How AI Can Handle All! Requests with Minimal Human Intervention

Achieving high levels of customer satisfaction while managing costs is a challenging balancing act. With its advanced language processing capabilities, ChatGPT can answer all customer requests at once, requiring only minimal human intervention for review and fact-checking.

Confidence Level on First Contact Resolution (FCR)

One of the critical success factors, is the AI's ability to provide a confidence level for First Contact Resolution (FCR). This metric indicates how certain the AI is that it has resolved the customer's query satisfactorily on the first attempt. The confidence level is determined by several factors, including the complexity of the question, the clarity of the customer's input, and the AI's training data.

Role of Reviewers

While AI solutions are highly capable, human reviewers play a crucial role in ensuring the quality and accuracy of responses. Reviewers are responsible for amending and fact-checking the AI's answers before they are sent out to customers. This human oversight is essential, particularly in cases where the AI's confidence level is below a certain threshold. Reviewers ensure that any nuanced or complex queries are handled appropriately, maintaining the high standards of customer service.

Increasing Automated FCR

As ChatGPT continues to learn and evolve, its accuracy and confidence in resolving customer queries improve. Over time, this leads to an increase in automated FCR rates. The AI becomes better at understanding and addressing a broader range of inquiries, reducing the need for human intervention. This gradual improvement not only enhances efficiency but also frees up human resources to focus on more complex and strategic tasks.

Implementing AI for Customer Service: A Practical Approach

To implement ChatGPT effectively in a customer service environment, adopt a batch processing method. This approach allows for efficient handling of customer inquiries while ensuring quality control through human review. Here's a step-by-step guide on how this can be done in practice:

1. Collect Customer Inquiries: List open customer inquiries into a centralized queue.
2. Create Batches: Organize these inquiries into manageable batches. let’s assume 100 batches of inquiries at a time.
3. Initial Response Generation: Use AI to generate responses for all inquiries within each batch. The AI processes the inquiries and drafts responses based on its training of previous answers and understanding.
4. Assign Confidence Levels: For each response generated, the AI assigns a confidence level indicating how likely it is that the response will resolve the customer's query satisfactorily on the first attempt (FCR). A level of 85% FCR is what top quartile companies have
5. Human Review: Reviewers examine the responses, amending and fact-checking as necessary. They ensure that the responses are accurate, appropriate, and aligned with company policies.
6. Reviewers must provide feedback on the quality of the responses. This feedback is used to retrain and fine-tune the AI, helping it to improve over time.

Based on the results of the human review, adjust the confidence thresholds for future responses.

If the reviewed responses consistently meet high standards, the threshold for automated send-outs can be increased. High-confidence responses can be sent without further review, while low-confidence responses may be flagged for additional human review before sending.

Continuous Monitoring and Adjustment

Continuously monitor the performance of both the AI and the human reviewers. Track key metrics such as response accuracy, customer satisfaction, and FCR rates. Use the insights gained from monitoring to iteratively improve the system. This includes refining the AI model, adjusting the review process, and optimizing the batch processing workflow.

Benefits of This Approach

By processing inquiries in batches and using AI for the initial response generation, the system can handle a large volume of inquiries efficiently. The human review process ensures that responses meet high standards, maintaining customer satisfaction and trust. The feedback loop allows for continuous learning and improvement of the AI, leading to better performance over time. Automating the majority of responses leading to cost savings while maintaining high service quality.

Join me on a journey through my past experience in Customer Operations and the new era of AI and technology that is aiding companies in becoming more customer-centric and staying at the forefront of innovation. There are 5,626 AIs related Software developments in Customer Operations today. Checkout my full list of Customer Operations AI use cases here

Yours, Carmen