EasyVista, the global technology company dedicated to empowering IT, today announced the launch of EasyVista Platform 2025.2, a significant update that deepens the use of AI across the platform while introducing practical enhancements in security, automation, and user experience.  

This latest release continues EasyVista’s strategy to embed AI into the fabric of everyday IT work, making it easier for organizations to streamline operations, drive efficiency, and prepare for what’s next without disruptive change.  

“AI isn’t just a promise, it’s now part of how work gets done in the EasyVista Platform,” said Loic Besnard, Chief Product Officer, EasyVista. “With 2025.2, we’re giving our customers smarter tools that fit naturally into their daily workflows, including automatically generating knowledge from incidents and providing sharper recommendations, as well as powering self-service with advanced language models.”  

Key updates in EV Service Manager include:  

• Auto-generated knowledge: Agents can create knowledge articles directly from incidents, saving manual effort and accelerating knowledge sharing.  

• More accurate suggestion cards: AI-powered recommendations have been fine-tuned to better assist agents and speed up resolutions.  

• LLM-based self-service: EV Pulse AI Conversations is a new chatbot powered by large language models (LLMs), providing more natural, context-aware support.  

The release includes a new home canvas, an individually configurable homepage where IT support agents can configure KPIs, charts, and other widgets. Other 2025.2 improvements include targeted improvements in security, reporting, and UX that help teams work more effectively and stay compliant.    

This release also comes as organizations are actively seeking better ways to leverage AI. A recent EasyVista survey of IT leaders found that 71% indicated AI is important to ITSM success, underscoring its increasing importance in operational strategies.  

When asked what would most improve their ITSM practices, 56% cited the need for easy-to-use AI and automation capabilities, ranking just behind additional training for IT staff. Automating workflows and introducing AI to boost efficiency are both top priorities for the next 12 months.  

EV Platform 2025.2 is available for all EasyVista customers.  

New global benchmark study reveals sharp gaps in tools, talent, and integration

Despite knowing they need to modernize, small and medium-sized businesses (SMBs) are falling behind on IT maturity, leaving themselves exposed to rising cyber risks, inefficiencies, and productivity losses. This is according to a new global benchmark study, “The State of SMB IT for 2026,” released today by EasyVista and OTRS Group. The report reveals that just 12% of SMBs have implemented a fully mature, proactive IT Service Management (ITSM) framework, even though more than half (56%) recognize it as a strategic opportunity to improve efficiency and drive business success. Nearly 40% of organizations still operate without well-defined or consistent ITSM processes, relying instead on basic ticketing systems, spreadsheets, or manual workarounds.    

Based on responses from more than 1,000 SMB IT leaders and practitioners, the report offers insights into the operational realities shaping IT strategies for organizations worldwide. It highlights a growing urgency among SMBs to modernize fragmented systems, improve security, and leverage emerging technologies like AI to drive business value.  

Pain Points Holding Back SMB IT  

According to the report, 67% are still relying on manual work or spreadsheets to bridge ITSM and ITAM processes and just 39% report full integration between infrastructure monitoring and ITSM workflows. SMBs name talent shortages (40%), budget constraints (40%), and security and compliance pressures (37%) as the top obstacles preventing them from advancing IT operations.  

AI Momentum is Building, But Still in the Discovery Phase  

The report shows that AI adoption is growing but SMB teams face cost and expertise barriers:  

• 71% of respondents say AI is rather or very important to ITSM success.  

• Early adoption is focused on asset tracking and reporting (35%), automating repetitive tasks (34%), and IT trend analysis for better decision making (33%).  

• Cost, data privacy and security, and a lack of expertise are the biggest obstacles in deploying Generative AI.  

SMB Priorities Point Toward Modernization  

Looking ahead, the top IT priorities for the next twelve months are:  

• 41% plan to prioritize IT security improvements. 
• 31% aim to automate IT workflows.  
• 30% plan to introduce AI to improve efficiency.  

However, delivering on these goals will require more than just new technology:  

• 62% say they need training and education to improve ITSM practices.  

• 56% want easy-to-use AI and automation capabilities.  

•  48% want access to affordable tools and software.  

“SMBs clearly understand that ITSM and ITAM aren’t just back-office functions anymore, they are critical to how the business runs,” said Keith Andes, Head of Product Marketing at EasyVista. “But our data confirms that too many teams are still stuck with fragmented, outdated systems that slow them down and open up risks. What they need are affordable solutions that are easy to integrate and built with automation and AI at the core. That’s how IT can ‘do more with less’ while helping the business grow.”  

Andreas Bender, Senior VP Business Transformation and Innovation, OTRS Group, added: “As IT systems become more critical to business operations, SMBs risk falling behind larger competitors if they cannot modernize effectively. To make the shift from reactive to proactive ITSM, SMB leaders must empower their IT teams with clear priorities, consistent processes, the right technologies, and continuous training. Proactive ITSM isn’t just a technical change – it’s a leadership-driven transformation that starts with strategic commitment.”  

Download the report at this link to learn more: https://info.easyvista.com/the-state-of-smb-it-in-2026

1. Introduction to IT Workflow Automation 

What is IT Workflow Automation? 

More efficiency, more optimization, more reliability: IT workflow automation is at the center of the scene for these simple and crucial reasons… which, in turn, reflect the demands of the market and of every company. 

In a context that we could define as one of “permanent digital transformation,” leveraging automation means reducing costs, increasing productivity, and enabling IT departments to focus on more strategic areas. It’s not just a matter of speed and savings; it’s a fundamental tool for improving IT performance and ensuring that business processes are always aligned with the growing expectations of the market. 

2025 Gartner®Market Guide for ITSM Platforms

Get the latest ITSM insights! Explore AI, automation, workflows, and more—plus expert vendor analysis to meet your business goals. Download the report now!

DOWNLOAD NOW

But what exactly are we talking about, more concretely? 

In short: IT workflow automation consists of automating repetitive manual tasks and processes using increasingly advanced and “intelligent” technological tools. Activities that used to require direct intervention from dedicated teams—such as ticket management or resource allocation—are now simplified and managed automatically, with error margins that grow smaller every day. 

The Role of AI in Optimizing IT Workflows 

Automation and artificial intelligence are naturally closely linked. We can say that AI adds an additional level of sophistication to IT workflow automation, allowing systems to “learn from experience” based on the input and data collected, and to make decisions autonomously. 

Thanks to AI, companies can not only automate processes but also predict and resolve problems in advance, further improving efficiency and reducing downtime. 

In short, there’s a vast field in front of us to explore, with the right attention, but also by moving ahead of the competition to gain a competitive advantage. 

2. Key Areas for IT Automation 

Automation of Service Desk Operations 

Automation can be applied to different areas of the IT department, each of which can benefit significantly from the reduction of manual workloads and the optimization of processes. 

One of the most important areas is certainly the service desk, which is often the first point of contact for users needing IT support. 

Automating operations such as ticket creation, prioritization, and handling of repetitive requests brings very tangible benefits and is something that can be achieved with great ease by relying on specialized solutions like those from EasyVista Service Manager. 

Incident and Problem Management Enhanced by AI 

Here’s another key area. Automation and AI systems can transform incident and problem management, identifying causes precisely and suggesting solutions in a timely manner. 

But the approach is no longer just reactive. With the use of machine learning algorithms, it is possible to predict the occurrence of incidents, reducing response times, mitigating the negative impacts on business operations, and embracing a more effective proactive approach. 

Automation of Workflows in IT Asset Management 

The IT assets of a company—large, small, or medium—multiply, need constant updating, must be secured, and need to function like a well-coordinated orchestra. To achieve this, once again, automation comes to our aid. 

Automating asset management processes, such as inventory, software updates, disposal of obsolete devices (and much more), is essential. It’s a matter of efficiency, security, but also compliance. 

3. How AI Improves IT Workflows 

AI for Predictive Maintenance and Monitoring 

The IT infrastructures of companies are becoming increasingly extensive and “elastic.” Therefore, they need to be constantly and in real-time monitored, identifying potential anomalies before they turn into serious problems. This predictive maintenance approach is one of the very tangible advances that AI systems have already brought. 

The benefits? Downtime is minimized, resource utilization is optimized, and the costs associated with extraordinary repair interventions can be avoided. 

Smart Ticket Routing and Prioritization 

Smart routing is a key element of ticket management. It’s the process that allows requests or tickets to be automatically routed within the service desk to the most appropriate team or technician, based on predefined criteria such as the type of request, priority, staff availability, or required skill level. 

Once again, the benefits are many, including the optimization of response times and workloads, the reduction of manual errors, and, once more, an overall improvement in efficiency. 

AI-Based Self-Service Solutions 

Another crucial strategy to optimize request management and avoid overloads is to implement self-service solutions. 

Integrating AI-powered chatbots and virtual assistants into self-service portals allows users to solve common problems without IT staff intervention. 

This not only reduces the workload of the service desk but also improves the end-user experience, providing quick and accurate responses to their requests. The dynamic, in short, is perfectly win-win. 

4. Implementing AI and Automation in IT Workflows 

Steps to Automate IT Workflows 

Automating IT workflows brings a significant chain of benefits. However, it requires a coherent and efficient implementation strategy. 

There’s no one-size-fits-all recipe; much depends on the structure of the individual company and the context in which it operates. But there are some key steps to consider in any case: 

• Set clear objectives for this transition; 
• Identify the areas with the greatest potential for automation; 
• Choose the right tools (a point we’ll return to in the next paragraph); 
• Integrate new solutions with existing systems; 
• Continuously monitor results to make improvements; 
• Pay close attention to staff training. 

Tools for Workflow Automation and AI Integration 

The tools that facilitate IT workflow automation and AI integration are diverse and continuously growing; among them, IT Service Management (ITSM) platforms, business process management (BPM) software, and AI-focused solutions for monitoring and analysis play a central role. 

Automation is at the heart of all EasyVista solutions and products, designed for companies of any size, with a strong emphasis on ease of implementation and use. 

Overcoming the Challenges of AI and Automation Implementation 

There’s no doubt: implementing automation and AI systems offers great advantages and enormous opportunities, still largely to be explored. But every innovation brings challenges that must be addressed. 

In this case, the challenges involve change management, data security, and integration with existing systems. Therefore, it is essential to plan an adoption strategy for advanced systems that includes careful employee training, adjustments to company policies, and the implementation of advanced and constantly updated security measures. 

5. The Benefits of AI and Automation in IT Workflows 

As we near the conclusion, let’s summarize the main benefits of AI and automation in IT workflows that we have already touched upon in the previous sections of this article. 

Greater Efficiency and Productivity 

Automating IT processes reduces execution times, minimizes errors, and allows teams to focus on higher value-added activities. In other words: it greatly improves efficiency and productivity. 

Improved Employee and Customer Satisfaction 

Greater efficiency also means greater employee satisfaction, as they are not overwhelmed by sudden workloads, often involving boring and repetitive tasks. All this positive impact, finally, is reflected on end customers. And we know how crucial this is in today’s business world. 

Reduction of Operating Costs 

Not only greater efficiency. Optimizing IT workflows also leads to a significant reduction in costs, thanks to reduced downtime, optimized resource use, and fewer manual activities. 

6. Conclusion 

The Future of IT Workflow Automation 

The future of IT workflow automation is closely linked to the development of new AI-based technologies and integration with increasingly advanced IT management tools. In front of us, we have vast opportunities, and—as always—the most attractive opportunities will be seized by those who know how to move ahead. 

AI and Automation as Strategic Resources in IT Management 

Let’s conclude with one certainty: we must think of AI and automation systems not just as technological tools, but as true strategic resources for IT management, capable of transforming operational efficiency and even the “mindset” of a company. 

The end result? The creation of a lasting competitive advantage. 

IT Service Management (ITSM) forms the backbone of efficient IT operations, ensuring smooth system functioning and timely incident resolution. In this domain, generative artificial intelligence (GenAI) is emerging as a transformative driver. 

The future is within reach—a future where an ITSM tool powered by AI can resolve problems before users even notice them. It enables the automatic generation of content from the knowledge base to find tailored solutions for specific issues. 

Let’s explore some concrete examples of this largely untapped potential. 

An organization using GenAI can quickly identify recurring issues from a software update, proactively develop a patch, and inform users before disruptions occur. 

Instead of manually categorizing and prioritizing tickets, the same organization automates these activities with unparalleled precision, ensuring critical issues are addressed first. 

These examples showcase the shift from reactive to proactive ITSM strategies, a structural transformation. Generative artificial intelligence is redefining standards of excellence in service management. 

Introduction to Generative Artificial Intelligence in ITSM 

Unlike traditional AI models designed for data analysis, predictions, classifications, or recommendations, generative models can create entirely original outputs. 

GenAI-powered tools learn to identify and interpret patterns within their training data sets, leveraging this understanding to generate realistic, context-informed artifacts. 

This ability to produce innovative solutions is especially valuable in IT Service Management (ITSM). Traditionally, ITSM has relied on structured workflows, predefined processes, and manual actions. 

The emergence of generative AI introduces dynamic, intelligent systems capable of learning, adapting, and innovating autonomously within IT environments. By leveraging generative models, ITSM platforms can transition from routine operations to adaptive, innovative processes. 

GenAI harnesses deep learning and natural language processing (NLP) to interpret complex data sets, enabling IT teams to respond faster and more accurately to a wide range of requests and queries. 

By doing so, ITSM platforms predict incidents, analyze patterns, and automate resolutions. Integrating GenAI allows organizations to enhance their ITSM frameworks, reduce operational costs, and improve user satisfaction. 

2025 Gartner®Market Guide for ITSM Platforms

Get the latest ITSM insights! Explore AI, automation, workflows, and more—plus expert vendor analysis to meet your business goals. Download the report now!

DOWNLOAD NOW

Key Benefits for ITSM Operations 

A recent Gartner survey highlights how generative AI has become the most frequently implemented AI solution across organizations. It surpasses other methods such as graphical techniques, optimization algorithms, rule-based systems, and other machine learning types. 

Integrating GenAI into existing applications seems the most effective approach for generative AI use cases, with 34% of respondents identifying it as their primary method. This is more common than rapid model customization (25%), fine-tuning custom models (21%), or using standalone GenAI tools like ChatGPT or Gemini (19%). 

Generative AI acts as a catalyst for expanding AI across the enterprise. Implementing GenAI in ITSM provides numerous benefits beyond operational efficiency: 

• Enhanced decision-making: GenAI offers real-time insights, enabling teams to make data-driven decisions. 
• Improved user experiences: NLP-based chatbots ensure faster and more accurate resolutions, reducing end-user frustration. 
• Large-scale automation: Routine tasks like ticket classification, prioritization, and escalation can be automated, freeing up human resources for strategic initiatives. 
• Proactive problem resolution: By identifying patterns in historical data, GenAI can predict and mitigate risks before they escalate. 

Generative AI also fosters innovation within ITSM, as its adaptive algorithms continually refine processes based on real-world interactions. 

Generative AI Use Cases in Service Management 

Modern IT environments face challenges such as high ticket volumes, prolonged resolution times, and inconsistent support quality. 

By integrating a generative AI layer into ITSM platforms, organizations can tackle these pain points, reshaping ITSM operations entirely. 

With intelligent automation, deep recommendations, and efficient service delivery, the entire ITSM ecosystem experiences significant improvement. 

Here are the key capabilities and use cases where generative AI plays a crucial role: 

• Intelligent categorization: Leveraging clustering and analytical AI technologies, incidents are classified based on fixed and organic data. By analyzing similar incidents, a GenAI layer determines probable root causes, ensuring precise, context-driven categorization. 
• Smart prioritization: Using sentiment analysis, business calendars, and service data, priorities are assigned more accurately. Open incidents are analyzed to quickly identify and rank critical problems, reducing downtime. 
• Problem detection: Recurring incidents can be identified through pattern recognition, preventing repeated issues and improving service reliability. 
• Smart escalation: GenAI predicts cases at risk of breaching SLAs (Service Level Agreements), enabling proactive escalation to senior teams before critical deadlines. 
• Risk analysis and advice: Evaluating past changes and assessing the risks and impacts of proposed changes ensures better decision-making and minimizes disruptions. 
• Root cause analysis: By clustering incident records, common traits like resolution steps or affected assets are identified, streamlining problem-solving efforts. 
• Change pattern clustering: Historical change analysis helps IT teams assess the potential impacts of proposed changes, improving decision-making and reducing approval risks. 
• Knowledge and known issues: By recommending relevant knowledge base articles, ticket resolutions happen significantly faster. 

These use cases underscore GenAI’s transformative potential in IT Service Management, addressing long-standing inefficiencies and ensuring faster, more reliable, user-centric support. 

Overcoming Challenges in Generative AI Adoption 

While the benefits of GenAI are clear, organizations face several hurdles when integrating this technology into ITSM frameworks: 

1. Data privacy and security: Ensuring compliance with data regulations is critical, as AI systems process sensitive information.
2. Lack of expertise: Teams must be trained to manage and optimize generative AI systems, requiring significant investment in continuous skill development. 
3. Integration complexity: Aligning GenAI layers with legacy systems can be challenging, demanding robust integration strategies. 
4. Cost considerations: High initial investments in tools and infrastructure may deter budget-constrained organizations. 

Despite these challenges, the long-term value of GenAI integration—improved efficiency, reduced costs, and superior service quality—justifies the effort and investment. 

As technology evolves, its capabilities will only expand, making generative AI an indispensable resource for ITSM strategies. 

Conclusion 

By enabling systems to analyze data, identify patterns, and create innovative solutions, GenAI allows IT teams to move beyond reactive problem-solving toward proactive, predictive, and adaptive strategies, enhancing every aspect of ITSM. 

EV Pulse AI by EasyVista is a prime example of how generative AI can transform ITSM practices, offering smarter and more efficient tools to meet the demands of modern IT environments. 

EasyVista has long been committed to empowering IT teams with flexible tools that create a positive and measurable impact. Within this customer-centric vision, EV Pulse AI emerges as a cornerstone of EasyVista’s technological roadmap. 

EV Pulse AI addresses today’s complex IT challenges with virtual support agents, intelligent incident categorization, and risk and root cause analyses powered by AI. These advanced features enable IT teams to collaborate efficiently and make data-driven decisions, driving productivity and agility in complex, dynamic IT environments. 

A Configuration Management Database (CMDB) constitutes the backbone of IT Service Management (ITSM). It is a comprehensive map of the IT infrastructure that enables organizations to manage IT resources more efficiently, ensure compliance, and make informed decisions. 

Despite its proven importance, many organizations fail to fully exploit the potential of their CMDB, and this tool risks transforming from a strategic resource into a source of frustration and inefficiency. The truth is that having a poorly utilized CMDB solution or one unsuited to business needs can be worse than not having one at all. 

Inaccurate or obsolete data leads to wrong decisions, slows down incident resolution, and reduces user satisfaction. However, when properly used and maintained, a CMDB simplifies ITSM operations and offers countless benefits. 

Understanding the Role of the CMDB 

Fundamentally, a Configuration Management Database functions as a central hub for storing and organizing detailed information about the elements that make up an IT environment. These elements, both tangible and intangible, support service delivery and infrastructure stability. 

Key Components of a CMDB 

Configuration Items (CI): these are unique entries that represent physical or logical IT resources or services. Each CI is cataloged with distinctive attributes, such as version, owner, and lifecycle status. 

Systemic interactions: a distinctive feature of any effective CMDB is its ability to describe how different components interact with each other. 

Descriptive metadata: information associated with each element, such as deployment environment, maintenance schedule, or ownership. Metadata enhances usability and enables accurate queries. 

Difference Between CMDB and Asset Management 

Although they sometimes overlap, CMDB and Asset Management systems have different objectives. Asset Management is aimed at financial oversight and contractual aspects of IT resources, such as purchase dates and depreciation, while a CMDB is oriented toward understanding system behavior and service architecture. Together, the two offer a comprehensive view of IT resources, but it is the CMDB that provides the information necessary for efficient incident resolution and planning. 

With a properly managed CMDB, IT teams gain meaningful insights into how infrastructure components are connected to each other, enabling more informed decision-making and smoother operations. This foundational knowledge is essential for adopting practices that enable real value from the investment. 

2025 Gartner®Market Guide for ITSM Platforms

Get the latest ITSM insights! Explore AI, automation, workflows, and more—plus expert vendor analysis to meet your business goals. Download the report now!

DOWNLOAD NOW

CMDB Implementation: The Most Common Pitfalls 

Today many CMDB initiatives fail to live up to expectations. This discrepancy between reality and expected performance is rarely due to inadequate technologies. Much more often, it is the result of underestimated operational challenges and execution not aligned with real business needs. Recognizing the most common pitfalls can help organizations fully exploit their CMDB’s potential by adopting proactive measures in a timely manner. 

The recurring reasons why many organizations fall into the trap of underutilizing their CMDB are: 

• Lack of clear objectives: implementing a CMDB without defined purposes leads to abnormal expansion of the scope of application and data overload. 
• Poor accuracy and data inconsistency: obsolete or incomplete data concerning CIs undermines trust in the CMDB. This is why regular audits and automated detection are essential. 
• Overly complex mapping: trying to map every CI and every relationship from day one adds unnecessary complexity and increases the risk of errors. 
• Manual processes more easily subject to errors: relying on manual updates slows down teams and introduces discrepancies. 
• Integration with other IT systems: seamless connectivity with monitoring, asset, and ITSM tools often requires advanced customization, which requires significant expertise to achieve. 
• Insufficient governance and ownership: without clear definition of responsibilities between teams, CMDB maintenance quickly ends up being neglected. 
• Lag behind the evolution of the IT landscape: with the proliferation of cloud, containers, and microservices, the CMDB must adapt or risks becoming irrelevant. 

Recognizing these critical issues is the first step toward adopting CMDB best practices and improving its performance. By addressing these problems with a clear strategy, robust tools, and continuous stakeholder engagement, organizations can get the most out of a CMDB while improving operations. 

Best Practices for Maximizing CMDB Value 

To fully exploit the potential of a configuration management database, organizations must go beyond simply implementing the tool and adopt a strategic approach. CMDB best practices are aimed at making the database accurate, relevant, and aligned with broader IT and business objectives. They are fundamental for maximizing its value and avoiding the most frequent errors. 

Define clear objectives: start small and scale gradually. Begin with a clear articulation of why a CMDB is needed and the expected business outcomes. It’s a “start-small” philosophy: first model only the most critical services, applications, or servers, demonstrate their value, and then expand to include network devices, databases, and the rest of the assets as the process matures. 

Establish solid governance, ownership, and stakeholder engagement. Governance remains the keystone of a reliable CMDB, but it must extend beyond IT operations. If the database is to reflect the organization’s real priorities, it is essential to designate those responsible for data management, service owners, and CMDB managers and actively involve stakeholders from security, finance, and business departments. 

Automate data collection and updating. Replace error-prone manual entry with automatic detection and integration tools that identify CIs and update attributes and lifecycle status in real time. It’s good practice to connect the CMDB to monitoring platforms, IT asset management, cloud service providers, and DevOps pipelines so that changes are captured the moment they occur. 

Focus attention on data quality: verify, refine, repeat. Evaluating data quality is not a one-time activity. It’s necessary to define standards for naming, classification, and mandatory attributes; validate data at every acquisition point; perform scheduled audits to detect obsolete or conflicting entries. It’s better to use dashboards to monitor completeness, accuracy, and timeliness, so you can act before quality deteriorates. 

Define clear CI relationships and categories. The true power of a CMDB lies in understanding how components interact with each other. Categories and naming conventions must be consistent to make the data model intuitive, easy to query, and ready for impact analysis. 

Seamless integration with ITSM processes. Incorporating the CMDB into incident, change, problem, and release workflows ensures that every ticket is enriched with current configuration data and technicians can immediately assess upstream or downstream impact. 

Monitor, measure, and communicate performance. Monitoring metrics such as CI accuracy, update frequency, change success rate, and mean time to incident resolution serves to have a precise measure of the value produced and identify optimization opportunities. 

Foster a culture of continuous improvement. Encouraging feedback, keeping documentation updated, investing in continuous training, and reviewing governance policies as technology and the business evolve means, essentially, treating the CMDB as a living system in constant evolution. 

Real Benefits of CMDB Optimization 

A CMDB is at the foundation of the entire service management lifecycle. When governed by the practices we have described, the CMDB can evolve to strengthen every aspect of ITSM. The benefits are evident. 

Holistic oversight and greater efficiency: by collecting detailed information about infrastructure and services in one place, teams gain a reliable end-to-end perspective, useful for planning upgrades or tracking problems. Identifying underutilized or duplicate resources helps reduce waste for greater efficiency. 

Faster incident response: the resource map stored in the CMDB reveals at a glance which business applications or services are impacted when a component malfunctions. This broad and detailed view accelerates classification, reduces downtime, and improves customer satisfaction. 

Greater risk awareness in case of changes: before implementing a patch or update, teams can perform real-time assessments of change impact, drastically reducing the likelihood of unexpected interruptions, enabling smoother workflows and a higher degree of preparation for audits. Accurate change impact analysis reduces downtime. 

Faster root cause identification: for persistent or recurring problems, historical configuration snapshots reveal which patterns repeat and allow identification of underlying causes. This makes it possible to intervene to implement lasting solutions instead of ineffective ones. 

To achieve these results requires continuous management, cross-functional collaboration, and constant commitment to improvement. In this context, technology can prove to be an extraordinary enabling element supporting change. 

The digital revolution we are immersed in is a permanent revolution. We live in an era where the ability to respond quickly, almost immediately, to changes has become an indispensable requirement for every company (and we would also say for every worker). 

In this context, data is the most precious and strategic resource: it represents the new capital (“the new oil,” as we often hear) on which to build operational efficiency, innovation, and competitiveness. 

In the field of IT Service Management (ITSM), the value of data is even more evident: it is thanks to real-time insights that organizations can make proactive decisions, optimize services, and prevent incidents. 

Be careful, though! It’s not enough to collect the maximum number of information possible: you need to transform it into Actionable Data in ITSM, that is, data ready to guide immediate and concrete actions. 

And it is precisely on this point that we have focused the continuation of this article: not yet another contribution on the abstract importance of so-called “Big Data,” but a very practical journey that starts from the advantages of Actionable Data, then focuses on their impact in ITSM, with the related challenges to face. 

From Simple Data to Actionable Data in ITSM 

The IT departments of any type of company collect enormous quantities of information every day: support tickets, performance metrics, system logs, user feedback; and much more. An enormous mass of raw data… which, however, serves little purpose if it is not “refined.” The real challenge consists in making this information immediately readable, organizing it, and contextualizing it. Only in this way can value and therefore competitive advantage be extracted from data. 

To put it another way: Actionable Data in ITSM means having processed and “deep” data that supports operational and strategic decision-making with maximum effectiveness and in the fastest possible times. 

In practice, intelligent use of data allows you to: 

• Move from reactive to proactive incident management 
• Optimize resource utilization and improve workflows 
• Drastically reduce downtime 
• Improve user experience through targeted and predictive interventions 
• Support continuous improvement and innovation tailored to your specific needs 

And we return to these advantages in more depth in the paragraph below. 

2025 Gartner®Market Guide for ITSM Platforms

Get the latest ITSM insights! Explore AI, automation, workflows, and more—plus expert vendor analysis to meet your business goals. Download the report now!

DOWNLOAD NOW

The Main Advantages of Actionable Data in ITSM 

From information to action: this is what happens with the breakthrough of Actionable Data in ITSM. Just above we listed some of the advantages that derive from their exploitation; here below we explore them in depth, grouping them into what seem to us the three most important cores. 

From Reactive to Proactive Management 

Real-time analysis allows identifying trends, anomalies, and weak signals that, if caught in time, allow intervention before serious problems manifest. 

Solutions like EV Observe from EasyVista, for example, allow analyzing data in real time and activating predictive alerts. 

In short, it’s the old (and always valid) adage: prevention is better than cure. Translated into more contemporary terms: moving from reactive management to a proactive approach that aims to make IT infrastructure anti-fragile and minimize the impact of incidents on business. 

Indeed, pushing the reasoning to its limit, a good exploitation of Actionable Data in ITSM significantly reduces the very number of critical incidents. 

Service Optimization 

Naturally, it’s not just about incident management. Through correct interpretation of data, it’s possible to identify bottlenecks, inefficiencies, and areas for improvement in normal IT workflows. 

It’s like having a very high-definition photograph of everything that happens in the company; a photograph that evolves from moment to moment. And this is the indispensable starting point for perfecting processes, optimizing resource utilization, and reducing downtime. 

The final result? Increased productivity and simultaneous reduction of operational costs. 

Also in this case, a practical example: a product like EV Service Manager helps optimize request management processes, improving efficiency and significantly reducing time-to-resolution. 

It’s a product that leverages the power of Artificial Intelligence systems; and this brings us directly to the next point, related to automation. 

Automation as a Trigger for Continuous Improvement 

Data is the starting point, but also the endpoint. It’s the basis on which to test, correct, and refine processes and strategies. Consequently, by constantly monitoring KPIs and operational metrics, organizations can trigger the virtuous spiral of continuous improvement to their IT services. 

And there is no continuous improvement without automation: the two sides can only reinforce each other; indeed, now we can say that one makes sense only in relation to the other. All this is valid on a large scale, on the IT macro-structure, but also on a small scale, on individual processes. 

A simple and very common example? In Customer Service systems, starting from the analysis of historical tickets, rules can be created to automatically manage recurring problems. 

Here, with a practical and absolutely everyday case, is the difference between simple data and Actionable Data in ITSM. And at this point, below, we are ready to isolate its key characteristics. 

The Key Characteristics of Actionable Data in ITSM 

We opened this blog post by immediately drawing attention to the difference between simple data and Actionable Data. However, we must be clear: it’s impossible to draw a sharp red line between one and the other. 

What can certainly be done, however, is to identify some characteristics that make data, precisely, “actionable.” Below we analyze 5 of them, choosing those that seem most decisive to us: 

1. Timeliness 

In ITSM, speed is crucial. Data must be collected, analyzed, and made available almost in real time to have real operational value. Only updated data allows for rapid decision-making on emerging criticalities, avoiding escalations that could compromise service quality. 

2. Relevance 

Not all collected data is truly useful. And utility is not measured in abstract terms. Actionable data, therefore, is always closely connected to business objectives and defined KPIs. It’s necessary to select, from the mass of available information, those truly significant for the specific ITSM context, filtering out background noise. 

All this, moreover, must be done in a largely automated manner; otherwise it becomes an immense and unsustainable job from all points of view. 

3. Accuracy 

Data quality is fundamental: errors, incompleteness, or inaccuracies can generate wrong decisions and worsen problems instead of solving them. It is therefore essential to adopt rigorous data governance practices to guarantee the reliability of the information on which IT strategies are based. Also in this case, much depends on the objectives of the individual company, the KPIs, the landscape in which it moves. 

4. Clarity and Readability 

Data must be useful to humans. Therefore, it must be presented in a clear and easily interpretable way by operational teams. Effective visualizations, intuitive dashboards, and synthetic reports help make data immediately usable, reducing the time needed for analysis and interventions. 

5. Contextualization 

Data makes sense only within a framework. To become truly “actionable,” it must be placed in a broader context: correlated to historical trends, integrated with other indicators, interpreted in light of business processes, but also of the specific productive sector in which the company is situated. Only this way can it guide truly effective choices. 

Challenges in Obtaining Actionable Data in ITSM 

Although the potential of Actionable Data in ITSM is enormous, there are several practical challenges that companies must face. Among these: 

• Information silos. When data is distributed in separate systems that don’t communicate with each other, it becomes difficult to have a complete and integrated view of IT processes. Overcoming silos requires information integration strategies, unified platforms, and management policies that favor sharing and transparency. 
• Lack of analytical skills. Data availability is not enough: specific skills are needed to analyze, interpret, and transform them into concrete actions. In short, the added value of human capital is needed. Investing in analytical training and, where necessary, integrating specialized figures like data analysts or data scientists becomes a priority for every modern IT team. 
• Legacy systems. Many organizations still find themselves bound to legacy systems that don’t natively support integration with advanced analysis tools. This limits data accessibility and their valorization. 

Fundamentally, it’s a very normal and human problem of habit and resistance to change: “it’s always been done this way, why change?” The answer lies in the fact that it’s the world around us that continues to change; and standing still is equivalent to losing ground. 

Conclusions 

We live in an era of permanent digital transformation. In this scenario, data analysis is the engine of IT competitiveness. Investing in collection, analysis, and action systems based on Actionable Data in ITSM is no longer an option, but an absolute necessity for those who want to aim for efficiency, proactivity, and continuous improvement of all IT processes. 

2025 Gartner®Market Guide for ITSM Platforms

Get the latest ITSM insights! Explore AI, automation, workflows, and more—plus expert vendor analysis to meet your business goals. Download the report now!

DOWNLOAD NOW

To meet increasingly complex needs, organizations’ technological infrastructure tends to expand to handle a particularly heavy workload. 

For this reason, having a robust Incident Management framework, capable of managing a growing number of users and operations daily, allows operational teams to maintain a resilient IT environment. 

Today, artificial intelligence supports the incident management process in all its phases, from incident detection to response and root cause identification. 

In particular, AI applications automate tasks such as incident categorization and prioritization, enhancing and speeding them up through advanced technologies like machine learning and natural language processing (NLP). 

In this article, we will explore how AI-driven incident management is much more efficient than manual management, which often results in delays and classification errors. 

What is Incident Management? 

Incident management is the structured process that identifies, records, analyzes, and resolves IT incidents. Effective incident management is essential to minimize downtime, provide timely responses, maintain IT service continuity, and ensure smooth service delivery. 

Incidents, in this context, refer to unplanned interruptions or degraded IT services, such as system crashes, performance slowdowns, or issues that affect user productivity. 

If well-organized and supported by appropriate tools, the incident management process allows IT teams to systematically and efficiently address these interruptions. 

Key activities in an incident management process include: 

• Incident Detection and Logging: Recognizing and documenting the problem. 
• Categorization and Prioritization: Classifying the incident and determining its urgency and business impact, then directing it to the most appropriate team for investigation. 
• Resolution and Closure: Implementing a solution and closing the incident. 
• Review (if necessary): Analyzing incidents to prevent future occurrences. 

The primary goal of this process is to restore normal service operations as quickly as possible, ensuring quality and minimizing the business impact of incidents. 

2025 Gartner®Market Guide for ITSM Platforms

Get the latest ITSM insights! Explore AI, automation, workflows, and more—plus expert vendor analysis to meet your business goals. Download the report now!

DOWNLOAD NOW

Traditional Incident Management Process vs. AI-Driven Incident Management 

Traditional incident management methods in IT operations can be time-consuming and prone to inefficiencies, whereas AI-driven incident management leverages technology to optimize various aspects of the process. 

Traditional ITSM platforms rely heavily on human intervention: operators manually categorize incidents based on their understanding of the issue. This approach is prone to errors, such as misclassification, inconsistent prioritization, and delays. 

AI incident management uses artificial intelligence technologies, like machine learning and NLP, to automate and optimize incident-related processes in ITSM platforms. 

An AI system can collect vast amounts of historical data on all events, including third-party data, and analyze it in real time to make more accurate decisions, surpassing manual methods in both speed and accuracy. 

How AI Transforms Incident Prioritization 

Incident prioritization establishes the order in which incidents must be addressed. 

Correct prioritization based on urgency and business impact gives precedence to high-impact incidents. Failing to prioritize correctly can lead to prolonged downtime, harming business operations. 

AI-based incident prioritization transforms the entire process. 

By instantly analyzing data streams from multiple sources and leveraging machine learning algorithms, AI reduces the need for manual intervention and ensures unprecedented precision and consistency. 

Let’s clarify the difference between a traditional incident management process and an AI-driven one by examining three critical areas. 

Incident Identification 

• Traditional IM: Different teams overseeing and resolving incidents collaborate to identify the cause but often lack complete visibility of key events, leading to diagnostic delays. 
• AI-Driven IM: AI automatically categorizes events and traces the incident to its source, providing immediate clarity and speeding up the resolution process. 

Task Assignment 

• Traditional IM: A responsible technician manually reviews the incident and assigns necessary tasks, often guiding team members on how to address the issue. 
• AI-Driven IM: AI provides a real-time map of incidents with grouped alerts (clusters), simplifying task assignment. 

Root Cause Analysis (RCA) 

• Traditional IM: Teams manually analyze incidents to determine the root cause, which is time-consuming and reactive. 
• AI-Driven IM: AI traces incidents back to their root cause and predicts potential future issues, making RCA faster and more proactive. 

Advantages of AI in Incident Categorization and Prioritization 

AI incident management offers a transformative approach that significantly improves speed, accuracy, and efficiency. 

Leveraging machine learning algorithms and historical data, AI can automate routine tasks, allowing IT teams to focus on critical issues and simplify overall workflows. Let’s delve deeper. 

Speed 

AI can instantly analyze incoming incidents, categorize them using predefined algorithms, and prioritize them based on urgency and impact. For example, if a server crashes during business hours, the system automatically assigns the highest priority to the event, ensuring it is addressed first. 

The ability to make faster decisions accelerates the entire resolution process, leading to shorter incident life cycles and quicker service restoration. 

Accuracy 

In manual incident management, incorrect categorization or prioritization can delay problem resolution or assign the wrong team. AI minimizes this risk by identifying recurring patterns based on historical data. 

For example, if an incident is incorrectly categorized as low-priority in a manual process, it may be overlooked, resulting in prolonged downtime. 

With AI, this risk is significantly reduced, as the system can recognize similar past incidents and assign them correctly from the start, ensuring resources are allocated efficiently and incidents are handled promptly. 

Efficiency 

AI not only increases the speed and accuracy of incident management but also significantly improves efficiency by automating repetitive tasks. 

In traditional settings, IT teams spend a lot of time manually categorizing and prioritizing incidents, wasting resources, especially during peak periods. By automating these tasks, AI allows IT teams to focus on more complex strategic issues that require specifically human skills. 

For instance, instead of spending time categorizing numerous routine help desk tickets, IT staff can work on system upgrades or resolving critical problems. 

In summary, integrating AI into incident categorization and prioritization processes is a game-changer for modern IT service management. 

Best Practices for Implementing AI in Incident Management 

Organizations aiming to successfully integrate AI solutions into their incident management workflows should adopt strategies that significantly enhance the effectiveness and impact of the AI applications implemented. 

Below are some best practices that can help optimize AI integration. 

• Identify Key Areas for Improvement: Focus on areas where manual categorization and prioritization are time-consuming or error-prone. 
• Leverage Historical Data: AI solutions perform better when trained on accurate historical data. The data must be clean, well-structured, and complete to improve system effectiveness. 
• Monitor Performance: Ensure AI applications adapt to new data and changing business needs. Regular feedback cycles can improve accuracy and performance over time. 
• Adopt Incident Management Software: Choose specific software designed to simplify and make incident management processes more efficient. 

The last point is particularly important. Today, support agents have access to tools that provide a comprehensive end-to-end view of all IT services, from infrastructure to endpoints, allowing them to proactively resolve problems before they impact the business. 

The Future of AI Incident Management: Overcoming Challenges and Seizing Opportunities 

While AI undoubtedly brings significant improvements, organizations may face some challenges during implementation. A primary hurdle is data quality, which must be structured and high-quality. Another critical issue is employees’ adoption of AI-based tools, as they may harbor doubts and uncertainties about the increasing role of automation. 

As AI technologies evolve, the prospect of fully autonomous AI incident management becomes increasingly likely: AI systems seem destined to manage most of the incident lifecycle, from detection to resolution, without human intervention. 

Soon, AI may even enable IT teams to anticipate incidents before they occur, using patterns and trends to predict potential system failures. 

In conclusion, AI-driven incident management is transforming how ITSM platforms handle incident categorization and prioritization, leading to improvements in speed, accuracy, and efficiency. 

2025 Gartner®Market Guide for ITSM Platforms

Get the latest ITSM insights! Explore AI, automation, workflows, and more—plus expert vendor analysis to meet your business goals. Download the report now!

DOWNLOAD NOW