AI is a collective term for methods that allow computers to perform tasks that typically require human intelligence.
One form of AI is machine learning (ML), where patterns are derived from large data sets either to better interpret the underlying data or to make specific predictions based on it.
A subfield of machine learning is Large Language Models (LLMs), which can understand and generate human language.
Applications of AI in customer service are not new. Many contact centers already use AI tools, for example, to detect linguistic content, concerns, and emotions in conversations, regardless of the communication channel (Intent Recognition, Advanced Speech Recognition) or to conduct (partly) automated, natural language dialogs in speech and writing, regardless of the language used (Conversational AI, Translation Services). Other applications include process automation, knowledge management, or churn prevention.
Initial business cases show that with AI support, the productivity of employees can be improved, their qualification level can be raised, and thus customer satisfaction increases. In the medium term, operational cost savings are expected.
There is no such thing as a free lunch.
To achieve these desired benefits, budgets must be invested wisely, and people must be involved purposefully. The challenge: Machine learning technologies are developing exponentially; the impacts on the economy and society are hard to predict. We face a complex challenge with business-related benefits and uncertain „second order“ effects on living and working together.
Next Best Action: Address Complexity with Transformational Competence
Transformational competence is described as the ability to:
• collaborate with others, think laterally, and find innovative solutions,
• balance conflicting or seemingly incompatible ways of thinking with empathy,
• become familiar with complexity and ambiguity,
• take responsibility for one’s actions and reflect critically, and
• create values that provide guidance to employees and customers.
What can decision-makers in customer service specifically do to make better decisions about using AI in customer service with transformational competence?
• Develop visions for the use of machine learning in customer service. Create spaces where you can connect operational experiential knowledge, procedural, and technical expertise with your teams and shape future scenarios. This gives you direction.
• Describe the key capabilities of your customer service (Capability Mapping) to achieve the KPIs derived from the vision. This operationalizes your focus.
• Provide your teams with appropriate AI training. This improves technical confidence.
• Formulate ethical and business guidelines for the further roadmap of AI development. This creates trust and cohesion within the team.
• Test AI applications in specific use cases. This way, you gather your own experiential knowledge and can better classify supposed expert opinions.
The AI-Roundtable – Ideation and Project Roadmap for the Use of AI Tools in Service and Sales
junokai coordinates, curates, and moderates tailored 2-day AI roundtables for individual companies. Knowledge is combined with creativity. The aim is to identify AI applications that can be implemented in the short term in CSS that benefit both customers and the company.
At the junokai AI Service Roundtable, decision-makers and their teams from service and sales units can experience AI technology in Customer Service and Sales. Internal AI project teams introduce themselves, and relevant external AI players present their USPs via speed dating.
The resulting opportunities for customers and companies are discussed, uncertainties are identified, and opportunities can be evaluated.
In a Design Thinking-oriented creative session, quick wins are defined, and roadmaps for the implementation of AI action areas are sketched out.
Sebastian Schmidt – Senior Consultant
junokai