Are you still in the customer service past? Have you started taking action to get a foothold in the future? These are questions you could answer today.
New solutions and products with AI are even in the soup! Since the launch of ChatGPT, the market has begun to witness the birth of a large number of creations that promise to “revolutionize the way in which x cosa is done” and yes, artificial intelligence and LLM's they have come to mark a before and after in all the processes of any company and that makes all businesses want to be part of this new wave, but do you really know this technology?
At Vozy we are quite repetitive in the need to know very well what we are getting into, how generative artificial intelligence provides us with value and, of course, the basic terminologies and the origin of technology. With this in mind, in our blog we have created a space where you will find a lot of valuable content that will guide you along the path of generative artificial intelligence from start to finish.
This time, we want to offer you a vision of the past and the future.
What was customer service like before GEN AI and what are the predictions for the future?
Before the arrival of generative AI, customer service was, to a large extent, a rigid process. Problem solving relied on answers established in pre-determined complex conversation flows, and virtual agents were unable to respond to complex requests or requests that stray away from the conversational flow. This resulted in unempathetic conversations, standardized responses and no context.
In addition:
- Establishing these conversational flows usually takes a long time, which slowed down the system implementation process.
- The ability to understand and process human language is limited to trained patterns and phrases. Example: chatbots that respond with predefined answers to questions such as business hours, return policies, etc.
- It requires manual updates to incorporate new knowledge or handle new queries.
- It can result in frustration for customers if their questions don't fit neatly into the predefined categories.
- Its ability to scale and handle more complex queries is limited.
- Problems with incomplete or biased data can lead to incorrect or inadequate answers.
- Difficulties integrating with other existing IT systems, which can lead to compatibility and operational problems.
While conventional AI customer care systems are useful for basic, repetitive tasks, they have significant limitations in terms of personalization, contextual understanding, flexibility, and scalability. These limitations negatively affect the customer experience and the overall efficiency of the system as queries become more complex and varied.
This is what the future of customer service looks like with GEN AI
With the integration of generative AI, the future of customer service is shaping up to be more efficient, personalized and proactive. Predictions indicate that companies will be able to offer highly personalized customer experiences, anticipating needs and problems before customers report them. For example, a virtual assistant could alert a customer to a delay in their delivery and offer alternatives before the customer is even aware of the problem. In addition, generative AI will allow:
- Analysis of large amounts of information customer service tools powered by generative artificial intelligence have the ability to analyze millions of conversations, databases and corporate information. This gives them the ability to provide more accurate, empathetic and personalized answers.
- Instant response time AI systems can respond immediately to inquiries, dramatically reducing wait times and increasing customer satisfaction.
- Consistency and precision Generative AI ensures a constant level of service, providing accurate answers based on vast databases and continuous learning. There is no variation in service quality due to human factors.
- Large-scale customization By knowing the customer's history, preferences and behaviors, companies can anticipate their needs and offer more relevant solutions.
- Predictive and proactive analysis For example, if a product has a high failure rate, generative artificial intelligence can alert affected customers before they experience the problem.
- Continuous improvement generative AI systems learn and improve over time. Every interaction provides valuable data that is used to continuously refine and optimize the service.
And this is just the beginning! In fact, this is not part of the future, this is already happening, so imagine all that could be achieved over time. For now, we invite you to continue learning and researching so that you have enough tools to allow you to give generative artificial intelligence the opportunity.
