ChatGPT has reignited interest in the broader domains of generative AI, AI and knowledge management (KM). In this article, we will focus on how generative AI can be harnessed for customer service automation and contact center agent augmentation.

Here are eight best practices for success.

1. Deploy Prudently

Instead of taking an all-or-nothing approach to deploying generative AI, we recommend using an activation framework based on risk and value. “Risk” could mean risk for the business (e.g., compliance) and/or the consumer (e.g., medical question), and “value” could mean customer value (e.g., value of a customer’s deposits or net worth) and/or the transactional value (e.g., shopping cart value).

  • Low risk, low value: An example is a cross-sell recommendation when a shopper is buying a commoditized product. High automation, enabled with generative AI and with no human supervision, can be deployed here.
  • Low risk, high value: An example is when a shopper is looking for advice on which laptop to buy. Since the risk is low, high automation can be applied to this scenario as well, but since the customer (e.g., B2B client) or transactional value (laptops can even cost thousands of dollars) is high, we recommend at least a “minimal” level of supervision—for validation of generated answers/advice.
  • High risk, low value: Even though the value is low, this scenario still warrants fairly high supervision since the risk is high. Since the value is low, businesses in a low-regulation environment can perhaps get away with some automation. An example is selling consumers a new account in a bank. The deposit may be minimal, but the cost of noncompliance can be high.
  • High risk, high value: Examples are medical advice sought by a consumer for a non-trivial illness or product advice for a complex product like medical equipment. We suggest going with no automation and human involvement before gaining more experience.

Read the full article here.