Vodafone Business Tech4Business, Zandvoort: How Generative AI Is Transforming Customer Contact
Generative AI is reshaping customer contact faster than any previous technology shift. At Vodafone Business Tech4Business in Zandvoort in 2024, we explored what this transformation really looks like beneath the surface. Many organisations are experimenting with Large Language Models for the first time, and while the potential is enormous, the landscape can feel overwhelming.
This session gave participants a clear view of what generative AI can offer customer service today and how companies can make responsible choices while the technology continues to evolve. The discussion covered the fundamentals of Generative AI, the practical value of LLMs in customer-facing interactions, and the techniques companies can use to keep these models reliable, secure, and aligned with their goals.
Understanding LLMs in Customer Contact
To use AI effectively in customer service, it helps to understand what Large Language Models actually do. At a conceptual level they take text, audio, or structured data and generate a meaningful response based on statistical relationships learned during training. This gives them the ability to recognise intent, extract relevant information, and produce helpful replies in natural language.
During the session, participants gained insight into how these models process context and how that enables both better routing and richer self-service flows. We also addressed the risks. LLMs are powerful and flexible, but without the right safeguards they can drift, hallucinate, or expose sensitive information. That is why strong validation and governance are essential when applying AI to customer contact scenarios.
Where Generative AI Creates Value
Organisations see two major opportunity areas. The first is direct interaction, where AI can support or automate conversations across channels. Customers receive faster, more consistent answers and support teams can focus on the requests that genuinely require human attention.
The second area is data insights. Models can process unstructured text and detect patterns that help organisations improve processes, training, or product design. This includes discovering recurring contact drivers, identifying friction in customer journeys, and surfacing opportunities to reduce unnecessary contact.
Throughout the session we illustrated these opportunities using practical, real-world examples. These examples helped participants understand how automation supports customer satisfaction rather than replacing the human element.
Techniques That Keep AI Under Control
Because LLMs can behave unpredictably, several techniques are increasingly important for companies that want safe and reliable outcomes.
Retrieval-Augmented Generation, or RAG, ensures the model only answers based on approved information. It combines the language understanding of the model with an organisation’s own knowledge sources so the AI stays accurate and grounded.
Fine-tuning allows a model to adapt to domain-specific language and phrasing patterns. This is useful for industries with specialised terminology or where tone of voice matters deeply.
Validation frameworks help teams detect mistakes, prevent escalation of small inaccuracies, and maintain quality over time. They play a key role in building trust, both internally and externally.
Insights From Emerging Research
One of the striking discussions in Zandvoort focused on new research into the “thought processes” of LLMs. These studies explore how models represent personality traits internally and how those traits can be influenced. While still early, this research suggests that controlled personality steering may become a meaningful design tool for organisations that want specific conversational styles or degrees of assertiveness.
Participants found this particularly valuable because it connects future innovation to practical design choices companies already face today. It also reinforces why a deeper understanding of model behaviour is becoming essential as AI plays a larger role in customer contact.
A Sector in Rapid Transition
The key message from the session was clear. Generative AI is not a distant vision. It is already transforming how organisations handle conversations, structure workflows, and analyse customer needs. At the same time the technology is evolving so quickly that companies need both curiosity and caution. A thoughtful, structured approach makes all the difference.
Events like Tech4Business help create that clarity. By sharing concrete examples, proven techniques, and honest limitations, organisations can make better decisions about how to bring generative AI into their customer contact landscape with confidence.
