Your company may be ready to take the plunge into GenAI-driven customer service, but your data has to be ready too.
Generative AI is transforming customer service
Thanks to GenAI technology, and GenAI frameworks like Retrieval-Augmented Generation (RAG), customer care is rapidly changing. Companies are eager to employ GenAI to enhance the customer experience, accelerate operations, and gain a competitive edge. Research shows that GenAI-driven customer service is among the top use cases. According to our survey, Enterprise Data Readiness for GenAI in 2024, more than 50% of organizations plan to deploy GenAI-powered customer service over the next 12 months – and its adoption is accelerating, especially in industries like healthcare and pharma, financial services, retail and telco, travel and hospitality.
Why customer service?
Customer service directly impacts customer satisfaction and loyalty, and in turn, core business metrics like customer acquisition and retention. It’s also costly, so any efficiency has a direct impact on a business’ bottom line. By implementing AI-driven customer service, enterprises can:
- Increase efficiency by using virtual assistants to help human agents be more efficient during calls – with quicker resolution time and less waiting time.
- Make interactions more personal by using GenAI to tailor responses based on individual customer data, behavior, preferences, and context.
- Reduce agent ramp-up time, allowing new agents to become productive quicker.
- Boost satisfaction by providing more accurate responses, more quickly.
- Increase scalability by increasing call volumes without increasing cost.
The stages of AI-driven customer service adoption
As enterprises integrate GenAI into customer service, they progress through different levels of maturity. Studying this maturity model can help you plan your GenAI journey more effectively.
- Today most organizations offer chatbots that can address simple, rule-based transactions.
- However, it’s not until the second stage, GenAI Rep Assist, that you see the first real transformation. In this stage, human agents can consult with virtual assistants to deliver insights about a certain customer by instantly accessing that customer’s (and only that customer’s) data.
- The shift to GenAI Customer Assist marks a crucial turning point, where AI evolves from a support tool to the primary customer interface. In this stage, GenAI can address any customer inquiry in a personalized, context-aware manner.
- The final stage, GenAI Autonomous Agents, anticipates the day when customer service will be proactive – predicting and addressing customer issues before they arise. For example, proactively informing customers about a network failure, together with the estimated time to resolution. With agentic AI technology, autonomous agents can scale exponentially without proportional cost increases, meaning that the business value of your GenAI investment in customer service will rise indefinitely.
While the potential for AI-driven customer service is great, so are the challenges that must be overcome. This includes the critical issues of data security and privacy, reliability of LLM responses, and overall enterprise data readiness for GenAI – as highlighted below:
Data security and privacy
Customer data is personal and might get into the wrong hands or leaked to the LLM. Therefore, enterprise data must be masked, encrypted, isolated, and compliant with all privacy regulations.
Reliability of LLM responses
You must ensure accurate, trusted, and personalized answers to every user question. If not, the impact of a wrong answer is immediate, negative, and potentially very damaging to your brand.
Enterprise data readiness for GenAI
Compliant, complete, and current customer data must be accessed in a split second. Additionally, cost and scale must be controlled, with potentially hundreds or thousands of queries – made by LLM agents, functions, and human users – executing at the same time.
Best practices for implementing AI-driven customer service
To minimize the challenges listed above, and maximize the benefits of GenAI, you must plan, invest in technology and training, and develop a clear strategy for implementation. Below are some best practices to consider:
- Determine your current maturity level
Use the maturity model above to assess where you stand. This will help solidify your strategy, technical needs, and investment parameters.
- Begin with a pilot – but make sure it’s secure, scalable, and real-time
Launching a pilot program can help you test the waters without buying a boat. Choose a specific customer service use case where GenAI would have the most immediate impact. But note that, according to Gartner, 30% of the projects will fail when you try to scale them. Why? Because basic requirements like security, scalability and conversational latency weren’t anticipated.
- Make sure your data infrastructure can support GenAI requirements
GenAI brings new requirements to the table, such as:
- Interactivity: In a chatbot scenario, the relevant customer data must be fetched in milliseconds for an immediate response to each user query.
- Up-to-date data: The data must be fresh and relevant, otherwise how could GenAI answer a question about a customer’s latest payment?
- Scale: Make sure you can scale your GenAI apps to support thousands of concurrent users and massive enterprise data volumes.
- Concentrate on employee training
Equip your customer service team with the skills they need to work with your GenAI apps effectively, including technical training and adapting to new workflows. Additionally, keep a human in the loop until you gain confidence in GenAI abilities.
- Make privacy and compliance a top priority
Enforce privacy controls – such as data masking, encryption, isolation, and synthesis – to ensure compliance with all relevant regulations and company policies.
Seize the opportunity presented by AI-driven customer service
GenAI is redefining customer service across numerous industries. Enterprises that embrace this technology and progress through the maturity model can expect significant improvements in efficiency, personalization, and customer satisfaction.
The key is to develop a clear strategy, work in phases, and address data challenges head-on. With the right plan and advanced GenAI models and frameworks, you can easily get your enterprise data ready for GenAI and start reducing costs and increasing customer satisfaction from day one.