How Leading Companies Use AI to Redefine Customer Service
Customer service means achieving the best outcome for the customer—with as little effort as possible. According to consulting firm Gartner, many companies are already feeling the pressure to implement AI. This trend is not only driven by business leaders but also by policymakers such as Emmanuel Macron, who has announced billion-dollar investments to keep Europe competitive in AI development.
A few years ago, AI hype was marked by inflated expectations, but today, we have reached the stage of productive deployment. AI Agents are not only more powerful than traditional chatbots but also enable real autonomous process automation.
How Companies Use AI in Customer Service Today
Many companies already use conventional chatbots. These rely on fixed rules and can answer simple customer inquiries. Another common use case is Agent Assist, an AI-powered support tool for customer service teams that helps with email and chat responses.
Less widespread, but with great potential, are automated case classification and quality monitoring. These technologies allow inquiries to be automatically categorized or service quality to be measured.
The least used but most promising development is Autonomous Case Resolution. AI Agents that not only support inquiries but handle them independently. This is the future, as these systems can carry out entire processes without human intervention.
Why AI Agents Work Today—But Didn’t Before
AI models have made significant progress in recent months. In particular, so-called reasoning models have improved dramatically. While older AI models generated responses based on the most likely next word, modern systems think step by step.
A good example is the O3 model from OpenAI, which recently achieved 25% correct answers in the "Frontier Math" test, a PhD-level exam. Just months ago, it was considered impossible for AI to handle such complex tasks.
Another key factor is the drop in costs. Eighteen months ago, it would have been economically unfeasible to answer an email with 30 AI-driven steps, as the costs were too high. Today, costs have dropped by a factor of 100, making AI adoption financially viable.
AI Assistant vs. AI Agent: What’s the Difference?
Many companies already use AI Assistants that support humans but do not act autonomously. The most well-known example is ChatGPT, where the human still decides whether to use the generated response.
An AI Agent, however, goes one step further. It can execute entire processes or individual process steps autonomously, without human intervention.
Example of an AI Assistant:
- Magic Reply: AI suggests a response to a customer inquiry, which the employee can review and send with one click.
- Text Corrections: Automatic adjustments to match the company’s preferred wording and tone.
- Knowledge Base Integration: AI identifies relevant articles from internal knowledge databases and suggests them to the employee.
- Real-Time Translation: Inquiries in foreign languages can be translated and answered directly.
Example of an AI Agent:
- Wrong package delivery: The AI Agent creates an automatic summary of the case and initiates a correction.
- Warranty claim: The AI retrieves order data, checks the claim, and generates a DHL return label.
- Address change: The AI Agent updates the address directly in SAP, without human intervention.
The key difference: While AI Assistants support humans, AI Agents handle entire tasks autonomously.
Why Supervisor AI Eliminates Fixed Workflows
Until now, automation has been controlled by rigid workflows. This meant that companies had to define a fixed process for each type of customer request.
For example, a workflow for an address change could look like this:
- The customer requests an address change.
- The system checks the request and sends a follow-up if data is missing.
- The new address is saved in the ERP system.
- The customer receives a confirmation.
The problem: Any process change requires manual adjustments to the workflow.
Supervisor AI eliminates this issue by enabling flexible automation without fixed workflows.
- An AI Agent recognizes that the request is a warranty claim.
- Another AI Agent searches internal systems like Confluence or Salesforce for relevant information.
- A specialized AI Agent executes the necessary action, such as generating a DHL return label or processing an address change in SAP.
- If a decision is needed, the request is forwarded to a human.
This creates dynamic process control, which adapts to new workflows without requiring reprogramming.
How Companies Successfully Implement AI Agents
The adoption of AI Agents happens in several steps:
- Start with an AI Assistant: The risk remains low because humans still have final control over responses.
- Analyze ticket data: This reveals which processes can be easily automated.
- Implement first AI Agents: Many companies begin with simple processes like address changes or warranty claims before automating more complex workflows.
Many businesses achieve significant efficiency gains within just a few months. For example, DPD reduced processing times by 30% in a short period using AI Assist by Typewise.
Hybrid Intelligence as the Future of Customer Service
Technology has evolved, costs have fallen, and companies that act now benefit from major efficiency gains.
- AI Assistants make customer service agents faster.
- AI Agents automate entire processes.
- Supervisor AI eliminates rigid workflows, enabling greater flexibility.
The future of customer service lies in Hybrid Intelligence—the combination of human expertise and AI-powered automation. Companies that invest in this now will gain a significant competitive advantage.
