Introduction
IT service management teams face growing pressure from escalating incident volumes, fragmented knowledge, and rising expectations for 24/7 service efficiency. Artificial Intelligence (AI), especially generative and agentic models, is transforming ITSM – from automating routine L1 support to enabling proactive incident prevention. This guide explores the most impactful AI use cases in ITSM, business value, and adoption challenges.
What is AI and Why Does It Matter in ITSM?
Definition of AI and Its Core Technologies
Artificial Intelligence (AI) refers to computer systems that can mimic human intelligence, performing tasks such as learning, reasoning, and decision-making. It is built on technologies like machine learning, natural language processing (NLP), and computer vision. These tools allow systems to recognize patterns, understand language, and make predictions based on data.
In IT service management (ITSM), AI enables smarter automation, real-time insights, and faster service delivery. By analyzing historical data, AI improves incident categorization, speeds up resolution, and powers intelligent virtual agents. The result is a shift from reactive ticket handling to proactive, data-driven service operations.
The Growing Role of AI in Transforming ITSM
AI is increasingly embedded in ITSM tools, turning routine service management into intelligent, adaptive workflows. Traditional rule-based systems are being replaced by machine learning models that learn from past incidents to improve categorization, routing, and resolution. This evolution allows IT teams to handle service requests with greater speed and accuracy.
Modern ITSM platforms now include predictive capabilities that identify risks and performance issues before they escalate. AI analyzes system logs, user behavior, and historical incidents to detect patterns that signal potential outages or failures. By acting on these insights early, organizations reduce downtime and improve service reliability.
Generative AI and intelligent agents are redefining user interaction within IT support. These systems provide contextual answers, automate task execution, and escalate complex cases when needed. As a result, IT teams can offer round-the-clock support, streamline operations, and improve employee satisfaction.
Key Statistics and Trends Highlighting AI Adoption in ITSM
As of 2024, 82% of organizations had implemented AI features in their ITSM practices, and 93% were open to using AI agents for service roles. However, only 27% had moved beyond pilot stages, and just 4% had fully integrated AI into their ITSM workflows. This shows most companies are experimenting with AI, but few are scaling it.
Top AI use cases in ITSM include process optimization (48%), risk advisory (46%), and knowledge discovery (42%), according to a 2024 survey of IT leaders. McKinsey also reported that AI use in IT grew from 27% to 36% in six months, the fastest growth across all business functions. Adoption is accelerating, but maturity varies widely.
Despite interest, 51% of organizations cite governance and compliance as top barriers, while 47% flag data security, and 41% lack internal expertise. Over half of IT leaders still don’t trust AI to operate without oversight. Closing these trust and skills gaps will be key to unlocking full AI value in ITSM.
Business Benefits of AI in ITSM
AI is driving measurable impact across ITSM by reducing manual workloads, improving responsiveness, and enabling more strategic decision-making. From automation to predictive insights, its applications are reshaping how service teams operate and deliver value.
1. Enhanced Productivity and Workflow Automation
AI reduces manual workload by handling repetitive tasks like ticket categorization, password resets, and access requests. This accelerates response times and allows IT staff to focus on complex, high-value issues. As a result, teams can manage more requests without increasing resources.
Automation also minimizes errors and ensures process consistency across the service desk. By learning from historical data, AI optimizes ticket routing, approval flows, and resolution paths. These improvements lead to faster turnaround times and stronger SLA performance.
2. Proactive Incident Prevention
AI continuously monitors system behavior to detect early signs of service degradation. By spotting patterns in logs and past incidents, it flags issues before they become outages. This proactive approach reduces unplanned downtime and improves reliability.
It also enhances planning by forecasting spikes in demand or infrastructure strain. With this foresight, IT teams can allocate resources more effectively and mitigate risks in advance. The result is fewer major incidents and a more stable IT environment.
3. Improved Knowledge Management
AI keeps the knowledge base up to date by analyzing past tickets and surfacing effective solutions. It reduces the need for manual updates and ensures agents and users can find relevant answers quickly. This shortens resolution times and lowers support volumes.
AI also identifies where knowledge gaps exist by tracking frequently unresolved or repeated issues. It suggests new articles to fill these gaps and improve self-service accuracy. Over time, this leads to more empowered users and fewer escalations.
4. Improved User Experience
AI-powered virtual agents offer fast, reliable support through natural language interactions. They handle routine issues instantly and are available around the clock. This improves user satisfaction while relieving pressure on service teams.
When a problem requires human help, the virtual agent transfers it with full context intact. This eliminates repetitive explanations and speeds up resolution. The experience feels seamless and responsive from start to finish.
5. Smarter Decision-Making and Strategic Governance
AI enables real-time visibility into service performance with dynamic dashboards and trend analysis. IT leaders gain actionable insights for staffing, investment, and continuous improvement. This shifts decision-making from reactive to strategic.
Governance also benefits, with AI monitoring compliance, surfacing anomalies, and supporting audits. It strengthens oversight and promotes consistent service delivery. With these capabilities, ITSM becomes more aligned with organizational goals.
To explore broader AI productivity gains across IT – beyond support – see our IT industry use cases post for real-world insights.
Challenges Facing AI Adoption in ITSM
Despite its potential, implementing AI in ITSM comes with real-world obstacles that can slow or limit progress. These challenges span data quality, governance, technical complexity, and the human trust required for successful adoption.
1. Fragmented and Low-Quality Data
AI systems depend on clean, structured, and connected data to function reliably. In many organizations, data is scattered across siloed tools, legacy platforms, and incomplete records. This fragmentation weakens AI’s ability to deliver accurate insights or automate workflows effectively.
Improving data quality requires more than just technical fixes – it demands process alignment and shared ownership across teams. Without consistent data input and real-time integration, even the best AI tools underperform. For most ITSM teams, data readiness remains a foundational hurdle.
2. Governance and Compliance Gaps
As AI takes on more decision-making, questions around governance and accountability become critical. Many organizations lack clear policies on how AI systems should behave, be monitored, or be audited. This can expose businesses to legal and regulatory risk.
Establishing governance frameworks requires alignment between IT, legal, and compliance teams. Without oversight, AI decisions may be inconsistent or opaque. Ensuring responsible AI use in ITSM is a strategic – not just technical – priority.
3. Lack of Internal Expertise
Deploying and maintaining AI tools demands new skill sets that many IT teams still lack. Data science, model training, and AI system monitoring often fall outside traditional ITSM capabilities. This skills gap slows adoption and limits the value of AI investments.
Organizations must invest in training, hiring, or partnering with vendors to fill these roles. Without the right talent, even well-designed tools go underutilized. Building internal confidence in AI also requires hands-on experience and iterative deployment.
4. Complex Integration with Legacy Systems
Many ITSM environments rely on legacy tools that were not built to support AI or data interoperability. Integrating AI into these systems can be technically challenging and time-consuming. This complexity increases implementation costs and delays time to value.
Successful AI adoption often requires modernizing infrastructure or introducing middleware for data flow and automation. Change management becomes just as important as system architecture. Without seamless integration, AI remains siloed and under-leveraged.
5. Inconsistent AI Output and Trust Issues
Despite improvements, AI tools can still produce unreliable or inconsistent results. Errors in prediction, context misunderstanding, or irrelevant recommendations undermine user trust. In ITSM, where accuracy and timeliness matter, even small mistakes can cause major disruptions.
Gaining user trust requires transparency, auditability, and human oversight in critical workflows. Many teams hesitate to allow AI full autonomy until its reliability is proven. Building confidence in AI systems is a gradual process that depends on measured, accountable use.
Specific Applications of AI in ITSM
1. Automated Incident Management
AI‑based incident automation uses NLP models and historical incident data to classify, route, and in some cases resolve tickets without human involvement. It ingests ticket metadata, such as issue descriptions, timestamps, and prior solutions, to prioritize and act using AI protocols integrated into ITSM platforms. This reduces manual triage, improves accuracy, and speeds up response, but requires strong data quality and careful training to minimize misclassification.
Organizations gain operational value through reduced MTTR and fewer escalations, enabling IT teams to focus on high‑value tasks. AI models continuously learn from new ticket resolutions to refine priority and routing logic over time. Ethical considerations include ensuring correct routing for sensitive or privileged tickets and auditing for fairness and transparency.
In highly regulated or high-volume environments, automation leads to cost savings and scalability. Enterprises must ensure integration with service catalogs and accurate service knowledge bases for reliable performance. Security concerns include protecting sensitive ticket data in AI workflows and maintaining clear governance over automated actions.
Real-world example:
At Broadcom, the Moveworks “1.Bot” chatbot automatically triaged and resolved approximately 38 % of IT tickets, processing 6,000 tickets/month and triaging over 72,000 tickets in one year.
2. AI‑Powered Virtual Agents & Chatbots
Virtual agents are deployed across enterprise messaging platforms like Slack or Teams to handle common support tasks such as password resets, access requests, FAQs, and status queries. These agents use multilingual NLP to understand user intents and integrate with backend systems like identity providers or service desks. This self-service model increases productivity and reduces ticket volume, but it must be continuously trained and monitored to avoid misunderstanding user queries.
Generative AI layers enable agents to craft context-sensitive responses and suggest new knowledge content dynamically based on user interactions and feedback loops. End-users benefit from 24/7 support that adapts conversationally to their needs, further democratizing support across global teams. Security and privacy are key: agents must correctly authenticate users and handle sensitive data within compliance frameworks.
Operationally, successful virtual agents drive down Level-1 ticket loads and improve resolution rates. They also enhance user satisfaction by providing immediate assistance with high accuracy. Still, organizations must monitor fallback rates to agents to identify gaps in the knowledge base and training logic.
Real-world example:
Broadcom’s Moveworks agent achieved up to 57 % auto-resolution of IT issues by improving its knowledge base and integration, significantly reducing Level‑1 workload.
3. Predictive Monitoring & Root‑Cause Analysis (AIOps)
Predictive AIOps platforms aggregate logs, monitoring metrics, incident histories, and event data to detect early-warning signals of system degradation or failure. Machine learning models analyze patterns across large datasets to cluster similar alerts and identify root causes proactively. This enables IT teams to resolve issues before user impact occurs, reducing noise and false positives.
These systems integrate with ITSM workflows to trigger changes or escalation automatically based on predicted severity. The predictive insights help optimize resource allocation, inform maintenance scheduling, and support decision-making. Yet model calibration and explainability remain critical to ensuring trust and reducing over-automation risks.
From a strategic standpoint, predictive monitoring enhances operational resilience and uptime. Organizations save on costs associated with unplanned outages and SLA breaches. Care must be taken around the accuracy of predictions and governance of automated responses.
Real-world example:
BMC HelixGPT and comparable AIOps platforms are frequently cited for reducing incident resolution times by as much as 70 % through event correlation and root-cause diagnosis improvements.
For examples of AIOps that detect system issues before they escalate, read about how tools like ServiceNow use AI to classify and resolve incidents automatically.
4. Knowledge Management Optimization
AI-powered knowledge management analyzes ticket data, agent responses, and FAQ usage to recommend content updates and identify gaps in existing documentation. It can draft new knowledge base articles using generative AI, summarizing incident resolutions in natural language. This ensures up-to-date and accessible information for self-service users while decreasing repetitive tickets and agent workload.
The system often includes summarization features to distill complex tickets into concise summaries, making transfer and escalation smoother. AI suggests article titles, tagging, and categorization to streamline KB publishing workflows. Governance is essential: all AI-generated content must undergo review to maintain accuracy and relevance.
Operational benefits include faster onboarding of new agents and improved deflection rates for common requests. Self-service adoption rises as end-users find reliable content without filing tickets. Organizations need policies to audit AI suggestions and keep content aligned with compliance or security standards.
Real-world example:
InvGate’s Service Management AI Hub generates knowledge article drafts in under 30 seconds from resolved incidents and powers contextual summaries to help deflect up to 15 % of support requests.
5. Predictive Change & Risk Management
AI-driven change risk analysis uses historical change tickets, CMDB dependencies, and incident outcome data to estimate the risk and impact of proposed IT changes. Models can flag high-risk changes, suggest optimal scheduling windows, or recommend additional validation steps. This helps reduce failed changes and supports proactive risk mitigation.
Machine learning algorithms simulate possible outcomes by comparing new change requests to past patterns of success or failure. Automated scoring helps prioritize changes and reduces rollback incidents. Ethical and technical challenges include completeness of CMDB data and transparency in scoring rationale.
From an operational standpoint, predictive risk management improves change success rates and fosters confidence in accelerated deployments. Teams make data-driven scheduling decisions and reduce service disruptions. Governance must ensure that human validation remains part of the automation loop.
Real-world example:
A managed-services IT firm in India applied AI to assess change risk, resulting in a 28 % improvement in change success rate while maintaining ITIL compliance and reducing rollback incidents.
6. Automated Asset Lifecycle Management
AI-powered asset management predicts hardware failures and end-of-life timing by analyzing telemetry, usage metrics, and warranty information stored in ITAM tools. ML models trained on historical asset data forecast risk of failure and proactively trigger replacement or maintenance workflows. This enables preemptive procurement and reduces surprise interruptions due to hardware issues.
The system integrates with lifecycle management tools to automate alerts and procurement requests when risk thresholds are exceeded. It also helps optimize budgeting by forecasting depreciation and usage trends. Challenges include ensuring comprehensive telemetry data and mitigating bias from skewed historical records.
Strategically, predictive asset management reduces downtime costs and extends hardware ROI. It improves planning visibility and aligns IT procurement cycles with actual usage patterns. Teams must maintain transparency about AI-driven recommendations and align them with business policies.
Real-world example:
Databricks (and firms like Aramex) using Freshservice AI reportedly saw bots handle up to 60 % of tickets, while predictive asset features reduced unplanned failures and enabled proactive maintenance planning.
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Let’s Build TogetherExamples of AI in ITSM
Real-world implementations of AI in ITSM highlight the tangible business value these technologies deliver. The following case studies showcase how leading organizations have successfully used AI to automate workflows, reduce resolution times, and improve service efficiency.
Real-World Case Studies
1. Broadcom: Automated Incident Handling with Moveworks
Broadcom launched Moveworks’ AI assistant, known internally as “1.Bot,” enabling employees to resolve IT issues like password resets, ticket status checks, and account unlocks directly through Google Chat. In its first year, the system automatically resolved 38% of IT tickets, processing 6,000 tickets/month and triaging over 72,785 tickets, dramatically lowering manual workload.
This deployment transformed Broadcom’s IT support by slashing mean time to resolution and reducing support backlog. It freed IT staff to focus on complex escalations and strategic initiatives, leading to significant productivity gains and cost reduction.
2. BMC: Intelligent Event Correlation with Helix AIOps
An enterprise-tier user of BMC Helix AIOps deployed event-correlation policies that grouped 66 events into a single actionable “situation”, significantly reducing event noise and enabling operators to focus on actual incidents. The platform’s intelligent grouping and root‑cause isolation shortened investigation time and improved operational efficiency by lowering alert fatigue.
In addition to noise reduction, the system was linked into ITSM workflows so that identified situations could trigger automation or escalation. As a result, teams saw a marked reduction in MTTR and stronger reliability across monitored services.
3. Databricks: Ticket Deflection & Asset Insight with Freshservice AI
Databricks implemented Freshservice’s AI-driven service platform, which delivered a 23% ticket deflection rate through contextual knowledge suggestions and workflow automation while maintaining around 96% customer satisfaction.
Within Freshservice, AI also flagged assets nearing failure or replacement thresholds based on telemetry and usage data. This predictive lifecycle support facilitated smoother IT operations and reduced downtime-related risk, embedding asset intelligence into ITSM workflows.
Innovative AI Solutions
AI is evolving ITSM through generative tools and autonomous agents that enhance speed, accuracy, and user experience. ServiceNow’s Now Assist integrates generative AI to summarize tickets, suggest resolutions, and deflect cases, achieving 54% form deflection and saving over $5.5M annually in a pilot deployment.
Agentic AI is also gaining traction, with companies like Deutsche Telekom deploying AI agents that handle routine tasks across IT and HR. Their internal agent serves 10,000 users weekly, streamlining service without increasing staff. These innovations reduce workload while maintaining control and compliance.
Explore how AI streamlines workflows and drives performance improvements in our guide to unlocking operational efficiency with AI.
AI-Driven Innovations Transforming ITSM
Emerging Technologies in AI for ITSM
Generative AI is reshaping ITSM by enabling automated ticket responses, summarizing incidents, and enhancing self-service through natural language processing. These models analyze text, classify issues, and predict resolution steps based on historical data, reducing the manual load on IT teams. The result is faster, more accurate support that scales with demand.
Computer vision is also gaining ground by interpreting screenshots and image attachments to enrich context and improve routing. In parallel, AIOps platforms use machine learning to detect anomalies, correlate incidents, and recommend fixes in real time. Together, these technologies bring predictive intelligence and operational agility to IT service management.
AI’s Role in Sustainability Efforts
AI contributes to sustainability in ITSM by enabling predictive maintenance and proactive incident management. By analyzing system logs and historical data, AI identifies patterns that signal potential failures before they escalate. This helps reduce downtime, extend asset life, and eliminate unnecessary hardware replacements.
Energy optimization is another key benefit as AI dynamically adjusts server loads and infrastructure usage based on demand. Smart systems can monitor performance metrics and automate resource allocation to minimize energy waste. These improvements lower environmental impact while maintaining IT efficiency.
How to Implement AI in ITSM
Implementing AI in ITSM isn’t just about choosing the right technology, it’s about aligning strategy, data, and people to drive lasting impact. Here’s a step-by-step approach to help you adopt AI in ways that are practical, scalable, and built for long-term success.
Step 1: Assessing Readiness for AI Adoption
Before introducing AI into your IT operations, take a step back to evaluate your current ITSM maturity. Start by identifying high-volume, repetitive processes like password resets, ticket triage, or standard incident responses. These areas typically provide quick wins and clear returns, making them ideal entry points for AI.
It’s also important to gauge organizational readiness beyond technical fit. AI adoption often challenges legacy workflows and team dynamics. Without executive backing and team buy-in, even the smartest tools will fall flat.
Explore our data analytics services to see how we help businesses implement AI solutions that scale.
Step 2: Building a Strong Data Foundation
Successful AI depends on the quality of the data it’s built on. IT teams must ensure they’re capturing clean, well-structured data across service tickets, system logs, user feedback, and configuration records. This foundation helps AI recognize patterns, make decisions, and drive automation.
Centralizing your data is just as important as collecting it. A unified system lets teams work from a shared source of truth and ensures AI models receive consistent, reliable inputs. Strong data governance helps maintain accuracy, privacy, and long-term scalability.
Explore why clean, well-governed data is the foundation of successful AI adoption in our data management guide.
Step 3: Choosing the Right Tools and Vendors
Not all AI tools are created equal, especially when it comes to ITSM. Look for vendors that offer domain expertise, configurable solutions, and seamless integration with your existing IT workflows. You want platforms that can scale with your team and align with your strategic goals.
Transparency is key when choosing a partner. Understand how your data will be used, stored, and protected throughout the AI lifecycle. A trustworthy vendor will offer support, security assurances, and regular updates to help you adapt as AI evolves.
Step 4: Pilot Testing and Scaling Up
Launching small-scale AI pilots lets you test value with minimal risk. Focus on a single use case, like automating ticket categorization or setting up a self-service chatbot. These pilots provide early insights and build momentum for wider adoption.
Use feedback from the pilot phase to improve both the AI system and your internal processes. Document lessons learned, refine your goals, and measure outcomes like time savings or reduced backlog. Once your pilot proves effective, you can scale with confidence.
Step 5: Training Teams for Successful Implementation
Getting the most from AI means helping your teams feel comfortable using it. Offer hands-on training and show how AI improves their work, whether through faster resolution or reduced repetitive tasks. This builds trust and boosts adoption across roles.
Encourage collaboration between IT staff and AI system managers. When humans and machines work together, service quality improves without sacrificing oversight. A skilled, empowered team is your most valuable asset in any AI-powered transformation.
To ensure successful AI integration, institutions should start with a clear roadmap. Our guide for tech leads outlines how to assess readiness and align stakeholders from the start.
Measuring the ROI of AI in ITSM
Key Metrics to Track Success
One of the most telling metrics in ITSM is productivity improvement. AI helps reduce manual workloads by automating common tasks like ticket classification, response generation, and user support. This allows IT teams to shift focus from repetitive troubleshooting to more value-driven initiatives, boosting both morale and performance.
Cost savings are another critical marker of ROI. Organizations can reduce Tier 1 support costs by implementing AI chatbots or automated workflows, leading to fewer escalations and faster resolutions. But equally important are the indirect savings, like minimizing system downtime, decreasing turnover, and accelerating onboarding through AI-generated knowledgebases.
To go deeper, look at resolution times, SLA compliance rates, and the volume of tickets handled without human intervention. AI’s true value lies in how it enables scalability without increasing headcount. When quantifying time saved, customer satisfaction gains, and operational agility, the ROI becomes clear and defensible at the executive level.
Case Studies Demonstrating ROI
A Forrester Total Economic Impact study commissioned by SymphonyAI found that an enterprise using AI-driven ITSM achieved a 204% ROI over three years, with savings of over $3.17 million. The organization deflected 35% of tickets through automation, cut average handling time by 75%, and saved nearly $850,000 across incident management and workflow optimization.
At Leeds United Football Club, Atera’s AI reduced ticket volume by 25–35%, enabling a lean IT team to support over 1,000 users more efficiently. Broader AIOps adoption shows MTTR reductions of up to 40%, significantly lowering downtime-related costs for large enterprises.
Common Pitfalls and How to Avoid Them
A frequent challenge in measuring AI ROI in ITSM is focusing solely on automation or cost savings. While important, these metrics often miss broader benefits like improved service quality, user satisfaction, and operational agility. A more strategic approach evaluates how AI aligns with and advances long-term business goals.
Equally common is the lack of baseline metrics to measure progress effectively. Without clear starting points and defined outcomes, it’s difficult to track value or justify expansion. ROI measurement should be continuous, aligning technical performance with business impact to ensure sustained and measurable success.
Learn how to evaluate AI model effectiveness and ROI with our practical guide on AI performance metrics.
Future Trends of AI in ITSM
Predictions for the Next Decade
AI in ITSM is rapidly evolving toward autonomous systems capable of handling entire service workflows. By 2030, Gartner projects that over 70% of enterprise service interactions will be resolved by AI without human input, driven by advances in generative AI, AIOps, and agentic automation.
To stay ahead, organizations must invest in AI-ready infrastructure and continuously improve data quality. Those that align early with these trends will gain faster resolution, lower costs, and greater scalability in managing increasingly complex IT environments.
To see which emerging technologies are shaping AI adoption in the next decade, our IT landscape trends recap and guide for business to intergrating AI in 2025 breaks down the must-watch shifts for business leaders.
How Businesses Can Stay Ahead of the Curve
To stay ahead in AI-driven ITSM, businesses must integrate AI into core workflows and foster a culture of continuous improvement. This means training teams, setting clear goals, and aligning AI use with long-term strategic objectives.
Choosing scalable, secure AI solutions and regularly reviewing performance is key to sustained success. By treating AI as an evolving capability – not a one-off investment – organizations can adapt quickly and lead in a rapidly changing IT landscape.
Conclusion
Key Takeaways
AI is fundamentally transforming IT Service Management by automating routine tasks, improving service accuracy, and enabling predictive operations. From generative AI that enhances self-service to AIOps that proactively detects incidents, these technologies are driving faster response times, higher efficiency, and better user experiences. When implemented strategically, AI not only reduces costs but also aligns ITSM with broader business goals.
Success with AI in ITSM hinges on a solid data foundation, the right technology partners, and a phased approach to adoption. Organizations that measure ROI effectively and invest in continuous improvement are seeing tangible gains in both productivity and service quality. As AI continues to evolve, its role in ITSM will shift from support to strategic enabler.
Moving Forward: A Strategic Approach to AI-Driven Transformation
As AI becomes central to modern IT operations, now is the time to rethink how your organization manages service delivery, resolves incidents, and scales support. From reducing ticket volumes to accelerating issue resolution and enhancing service reliability, AI in ITSM is no longer a future concept, it’s a current business imperative.
At SmartDev, we help organizations design and implement AI-driven ITSM solutions that deliver measurable impact. Whether you’re looking to automate workflows, adopt AIOps, or build intelligent self-service experiences, our team ensures your AI investments align with your goals.
Explore our AI-powered software development services to discover how we turn ITSM into a strategic driver of agility, efficiency, and innovation.
Contact us today to start building a smarter, more resilient IT service management strategy powered by AI.
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References:
- The state of AI: How organizations are rewiring to capture value | McKinsey & Company
- State of AI in IT 2024 | Atomicwork
- The advent of AI agents in ITSM: Perception and future impact | ManageEngine
- AI in ITSM – An Overview | APMG International
- AI for ITSM: Practical use cases, benefits, architecture, implementation and development | LeewayHertz
- The Impact of AI and ML in ITSM | Motadata
- New BMC Helix ITOM Release Introduces AI and OpenTelemetry Tracing and Enhances Usability to Reduce MTTR | BMC Software
- Harnessing the power of intelligent event correlation to reduce event noise and improve MTTR | BMC Documentation
- Broadcom Integrates Its IT Knowledge Base With AI | Moveworks
- How Are Companies Using AI Agents? Here’s a Look at Five Early Users of the Bots | The Wall Street Journal
- Total Economic Impact Study Reveals 204% ROI for SymphonyAI Enterprise IT Service Management Customers | SymphonyAI
- Improving IT Support by Enhancing Incident Management Process with Multi-modal Analysis | arXiv
