How to avoid ChatGPT and automation failure in the contact center

There is an inescapable cycle in customer service today. Consumers continue to expect more from companies. To satiate expectations, brands will implement technologies like ChatGPT and other automation tools, bringing greater speed, convenience, and personalization than ever before. As a result, customers demand even higher levels of service.

When businesses find themselves trapped in this vicious cycle, they often accelerate deployment timelines, skipping over testing and training. While the temptation is real, companies cannot forgo the necessity of carefully, intentionally, and intelligently integrating generative AI into their customer service environments and contact centers.

Accuracy concerns of generative AI

Generative AI is powerful but not perfect. For example, it can sometimes create content or answers that might be plausible but factually inaccurate. These erroneous yet authoritative-sounding responses can negatively impact customer experience and even have legal implications in certain use cases.  

Businesses must recognize that generative AI is not all-knowing, and it is their responsibility to implement the necessary governance and guardrails to reduce these errors from occurring. With the proper training and regular updates, companies can ensure that their solutions use the most timely, accurate, and reliable data to generate responses.

Data scientists can help continuously fine-tune AI models through routine tests and audits. Prompt engineers can also act as the first defense against wrong answers.

Controlling the quality of generative AI responses  

Generative AI solutions can enhance service interactions insofar as they understand the appropriate context in which they operate. Consider the frustration a layperson would experience if an AI assistant responded to their question with highly technical language and industry jargon. Or if a generative AI solution divulged several one-star reviews after a caller asked it to read the latest ratings.

Again, brands can overcome quality challenges through training and implementing the proper guardrails to prevent non-answers or overly generic responses. By having prompt engineers constantly perfect Large Language Modules (LLM) over time, an AI solution will refine its answers, making them less broad and more nuanced.

Moreover, with prompt engineering and training, generative AI can automatically tune up or down the complexity of its responses to fit the use case. Creating customer profiles can also prevent misalignment between the answer and the questioner.

Maintaining privacy and compliance

Additional generative AI concerns include privacy, such as unintentionally disclosing confidential information, and compliance – namely, PCI, HIPAA, and other industry-based compliance rules.

Companies must involve their legal and compliance teams with any generative AI deployment. In particular, it is best to partner with a trusted provider when integrating an LLM into one’s customer service platform or contact center.     

How IntelePeer can help integrate ChatGPT into your contact center 

On its own, ChatGPT can only respond to questions or statements. For instance, once it has a customer’s credit card information, it can’t hand that data off to a CRM or point-of-sale solution.

However, communications automation platforms like IntelePeer’s Smart Automation can facilitate the necessary automation and orchestration aspects. What’s more, IntelePeer has the expertise to help you integrate your generative AI solution with your contact center in a way that does not degrade accuracy and quality or disregard privacy and industry compliance. 

Watch this special round-table webinar to learn more from our expert panelists on this very topic or contact us today to find out more about our communications automation platform.