Artificial intelligence (AI) has made great strides, and is now used in many industries to make interactions faster and more effective. But what is AI and how does it work in the context of contactability? AI refers to systems capable of performing tasks that normally require human intelligence. These tasks include understanding language, solving problems, and in this case, interacting with users automatically.
In contactability, AI is primarily used to improve communication between businesses and customers. Instead of having a person answer every message or call, AI systems, such as virtual assistants or chatbots, can do it automatically.
How does AI work in contactability?
- Natural Language Recognition: AI systems use natural language processing (NLP). This allows them to understand human language, whether written or spoken.
- Auto Reply: Once the AI understands what the customer is asking, it looks for the best answer.
- Machine learningWhat makes AI special in contactability is that it learns from every interaction. If AI encounters a new question that it doesn’t know how to answer, it doesn’t get stuck. Instead, it stores the information, analyzes it, and improves its responses for the next time a similar interaction occurs.
How does the AI ecosystem that trains AI work in Laraigo?
At Laraigo we have automatic and cyclical learning of virtual assistants through Artificial Intelligence. Here's how it works:
1. User interaction with the virtual assistant
The cycle begins when a user interacts with a virtual assistant through various channels, such as web chat or social media. The watsonx Assistant AI model interprets the message through intents and responds accurately. For example, if a customer asks about shipping options, the assistant uses natural language processing (NLP) to interpret the query and provide the information without human intervention. The system understands different forms of the same question and responds in real time.
2. Collecting and storing data with IBM Cloud Object Storage
Each interaction generates a range of data, such as customer history, preferences, or frequently asked questions. This data is securely stored on a storage platform such as IBM Cloud Object Storage for future analysis.
3. Automatic analysis of interactions with LLaMA 3
After storing the data, the generative AI system Calls, processes all information automatically. This generative AI analyzes each interaction in order to detect patterns, identify the most common intentions and find areas where the interaction can be improved.
If 100 different customers ask about a new product launch, the AI will detect that there is a growing interest in that topic. From this information, the system can adjust its future responses, suggesting details about the product to other customers who might be interested without them asking, and the AI will then be able to adjust that response to be more accurate next time.
4. Generation and adjustment of intentions
AI analyzes interactions to identify customer “intents”—the goals behind each query. These intents are then continuously grouped and adjusted to improve responses. For example, if multiple users ask about changing the shipping address, AI groups all those queries under a single intent and adjusts the response to cover different ways of asking.
This process of generating and matching intents is often done manually to optimize AI performance. Traditional AI, such as watsonx Assistant, plays a crucial role in this process by managing the rules and structured data that facilitate the identification and matching of intents, ensuring smoother and more accurate interactions.
5. Autonomous training with machine learning
An AI system’s autonomy is achieved when it automatically integrates new intents and response examples through machine learning algorithms. This means that the AI is continuously trained and adjusted without the need for manual intervention. For example, if the AI detects that the responses about a particular service are not satisfying customers, it adjusts the responses to be more complete and useful. So, the next time a customer makes an enquiry about that service, the AI will provide an improved response based on previous data. All of this happens automatically, with generative AI continuously optimising traditional AI without human intervention.
6. Continuous optimization of the contact flow
Finally, the AI system continuously optimizes the contactability process. By learning from each interaction, the AI becomes more efficient and effective, improving both the user experience and the management of company resources.
A virtual assistant that has gone through several learning iterations can handle a wider variety of questions and respond more quickly and accurately. This reduces the need for human intervention, which lowers costs and allows agents to focus on more strategic tasks, improving overall service efficiency.
Continuous optimization also has a direct impact on operational efficiency. By reducing the number of repetitive questions that need to be handled by humans, companies can allocate their human team to resolve more complex queries, improving overall service. Additionally, AI continues to learn from these more complex interactions, which in the long run will also reduce the need for escalation.
The AI ecosystem that trains AI represents an evolution in contactability. With Laraigo, from the first interaction to autonomous analysis and training, each interaction becomes more efficient. This ability to learn and optimize allows companies to offer more personalized experiences, reducing costs and optimizing human resources.