AI Archive | OTRS Fri, 19 Dec 2025 09:02:10 +0000 en-GB hourly 1 https://otrs.com/wp-content/uploads/2018/03/cropped-OTRS-LOGO-without-tagline-32x32.png AI Archive | OTRS 32 32 The Future of Service Management: Automation, AI and Beyond IT https://otrs.com/blog/customer-service/the-future-of-service-management/ Thu, 04 Dec 2025 08:31:49 +0000 https://otrs.com/?p=222294

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

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

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.

Contact Experts
Laura Diral
]]> AI in SMB IT: Status Quo and Solution Strategies https://otrs.com/blog/ai-automation/ai-in-smb-it/ Thu, 28 Aug 2025 06:05:17 +0000 https://otrs.com/?p=219297

AI in SMB IT: Status Quo and Solution Strategies

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AI in SMB IT: Status Quo and Solution Strategies

Artificial intelligence has promised to change customer behavior and revolutionize IT operations. In fact, it is already steadily doing so today. The hype is massive and its relevance is enormous. Many companies and individuals fear they may not be able to keep up. They worry about falling behind.

Naturally, corporations and large enterprises are better equipped with resources. They easily adapt to technological innovations such as the current AI wave. For small and medium-sized businesses (SMBs), however, the challenge is greater. Between expectations, outside perceptions, and the practical reality, a significant barrier arises for SMBs.

This article, based on recent survey data, examines what is realistic for SMBs when it comes to AI adoption. It also considers what remains wishful thinking. And, it offers recommendations for appropriate IT strategies. In contrast to AI euphoria, this article creates a realistic picture of the current status quo.

Background: Pressure Is Increasing

Developments in the digital world often move at tremendous speed.The digital transformation was a major challenge, especially for SMBs. Now, the perceived need to roll out AI on a large scale brings even greater hurdles.

The budget issue is particularly striking. SMBs must operate with limitations. They make tough choices and face pressure to keep pace with major developments and trends.

Understanding where they can actually deploy AI and where they cannot, provides IT decision-makers with much-needed clarity. This cuts through exaggerated AI market claims.

Study “The State of SMB ITSM for 2026”: Pragmatism Prevails

Our study “The State of SMB ITSM for 2026” shows that SMBs primarily look for concrete and low-risk ways to improve IT productivity.

56% say they need user-friendly AI and automation to enhance their ITSM practices. The solutions should be intuitive to use, quick to implement, and deliver results quickly.

This makes one thing clear. SMBs are not incorporating AI into their IT strategies as a disruptive force. For example, they are not replacing human agents with AI-powered virtual agents. Instead, SMBs are using AI to supplement or optimize existing workflows.

The most important use cases to optimize ITSM and ITAM (IT Asset Management) processes are as follows:

  1. Asset tracking and reporting (35% of respondents)

  2. Automation of repetitive tasks (34%)

  3. Trend analysis for decision-making (33%)

  4. Continuous process improvement (32%)

  5. Predicting and preventing IT incidents (30%)


More Evolution Than Revolution

This reflects their pragmatic view. SMBs mainly use AI to save time, gain better insights, and reduce errors. It’s about optimizing existing workflows—more of an evolution than a full-blown AI revolution in IT.

Large international corporations, on the other hand, demonstrate more radical scenarios. In some cases, first-level support is almost entirely taken over by GenAI features. AI agents handle troubleshooting processes.

SMBs don’t have to go that far. More disruptive technologies play only a secondary role among respondents.This includes end-user chatbots (24%), sentiment analysis (22%), or translation services (16%).

This suggests that SMBs currently do not view generative AI as a major game changer. They do, however, see it as an important enabler of IT processes. The potential of AI is evident. It simply unfolds on a much smaller scale compared to many larger companies.

High Relevance, but Practical Obstacles

The high relevance of AI—even for small business owners—should not be underestimated. The fact that adopting AI in SMBs is fragmented and secondary to primary processes is due to practical hurdles more than to willingness.

Nineteen percent of surveyed executives and IT professionals cited budget constraints as the biggest barrier to introducing generative AI into IT operations. Seventeen percent pointed to a lack of in-house expertise.

In contrast, doubts about AI’s value are not holding respondents back. Only 6% named limited use cases as the biggest obstacle. Five percent cited unclear ROI and 3% saw no need for generative AI at all.

Not surprisingly, 71% of respondents are convinced that AI in ITSM is critical for success and will be among the top five priorities for 2026. Similarly, 30% consider the introduction of AI tools the most important IT priority in the next 12 months.

About the Report

“The State of SMB IT for 2026” is based on an online survey conducted between March 14 and April 4, 2025, on behalf of EasyVista and OTRS AG. A total of 1,051 executives and IT professionals from companies with 51 to 1,000 employees in Brazil, Germany, France, the UK, Italy, Spain, Hungary, Portugal, Malaysia, Mexico, and the USA took part.

View Compact Report Infographic
View Full Report

Solution Strategies

The study points to cautious optimism, not blind enthusiasm for AI adoption in SMB IT. Respondents are well aware of both the high potential and benefits of AI. They also understand the practical limitations of their companies which include budget and in-house know-how.

This strongly correlates with the fact that IT is viewed differently in SMBs. ITSM leaders view IT as a strategic business driver. In contrast, they are often in early maturity stages and rely on fragmented tools. They are also more reactive than proactive in their approach.


Recommendation #1: Take Incremental Steps

This clearly shows that SMBs recognize the signs of the times. They understand that a large-scale shift to AI-driven processes would practically overwhelm them. A realistic and promising approach, therefore, lies in incremental improvements with clearly measurable benefits. Examples include automating repetitive tasks, improving real time inventory management, or optimizing customer service.


Recommendation #2: Embrace Selectivity

In plain terms, the breadth of AI options is overwhelming and nearly impossible to grasp in full. The best approach is, therefore, an as-much-as-you-can strategy. SMBs should take action wherever possible and where clear benefits exist. But they must act consciously within firm boundaries, since a comprehensive approach is out of reach.

This explains the observed gap between awareness and implementation. The gap results in a demand for affordable, easy-to-integrate, and tailored AI solutions.

The key lies in setting clear priorities for AI adoption. Leaders must equip teams with the right technology and training. The focus should be on tools that deliver time savings, enhance quality, and provide strategic value.

In short: SMBs neither have to nor can fully ride the AI hype. Their challenge is to precisely identify and implement those AI solutions that promise the greatest benefit for their individual needs.

OTRS AI Services

Since SMBs implement AI step by step and with pragmatism, they need a flexible, scalable, and cost-conscious model.

They can find such a model in OTRS AI Services. OTRS users can flexibly book AI services via a credit-based system (more info here). Available features include intelligent ticket classification, AI-powered response generation, and unified knowledge access.

The services—based on the machine learning and a Large Language Model (LLM)—are designed to deliver high-quality customer experiences, improved workflows, and greater productivity.

View Datasheet
Overview, Pricing, FAQ

Conclusion: Practical Support, Not Hype, in Focus

With the growing presence of artificial intelligence (AI) in professional IT environments, the pressure on SMBs is increasing. On the one hand, there’s the hype and the urge to stay up to date. They must deliver products or services as quickly as possible and protect customer relationships. Both of which can be enhanced with AI.

On the other hand, there are financial, staffing, and skill-related constraints.

For SMBs, AI capabilities are more about practical support of existing processes and relief. For example, if SMBs are often understaffed, AI can help bridge this gap. This pragmatic approach reflects today’s reality. The pursuit of “unlimited AI-powered performance,” however, remains an ideal that SMBs cannot (yet) pursue.

Therefore, SMBs need easy-to-implement solutions that provide quick and straightforward support, ease the burden on employees, and create greater value.

Tobias Kortas
]]> Device Management Software and Its Connection to Service Management https://otrs.com/blog/itam/device-management-software/ Tue, 26 Aug 2025 06:46:50 +0000 https://otrs.com/?p=219049

Device Management Software and Its Connection to Service Management

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Device Management Software and Its Connection to Service Management

Modern IT landscapes are complex—and growing even more so. Countless assets and a wide variety of devices are managed by IT teams. At the same time, the business expects IT to deliver strong services.

A dedicated Mobile Device Management (MDM) solution is not only a key component for handling these challenges effectively. It also enables outstanding monitoring, significant time savings, and a high Return on Investment (ROI).

This article outlines how device management software creates real added value. It considers the benefits of pairing MDM solutions with a ticketing system. The article examines its role in  IT Service Management (ITSM) or Enterprise Service Management (ESM). Finally, it gives an overview of  various budget considerations.

What Is Device Management Software Today?

Mobile Device Management refers to software solutions and related strategies that efficiently manage, monitor, and secure endpoints regardless of location or operating system. Endpoints are devices such as laptops, tablets, or smartphones. Intelligent device management means that devices running in the environment are remotely identified, monitored and maintained.

Integrations with other tools are essential to gain a holistic view of IT environments. In addition, automation provides smart ways to save valuable resources.

Learn more about our device management solution

Connections and Overlaps

Mobile Device Management is part of IT Asset Management (ITAM). Today, MDM is evolving into intelligent endpoint management. This combines MDM with customer management—leveraging AI-driven analytics and increasingly relying on automation.

In modern IT operations, it makes sense to connect device management with IT Service Management (ITSM). Device management helps support hardware. ITSM supports service processes. Request management, problem management, incident management, or change management are examples of service processes.

On this basis, IT environments can be managed holistically with ease.

ITSM becomes Enterprise Service Management (ESM) when its principles are extended to other areas of the business.. Device management also complements ESM. It helps teams manage both services and technology through a central platform, structured processes, and clear responsibilities. While ESM orchestrates services, MDM becomes a crucial service component (more on this later).

Learn more about device management

Key Functions—and Their Role in Providing Services

When combined with service management, Mobile Device Management brings several significant practical advantages. Even small teams can gain a surprisingly good overview of large diversified IT environments.

After device enrollment, MDM functions and service management work together to offer a number of benefits.

  1. Device history and inventory data: Tckets can be auto-populated with prior device information. This could include device properties or earlier service cases. This saves time, provides clarity, and marks the first step toward adequately resolving a support request. It also helps technicians understand if someone is using a personal device.

  2. Software and patch management: Installations, updates, and patches can be managed across many devices through MDM software. This helps proactively avoid disruptions which aligns perfectly with proactive problem management. Teams can eliminate root causes before they lead to problems and incidents.

  3. Remote maintenance: Being able to easily maintain devices remotely is essential in MDM. For instance, if a device is lost or stolen, teams can make sure work data is not compromised by remotely wiping the device. When done reliably, first-level support experiences huge relief, as users contact support far less often with maintenance issues.

  4. Automatic escalations: Device security is easier to manage. For instance, if devices violate security or compliance policies, automatic escalations can be triggered. This resolves issues as quickly as possible.

  5. Policy management: Policies can be directly integrated into change management processes. This includes information on how devices, apps, and data should be used.

Integration with a Ticketing System

It’s already clear that Mobile Device Management has strong relevance for service management. To make work easier, the mobile device management solution should be integrated with a ticketing system.

Here’s how integration with a ticketing system makes sense:

Learn more about ticketing systems
  • Relevant device information is automatically available in tickets through a shared data foundation.

  • Events within MDM tools automatically trigger ticket creation.

  • Response times and SLA (Service Level Agreement) compliance improve.

  • Self-service portals can integrate device-related content (e.g., tailored suggestions for a “slow device”).
The combined power of device management and a ticketing system propels IT teams forward.

The key lies in having all device data and service processes in view. In this way, teams can act efficiently and logically.

Device Management in the Context of Enterprise Service Management (ESM)

Device management software plays an increasingly strategic role in Enterprise Service Management (ESM). It benefits the IT department but all other areas of the business.

A typical example is employee onboarding. HR initiates a service request. By using device management, IT can automatically provide, configure, and deliver the appropriate device. At the same time, these steps can be documented, managed, and tracked through the central ticketing system.

This is an excellent example of ESM in action.

In short: When device management is systematically integrated into the ESM platform, seamless, end-to-end processes emerge that increase efficiency and transparency across the enterprise.

IT saves time and is positioned as a driver of strategic services. IT becomes the heart of the digital organization.

AI in Device and Service Management

Artificial Intelligence (AI) holds a prominent place in service management. But it also optimizes and accelerates processes in device management.

Several use cases for AI come into play. For instance, device and ticket data can be used to generate predictions that support maintenance processes. AI also enables intelligent routing decisions in device management, such as when certain device types are frequently affected.

In service management, AI applications help in many ways. They can:

  • classify tickets,
  • generate responses,
  • provide real-time translations, or
  • perform sentiment analysis. Sentiment analysis gauges the emotional tone of inquiries.

AI creates numerous opportunities. It accelerates processes. It helps teams handle higher volumes, achieve better results, and generate forward-looking insights. The potential in this area is far from fully realized.

Learn more about AI features in OTRS

How Integrated Device Management Software Helps Save Budget

Using resources intelligently, acting efficiently, achieving Return on Investment (ROI): these have always been important in business. Today, they are even more critical due to increasing market pressure.

When organizations ask whether to implement an MDM solution, budget is taken into account in two ways:

  1. The solution must be worth its price. The price includes the acquisition cost. It also includes factors that go into Total Cost of Ownership, such as training or maintainance.

  2. The software should pay off and generate more financial value than it costs. Ideally, benefits such as productivity gains, automation, or error reduction should outweigh the expenses.


This is precisely what integrated device and service management achieves:

  • By reducing manual effort (e.g., in incidents and problems), support costs decrease.

  • Proactive monitoring extends device lifespans, reducing the need for costly replacements.

  • By providing key context information, device management enables faster and more comprehensive problem resolution.

  • License and asset management are optimized, ensuring licenses and devices are used more efficiently and in a coordinated manner.

  • Transparency on device status and usage enables well-founded, targeted investment decisions.

  • Remote device management makes it easier to enforce security, thus protecting the business from potential fines.

Conclusion

Device management plays a crucial role in IT operations and strongly overlaps with ITSM and ESM. It can also be described as the data-driven backbone of AI-powered automation.

Efficiency, security, and cost control are pressing topics—heavily supported by intelligent, integrated device management. That’s why it makes sense to integrate device management with a ticketing system or an ESM platform. It saves costs long-term, unifies processes, and maintains a holistic overview of IT-related workflows.

At the same time, device management remains a vital subcategory of IT Asset Management. It enables comprehensive device administration and application management regardless of location and operating system. This creates the foundation for fast remote support, delivers valuable automation, and ultimately saves considerable time and money.

Organizations that successfully leverage MDM software solutions to manage devices lay the foundation for intelligent data use and integration with ITSM and ESM processes. This includes automation and AI benefits.

Contact experts
Tobias Kortas
]]> Large Language Models (LLMs) and Machine Learning: Background and Use in Customer Service https://otrs.com/blog/ai-automation/large-language-models-llms/ Tue, 12 Aug 2025 10:47:02 +0000 https://otrs.com/?p=218364

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

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

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.

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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:

  1. Determining whether they can trust the outputs

  2. Difficulty validating AI decisions or recommendations

  3. Dealing with bias and discrimination

  4. Protecting sensitive data

  5. 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

Learn More About Our AI Services

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:

  1. LLMs as supportive tools – used occasionally to speed up and enhance manual processes.

  2. 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.

Contact our Experts
Tobias Kortas
]]> AI in Customer Service https://otrs.com/blog/ai-automation/ai-in-customer-service/ https://otrs.com/blog/ai-automation/ai-in-customer-service/#respond Tue, 18 Mar 2025 08:22:19 +0000 https://otrs.com/?p=211121

AI in Customer Service

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AI in Customer Service

Why is AI important in service?

The feeling that customers take from a company is firmly influenced by the level of service received. Artificial intelligence can provide significant support and have a positive influence in this area.

AI is changing customer service. It offers support ranging from minor assistance to comprehensive virtual assistants. The AI spectrum is broad and supports customers and employees in a variety of ways.

One common factor is this: The technology should allow for quick, always available, easy, and flexible support.

This translates into better service delivery for customers. It also builds a stable foundation for companies thanks to satisfied customers and more profitable work.

In short: AI enables companies to offer their customers better service, save costs and gain a competitive edge.

Advantages of AI in customer service

AI can achieve a lot if you use it correctly. Here is a quick overview of some important benefits.

#1 Personalization

AI makes it possible to provide customers with personalized experiences and context-related support. This makes the service more pleasant and tailored to them. AI achieves this by analyzing customer data, making individual recommendations, designing targeted communication on the preferred channels and providing automated reminders.

#2 Shorter waiting times

The biggest annoyance in service is long waiting times. Artificial intelligence can significantly reduce response times and enable support to be always available. Shorter waiting times are achieved through the use of AI chatbots and workflow automation. It also supports employees with real-time information.

#3 Improved employee experience

Repetitive tasks and easy-to-solve queries take up a lot of time for customer service teams. They distract from more complex cases and more important tasks. If AI can provide support here, employees are relieved, work more effectively and are more satisfied.

#4 Increased customer loyalty

AI guides customers through the service and provides them with a better customer service experience.

When AI tools work properly, they make a significant contribution to improving customer satisfaction. This significantly increases customer loyalty. After all, it is often negative service experiences that make customers want to switch. When you consider the importance of a good customer base, loyalty becomes a decisive competitive factor.

#5 Cost efficiency

Those who make targeted use of AI in customer service have the opportunity to save money on several levels.

For example, chatbots and virtual assistants help employees handle fewer standard questions. This leads to faster processing times. Also, support teams work more efficiently and can provide 24/7 support at no extra cost.

AI also avoids expensive escalations because it can proactively identify and solve support problems.

Potential AI Disadvantages

Artificial intelligence does far more good for the service than harm. Nevertheless, there are some scenarios in which it can be detrimental.

#1 Missing the human factor

AI should not replace human agents. It should complement their work so that customers receive the best possible service through the combined effect.

Offering empathy, handling emotions, creating solutions, and providing support are important skills. Only well-trained employees can bring these skills to the table. In addition, people bring practical experience that an AI systems cannot have in this form.

The key is to build a strong AI-human team. This team should combine their strengths to create real benefits.

#2 Dependence on technology

Companies should not become too dependent on AI technology. They should always offer alternatives to AI-driven processes and tasks. After all, errors or failures in AI can severely impair support if you rely too heavily on it and it breaks.

#3 Lack of contextual understanding

Modern conversational AI can recognize context and provide precise answers based on this. However, difficulties can arise with unexpected queries and the interpretation of complex problems. This can sometimes lead to incorrect answers.

AI in customer service: examples

There are many ways to use artificial intelligence to optimize support and other customer service operations within companies. AI services can generate significant added value, particularly when using a customer portal or ticket system.

AI chatbots

Chatbots are a very typical use case for automated customer service. They provide low-threshold access to relevant information and knowledge. They mainly handle the first contact or do research for customers. This happens before customers reach out to an employee for detailed information or specific solutions.

Virtual assistants

These assistants provide employees with comprehensive support in their work. A virtual assistant can take on a whole host of smaller AI services that benefit agents. For example, it creates suggested answers, provides background information or summarizes previous conversations. Overall, many AI applications in support can be summarized under the umbrella term virtual assistant.

Intelligent ticket classification

Before processing a request, it often takes time to review tickets and assign them to the right category. An AI application can significantly speed up this process by automatically categorizing ticket content correctly. By quickly and correctly assigning tickets to the right department, we can process support and service requests on time.

Automated responses

Formulating a good answer to an inquiry can take a lot of time. This is a particularly important problem when there is a high volume of inquiries.

AI assistants can formulate suitable answers based on ticket information, which the respective support employee only needs to check. This speeds up processing. It also avoids errors and inconsistent answers.

Sentiment analysis

By noticing the mood in queries, AI can quickly understand how a customer feels. This happens before an agent contacts them. Depending on whether an enquirer is frustrated, satisfied or neutral, different approaches are advisable. If the AI detects a high level of frustration, for example, a rapid escalation is the right course of action.

In general, sentiment analysis helps agents to act with empathy from the outset and offer customers a better service experience.

Real-time translation

Service requests can come in many different languages. There is often a language barrier between user and agent. Real-time translations compensate for this.

Users and agents compose messages in their preferred language. AI then creates automatic translations and the agent reads the message in their preferred language. This means that multilingual communication is not only possible, but it is also fast.

Suggested solutions (knowledge base)

The path to a suitable solution can also be shortened. This can be achieved, for example, when an AI tool directly suggests suitable answers from the knowledge base. This means faster and more accurate solutions are suggested to the customer. They may also be suggested in real-time to an agent who is helping a customer directly.

Best practices for the optimal use of AI

Artificial intelligence provides support in many areas of customer service. However, it is not enough to implement it without detailed strategic and practical considerations.

Various stakeholders are increasingly demanding the use of AI. However, how exactly companies deploy specific AI applications is proving to be more crucial.

The following approaches make sense:

1. Combine AI skills with human strengths

AI offers added value in service delivery, but it also has significant weaknesses. For example, the pure use of technology is clearly at a disadvantage when customers require empathy and comprehensive support. Companies should use AI in customer service in such a way that they combine human strengths with machine strengths.

This often happens automatically, but has its pitfalls when companies use AI extensively and ambitiously. In principle, the technology must support people in a targeted manner and not replace them.

2. Get the best out of personalization

Artificial intelligence comes with the great advantage of personalization. It can create completely individual customer experiences based on preferences and previous interactions. This offers great potential that many companies are not taking into account. Instead of simply implementing chatbots and minor efficiency improvements, it is advisable to use AI to create highly individualized customer experiences.

3. Establish clear boundaries

First, customers need to understand how much they are interacting with a human or an AI. This helps set clear expectations and avoid disappointment. Customers should also be able to switch from an AI application, such as a chatbot, to an employee easily. To improve services, AI and humans work hand in hand wherever possible.

4. Enable multi-channel communication

AI helps customers interact more. They can choose their favorite way to communicate. Therefore, the technology must work smoothly and without issues on different channels, like chat, phone, email, and social media. For example, if a particular customer likes to interact via email, this must be easily possible.

5. Make improvements

Many people expect that AI systems in customer service should work perfectly right from the start. In reality, however, mistakes do occur. Nothing works as intended straight away.

Companies would do well to learn from this and optimize their processes step by step. In terms of continuous improvement, it is advisable use machine learning, feedback from employees and customer input.

Conclusion: Combining strengths correctly

Artificial intelligence means enormous progress in many areas. Customer service also benefits from this to a large extent.

A distinction must be made between AI that takes over support and AI that supports employees. In reality, it is primarily about the latter. Virtual assistants, AI chatbots, and automation aim to give efficient support. They help improve customer service for users and service providers.

Companies should understand the different aspects of artificial intelligence. They need to create strategies to use it effectively in their services. On the one hand, there is pressure in this direction: customers are increasingly demanding it. On the other hand, AI reveals many practical advantages that enable companies to work more efficiently and successfully.

In practice, this also means a remarkable opportunity for typical human strengths. If an AI helps with routine tasks and simple cases, there is more room for human interaction. People can apply empathy, creativity and complex problem-solving skills to support more intensive cases.

Tobias Kortas
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]]> https://otrs.com/blog/ai-automation/ai-in-customer-service/feed/ 0 Knowledge Management – The Path to Profiting from Experience https://otrs.com/blog/itsm/knowledge-management/ https://otrs.com/blog/itsm/knowledge-management/#respond Wed, 31 Jul 2024 10:00:10 +0000 https://otrs.com/?p=90655

Knowledge Management – The Path to Profiting from Experience

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Knowledge Management – The Path to Profiting from Experience

Knowledge is a crucial factor in the corporate context – and is becoming increasingly important. In most companies, information trapped with one person will stiffle efficiency and innovation. Instead, knowledge should be available quickly, validly and in the right format so that many people can use it. Knowledge management is the practice of making effective use of knowledge and information.

What is Knowledge

In short, knowledge is awareness of and understanding about a certain topic or fact.

It is important to differentiate the term knowledge from those of information or data as follows:

Data -> Information -> Knowledge

  1. Knowledge = opinions, processes, ideas that stem from knowing about the information
  2. Information = data provided with context
  3. Data = measurements or objective details

Another term that is often confused with knowledge is skill. Typically, knowledge can be transferred from one person to another. When it comes to knowledge within a company, is often shared through writing or video.

In contrast, skills – for example, how to drive a car safely – can usually only be learned. They must be learned and put into practice by someone.

Knowledge Management: A Definition

Knowledge management (KM) is a structured process for capturing, organizing, storing, sharing and using knowledge or information.

Knowledge management is a multidisciplinary approach. It draws on learnings from many subject matter experts. These experts share knowledge with others, generally through a knowledge management program.

This provides everyone with adequate access to available knowledge. It does so in an easily accessible way that offers knowledge users an overview and structure. This makes it easier for people to put the knowledge to use.

Against this background, the challenge for companies is to use their inherent know-how profitably. They need to benefit as much as possible from their own expertise.

Why is knowledge management important?

By implementing a knowledge management process, companies can operate more efficiently, increase their innovation and gain competitive advantages.

The challenge is to separate the important from the unimportant. It is also necessary to provide truly relevant information to the right addressees and store it in the right places. This is where knowledge management tools come into play. They make knowledge content accessible as well as provide structure.

Effectively managing and access knowledge gives companies a competitive edge.

Relevant steps in knowledge management

Knowledge management is made up of the following steps and components:

  • Identify relevant knowledge
  • Capture and document knowledge
  • Save knowledge
  • Distribute and pass on knowledge, e.g. through knowledge base articles
  • Use and apply knowledge
  • Preserve knowledge and protect confidential information

It's not enough to know - you also have to apply. It's not enough to want - you also have to do. Johann Wolfgang von Goethe

Johan Wolgang von Göhte

Knowledge management in a company is a continuous process that never really comes to an end. Those responsible must constantly maintain, update, renew and supplement know-how. It must be up to date and truly helpful.

Knowledge Management Use Cases

Using knowledge in the corporate world is important. Here are two use case examples that illustrate how it may be a competitive advantage.

Example #1: A company develops an AI strategy

A company wants to establish itself more strongly in the field of artificial intelligence (AI). So far, however, there have been very few points of contact with the topic. In other words, no one has been explicitly assigned to it.

The Chief Information Officer (CIO) must develop a strategy. As a first step, they source all of the existing knowledge in the company:

  • The relevant knowledge that employees already have can be collected in an internal company forum.
  • Any existing self-service portal is searched for relevant information.
  • The existing knowledge is consolidated in a knowledge base.

Now, there is foundation on which to build. An AI strategy is developed based on the company’s own knowledge. In this way, knowledge about AI is used collectively and transferred into a target-oriented strategy.

Example #2: A team develops new software

A group of developers is in the process of creating new software. They need detailed information about previously developed modules, technologies used and frequently occurring problems for this extensive project.

This means that prior history, or knowledge, about the software must be accessible. Fortunately, a variety of items are available in a wiki (a type of knowledge base). This includes best pratices, peer and code reviews, project templates, checklists, etc.

By also documenting details such as code snippets and individual bug fixes, knowledge about the entire project is tracked The information serves as a basis for continuous improvement.

In the end, other people from the developer’s company can use this as an important reference for similar projects. This closes the cycle of learning and improvement.

The Benefits of Knowledge Management

Solid knowledge management leads to specific benefits that have an extremely positive impact. Specifically, the advantages are as follows.

Advantage #1: Work effectiveness

With the right knowledge at the right time, companies and their employees can expand their potential. This is a basic prerequisite for being effective and achieving goals.

Know-how pushes work in the right direction. This adds value and makes a real difference. When the right information is available to everyone at the right time, people are able to convert it into tangible results.

Advantage #2: Creativity and innovation

Ideas, suggestions, information, leanings, knowledge objects – all of these stimulate creativity and innovation. The basic concept is to build on what already exists in order to develop something new. Those who are well informed broaden their horizons and are much more likely to develop new approaches.

This is not just a basic scientific principle. It is also helps companies develop concepts that are in line with certain circumstances and market situations.

Advantage #3: Better products and services

By making best practices and experiences more accessible, companies can continuously optimize the quality of their products and services. Among other things, the principle of continuous improvement helps to increase efficiency and customer satisfaction.

This is in line with consistent knowledge management. It is the best way for organizations to pool their expertise, skills and experience to ultimately provide better products, customer support and related services.

Advantage #4: More efficient problem solving

By quickly accessing knowledge from past projects and similar challenges, companies can solve their current and future problems more efficiently.

With a well-structured knowledge management system, employees can quickly see how someone has successfully solved a similar problem. They do not have to reinvent the wheel. It is often sufficient to use best practices for similar cases as a guide and adapt this to the current situation. This speeds up the problem solving for the new case.

Advantage #5: Better decisions

Good knowledge management also makes it easier for companies to make targeted and logical strategic decisions. This is because the required information is available to people quickly.

After all, the quality of a decision often depends heavily on the information used to inform it. It’s a simple calculation: only those who have the right knowledge at their disposal can make truly logical and target-oriented decisions.

All change creates fear. And the best way to combat this is to improve knowledge.

Ihno Schneevoigt, former Managing Director at IBM Germany

Knowledge Management: Methods

The aim of knowledge management is to deal effectively with the knowledge available.

Knowledge should…
… not be lost.
… have a clear benefit.
… be available to as many employees as possible.
… bring clear added value.
… be applicable in the right situations.

Let’s look deeper into the knowledge creation process.

Identify knowledge

The first step is to identify existing knowledge. This often begins inconspicuously through conversations, team chats or group discussions. A more direct method is expert interviews. Documents, reports or minutes are also relevant sources of knowledge.

Collect and store knowledge

Both individual and collective knowledge must be well recorded. Individual knowledge can come from interviews, recordings or lectures. Collective knowledge (swarm intelligence) comes is created during meetings and can be found in minutes and recordings. Companies should store this knowledge digitally and make it centrally accessible.

Create a knowledge base

A knowledge base is at the heart of effective knowledge management. Knowledge base articles, FAQs and instructions can be essential for companies because they provide they structure and organization.

They also form the basis of a digital self-service for customers.

Actively use and apply knowledge

Even the best knowledge base is ultimately only a means to an end. Customers and employees should be able to use it frequently and satisfactorily. In general, it is often a decisive goal to be able to use the available knowledge as comprehensively and effectively as possible.

Optimize and enrich knowledge

Knowledge is not a static thing. It is constantly evolving. Organizations and individuals should regularly evaluate existing knowledge, identify “needs” and look for new sources of knowledge. The overriding question here is which knowledge is useful and up-to-date.

The SECI model

The SECI model has established itself as a systematic approach to knowledge management. It is one of the most widely used methods for systematic knowledge management.

SECI stands for Socialization, Externalization, Combination and Internalization. The model describes the process of how companies create and transform knowledge.

The SECI model depicts knowledge development as a dynamic cycle. It was developed by the two Japanese knowledge theorists Ikujiro Nonaka and Hirotaka Takeuchi.

This cycle can be applied to different levels of a company, such as individual, group or ove level. It promotes a learning organization that is constantly evolving and generating innovations.

 

The four phases of the SECI model are:

Socialization

In this phase, knowledge is transferred from one person to another through conversations, observation, imitation or shared practices. Examples include mentoring, on-the-job training and informal meetings. For example, an experienced salesperson might take a trainee along to a prospect meeting.

Externalization

Now tacit knowledge (that which is personal and experienced) is transformed into explicit knowledge (that which is codified and captured). In other words, the knowledge is recorded in some way such as in a document, model or concept. In the example above, this could be as simple as the trainee writing a report on the interview.

Combination

In this phase, knowledge from various sources is combined to create new knowledge. The aim is to collect and process information, combine data and systematize knowledge. This might involve several trainees comparing their reports in order to draw more informed and better conclusions.

Internalization

Now the explicit knowledge is transformed into implicit knowledge. It is internalized through practical application and experience. It is finally integrated into people’s actions. In the example, this means that the trainees apply the acquired skills in practice.

Tips for Successful Knowledge Management

In addition to understanding types of knowledge management, it is worth using a few tools, tactics and strategies too. This helps the company use of existing knowledge resources in the best ways.

Tip #1: Promote knowledge

sharing in a targeted manner
Knowledge management is far more useful when it is practiced by the entire company. In the best case scenario, employees actively participate in knowledge exchange and share relevant content immediately.

Companies can encourage this by calling for an open exchange of ideas, information and knowledge, involving key people with critical knowledge, and valuing or rewarding the exchange.

Tip #2: Actively use collaboration platforms

Lively exchanges bring relevant knowledge content to light. This requires good collaboration tools and document management systems. These make it easy to share information, ideas and concepts.

Tip #3: Create templates for documentation

If it is easy to create knowledge content, this will happen much more frequently. All too often, employees are overwhelmed by the task of creating useful and meaningful documents. Templates can be an important aid. Plus, they help standardize the documentation.

Tip #4: Incorporate security measures

Sharing relevant knowledge is a good idea. However, not every single person should be able to access it. It is important to implement access restrictions that protect confidential and sensitive content from unauthorized access.

Tip #5: Measure results

It is advisable to use metrics and key figures to evaluate the success of knowledge management. This can be as simple as the number of knowledge contributions created. It is also possible to record the extent to which a knowledge base is used. Other metrics include how much time employees saved or how many innovations emerge.

Relevant connections

Knowledge management only reveals its strengths in combination with other information management disciplines and tools.

Knowledge management and information management

Information management is closely linked to knowledge management. It enables companies to make targeted use of information as a strategic resource. Information must first be available in the right form. Then, it can be transformed into knowledge content.

Often, however, information and knowledge cannot be clearly separated from each other. This means that information management and knowledge management merge seamlessly.

The relevant difference between the two is that knowledge combines factual with experiential information.

Knowledge management and knowledge databases

Knowledge databases are an important means of recording knowledge centrally, clearly and easily retrievable. They are an important component of knowledge management, which also covers other areas.

Databases prove to be essential for recording, maintaining and retrieving relevant information. Knowledge management also controls how knowledge is used and how it is strategically deployed.

Knowledge management and information flows

Information flows describe the path that information takes within a company. Sender-recipient relationships are defined here.

Functional information flows are required in order to obtain relevant knowledge. Because communication does not necessarily take the most effective routes, information management directs the information.

For instance, two team members share important project experiences in an informal conversation (information flow) and ultimately come to the conclusion that this knowledge should be shared (information management). Based on the conversation, a knowledge base article (knowledge management) is created with lots of relevant and exciting information, learnings and experiences from the project.

Knowledge management and artificial intelligence

Artificial intelligence is playing an increasingly important role in the effective handling of knowledge. Recently, users have become accustomed to consulting generative AI tools. Typically, these are AI chatbots that promise simple, fast and intuitive access to relevant knowledge content.

However, the benefits of artificial intelligence – such as increased efficiency, time savings and optimized decision-making – can be used in a variety of ways. AI summaries of texts, for example, are often useful for gaining a better overview. This captures relevant content more quickly and easily transfers it into knowledge base articles.

Conclusion: knowledge management – an important discipline

Knowledge is of enormous importance. It is a decisive factor in the modern corporate world. Companies that operate a knowledge management system can make much better use of their information, ideas, experience, learning, values and skills.

To achieve this, use a suitable knowledge database, systematically generate documentation, and use the knowledge content. Drawing on models, such as SECI, and some best practices can bring great added value.

Find out how OTRS can support you with knowledge management.

Contact our experts
Tobias Kortas
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