automation Archive | OTRS

The Future of Service Management: Automation, AI and Beyond IT

Laura Diral — Thu, 04 Dec 2025 08:31:49 +0000

The Future of Service Management: Automation, AI and Beyond IT

2025-12-04
Laura Diral

Customer Service

As organizations prepare their strategies for 2026, service management stands at an important turning point. The coming year will bring rapid technological shifts, rising expectations and the need for operating models that can adapt with greater speed and reliability. Many teams are now evaluating how to position themselves for what lies ahead, how to simplify growing complexity and how to make service delivery more strategic across the entire business.

Several trends are already shaping this outlook. Automation is evolving into a fundamental capability for efficiency. AI is becoming part of everyday operations. Integration is emerging as the base for transparent and connected workflows. Security is more intertwined with service quality than ever before. And service management continues to expand beyond IT into enterprise-wide practices. Understanding these developments helps organizations refine their plans for the next year and build service ecosystems that support long term resilience and business value.

This growing clarity also highlights how central service management has become. IT is now expected to provide consistent service, adapt to new demands and maintain control over increasingly complex environments. The upcoming year will amplify these expectations. Businesses want faster delivery, stronger self-service options, better visibility and more predictable operations.

Meeting these expectations requires a departure from reactive work. It demands structured processes, connected platforms and a clear approach to how technology supports the organization. The future of ITSM will be shaped by the ability to reduce complexity and deliver clear, reliable service at every touchpoint.

#1 Automation as a foundation for consistent services

Automation has progressed from exploratory use to a structural requirement. Rising ticket volumes, resource constraints and distributed work environments have made manual processes impractical. Organizations now look for automation to increase consistency, while strengthening service quality.

In 2026, automation will influence far more than simple tasks. It will support lifecycle operations, accelerate approvals and help unify actions across different systems. It will also free teams to focus on improvements that have long been delayed by daily operational pressure.

The evolution is easy to see. Organizations that invest in automation gain the resilience needed to maintain high performance, even during periods of change.

Automation becomes the backbone of stability, enabling IT to deliver predictable and scalable service experiences.

#2 AI shapes the future of service management

AI is poised to play a much greater role in daily operations in 2026. Rather than serving as a distant innovation topic, AI is increasingly embedded into the practical work of service management. It supports classification, identifies trends, enriches communication and provides insights at a speed that human teams alone cannot match.

Findings from the new report by EasyVista and OTRS – The State of SMB IT for 2026 – reflect this shift. Most organizations consider AI in ITSM as important for successes and are already using it to enhance asset tracking, automate tasks and support user interactions through chatbots.

AI generated analysis also helps teams anticipate demands and detect patterns that would otherwise remain hidden. Building on this momentum, AI will continue to evolve into a dependable part of the service ecosystem, helping organizations respond faster, interpret data more effectively and maintain service quality in complex environments.

#3 Integration becomes the foundation of modern ITSM

As service environments grow, integration emerges as one of the most critical trends shaping the year ahead. Many organizations still operate with separate solutions for ticketing, asset management, monitoring and remote access. This creates unnecessary complexity, slows collaboration, makes data difficult to trust.

In 2026, the ability to integrate systems will determine how efficiently IT teams can work. Integrated platforms eliminate blind spots, cut unnecessary work and create a clear path for every request from start to finish. When the entire service landscape is unified in one ecosystem, information becomes clearer and service delivery gains both speed and context.

Integration also improves decision making. With unified data, IT teams can understand dependencies, identify recurring issues and act with more confidence. It strengthens governance and supports risk management by ensuring that changes, incidents and assets are always connected to reliable information.

Ultimately, integration transforms service management from a series of isolated tasks into a coordinated and transparent operating model. It becomes the underlying structure that supports automation, AI and every strategic improvement that follows.

#4 Security rises as a strategic IT imperative

Security has become inseparable from service management, and this trend will intensify in 2026. Hybrid environments, mobile devices and cloud applications have increased the attack surface, making security a continuous practice rather than a periodic initiative.

The EasyVista and OTRS report, The State of SMB IT for 2026, highlights this reality. Many organizations struggle to secure devices, manage endpoint risks and maintain reliable asset visibility. Cybersecurity disruptions remain one of the most significant impacts of IT incidents, demonstrating how deeply security and service continuity are connected.

As organizations prepare for the next year, security will influence ITSM strategies in several ways. Accurate asset inventories will be prioritized. Remote access will require stronger controls. Patch and update processes will become more automated. And monitoring will need to be integrated into service workflows to ensure rapid response.

Security now stands as a core requirement for stable service operations and must be woven into processes, tools and culture.

#5 Enterprise Service Management extends beyond IT

The future of service management will reach far outside the IT department. Many organizations are already adopting structured workflows for HR, Finance, Customer Service, Facilities. This approach allows teams to manage requests, tasks and documentation with greater transparency and accountability.

In 2026, this evolution will gain speed. As organizations push for efficiency and consistency, service management will serve as the common framework for how work is requested and delivered across the business. The outcome is smoother employee experience and a more coordinated flow of information between departments.

Enterprise Service Management (ESM) also supports decision making. With common workflows and shared data, leaders gain clearer insights into bottlenecks, resource needs and service quality across all functions.

#6 Skills and culture remain the drivers of continuous growth

Technology continues to evolve quickly, but the success of ITSM still depends on people. Modernizing processes, adopting AI or integrating platforms require teams who understand how to operate them and how to adapt them to business goals.

Training, change enablement and clear governance will therefore remain essential in 2026. Teams need the confidence to manage new capabilities and the clarity to align their work with strategic objectives. Without these foundations, even the best platforms will not deliver their full value.

Organizations that prioritize skills development will progress faster, maintain higher quality and experience fewer disruptions when adopting new technology.

Conclusion: shaping the next phase of service management

The outlook for 2026 reflects a service environment that is evolving quickly and becoming more interconnected. Automation, AI, integration, security and enterprise-wide workflows will guide how organizations strengthen their operations and support future growth.

Service management is moving beyond its traditional boundaries: it is becoming a strategic capability that influences business resilience, employee experience and long-term innovation. The organizations that succeed will be those that plan with clarity, invest in sustainable improvements and build service ecosystems that are transparent, integrated and ready for the demands ahead.

Laura Diral

Customer Service

Service Desk Automation: Best Practices for Greater Efficiency

Author OTRS — Thu, 18 Sep 2025 07:26:22 +0000

Service Desk Automation: Best Practices for Greater Efficiency

2025-09-18
Author OTRS

Best Practices, Customer Service

In recent years, IT has undoubtedly made impressive progress – including in service desks. Yet, service desk employees confirm that this work is still plagued by numerous inefficiencies.

The solution lies in service desk automation. If implemented smoothly, automation for service desks can deliver highly valuable improvements. Teams see higher productivity, increasesd cost savings and greater value creation.

The best practices presented here show how this can be achieved.

The Problem with an Inefficient Service Desk

Traditionally, when an end user encountered an IT issue, they contacted the service desk. The agent handling the request would first put the caller on hold. They would create the necessary documentation to officially open the ticket.

Only then could the problem be resolved or escalated. And only once the process was completed would the end user receive feedback. Depending on the severity of the problem, this could take minutes, hours, or even days.

This outdated approach frustrated both customers and employees alike. It is also extremely inefficient.

Automations and AI integrations provide a far better solution. They guide the service desk reliably into the modern age while saving valuable time, effort, and opportunity costs.

"Automation applied to an efficient operation will magnify the efficiency. Automation applied to an inefficient operation will magnify the inefficiency."

Bill Gates

10 Best Practices for Service Desk Automation

The following best practices show how organizations can work more efficiently. Service desk automation makes the best use of team resources.

1. Leverage Knowledge Management

Knowledge management is a central element of service desk automation. It allows both routine activities and complex processes to be handled in a more structured, organized, and standardized way.

To enhance service, use tools that make knowledge more engaging and interactive.

Example: The common HR question “What is parental leave?” can be broken down into a simple workflow. A tool that creates decision trees with automated workflows makes complex issues more dynamic. This is because it can account for multiple variables. Examples of variables are who needs help or what device requires support.

2. Reset Passwords and Unlock Accounts

Nowadays, almost everything requires a password. Therefore, a common issue is that people forget them regularly.

Automated, knowledge-based workflows should help. They can be made accessible through service portals. End users can follow the required steps to reset their passwords without contacting the service desk.

This significantly reduces first-level tickets and increases satisfaction, since users can solve problems on their own. Password managers provide further relief by storing all employee passwords in a central system. This requires a single master password.

3. Provide Automatic Answers and Solutions

Once again, knowledge management plays a key role here. Imagine an employee wants to install a new system on their laptop. There may already be a process in the IT service catalog.

If, instead, they contact the service desk, the assigned agent has to spend time handling it. While such a request may not seem critical, frequent interruptions of this kind have a clearly negative effect. They divert focus from truly important incoming tickets.

The right self-service automation tools give employees a way to ask questions and receive answers without requiring human intervention. Knowledge workflows can be integrated into portals, applications, community platforms, and AI chatbots.

4. Use AI

The use of artificial intelligence in the service desk offers companies clear benefits. By automating processes and preventing unnecessary tickets, operating costs decrease while sources of error are reduced.

At the same time, AI eases operational workloads. It handles routine tasks and gives employees valuable time for strategic and value-adding activities.

Customer satisfaction also rises. Users benefit from faster response times, more accurate solutions, and personalized support available around the clock. This strengthens trust and loyalty.

In addition, machine learning ensures that with each ticket creation, the AI-driven system becomes more efficient. This advances service delivery desk over time.

5. Route Tickets to the Right People

Too often, tickets end up with the wrong IT staff; teams spend too much time each day sorting new tickets. This is inefficient and negatively impacts the customer experience.

Sorting tickets can be automated. Teams can use an ITSM platform that automatically routes tickets to the correct departments from the start. Personalized dashboards and rules define the entire ticket flow.

When teams automate ticket routing, customers get the support they need more quickly. Agents spend less time dealing with administrative hassles.

6. Provide Timely and Regular Status Updates

One of the greatest frustrations for end users is not knowing the status of their issue. They want to know how much longer it will take to resolve. Automated ticketing systems can define rules to send updates to customers on time. This greatly reduces follow-up inquiries to the service desk.

Plus, if a ticket resolution falls outside the service level agreement (SLA), automated notifications can trigger escalation. This ensures the issue is not forgotten and still receives appropriate attention.

7. Escalate Critical Incidents

Some organizations have support teams available around the clock – but most do not. In the evenings or on weekends, systems or tickets are often not actively monitored. In these cases, it is crucial to have an automated system that immediately escalates critical issues in real time. This way they do not wait until regular business hours.

8. Measure Productivity

Data collection can also be automated to provide a comprehensive view of team performance.

This includes common service management measures and metrics such as:

MTTR (Mean Time to Recover)
First Contact Resolution rate
Number of tickets recorded monthly/weekly
Number of service requests recorded monthly/weekly
Percentage of escalations
SLA compliance rate
Business hours lost due to outages

The right ITSM tools display this data in dashboards – individually visualized for team members, managers, or executives.

9. Close Tickets Automatically

Some requests take longer than others but should not remain open too long. With service desk software, rules can be defined to automatically close tickets when necessary. An example of this would be when the customer does not respond within a certain timeframe. This reduces manual effort for agents who have many cases to keep up with.

10. Collect Customer Feedback

There are many ways to measure the performance of a service desk. However, even the most positive metrics have limited value if service quality is lacking.

A good solution is to regularly conduct surveys and send them to customers. The best way to ensure continuous feedback is to automate the process. There are tools that automatically capture, collect, and prepare feedback for evaluation.

How OTRS Drives Service Desk Automation

Thanks to automated workflows in the service desk, OTRS ensures that no steps are ever overlooked. Team members can easily manage requests based on flexible templates and communicate directly with customers via the system.

Automatic notifications and intelligent ticket assignments significantly shorten otherwise time-consuming decision-making processes. In addition, custom ticket fields, clearly defined process management, and reusable process templates enable more efficiency, transparency, and better results.

Conclusion: Service Desk Automation Delivers High Value

Companies benefit extensively from service desk automation. It increases efficiency, enhances customer satisfaction, and reduces redundant tasks.

But such automation offers even more potential: it improves service quality for employees, enables tailored customer experiences, and creates transparency about achieved performance.

In short: with the right technologies and ITSM tools, the work of a service desk can be significantly improved. This is critical – not only for employees and customers but also for overall organizational growth.

Author OTRS

Best Practices, Customer Service

Large Language Models (LLMs) and Machine Learning: Background and Use in Customer Service

Tobias Kortas — Tue, 12 Aug 2025 10:47:02 +0000

Large Language Models (LLMs) and Machine Learning: Background and Use in Customer Service

2025-08-12
Tobias Kortas

AI & Automation

Artificial intelligence (AI) is bringing striking improvements to customer service. The challenge, however, is that many organizations still don’t know how to make practical use of it. The excitement is real, and daily uses are varied. However, the true business value is slow to reach many companies.

To use AI effectively, it takes a deeper understanding of the mechanisms behind it. This article explores what Large Language Models (LLMs) and Machine Learning (ML) can accomplish in customer service.

What Are LLMs and ML—and How Do They Work?

Large Language Models and Machine Learning algorithms are transforming customer service. They are becoming important tools for companies. These tools help them stay competitive, provide quick support, save time, and keep high performance.

What Are Large Language Models?

Large Language Models (LLMs) are a powerful type of artificial intelligence (AI) designed to understand and generate human language. They are machine learning models that process natural language (Natural Language Processing – NLP).

LLMs understand text, analyze it, and generate coherent responses or perform language-related tasks. Neural networks that are similar in design to the human brain make this possible. The network’s training process requires massive amounts of text so that the model can learn and build connections.

There are many types of models that are differentiated by how the model is trained.

Fun Fact #1: To read the amount of text used to train GPT-3, a human would need to read around the clock for 2,600 years.

Fun Fact #2: A large language model performs many calculations. If a human could do one billion operations each second, it would still take over 100 million years.

When it comes to handling text, LLMs can:

Generate text
Create summaries
Continue or extend text
Translate languages
Rephrase sentences
Classify data
Categorize topics
Detect sentiment (Sentiment Analysis)
Fraud detection

They also function as chatbots, answer questions, and can even perform basic programming tasks.

These capabilities make LLMs increasingly popular in the business world. They support customer service with chatbots, sentiment analysis, translations, summaries, and information delivery.

What sets them apart: In 2017, developers introduced transformer models. This was a game changer because it lets LLMs decide how important information is in a sequence. It also processes NLP-related information much faster.

Use in business: In addition to training one’s own LLM, companies can be licensed. This means that the LLM can provide usable results right out of the box.

Companies can improve a pre-trained model by adding specialized data. This helps the model fit specific tasks, industries, or language styles. This results in more precise and context-aware outputs.

What Is Machine Learning?

Machine Learning (ML) is the foundation of Large Language Models. ML-based programs learn from example data rather than being explicitly programmed with rigid data. These models learn to recognize patterns and apply them to new data without needing additional instructions.

After the initial learning phase, it is fine tuned. Reinforcement learning is used. This is the practice of teaching the model which, among multiple options, is the best fit. The algorithm learns to make better decisions over time.

A simple example: A program initially doesn’t know what a cat looks like. The program learns from thousands of images and can later recognize a cat without being told what it looks like.

A more advanced example is sentiment analysis. A model learns how different emotions are expressed through various sample data and can then detect customer sentiment. This gives support agents quick orientation, allowing them to dive deeper into critical cases and respond accordingly.

Learn how OTRS makes your support more efficient with its AI services and download the OTRS AI data sheet.

Background: LLMs and ML Are on the Rise

Artificial intelligence continues to gain momentum. The challenge is not in understanding its potential but in turning that potential into tangible business outcomes. Yet, teams have difficulty applying tools, like ChatGBP, in meaningful, business-specific ways.

Our report is called “The State of SMB IT for 2026” It shows that 71% of small and medium-sized businesses (SMBs) believe AI is important for their IT service management (ITSM) success. However, most are still just starting to adopt it. For SMBs, AI is less of a disruptive force and more of an enhancement to existing workflows.

According to the report, the adoption of AI systems correlates with ITSM maturity. Without a good ITSM or ITAM system, AI has limited uses. It would only be able to help with chatbots, sorting tickets, or creating knowledge base articles.

AI, LLMs, and ML are already making a difference in service management. They are providing clear efficiency gains.

The bottom line: These technologies currently support manual processes rather than fully replacing them.

Role in Customer Service

Large Language Models are an excellent fit for customer service. Put simply: LLMs can significantly optimize customer service. These AI-driven applications support a wide variety of tasks and make a real difference.

Customers get fast and helpful answers. Agents save time and effort. Businesses enjoy smoother processes, more productive workers, and happier customers.

Example Use Case

This even applies to complex cases. Imagine a customer who is referring to issues following the implementation of a particular software. The assigned agent can quickly summarize past ticket interactions using AI. They can also detect the customer’s mood with sentiment analysis.

Additionally, they receive a suggested response in just seconds with the agent just needing to review it.

In such cases, the time savings are enormous, and the results—thanks to a combination of AI tools—are likely to be highly helpful.

Even without agent involvement, LLMs are taking on a key role by responding to inquiries instantly. They are able to offer around the clock services. This relieves staff and automates routine tasks. Chatbots and AI-driven knowledge bases are great examples of this.

Progress Through Machine Learning

Machine learning doesn’t just represent the potential of LLMs—it powers their ongoing evolution. LLM-generated outputs may start by handling simple questions, like service-level 0 or service-level 1 inquiries. However, their abilities can improve. They can eventually deal with complex issues and match the skills of experienced workers.

Tips for Using LLMs and ML

There’s no doubt about it: LLMs and ML are growing quickly. They are getting great results and will likely exceed our expectations in the future.

Therefore, the question isn’t whether to use these technologies—but how. In other words, getting the most out of LLMs and ML is crucial both now and in the future.

Below are practical tips for leveraging their strengths while effectively addressing challenges.

Make the Most of the Benefits

The potential benefits of AI are vast, powerful, and varied. You just need to know them—and know how to use them.

Here are key examples of how LLMs can drive meaningful improvements in customer service:

#1 Use LLM Capabilities Strategically for Automation

Many users apply LLMs in a fragmented way, supporting manual processes. In reality, LLMs can fully take over tasks that previously required manual effort. For example, in customer service, models can generate responses, handle entire support conversations, and even automate documentation or FAQ creation.

Ideally, users who understand the full scope of LLM capabilities should use them to the fullest extent possible. This saves time and often yields more consistent and better results.

#2 Enhance Precision and Quality

LLMs are often recommended for routine tasks, process automation, and increasing output. Advanced machine learning allows for high quality. LLMs not only understand language well but can also generate it accurately. This makes it possible to produce well-crafted emails and reports, clear summaries, rewrites, and accurate translations between languages.

#3 Find Creative Solutions and New Ideas

Thanks to their vast training data, LLMs can surface knowledge from many different areas and connect the dots. This can lead to creative, unconventional solutions and ideas that users wouldn’t come up with on their own.

Overcoming Challenges

In general, AI, LLMs, and ML offer significantly more advantages than problems. Still, there are some challenges. The sooner users understand them, the better they can manage them.

Here are the most common challenges users face:

Determining whether they can trust the outputs
Difficulty validating AI decisions or recommendations
Dealing with bias and discrimination
Protecting sensitive data
Navigating legal and ethical uncertainties

Below are a few key challenges explained:

#1 Dealing with Hallucinations

One of the biggest challenges in generative AI is output accuracy. While most results are factual, people should still check the outcomes—especially in complex scenarios.

Sometimes AI “hallucinates”—generating information that sounds right but is factually incorrect. This happens because predictions are based on probability (the most likely next word) rather than truth verification.

You can reduce hallucinations by providing LLMs with context—such as relevant documents—which helps generate more accurate, context-aware responses.

#2 Identifying Bias

This challenge is closely related to accuracy. Biases may be factually correct but still present a skewed view of reality.

For instance, LLMs can reproduce social stereotypes—like defaulting to male doctors and female nurses. In addition to ethical bias, linguistic (e.g., overly polite wording) or geographic (e.g., US-centric examples) bias may appear.

With experience, users can easily identify these. Mature applications and diverse training data help minimize them—especially with fine-tuning using curated datasets.

#3 Protecting Sensitive Data

LLMs should comply with data protection regulations like the GDPR and must not expose personal data. Users should avoid sharing personal or sensitive data unless absolutely necessary—and then only if they’re sure how that data is being handled.

LLMs and Machine Learning at OTRS

Today’s customers expect outstanding service experiences: fast, knowledgeable, thorough, and up to date. In ITSM, that includes being able to handle large ticket volumes while maintaining high service quality and satisfaction.

OTRS’s AI services bring LLMs and machine learning to the next level. Our AI learns from data, understands context, and generates relevant answers—automating previously time-consuming service tasks.

This improves efficiency and the quality of customer service. It also helps businesses grow, giving them a clear edge over competitors.

Available AI services include:

Ticket classification and service description
AI-generated responses
Sentiment analysis
Real-time translations
AI-generated summaries

Conclusion

Large Language Models and Machine Learning are becoming increasingly important in customer service. When used for automation, standardization, or personalization, they can significantly enhance efficiency, customer experiences, and satisfaction.

It’s not just about saving time on routine tasks. It’s also about quality.

LLMs provide new insights and effective solutions. They also offer sentiment analysis. These create a strong base for better service.

In the future, a key differentiator will be how businesses use LLMs. There are two main approaches:

LLMs as supportive tools – used occasionally to speed up and enhance manual processes.
LLMs as disruptive technology – used to replace manual processes altogether.

The first approach keeps the focus on manual labor; the second is technology-driven. The truth is that businesses using LLMs only sometimes are just starting to see their full potential in customer service.

Tobias Kortas

AI & Automation

Features in OTRS: AI Use Cases and Benefits

Tobias Kortas — Tue, 01 Jul 2025 06:00:27 +0000

Features in OTRS: AI Use Cases and Benefits

2025-07-01
Tobias Kortas

AI & Automation, Using OTRS

Today’s service teams have high expectations. They must provide fast, personalized, and high-quality support. This support often needs to be across many channels, in different languages, and under pressure.

Even the most dedicated employees find it hard to keep up. They feel overwhelmed by repetitive tasks, slow ticket triage, and time-consuming research.

To ease this burden and provide noticeably better service, teams need new solutions. Artificial Intelligence (AI) applications accelerate responses, streamline workflows, and increase productivity. OTRS’ AI features automate repetitive tasks and enable fast, high-quality, and transparent ticket handling.

Why AI Is a Key Driver of Efficiency

AI features and applications automate tasks and increase efficiency, allowing companies to accomplish more with fewer employee resources. Every agent should have an AI assistant. This helps speed up ticket processing and gives accurate answers. It also frees teams from boring routine tasks.

In short: When agents work smarter, not harder, they get better results with less effort. This leads to more satisfaction overall.

This reduces pressure and improves customer relationships.

It supports key performance indicators (KPIs). It aims to maximize the return on investment (ROI) for AI solutions. It also focuses on improving important business metrics. These include Customer Retention Rate (CRR) and Net Promoter Score (NPS).

Additionally, it looks at employee satisfaction and productivity.AI-powered systems keep improving over time. They build on the base model and become more valuable with each interaction.

AI-enhanced systems significantly outperform traditional software, particularly in terms of efficiency and service quality.

AI Features in OTRS

As a ticketing system, OTRS doesn’t just provide AI tools—it gives users the power to work more efficiently.

AI applications are only as good as the time savings, quality improvements, and service enhancements they generate.

Here’s an overview of the AI services available in OTRS:

Ticket Classification and Service Description

This AI feature automatically analyzes and categorizes tickets. Not only does this save time, but it also ensures standardized, accurate ticket assignment. It can also trigger automated workflows such as escalation management.

This feature uses automated service descriptions. It creates consistent and meaningful summaries in seconds. These summaries include keywords and common request types. This significantly reduces manual documentation and forms the foundation for further AI use.

AI-Powered Response Generation

This service generates context-aware responses based on knowledge base entries. Agents review the natural language reply and send them directly. This greatly speeds up response times and helps solve problems better.

It also makes sure that answers are clear, helpful, and correct. This removes the need for long manual responses and searching through knowledge bases.

Sentiment Analysis

By using a large language model, OTRS AI features identify the emotional tone of incoming messages. They system determines how urgent or emotionally charged a request may be. Agents deal with cases differently based on a customer’s mood.

Sentiment analysis provides a quick overview and helps agents craft thoughtful, empathetic replies.

Real-Time Translation

This feature breaks down language barriers by instantly translating both incoming and outgoing messages. It enables seamless multilingual communication, allowing everyone to converse in their native language. This saves time for agents and enhances the customer experience.

Unified Knowledge Access

Responses need to be fast, accurate, and based on the latest information. This service integrates with both internal and external sources to ensure responses are current and consistent.

Accessing AI Services

AI features in OTRS are provided through credit packages that are tailored to specific needs. The features are microservices in OTRS. They are easy to set up and grow with your support operations. This also helps improve agent performance.

Pricing is simple and scalable: each AI action—such as ticket classification—costs one credit.

Ideal Use Cases for AI in OTRS

AI in a software solution like OTRS is useful in a wide range of scenarios. It’s especially beneficial when the goal is to save time, enhance the user experience, or increase precision. In these cases, automation, pattern recognition, and language processing pay off significantly.

Here are some ideal scenarios:

High ticket volumes: Service teams benefit from automation and easier scalability.
Multilingual environments: AI supports the setup of international, multilingual customer support.
Onboarding and productivity: AI shortens ramp-up time and boosts employee efficiency.
Improving customer experience: AI provides tools to better understand and serve unhappy customers.
Cost reduction: For cost-conscious businesses, AI helps reduce cost per ticket.

Benefits of Using AI

Using a dedicated ticketing system is already a big step forward for many organizations. Adding focused AI functionality takes productivity to the next level and helps evolve service management even further.

Here are five key benefits:

#1: Faster Resolutions

A key strength of generative AI is speeding up processes and reducing routine workloads. In ticketing systems, this means automatic ticket classification, priority assignment, forwarding, and response generation.

All of this speeds up the process, enabling quicker—and often better—resolutions. It eases the agents’ workload and, more importantly, increases customer satisfaction.

#2: Streamlined Workflows

One of the biggest challenges at work is the overload of routine tasks. These tasks prevent employees from focusing on strategic or creative work. AI frees them from these constraints, allowing for more value-driven tasks.

Sometimes, workflow management is less about perfecting processes and more about enabling employees to follow them without disruption.

#3: Improved Accuracy

The real power of AI lies in combining human and machine strengths. For example, as an agent builds a relationship with a customer, AI gives helpful case information. This information comes from internal or external sources in real time.

Agents can then filter what’s useful for the specific case—resulting in highly relevant, well-structured answers. Enhanced responses with rich detail are received by customers.

#4: Better Relationship Management

Empathy is a human strength. However, AI is very good at analyzing large amounts of data. This includes finding sentiment in text.

Sentiment analysis helps agents detect emotions quickly and prioritize tickets that may indicate frustration or urgency.

AI also supports personalization. It recommends actions based on historical data and understands each customer’s specific preferences and expectations. Summary generation helps employees quickly gain an overview—something that would otherwise require significant time and effort.

#5: Multilingual Support

Language barriers are one of the biggest obstacles to fully understanding issues and crafting appropriate solutions. Even when people share a language, fluency may not be enough to communicate complex details effectively.

Integrated translation eliminates this barrier. It enables multilingual support, regardless of the customer’s original language. Agents view requests in their chosen language. The system automatically translates their replies into the ticket’s original language.

Conclusion: Smart AI Usage Drives Business Forward

AI models are a game changer in ticketing systems—helping save time, improve visibility, and deliver more personalized service. When used effectively, customers clearly feel the benefits of AI.

A core rule of process automation is to first optimize workflows, then automate them. Similarly, AI should be implemented gradually in areas where it delivers high value.

OTRS’ AI credits provide a clear and flexible way to use AI features. This makes it easy to meet increasing support needs.

Tobias Kortas

AI & Automation, Using OTRS

Mobile device management: definition, applications and best practices

Tobias Kortas — Tue, 05 Nov 2024 08:15:44 +0000

Mobile device management: definition, applications and best practices

2024-11-05
Tobias Kortas

Best Practices, Digital Transformation, ITAM

What is device management?

Device management means that people in charge, usually IT administrators, provide, set up, and monitor applications for devices. These devices include desktop PCs, laptops, tablets, and smartphones. They also take steps to prevent issues, like performing updates.

This includes the following areas:

Configuration management: setting up and customizing devices
Security management: enabling adequate protection, for example through updates, firewalls, encryption, or geofencing
Monitoring: monitoring the status and use of the devices
Support: supporting users by providing instructions and resolving problems and incidents
Compliance: ensuring that all devices comply with regulatory and internal guidelines

Definition: Device management

With good device management, companies can run their IT operations smoothly. They can also gain control and security. This allows them to act strategically and use their IT resources effectively.

A device management server often works with an application on the client device. It can also delete contact data and other settings. This is useful for lost or stolen devices, as well as for devices of terminated employees.

Why device management is so important

Device management offers excellent control over an unlimited number of devices. Enterprise mobility management (EMM) professionals save time and reduce stress by having secure, reliable, and up-to-date information. They also benefit from a clear overview of devices. In addition, thanks to remote maintenance, automatic provisioning and zero-touch provisioning, those involved can act from anywhere.

A key factor is that automation reduces downtime and manual effort. This helps both IT teams and their clients. It makes everyday life easier. Tedious processes and time-consuming IT problems become things of the past.

With better device management, organizations save money. They use their devices and applications more effectively. This helps employees use their time more efficiently.

Security benefits

Good device management greatly improves security. Administrators gain better control and can act quickly in emergencies. This helps protect against data loss, malware, and unauthorized access.

In terms of security, teams can:

Respond to security incidents and anomalies in real time
Remotely lock or wipe stolen or lost devices
Enforce password requirements, encryption or device shutdowns
Prevent unauthorized apps and software from being installed
Manage updates centrally to fix vulnerabilities

Mobile Device Management

When discussing device management, people often mention mobile device management (MDM). MDM focuses mainly on mobile applications and devices.

What is mobile device management?

Mobile device management means that IT administrators manage and secure mobile devices such as laptops, smartphones or tablets. Important requirements include the ability to remotely control and configure devices, install applications on them, and lock and secure stolen or lost mobile devices.

Companies generally accomplish mobile device management by using a special device management solution.

MDM software

Those responsible often use special software for mobile device management (MDM). It’s hard to manage many mobile devices in an organization. A dedicated system is almost necessary for this task.

Organizations also use this to save time, improve security, and make the best use of money and resources. MDM solutions should help by giving a clear overview, automating some tasks, and allowing bulk actions.

MDM tools focus on the following activities:

Distributing and managing apps
Monitoring device activities
Implementing restrictions and blocking activities
Determining the locations of mobile devices (geolocation)
Checking installations
Complying with security guidelines

Use cases for mobile device management

There are many industries, organizations and companies for which efficient device management is extremely important. This is even more important when many devices are in use. There are often many applications and information on these devices. A strong need for security also exists.

Scenarios like devices infected with malware or viruses can let hackers access sensitive data. These situations are not just a fantasy. They are all too often a reality.

Tobias Kortas

Here are some striking examples of how companies use mobile device management in a dedicated way.

Example #1: School

A school manages the devices for students and teachers. This includes setting restrictions, using geofencing, and automatically installing updates. For example, the administrator can install or block apps on student tablets used in class. They can also update the devices, limit Internet access, or lock lost devices from a distance.

Example #2: Enterprise

A large company uses device management to optimize its own IT processes and manage numerous implementations simultaneously. One challenge is separating corporate data from personal data on employee-owned devices. Another challenge is enforcing strong security policies on all devices.

Example #3: Government agency

A government agency needs a safe digital space. This space should allow for easy device management and smooth daily operations.

For example, this could mean properly securing all official laptops that hold confidential citizen data. You can do this using a mobile device management solution. This works with encryption, regular updates and patches as well as blocking unauthorized applications

Example #4: University

At a university, lecturers, staff and students use many different devices. With mobile device management, you can efficiently manage all of these aspects, including comprehensive security and usage restrictions. An important task is to make sure students can access academic resources. This includes e-learning platforms and library databases.

Example #5: Medium-sized company

A medium-sized company wants to improve the security of its devices. It also wants to keep them in good condition. Additionally, the company aims to solve any IT problems quickly and effectively. For example, a company might equip its field staff with laptops and smartphones.

Now, companies must install and update apps and software, like CRM or project management tools, on all devices. A dedicated mobile device management solution guarantees that everyone involved can work effectively, securely and conveniently.

BYOD and MDM

The BYOD principle is important in today’s corporate world. This world values flexibility, agility, and different ways of working.

What is BYOD?

The idea of “Bring Your Own Device” (BYOD) is popular with younger workers. This means they use their own devices for work. It offers a high degree of flexibility and freedom, but also mixes work and private life (work/life blending).

IT administrators face a challenge. Devices not owned by the company are very hard to manage and control. This leads to the need for professional device management that includes both company-owned and employee-owned devices.

A key challenge with BYOD is to clearly separate personal and work data on a device.

How mobile device management supports BYOD

Although BYOD is a challenge for companies, it is by no means a hurdle. Measures can be taken to adequately prepare for this.

It is clear that dedicated mobile device management is the best way forward. This approach helps monitor, control, and manage many devices effectively. This guarantees secure, controlled and legally compliant use of private devices in the corporate environment.

This way, the onboarding of company-owned devices and employees’ personal devices can happen smoothly. Administrators can configure them with the necessary and desired settings while ensuring security at all times.

More security

Device management solutions make it possible to securely integrate personal devices into a company network. This works by using methods like encryption, strong password protection, and remote wipe. Remote wipe deletes data if a device is lost.

Access management

Sophisticated mobile device management makes it possible to effectively control who has access to which company resources. This effectively protects sensitive data and prevents unauthorized access. It also helps to keep track of the device inventory.

Separation of data

A good device management solution keeps professional and personal data separate on a device. This way, you keep company data secure and protect personal data.

Monitoring

IT administrators can monitor activities on private devices used for work. This allows them to intervene quickly if any problems arise.

Compliance

By consistently applying compliance and data protection requirements, private devices also meet the same compliance requirements.

Device management solution: important management features

Device management is a field for which companies usually use a dedicated software solution. It is therefore important to take a closer look at the characteristics that make for an appropriate solution.

The following are the most important functions that a device management solution includes.

Multi-platform device management

This is about mobile application management – distributing, updating and managing software across different platforms. The main benefit is the independence and mobility it offers. Devices can be managed anywhere and anytime. They can also work with different operating systems.

Device monitoring and tracking

Administrators receive real-time information on device statuses, usage patterns and locations. This means they can see everything important at all times. They can also track what is happening with each device.

As a result, they can often access a device remotely and take logical action. For example, admins can block certain apps or update devices with just a click.

Remote support and troubleshooting

Having remote access to devices and being able to initiate the right measures is a huge advantage. It means that IT teams can support the end user quickly and effectively with remote access in the event of problem. This is a big productivity boost, because unresolved IT problems can slow down individual users and whole groups.

Security and compliance

Adequate mobile device management makes everyday life easier and makes the work of IT administrators much more effective. It has a clear and direct impact when things become serious, especially regarding security. If someone steals or loses a device, you can block and delete it remotely. Encryption and the enforcement of passcodes also increase security and compliance.

Zero-touch provisioning

Process automation makes sense in many areas. In this case, software and updates are on an MDM server. This server can automatically or on-demand send updates and installations directly to a device.

Everyone involved has to invest significantly less time and effort. You can carry out configurations with minimal effort.

Find out how OTRS can help you with device management.

Best practices for
device management

Organizations can benefit from device management in many ways. They can save time and money, improve security, and ensure compliance. Device management also helps with scalability.

The best practices mentioned here show how professional device management can be better implemented and its benefits maximized.

Best practice #1:
Use geofencing

Geofencing, or “geographical fence,” lets administrators limit device use based on where they are. If a device is located within a defined area, the system automatically restricts or blocks certain functions.

For example, employees are often only allowed to access sensitive company data within an office building. Access from home or even from abroad is then automatically blocked thanks to geofencing. In schools, this technology ensures that devices are used only for teaching and learning.

Best practice #2: Central administration with

Unified Endpoint Management (UEM)

The term Unified Endpoint Management (UEM) describes a central platform for managing and securing end devices in an organization. The aim is to simplify IT processes and eliminate security risks.

MDM and UEM solutions help. They enforce uniform rules for devices - regardless of whether they are company-owned or private, or whether they are used in the office or at home.

Tobias Kortas

Best practice #3: Introduce clear BYOD guidelines

If employees want to use their own devices for work, they should link this to clear guidelines. This should requireme them to access internal company data and resources securely.

Here are some examples of guidelines:

Require device enrollment
Clearly separate personal and business data
Implement encryption and password protection
Enable an option to delete company data remotely without affecting personal data

Best practice #4: Maintain an inventory list carefully

Every device that is used in an organization or network should be listed on an inventory list.

The following information is usually included for each device:

User
Device model
Operating system
Serial number
Installed applications

It is important that the relevant information is up-to-date, correct and complete. For example, regular audits can easily guarantee that no unauthorized users or devices are accessing a network.

Best practice #5: Consider the entire life cycle

Devices travel a long way in organizations. Administrators and other stakeholders, provision, implement and monitor devices. However, this is not the end of the story. The life cycle includes procurement to deployment, maintenance, user changes and disposal.

If there are well-defined processes in place, this works well. Devices are always up to date. They do not pose any unnecessary risks – such as unsecured sensitive data. Users do not use them beyond a defined service life.

Best practice #6: Run regular backups

Regular backups are essential, especially when it comes to important company data. Test backups to ensure that you can restore data completely and correctly in an emergency.

Best practice #7: Combine with a ticket system

Combining a highly developed device management system with a ticketing system makes sense in many respects. Users benefit from an all-in-one solution. They can combine areas such as ITSM with efficient device management.

Combining inquiries, problem management, service processes and device management creates excellent control over IT-relevant processes.

Conclusion: The many advantages of efficient
device management

Mobile device management is an area that can play a huge role for companies, organizations and institutions. Sophisticated device management – supported by an adequate software solution – make IT administrators’ day-to-day work easier.

Organizations as a whole also benefit from greater efficiency, functional processes and fewer IT problems. The time savings alone have significant monetary value, not to mention the reduced risks from security gaps.

It is important to take a closer look at this area. Implement a suitable solution and apply best practices. By using it consistently and integrating it into your day-to-day IT work, you will benefit immensely. You will also ensure a high return on investment.

Find out how you can make the most of device management.

Tobias Kortas

Best Practices, Digital Transformation, ITAM

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