AI & Automation Archive | OTRS https://otrs.com/blog/ai-automation/ 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 & Automation Archive | OTRS https://otrs.com/blog/ai-automation/ 32 32 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

TOC   
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
]]> 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

TOC   
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.

Register now

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
]]> Features in OTRS: AI Use Cases and Benefits https://otrs.com/blog/using-otrs/ai-features/ Tue, 01 Jul 2025 06:00:27 +0000 https://otrs.com/?p=216187

Features in OTRS: AI Use Cases and Benefits

TOC   
Features in OTRS: AI Use Cases and Benefits

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

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

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

Why AI Is a Key Driver of Efficiency

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

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

This reduces pressure and improves customer relationships.

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

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

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

AI Features in OTRS

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

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

 

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

Ticket Classification and Service Description

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

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

AI-Powered Response Generation

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

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

Sentiment Analysis

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

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

Real-Time Translation

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

Unified Knowledge Access

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

Accessing AI Services

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


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

Learn more about AI credits

Ideal Use Cases for AI in OTRS

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

Here are some ideal scenarios:

  • High ticket volumes: Service teams benefit from automation and easier scalability.

  • Multilingual environments: AI supports the setup of international, multilingual customer support.

  • Onboarding and productivity: AI shortens ramp-up time and boosts employee efficiency.

  • Improving customer experience: AI provides tools to better understand and serve unhappy customers.

  • Cost reduction: For cost-conscious businesses, AI helps reduce cost per ticket.

Benefits of Using AI

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

Here are five key benefits:

#1: Faster Resolutions

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

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

#2: Streamlined Workflows

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

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

#3: Improved Accuracy

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

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

#4: Better Relationship Management

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

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

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

#5: Multilingual Support

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

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

Conclusion: Smart AI Usage Drives Business Forward

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

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

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

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

TOC   
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
.blog-right-sidebar .blog-left-sidebar{ width:10%; } .blog-main-content{ width:80%; } .toc-hidden .maintoc-container{ width:1px; height:1px; overflow:hidden; } .toc-hidden .blog-left-sidebar,.toc-hidden .blog-right-sidebar{ width:10%; } .toc-hidden .blog-main-content{ width:70%; } .toc-hidden .blog-right-sidebar{ width:20%; } .firstloadanimation .newblogentrance{ transition:all 2s; }
]]> https://otrs.com/blog/ai-automation/ai-in-customer-service/feed/ 0 Automated customer service: advantages, best practices, examples https://otrs.com/blog/customer-service/customer-service-automation/ https://otrs.com/blog/customer-service/customer-service-automation/#respond Wed, 19 Feb 2025 15:55:45 +0000 https://otrs.com/?p=210973

Automated customer service: advantages, best practices, examples

TOC   
Automated customer service: advantages, best practices, examples

What does customer service automation mean?

With automated customer service, companies streamline processes and create the possibility of fast around-the-clock supports. This leads to the desired answers and resolutions without delay.

There are various ways to automate customer service. This does not, however, mean that companies should forgo the personal touch altogether. In many cases, automation simply offers additional options for receiving support.

Customer contact and automated services

Examples of how customers interact with automated services include:

  • A customer uses the automated service, achieves her goal and requires no further service.
  • A customer relies on the automated service to start a process. They still need human intervention due to the request complexity.
  • A customer contacts support, but is told that they can use the self-service options more quickly and conveniently.
  • An AI chatbot switches on.

Examples of automated customer service

Customer service automation can mean resolving customer queries by using web chat functions, messages or knowledge bases. This is done without contacting a human agent.

There are various to support customer service with automation:

  • Internal workflow automation. By using software to structure inquiries and communication, the burden on service employees is relieved. Customers have the information they need more quickly.
  • AI-controlled chatbots can answer inquiries via the app or website.
  • Self-service options like knowledge base articles, FAQs, and forums help customers easily find assistance.
  • Automated emails are sent to customers to inform them about processes, updates and statuses.
  • Interactive voice response systems answer customer calls.

Advantages of automated customer service

Automating services is not an end in itself. Companies expect benefits that can best be expressed in concrete, relevant figures.

Here are the benefits that customers, employees and the company as a whole can directly perceive.

Advantage #1: Constant availability

24/7: Admittedly, this term is overused, but it makes a lot of sense when it comes to automated customer service. People can hardly provide constant availability. Even with generous service times, competent contact persons are often very busy and difficult to reach.

An undisputed advantage of automation and self-service is the ability to access information exactly when it is needed.

In a busy life full of tasks and appointments, customers want to initiate support when it suits them. This is often in the evening, for example, when most service employees are also off work. Customer satisfaction is boosted when customers are helped on their own schedules.

Advantage #2: Personalized experiences – better customer experience

When a customer service agent is in contact with a service employee, there is personal contact. The employee usually does not know the customer’s individual preferences and needs. It is difficult to tailor the answers to the customer.

Artificial intelligence (AI) and machine learning (ML) can improve this by recognizing preferences and patterns. This allows for tailored responses. Recommendations are also generated based on previous purchases and customer service interactions.

Automated customer service now has many ways to respond to individual concerns. These systems can adapt to customer needs and provide targeted, tailored content.

Such automation enables customers to find what they are looking for more quickly, resulting in greater satisfaction and loyalty. In turn, companies can improve key performance indicators, such as the customer retention rate (CRR) or sales figures.

Advantage #3: Saving time and money

When customers make inquiries, they want quick responses. In today’s fast-paced world, hardly anyone wants to wait long for helpful answers.

On the other hand, it is also important for companies to save time. With automation, employees are able to take on fewer repetitive tasks and can concentrate more on value-adding work.

On the one hand, cost savings are achieved because employees act more efficiently. They achieve more in the time available to them.

Meanwhile, dissatisfied customers cause high costs. These costs include direct costs and lost opportunities. Clever automation can help avoid these issues.

Advantage #4: Fewer errors and better quality

Mistakes happen – event in customer service. This cannot always be avoided, but it does have many negative effects. Automation prevents such errors. This is always the case when processes are already well defined through process optimization. 

Consistency exists because the same workflows are used in automated service. By replicating successful procedures again and again, companies achieve a consistently high quality level.

Advantage #5: Scalability

In traditional customer service, extra inquiries causes problems. Additional support agents, resources and costs are needed to cover the increased demand.

With automation, on the other hand, there are no physical limits to the volume of service that can be delivered. AI powered chatbots or virtual assistants, for example, are in continuous use without any restrictions whatsoever.

Advantage #6: Continuous improvement

Automated customer service sometimes used to have a reputation for not being ideal. There were a number of reasons for this:

  • Automations had their limitations with individual and complex single cases.
  • Early automation solutions were immature and had many weaknesses.
  • Hardly any solution is perfect from the outset. It must evolve.

The principle of continuous improvement is useful here. Systems learn from data, which includes input and experience. Customer feedback is also important for making improvements.

Customer service automation challenges

There are certainly challenges with automated customer service. These can be overcome with cleverly placed and well thought-out solutions that support and complement traditional customer service.

Here are a few topics to consider:

  • Customers who greatly appreciate human interaction feel a loss of the personal touch. This is more obvious when they deal exclusively with automation tools.
  • Some customers may view automation as a restriction rather than an enrichment.
  • Customers who need extensive support get lost in automation functions and get nowhere. This could lead to frustration.
  • Some are directed to a knowledge database or a chatbot. They may feel rejected and thus perceive service quality as lower.
  • Service automation must be thought through thoroughly. This prevents large investments on which there is no return.

The problems mentioned can be mostly avoided if automation is done correctly and best practices are used.

Best practices for automated customer service

The following best practices are useful for automating customer service.

Best practice #1: Analyze customer needs in detail

Customer needs are usually well known, but what kind of automation customers want often remains unresolved. The question must be how to best to meet the customer’s interests.

For example, customers may not be sufficiently informed about the status and progress of their inquiries. Tthey may have to spend too much time on support issues. Customers may want quick, simple access to relevant knowledge.

Review customer data to understand the needs that customers have.

Best practice #2: Use human, empathetic language

People want humanity. It’s a natural need. At the same time, the advantages of automated services cannot be denied. Fortunately, humanity and automation can be combined.

Whether an automated system relies more on written or spoken language, it should feel empathetic to customers and convey a feeling of “good care”. You can achieve this by carefully shaping the tone of an automated service. Avoid using stiff, robotic language.

Best practice #3: Offer alternatives to automation

Nobody likes to be presented with something that has no alternative. Automation must feel like an extension or improvement of the customer service experience. It should not feel like a restriction. Customers who do not want automation should not have to use it.

There should always be a way to bypass the automated system and easily communicate a human agent. Customers have their own individual preferences and are happy when the service successfully covers them.

Best practice #4: Keep transparency and data protection high

Trust and genuine customer loyalty only work with honesty and sincere communication. This means that it must be clear to customers when they are interacting with an automated system.

They should also know how their data is being used. It goes without saying that companies must comply with all relevant data protection guidelines.

Best practice #5: Review and adapt regularly

We are constantly confronted with new developments. The validity of information changes. Knowledge databases, FAQs, predefined answers or chatbot capabilities are often not well-maintained after implementation. Meanwhile user needs – due to numerous external influences – are subject to constant change.

As yesterday’s information cannot generate tomorrow’s added value, regular reviews and associated updates must take place. This creates reliability, trust and good customer loyalty.

Best practice #6: Choose the right software

Many automation projects face problems because they rely on different tools. Each tool is only useful to a certain point. Instead, companies should choose centralized platforms that offer structure and can be integrated into their individual workflow management.

High-quality software is highly functional, flexible, customizable and scalable (changes can be adapted). In addition, the system used must fit the project plan and not be a “makeshift” solution.

A brief look into the future

It can be assumed that customer service will become even more automated in the future thanks to artificial intelligence. The biggest advantage here is personalization. Service will respond to the individual needs of a customer in greater depth.

Generative AI can start right at the beginning of the process by being used to train employees. For example, highly unique scenarios were previously a weakness in service.

This will no longer be the case. Customers will receive with automated suggestions based on their profile. AI scripts will be able to solve customer issues instantly.  

Providing customers with solutions before problems happen is key. This could greatly improve the efficiency of support teams using AI bots in the future. AI technologies can quickly find the causes of problems. They can also handle tasks like managing escalations or creating support tickets.

Future bots will not be based on scripts, but will be dialog-based. This means they will be  individually tailored to customers by recognizing their moods and evaluating existing data and activities. This means that customers can receive helpful solutions tailored to their needs in real time.

Customer service automation with OTRS

OTRS offers a dedicated software solution that is specially tailored to the needs of customer service and support. It can be flexibly adapted to individual company requirements. Quickly responding to customer questions, creating transparency, and standardizing processes helps the customer service team provide better service.

Automation and self-service aspects are evident in this way:

  • Thanks to automated workflows, all steps are completed reliably and well.
  • Context-dependent FAQs create clarity for customers and reduce the workload of service teams.
  • Automatic notifications and ticket assignments save time. They shorten response and inquiry times.
  • Thanks to the intuitive integration of a knowledge database, users can quickly find exactly what they are looking for.
  • Customers can plan their preferred service times according to the availability of the service team.
Learn more

Conclusion: automation enriches service delivery

Automated customer service is gaining ground. It offers considerable benefits in many areas, from which companies, customers and employees can benefit. Faster solutions and better service experiences help everyone. They also make time use more effective and increase flexibility.

Some people do complain about the loss of personal touch in automated services. Others enjoy the independence gained and the reduced number of interactions. It is important that both groups are listened to and see their needs met by the services offered.

Companies must now take care to implement automated services in the right way to increase customer value. Such solutions require a good concept – including overarching goals and purposes – as well as structured implementation.

Find out how OTRS can support you with automated customer service.

Contact our experts
Tobias Kortas
]]> https://otrs.com/blog/customer-service/customer-service-automation/feed/ 0 AI in ITSM – Importance, Examples and Study https://otrs.com/blog/ai-automation/ai-in-itsm/ https://otrs.com/blog/ai-automation/ai-in-itsm/#respond Mon, 03 Jun 2024 13:43:11 +0000 https://otrs.com/?p=88623 https://otrs.com/blog/ai-automation/ai-in-itsm/feed/ 0 What are the advantages of artificial intelligence? https://otrs.com/blog/ai-automation/benefits-of-artificial-intelligence/ https://otrs.com/blog/ai-automation/benefits-of-artificial-intelligence/#respond Thu, 02 May 2024 12:26:44 +0000 https://otrs.com/?p=85940 https://otrs.com/blog/ai-automation/benefits-of-artificial-intelligence/feed/ 0 AI summaries of texts: tips and importance in ITSM https://otrs.com/blog/ai-automation/ai-summaries/ https://otrs.com/blog/ai-automation/ai-summaries/#respond Fri, 12 Apr 2024 07:38:06 +0000 https://otrs.com/?p=84117 https://otrs.com/blog/ai-automation/ai-summaries/feed/ 0 AI chatbot: advantages and tips for companies https://otrs.com/blog/ai-automation/ai-chatbot/ https://otrs.com/blog/ai-automation/ai-chatbot/#respond Thu, 11 Apr 2024 07:50:44 +0000 https://otrs.com/?p=84394 https://otrs.com/blog/ai-automation/ai-chatbot/feed/ 0 Workflow automation – definition, advantages and tips https://otrs.com/blog/ai-automation/workflow-automation/ https://otrs.com/blog/ai-automation/workflow-automation/#respond Fri, 26 Jan 2024 08:56:47 +0000 https://otrs.com/?p=78477 https://otrs.com/blog/ai-automation/workflow-automation/feed/ 0 Process automation: More freedom to drive added value https://otrs.com/blog/processes-workflows/process-automation/ https://otrs.com/blog/processes-workflows/process-automation/#respond Thu, 21 Dec 2023 09:26:10 +0000 https://otrs.com/?p=78001 https://otrs.com/blog/processes-workflows/process-automation/feed/ 0 HR Processes – Digitization and Automation as the Key to Success https://otrs.com/blog/processes-workflows/hr-processes-digitization-and-automation/ https://otrs.com/blog/processes-workflows/hr-processes-digitization-and-automation/#respond Wed, 14 Dec 2022 13:00:47 +0000 https://otrs.com/?p=59068 https://otrs.com/blog/processes-workflows/hr-processes-digitization-and-automation/feed/ 0 Why process automation is easier with experts https://otrs.com/blog/processes-workflows/why-process-automation-is-easier-with-experts/ https://otrs.com/blog/processes-workflows/why-process-automation-is-easier-with-experts/#respond Wed, 09 Feb 2022 14:50:18 +0000 https://otrs.com/?p=42899 https://otrs.com/blog/processes-workflows/why-process-automation-is-easier-with-experts/feed/ 0 Business Process Automation Trends: Where Do Companies Stand? https://otrs.com/blog/processes-workflows/business-process-automation-trends/ https://otrs.com/blog/processes-workflows/business-process-automation-trends/#respond Mon, 13 Dec 2021 11:50:58 +0000 https://otrs.com/?p=40036 https://otrs.com/blog/processes-workflows/business-process-automation-trends/feed/ 0 Automate Process Workflows to Improve Service https://otrs.com/blog/ai-automation/automate-process-workflows-to-improve-service/ https://otrs.com/blog/ai-automation/automate-process-workflows-to-improve-service/#respond Wed, 08 Sep 2021 11:58:50 +0000 https://otrs.com/?p=36324 https://otrs.com/blog/ai-automation/automate-process-workflows-to-improve-service/feed/ 0 How artificial is artificial intelligence? https://otrs.com/blog/ai-automation/how-artificial-is-artificial-intelligence/ https://otrs.com/blog/ai-automation/how-artificial-is-artificial-intelligence/#respond Mon, 28 Jun 2021 08:33:44 +0000 https://otrs.com/?p=33830 https://otrs.com/blog/ai-automation/how-artificial-is-artificial-intelligence/feed/ 0 Are You Asking the Right Questions About Automation? https://otrs.com/blog/ai-automation/are-you-asking-the-right-questions-about-automation/ https://otrs.com/blog/ai-automation/are-you-asking-the-right-questions-about-automation/#respond Mon, 11 Mar 2019 09:00:23 +0000 https://otrs.com/?p=16794 https://otrs.com/blog/ai-automation/are-you-asking-the-right-questions-about-automation/feed/ 0 Five Building Blocks for Successful AI Adoption https://otrs.com/blog/ai-automation/five-building-blocks-for-successful-ai-adoption/ https://otrs.com/blog/ai-automation/five-building-blocks-for-successful-ai-adoption/#respond Mon, 25 Jun 2018 09:31:48 +0000 https://otrs.com/?p=8713 https://otrs.com/blog/ai-automation/five-building-blocks-for-successful-ai-adoption/feed/ 0