow and PyTorch
- Documentation and community forums — Core frameworks illustrating practical capability.
- arXiv
- cs.AI, cs.LG, cs.CV, cs.CL — Pre-print research.
- NeurIPS and ICML
- Conference proceedings — Top-tier academic research.
- ACM and IEEE
- Journals and proceedings — ACM Computing Surveys; IEEE Transactions on AI.
- Kaggle
- Datasets and competition solutions — Applied machine learning on real-world problems.
- The Alan Turing Institute
- Research and reports — Responsible and applied AI.
- NIST
- AI Risk Management Framework — Voluntary framework for managing AI risk.
- European Commission
- AI Act — Risk-tiered legal framework for AI.
- Ethics Guidelines for Trustworthy AI — Principles for responsible development.
- Partnership on AI
- Research and best practice — Responsible AI development.
- AI Now Institute
- Annual reports — Social implications: power, inequality, rights.
- ACM FAccT
- Proceedings — Fairness, accountability and transparency.
- Data & Society
- Publications — Social implications of data-centric technology.
- WIPO
- Conversation on IP and AI — Intellectual-property implications of AI.
- IEEE Global Initiative on Ethics of A/IS
- Ethically Aligned Design — Recommendations for ethical AI design.
- Center for AI and Digital Policy
- Policy briefs — Accountable AI policy.