Think big, start small - The smart entry into artificial intelligence

Last updated: 16.04.2026 08:00

Artificial intelligence is changing how companies communicate, work and grow. AI opens up enormous potential, particularly in the area of communication solutions: from automated customer interactions to intelligent analyses. But many companies want everything at once. Fully automated, autonomous AI agents that reliably take on complex tasks - and preferably tomorrow. But this is rarely how successful AI implementation works.

The key to sustainable AI success lies in a simple principle: start small and then, based on your own individual service processes, look at which use cases AI can actually be a relief.

Why "from 0 to 100" rarely works

The desire for maximum automation is understandable. However, AI systems require high-quality data, clear processes and continuous training and monitoring. If you want too much too quickly, you run the risk of

• Unreliable results

• Low acceptance by customers

• Poor customer experience

• Frustrated customer service employees

• High costs

In short: Great visions are important! But the way there should be strategically structured. The key to sustainable AI success lies in a simple principle: think big - start small - learn quickly - scale in a targeted manner.

Define a clear use case

Don't start with "AI for everything", but with a specific problem. Analyze your individual customer service processes and identify potential for improvement in the processing of concerns where AI can provide support. A clear use case creates focus and measurable results, for example

• Shorten processing times: concerns should be resolved more quickly (e.g. through automation or better prioritization).

• Increase customer satisfaction: customers should receive faster, clearer and more consistent answers.

• Reduce customer service costs: More efficient processes reduce personnel costs and unnecessary duplication.

• Improve first-contact resolution: Problems should be resolved as directly as possible on first contact, without referrals.

• Relieve employees: Recurring, simple queries are automated so that employees can concentrate on complex cases.

• Reduce error rate: Standardized processes and support systems ensure fewer incorrect or inconsistent responses.

• Increase transparency and traceability: Processes are more clearly structured and better documented.

• Enable scalability: Customer service can function stably even when the number of inquiries increases.

• Improve response times (e.g. 24/7 availability): Digital solutions enable customers to receive support outside of business hours.

• Better use of data: Insights from inquiries are systematically evaluated in order to improve products and processes.

Start with supporting AI

Instead of immediately relying on autonomous agents or AI agents, start with AI as an assistant for

• Identification and legitimization of the customer

• Smart request recognition

• Completeness check of all relevant information

• Intelligent routing to the right contact person

• Structured presentation of all relevant customer data to start the conversation

• Suggestions for answer formulations and active recommendations for action

• Support with data collection

• Automatic summaries of conversations

This reduces risks and increases productivity at the same time.

Gain experience and build trust

Employees need to understand and trust AI. Small, successful projects help with this because they

• increase acceptance within the team

• reduce fear of contact

• continuously improve the solution through feedback

Automate step by step

The next step is only worthwhile once processes are stable and the AI is working reliably:

• Partial automation → Full automation

• Assistance system → semi-autonomous agent

This is how AI grows organically within the company.

Practical example: AI in customer service

A typical start can look like this. Each step builds on the previous one - so everything remains controlled and measurable.

1. AI pre-qualifies calls (request recognition, identification, legitimization)

2. AI prioritizes and distributes requests intelligently

3. AI suggests answers for customer service employees

4. AI makes recommendations for action based on company knowledge

5. AI supports the collection of data in third-party systems

6. AI answers simple standard inquiries automatically

7. AI agent takes over more complex dialogs independently

Conclusion: Success is a process, not a leap!

AI unfolds its full potential not through one big leap, but through smart, iterative steps. Companies that start small and scale in a targeted manner are more successful in the long term and avoid expensive false starts.