AI in customer service: what it really does
AI solutions for customer service that take the pressure off your team, reduce costs and keep your customers happy? They exist – if you approach them the right way.
Brief overview (TL;DR)
- What AI means in customer service: Intelligent systems understand enquiries, prioritise them, resolve standard cases partially automatically and initiate processes.
- Areas of application: Chatbots, AI agents, intelligent routing and backend automation. Modern generative AI enables more natural dialogues and more precise evaluations.
- Key advantages: Faster responses, less routine work, lower costs per contact and better service quality. Customers receive 24/7 support and consistent responses across all channels.
- Legal & compliance: The EU AI Act and GDPR are clearly regulated and enforceable. AI applications in customer service are considered low risk, but require transparency (labelling of AI use).
- How to get started: Define focus areas → Integrate tools → Train your team → Continuously improve. Small pilot projects deliver fast, measurable and scalable results.
What exactly does AI mean in customer service?
Artificial intelligence helps service teams understand customer enquiries faster, automate recurring tasks and respond more effectively. This is made possible by technologies such as natural language processing, automatic classification, content summarisation and intelligent search – but also by generative models for response suggestions or automation in connected systems.
AI can analyse emails and assign categories or urgency levels, suggest relevant knowledge articles, or directly initiate or even handle processes such as refunds on its own.
Typical areas of application
- Automatically answer common questions: AI recognises standard enquiries about order status, returns or technical questions and provides appropriate answers. Teams gain time for complex issues.
- Process unstructured data: Screenshots, PDFs, or emails are automatically read and structured. This results in clean tickets and smoother processes.
- Intelligently prioritise tickets: AI assesses urgency and forwards enquiries to the appropriate team. This shortens response times and avoids escalations.
- Relieve the burden on employees: AI suggests wording, summarises content and provides relevant information. This speeds up responses and makes training easier.
- Support with copilots: AI-powered assistants help employees by suggesting responses, summarising content, or providing context from various systems. They work in the background, improving response quality and reducing workload.
- Control background processes: With complete information, AI automatically triggers forwarding, status changes or workflows – fewer clicks, more stability.
- Recognising patterns and trends: AI analyses large volumes of enquiries and identifies recurring problems or changes in sentiment. A basis for informed decisions.
Advantages of AI in customer service
For customers
Shorter waiting times & 24/7 availability
51% of customers prefer a bot to a human when they want an immediate response.
Consistent responses across all channels
Information is always synced and up to date, as AI accesses a central knowledge base.
Faster solutions, happier customers
More precise intent recognition and context understanding leads to less back and forth.
For businesses
Relief from routine work
Frequently asked questions are answered automatically, allowing staff to concentrate on complex cases.
Efficient & scalable
Handle higher volumes of enquiries without having to increase the size of your team.
Lower cost per contact
Automation reduces processing time and costs per ticket while maintaining the same level of service quality.
CX & AI: Dive deeper now!
Three (German-language) webinars – three perspectives on AI in customer service:
- Module 1: How is AI changing customer behaviour?
- Module 2: What opportunities and risks arise for employees?
- Module 3: What strategic decisions must companies make?
Comparing technologies: chatbots, AI agents, etc.
Not all bots are the same – and not every system that talks to customers is truly intelligent. Various types of AI-powered tools have become established in customer service:
- Rule-based chatbots follow fixed if-then rules for clearly defined FAQ cases. They are robust and reliable, but reach their limits as soon as users deviate from the script.
- AI chatbots use machine learning and NLP to understand more natural language. They recognise synonyms, learn from interactions and become more precise.
- Conversational AI understands context, intent and conversation flow – not just keywords. Enables natural dialogues with follow-up questions and seamless handovers to humans.
- AI agents go beyond answering questions: they retrieve data from backend systems, create labels, trigger workflows (return labels, address verification) and work autonomously across multiple systems.
Example: Automated returns with Zendesk + Leafworks
This is how the process typically works:
- Customer initiates a returns request in chat
- AI (AI agent) extracts relevant data and compares it with backend data
- If the criteria are met, a returns label is automatically generated
- Customer receives the label immediately; systems are updated
- If problems arise, the case is seamlessly transferred to the support team
Result: Significantly fewer manual steps, faster response times and consistent processes.
A powerful lever: automation in the backend
- Automatic categorisation & prioritisation: Requests are analysed and sorted by topic and urgency – each ticket ends up directly in the right queue.
- Data extraction from free text: Names, order numbers or products are recorded in a structured manner. Employees have all the information immediately available.
- Identify trends and anomalies: AI identifies patterns in real time – such as clusters of certain products or churn risks. This enables proactive action.
- Trigger automated actions: Keywords initiate follow-up processes: a “package not arrived” message triggers a status check, while “wrong item” creates a voucher. This backend automation remains invisible to customers, but saves time and money – teams can focus on complex cases.
Market overview (excerpt)
The major platforms offer integrated AI functions with different focuses:
Zendesk offers generative AI bots, intelligent ticket classification and agent co-pilot:
- AI Agents Essential: Self-service bots across all channels
- AI Copilot: Assists with response suggestions and summaries
- AI Agent Advanced: Fully automated ticket solutions
Learn more about the possibilities of Zendesk AI.
Other providers:
- Salesforce: Einstein GPT combines CRM data with language models for contextual responses
- Freshdesk: Freddy AI offers context-based recommendations and smart response suggestions
The best platform for you depends on your systems, data and goals – we provide honest and pragmatic advice to find the right solution.
How to successfully integrate AI into your customer service
- Analysis & goals: Where does most of the effort lie? Which enquiries are repeated? Define measurable goals such as “Increase first-time resolution rate by 15%”.
- Prioritise quick wins: Start with frequent, structured enquiries: status queries, returns, password resets. This creates acceptance and provides initial data.
- Tool selection & integration: Choose solutions that fit your systems. Pay attention to interfaces and data quality – existing platforms often already include AI modules.
- Pilot & Enablement: Test a clearly defined area with measurable KPIs. Train the team early on and gather feedback.
- Rollout & optimisation: Establish continuous monitoring, plan regular reviews and scale to additional use cases.
Data protection & law: EU AI Act and GDPR in customer service
When using AI in customer service, transparency and data protection are paramount. The EU AI Act (in force since August 2024) classifies customer service applications as “limited risk” in most cases – which means, above all, that they must be labelled as such. Users must be able to recognise that they are interacting with AI.
At the same time, the GDPR applies: personal data may only be processed lawfully, for specific purposes and to a minimum extent. For AI, this means clear deletion and anonymisation deadlines as well as secure infrastructure.
Practical implementation:
- Add AI information to the privacy policy
- Establish processes for data deletion/anonymisation
- Human-in-the-loop for generated responses
- Train employees
→ More about the EU AI Act – and what it means for your customer service
If you made it to this ...
AI is not an end in itself, but a tool for measurable improvements. Used correctly, it relieves teams, speeds up responses and stabilises service quality – visible to customers and measurable for companies.
However, without a well-thought-out strategy, much of its potential remains untapped. Companies should therefore introduce AI in a targeted and measured manner, rather than blindly following the hype.
👉 Learn more: The deepdive webinar series “AI & CX” (held in German) uses practical examples to show how technology is changing people and organisations and what course you need to set today.
Why Leafworks?
Leafworks specialises in helping companies find the best AI solutions for their customer service. We are the market leader and DACH Partner of the Year for Zendesk.
With a wide range of experience in the integration and implementation of tailor-made customer service solutions, we not only provide consulting services, but also take care of technical integration and optimisation – with the aim of not only finding (or developing) the best solution, but also implementing it quickly and then remaining at your side as a partner.
Let’s talk about your wishes and ideas!
Robert Cwicinski
Customer service expert
- CRM
- Automation
- Integrations
- Business Intelligence/BI
- Zendesk
- Digital Transformation
- Custom MVPs
FAQs about AI in customer service
What can AI do in customer service?
Chatbot vs. AI agent – what is the difference?
Chatbots primarily provide answers based on FAQs. AI agents go one step further: they look up data, check authorisations and carry out actions independently.
Telephone or chat – where is AI more worthwhile?
Text-based channels are easier to automate. Telephony is worthwhile for clear, standard conversations, but requires reliable speech recognition and intent analysis.
How is data protection guaranteed?
Through GDPR-compliant processes, transparent communication, data minimisation and technical measures such as role/rights assignment and logging.
When does AI become cost-effective in customer service?
AI in customer service is particularly worthwhile when volume and repetition come together. A simple rule of thumb: the more frequently an issue occurs and the more standardised it is, the more likely automation will pay off.
Even with just a few hundred similar enquiries per month, a good bot can be worth the investment – because it works tirelessly and takes the pressure off the team.
It’s best to start with 1–2 use cases and look at the figures:
- Does the bot reduce processing time?
- Does the first contact resolution rate increase?
- How is customer satisfaction developing?
These KPIs enable you to quantify your return on investment (ROI). Many companies see positive effects after just a few months. For example, a recent evaluation reports that leading organisations in customer service achieve up to 8 times ROI on AI investments. However, it is important to start small and scale up based on measurable results – then AI will pay off step by step.
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