AI in IT: What It Is, How to Use It, and What's Coming Next

Artificial Intelligence (AI) is making a significant impact across various industries, and IT is no exception. From optimizing operations to enhancing service management, AI in IT is revolutionizing how organizations manage their technology infrastructures.

This blog post will explore how AI is transforming IT functions, the benefits it offers, and where it may go next.

What is AI in IT?

AI in IT is the application of artificial intelligence to information technology functions to streamline, automate, and enhance how organizations manage their IT infrastructure, services, and operations. In practical terms, AI enables systems to perform tasks that traditionally required human intelligence, such as decision-making, pattern recognition, anomaly detection, and problem-solving, at a scale and speed no human team can match.

In the context of IT, AI is used to automate routine tasks, improve system efficiency, and provide data-driven insights that support business growth. These can range from AI-driven automation in IT operations (AIOps) to machine learning models that predict system failures.

The scale of adoption reflects this shift, where 88% of organizations now regularly use AI in at least one business function, up from 78% the year before, according to McKinsey's State of AI 2025 survey. Also, the global AIOps platform market alone is projected to grow from $4.9 billion in 2023 to $46.2 billion by 2031 (The Insight Partners). This shows how deeply AI is becoming embedded in IT infrastructure and operations.

The use of AI in IT helps organizations:

• Optimize performance
• Reduce downtime
• Improve overall service delivery

This evolving technology is reshaping how IT teams handle everything from network management to cybersecurity.

Watch Our Video On AI in IT: Transforming Operations with Practical Solutions

Generative AI and Agentic AI in IT: A New Layer of Capability

Earlier forms of AI in IT, like monitoring algorithms, predictive analytics, rules-based automation, required IT teams to define what to look for and what to do. Generative AI and agentic AI change the equation, where these systems can generate content, reason through problems, and take multi-step actions with minimal human instruction.

Now, more than 80% of enterprises will have used generative AI APIs or deployed generative AI-enabled applications in production, up from less than 5% in 2023, according to Gartner.

Within IT specifically, the most widely deployed generative AI applications are:

• IT copilots and virtual agents: AI assistants embedded in ITSM platforms that draft responses to tickets, summarize incident threads, and suggest resolutions based on past cases. This reduces Average Handling Time (AHT) for service desk agents.
• Code generation and review: AI tools that auto-complete code, flag security vulnerabilities in pull requests, and generate unit tests. This accelerating development cycles and catching issues before deployment.
• Automated documentation: Generative AI that drafts or updates knowledge base articles from resolved tickets, keeps system architecture docs synchronized, and reduces the manual effort IT teams spend on documentation maintenance.
• Intelligent log analysis: Large language models that parse error logs and alert streams in plain language, surfacing root causes IT engineers can act on without requiring deep platform expertise.

Agentic AI takes this further. Rather than responding to a single prompt, agentic systems can execute multi-step IT workflows autonomously, such as detecting an anomaly, diagnosing the cause, triggering a remediation script, and filing an incident report, all without human intervention. IBM watsonx Orchestrate and similar platforms are already being used by enterprise IT teams for this type of autonomous operations.

For IT teams, the practical question is where to start. The highest-ROI entry points for generative AI in IT are typically service desk automation (handling tier-1 tickets end-to-end) and developer productivity tooling, both areas where the output is measurable and the risk of a wrong answer is limited.

The functional areas in the next section each reflect how both traditional AI and these newer generative and agentic capabilities are being applied, from AIOps and service management through to security and disaster recovery.

Using AI in IT: Ways, Benefits, Apps, and How-to

IT Functional Areas Where AI is Currently Used

AI has already made significant inroads into several IT functions. Below are key areas where AI is currently being implemented:

1. IT Operations (AIOps)

   How AI is used here: AI helps monitor IT systems in real-time, identifying anomalies, predicting outages, and automating responses to common issues.

   The latest evolution of agentic AI platforms can now execute multi-step remediation workflows without human intervention. The can detect an anomaly, diagnose the root cause, trigger a fix, and file an incident report, all autonomously.

   Benefits: 

   • Reduces downtime
   • Improves system performance
   • Frees up IT teams to focus on more strategic tasks

   Applications:

   • Dynatrace: Offers full-stack monitoring using AI to automate root-cause analysis.
   • Splunk: Provides AI-driven insights to streamline data management and alerting in real-time.
   • Moogsoft: Detects IT issues early with its AI-based platform to automate incident responses.
   • IBM Watson AIOps: Leverages AI to proactively manage IT operations by predicting and resolving incidents in hybrid environments.

   Steps for how to implement:

   • Identify repetitive tasks and failure points: Start by mapping out areas where manual tasks are causing delays, such as frequent system checks or recurring outages.
   • Evaluate AIOps solutions: Research platforms that fit your tech stack and goals. For example, use Dynatrace if your priority is deep visibility or Moogsoft for incident correlation.
   • Run a pilot project: Deploy the solution in a small, controlled environment to measure its impact on system monitoring and issue resolution.
   • Automate low-risk fixes: Configure automated responses for common issues (e.g., restarting a failed service) while keeping more complex problems for manual review.
   • Continuously improve: Use analytics from the AIOps platform to refine automation policies and enhance future responses based on incident trends.

2. Service Management (ITSM)

   How AI is used: AI-powered tools enhance IT Service Management (ITSM) by automating ticket management, chatbots for customer support, and predictive analytics for service improvements. AI reduces response times, automates repetitive tasks, and improves user satisfaction by resolving issues proactively.

   Generative AI has extended this further. AI copilots embedded in ITSM platforms now draft ticket responses, summarize incident histories, and suggest resolutions based on similar past cases. This moves beyond rule-based routing to context-aware assistance.

   Benefits:

   • Faster ticket resolution
   • Reduced workload for IT service desks
   • Better resource allocation

   Giva IT Help Desk and ITSM Cloud Software: These solutions leverage AI to streamline ticket management. Processes are further enhanced by automating classification and routing based on urgency and category. The applications feature real-time dashboards that track service desk performance, ensure SLA compliance, and reduce workload through intuitive self-service portals. Giva's platforms lower Total Cost of Ownership (TCO) and increase productivity by enabling automated workflows and actionable insights.

   Browse the Giva product suite for more information.

   Steps for how to implement:

   • Analyze service desk bottlenecks: Identify common service desk challenges like delayed ticket resolution, high volumes of repetitive requests, or gaps in customer support.
   • Select an AI-enhanced ITSM tool: Evaluate platforms like Giva's Help Desk in the Cloud. Focus on features such as auto-ticketing, predictive issue detection, and virtual agents.
   • Deploy chatbots and virtual agents: Consider implementing chatbots to handle basic user requests and FAQs. Test the chatbot's performance to make sure it can escalate complex queries to human agents seamlessly.
   • Automate ticket assignment and resolution: Configure rules within the ITSM platform to assign tickets automatically based on predefined conditions (e.g., issue type or priority). Implement auto-resolution for repetitive issues like password resets.
   • Monitor service metrics and refine workflows: Track KPIs like average resolution time, customer satisfaction, and ticket backlog. Use the platform's AI insights to identify further optimization opportunities and adjust workflows accordingly.

3. IT Asset Management (ITAM)

   How AI is used: AI optimizes IT asset management by automating the tracking, auditing, and maintenance of hardware and software assets. AI tools provide real-time visibility into asset lifecycles, predict hardware failures, and maintain compliance with software licensing.

   Benefits:

   • Enhanced asset tracking
   • Reduced maintenance costs
   • Better regulatory compliance
   • Prevention of hardware and software downtime

   Steps for how to implement:

   • Assess existing assets and processes: Identify gaps in current asset tracking and note any compliance risks or resource inefficiencies.
   • Deploy Giva IT Asset Manager: Integrate the platform with your existing infrastructure to automate asset management.
   • Set up preventive maintenance schedules: Use AI-powered insights to predict maintenance needs, such as critical software upgrades, and avoid costly downtime.
   • Automate compliance tracking: Configure automatic license monitoring and audits to remain compliant with software agreements.
   • Leverage dashboards for optimization: Use Giva's dashboards to monitor asset performance and adjust strategies based on usage patterns and predictive analytics.

   Read more: IT Asset Management Lifecycle: Tools, Processes and Best Practices

4. IT Governance, Compliance, and Cost Optimization

   How AI is used: AI supports governance by automating compliance monitoring, streamlining audits, and generating reports aligned with frameworks like GDPR, HIPAA, and SOX. For cost optimization, AI-powered tools analyze IT expenditures, forecast future needs, and identify areas for savings. This allows organizations to remain compliant while minimizing costs.

   Benefits:

   • Automated policy enforcement and compliance tracking
   • Faster audits with AI-generated reports
   • Optimized IT budgeting through accurate forecasts

   Applications:

   IBM Apptio Planning: This platform provides AI-powered forecasting, budgeting, and variance analysis tools, enabling continuous planning cycles and financial management. Apptio helps organizations align their tech investments with business priorities by automating data collection, streamlining variance analysis, and eliminating redundant costs.
   • ServiceNow GRC: Automates workflows for governance, risk, and compliance. The platform enables organizations to track risks and audit readiness more efficiently.

   Steps for how to implement:

   • Define compliance frameworks and budget priorities: Align governance policies and spending goals with your organization's objectives.
   • Deploy the right tools: Use platforms like Apptio to automate compliance and optimize budgeting processes.
   • Set alerts for risks and budget variances: Configure AI-powered notifications to address policy breaches or financial discrepancies early.
   • Review dashboards regularly: Leverage real-time insights to monitor compliance status and adjust spending plans as needed.

5. Disaster Recovery and Strategic IT Roadmap Planning

   How AI is used: AI strengthens disaster recovery by automating backups, predicting failures, and simulating disaster scenarios. For IT strategy and roadmap planning, AI helps forecast future needs, aligns IT investments with business goals, and adapts strategies to market trends.

   Benefits:

   • Faster recovery through automated failovers and predictive backups
   • Enhanced resilience with AI-driven scenario simulations
   • Smarter investment decisions by aligning roadmaps with evolving business demands

   Applications:

   • Rubrik Polaris: Automates backup and recovery processes with predictive analytics.
   • Planview Enterprise One: Uses AI to forecast resource requirements and align IT plans with business priorities.

   Steps for how to implement:

   • Identify critical systems for disaster recovery automation: Assess which applications or systems need priority in case of failures.
   • Simulate potential disaster scenarios: Use AI-driven tools to test different failure conditions and optimize your recovery strategy accordingly.
   • Implement predictive backup management: Set up platforms like Rubrik Polaris to monitor infrastructure health and trigger backups proactively.
   • Develop an agile IT roadmap: Leverage AI platforms like Planview to continuously adapt your roadmap based on forecasts and business needs.
   • Monitor recovery and strategy KPIs: Use dashboards to track progress, and fine-tune your plans based on performance metrics.

6. Security and Threat Detection

   How AI is used: AI plays a vital role in cybersecurity by detecting unusual activity, identifying potential breaches, and automating responses to common threats. Machine learning models can detect anomalies in network traffic, while predictive analytics can alert for vulnerabilities before they are used. This proactive approach reduces response times and strengthens IT security.

   Agentic security systems are beginning to go further by autonomously blocking suspicious IPs, quarantining compromised endpoints, or disabling accounts in response to confirmed threat patterns, without waiting for human approval on each action.

   Benefits:

   • Faster detection of security incidents
   • Reduced risk of data breaches
   • Automated response to common cyber threats

   Steps for how to implement:

   • Deploy AI-powered SIEM platforms: Use Security Information and Event Management tools with AI to centralize data, detect threats, and automate alerts.
   • Use network anomaly detection models: Continuously monitor network traffic patterns to identify suspicious or unexpected activity.
   • Configure automated responses: Set up predefined workflows for common attack scenarios, such as blocking IPs or disabling compromised accounts.

IT Functional Areas Where AI is Currently Not Used (But Could be Coming)

1. Software Development Lifecycle Optimization

   Potential Use of AI: AI could predict bottlenecks in the software development process, suggest optimal coding solutions, and even perform automated code refactoring. It might also help streamline testing by dynamically generating test cases.

   Potential Benefits: 

   • Faster development cycles
   • Fewer bugs
   • Optimized testing processes

   Steps for how to implement:

   • Explore AI-based coding tools: Test out tools that help with code analysis and bug detection.
   • Stay updated on code completion trends: Look beyond GitHub Copilot for emerging AI-powered development tools.
   • Run pilot tests for AI-enhanced testing: Try out AI tools for automating tests and improving quality control.

2. IT Documentation Management and Knowledge Creation

   Potential Use of AI: AI could automate the creation and management of IT documentation by dynamically generating documentation as systems evolve. It can also generate knowledge base articles based on support tickets and troubleshooting logs.

   Potential Benefits: Updated documentation in real time and reduced manual effort from IT teams.

   Possible Products/Applications: Future tools could emerge that integrate with service desks or infrastructure platforms to generate live documentation.

   Steps for how to implement:

   • Monitor AI tools in the market for knowledge management automation.
   • Run a small test to convert support tickets into knowledge base entries using AI.
   • Use AI tools to keep system architecture diagrams and documentation synchronized.

3. IT Vendor Management and Procurement Automation

   Potential Use of AI: AI could streamline vendor selection by analyzing historical performance data and market trends, recommending optimal vendors based on current needs. AI might also automate procurement processes by managing bids and contracts autonomously.

   Potential Benefits: 

   • Reduced manual effort
   • Better vendor performance tracking
   • Faster procurement cycles

   Possible Products/Applications: Platforms focused on vendor relationship management (VRM) might adopt these features.

   Steps for how to implement:

   • Explore AI tools that can analyze vendor performance metrics.
   • Test AI for contract review and management tasks.
   • Implement workflows that allow AI to assist with vendor comparisons based on historical data.

4. IT Workforce Planning and Skills Development

   Potential Use of AI: AI could predict future workforce needs based on project pipelines and business growth, identifying skill gaps and recommending training programs for IT staff. AI might also create personalized learning paths based on employee performance and feedback.

   Potential Benefits: Better alignment of IT staffing with business needs and improved employee skill development.

   Steps for how to implement:

   • Pilot AI tools that assess current skills and recommend targeted training.
   • Integrate predictive models into workforce planning software.
   • Use AI to align training programs with upcoming IT projects and trends.

Challenges of Implementing AI in IT

As with any new technology, using AI in IT brings new difficulties. While the benefits are significant, organizations need to anticipate and manage the risks involved, such as the following:

• Data privacy and compliance: AI systems often process sensitive information, raising regulatory and security concerns.
• Integration with legacy infrastructure: Many organizations still operate on outdated systems that are difficult to connect with AI solutions.
• Skills gaps: IT teams may lack the expertise to implement, monitor, and use AI effectively.
• Upfront costs: Deploying AI requires investment in tools, training, and sometimes new infrastructure.

Companies need strong governance frameworks, roll out AI tools in phases to minimize disruption, and invest in training programs for staff. Aligning AI initiatives with measurable ROI helps keep the process strategic and sustainable.

Industry-Specific Use Cases of AI in IT

How AI might be used in IT and the challenges faced can also vary based on industry. Different ones are using it in ways that work well with their unique priorities and challenges:

• Healthcare: AI can help keep operations HIPAA-compliant, automate EMR system maintenance, and predict hardware failures in clinical environments to keep patient care uninterrupted.
• Financial Services: Banks and insurers apply AI to enhance fraud detection, strengthen cybersecurity defenses, and maintain compliance with strict financial regulations.
• Higher Education: Universities use AI-powered help desks and chatbots to improve student support while predictive IT allocation keep systems meeting peak demand.
• Nonprofits: With limited budgets, nonprofits gain efficiency by automating routine IT management tasks, using resources effectively while maintaining reliable service.

What AI Means for IT Professionals and Careers

One of the most common questions IT professionals have about AI is straightforward: What does this mean for my job?

The data points in one direction: transformation, not elimination. According to a CIO analysis, 92% of IT jobs will see moderate or high transformation due to AI advances.

AI is reshaping what IT roles do, not making them obsolete. The shift looks like this in practice:

• Tier-1 support tasks, like password resets, access requests, common troubleshooting, are increasingly handled by AI agents, freeing help desk staff for more complex, judgment-intensive work
• IT operations engineers are moving from reactive alert-watching to proactive policy-setting, defining what AI systems should do when anomalies occur, rather than manually responding to each alert
• Cybersecurity analysts are working alongside AI threat-detection tools, spending more time on threat strategy and less on manual log review
• Developers are using AI coding assistants to accelerate routine implementation, shifting their attention toward architecture, security review, and higher-level problem-solving

The skills that are growing in demand as AI takes on routine tasks include AI-driven data analysis, prompt engineering, AI governance, and the ability to evaluate and manage AI system outputs. For IT professionals, the practical implication is not "learn to code AI models" but rather "learn how to direct AI tools effectively and maintain accountability for what they produce."

The Bottom Line: AI in Information Technology

AI is reshaping IT by automating tasks, predicting issues, and improving operations. While AI in Information Technology is already driving value in areas like operations and service management, there are still untapped opportunities for innovation.

By proactively exploring AI in emerging areas, organizations can future-proof their IT strategies and stay ahead in a rapidly evolving landscape. The time to act is now — those who adopt AI early will be best positioned to thrive.

Giva Brings AI Technologies to Help Desks and ITSM

Giva's Help Desk and ITSM Software solutions can help streamline your service and support operations.

• Giva's AI Copilot allows agents to effortlessly refine responses and quickly access and format solution information, empowering them to address customer issues with accuracy and efficiency.
• Our Knowledge Base AI Copilot summarizes search results so agents and customers alike do not necessarily need to view each knowledge article for the information they are looking for.

Let Giva partner with you to help you continue to increase customer satisfaction for your support teams. Book a free Giva demo to see our solutions in action, or start your own free, 30-day trial today!