artificial intelligence Archive | OTRS

AI in SMB IT: Status Quo and Solution Strategies

Tobias Kortas — Thu, 28 Aug 2025 06:05:17 +0000

AI in SMB IT: Status Quo and Solution Strategies

2025-08-28
Tobias Kortas

AI & Automation

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:

Asset tracking and reporting (35% of respondents)
Automation of repetitive tasks (34%)
Trend analysis for decision-making (33%)
Continuous process improvement (32%)
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.

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.

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

AI & Automation

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

AI in Customer Service

Tobias Kortas — Tue, 18 Mar 2025 08:22:19 +0000

AI in Customer Service

2025-03-18
Tobias Kortas

AI & Automation, 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.

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

AI & Automation, Customer Service

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AI in ITSM – Importance, Examples and Study

Tobias Kortas — Mon, 03 Jun 2024 13:43:11 +0000

Recently, there has been a lot of AI hype in ITSM. Companies have recognized that artificial intelligence (AI) and automation can significantly increase efficiency, shorten resolution times, improve service availability and much more. Find out all about how AI is revolutionizing IT service management here.

What are the advantages of artificial intelligence?

Tobias Kortas — Thu, 02 May 2024 12:26:44 +0000

Artificial intelligence (AI) is a defining topic in the corporate world. Plenty of organizations are implementing AI systems to automate tasks or make better decisions, for example. This article looks at the specific advantages and disadvantages of artificial intelligence - and how companies can make the best possible use of this technology.

AI summaries of texts: tips and importance in ITSM

Tobias Kortas — Fri, 12 Apr 2024 07:38:06 +0000

Artificial intelligence (AI) is becoming increasingly important. The main challenge for companies is using the new technologies effectively. AI-supported summaries of content are a useful way to act more easily, quickly and productively. This article shows how this can best be achieved – and how summaries can be connected to knowledge bases.

AI chatbot: advantages and tips for companies

Tobias Kortas — Thu, 11 Apr 2024 07:50:44 +0000

Artificial intelligence (AI) is already part of everyday life in plenty of areas. One of these areas is chatbots. Conversational AI chatbots are one of the most common applications of generative AI. This article focuses on their advantages, how they work and tips for using them.

How to Build a Knowledge Base – and Get the Most Out of It

Tobias Kortas — Thu, 28 Mar 2024 08:02:27 +0000

Knowledge is an important success factor for companies, but it often disappears into the black hole of various storage locations. In short: knowledge alone - especially in the hands of individuals - is not enough. It must also be easily accessible and organized. This article shows how customers and employees can use it quickly and flexibly with the help of a database.

Self-service: what it means

Tobias Kortas — Wed, 07 Feb 2024 16:52:25 +0000

Customers want one basic thing: quick solutions to their concerns and problems. An intelligent self-service portal makes this possible, improving the customer journey and saving service employees’ time. However, it is important that companies approach this area in a precise and structured manner.

Process automation: More freedom to drive added value

Tobias Kortas — Thu, 21 Dec 2023 09:26:10 +0000

Challenges, such as high costs and competitive pressure, characterize the professional world. At the same time, many companies are squandering their potential with unnecessarily long routine tasks. The solution is to automate repetitive business processes. This article shows exactly what this means and how it can work for your business.

How artificial is artificial intelligence?

Nils Leideck — Mon, 28 Jun 2021 08:33:44 +0000

Artificial intelligence has been part of our lives for a long time. What exactly do we mean by this, how is machine learning to be classified and is AI used everywhere?

Business Trends to Watch in 2020

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What are the trends for 2020 when it comes to your company? We give you an overview.

IT Focus in 2019: What Are the Top Priorities for German and American CIOs?

Author OTRS — Mon, 14 Jan 2019 10:00:34 +0000

The differences between Germany and the USA are small, and one sector stands out the most.

Five Building Blocks for Successful AI Adoption

Guest Author — Mon, 25 Jun 2018 09:31:48 +0000

Chat bots and optimized search functions are exciting for customer service organizations everywhere, yet companies need to lay some groundwork before the benefits of AI can really take root.

artificial intelligence Archive | OTRS

AI in SMB IT: Status Quo and Solution Strategies

AI in SMB IT: Status Quo and Solution Strategies

Background: Pressure Is Increasing

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

More Evolution Than Revolution

High Relevance, but Practical Obstacles

About the Report

Solution Strategies

Recommendation #1: Take Incremental Steps

Recommendation #2: Embrace Selectivity

OTRS AI Services

Conclusion: Practical Support, Not Hype, in Focus

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

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

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

What Are Large Language Models?

What Is Machine Learning?

Background: LLMs and ML Are on the Rise

Role in Customer Service

Example Use Case

Progress Through Machine Learning

Tips for Using LLMs and ML

Make the Most of the Benefits

#1 Use LLM Capabilities Strategically for Automation

#2 Enhance Precision and Quality

#3 Find Creative Solutions and New Ideas

Overcoming Challenges

#1 Dealing with Hallucinations

#2 Identifying Bias

#3 Protecting Sensitive Data

LLMs and Machine Learning at OTRS

Conclusion

AI in Customer Service

AI in Customer Service

Why is AI important in service?

Advantages of AI in customer service

#1 Personalization

#2 Shorter waiting times

#3 Improved employee experience

#4 Increased customer loyalty

#5 Cost efficiency

Potential AI Disadvantages

#1 Missing the human factor

#2 Dependence on technology

#3 Lack of contextual understanding

AI in customer service: examples

AI chatbots

Virtual assistants

Intelligent ticket classification

Automated responses

Sentiment analysis

Real-time translation

Suggested solutions (knowledge base)

Best practices for the optimal use of AI

1. Combine AI skills with human strengths

2. Get the best out of personalization

3. Establish clear boundaries

4. Enable multi-channel communication

5. Make improvements

Conclusion: Combining strengths correctly

AI in ITSM – Importance, Examples and Study

What are the advantages of artificial intelligence?

AI summaries of texts: tips and importance in ITSM

AI chatbot: advantages and tips for companies

How to Build a Knowledge Base – and Get the Most Out of It

Self-service: what it means

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