GenAI for Customer Service: Pitfalls and Prescriptions for Success - eGain

Per McKinsey, GenAI can enhance customer engagement productivity by up to 45%! However, most early GenAI projects in customer service have failed to meet their goals in operational cost reduction and CX improvement. What are the pitfalls to avoid and best practices to adopt for success? Here are some that we have learned from our client deployments:

Garbage-in-garbage-out: Enterprises often have inconsistent knowledge and content in silos strewn across the organization with no common framework to verify and establish content credibility. When these silos are used to feed GenAI, it spits out inaccurate answers. The result? Plummeting user trust and adoption. The solution? Set up a centralized AI knowledge hub that unifies and orchestrates all the building blocks needed for modern KM while leveraging trusted content and knowhow as a foundation. Make sure to have human experts in the loop!

“New hire” needs guidance: Effective prompt management is essential for obtaining trusted answers from GenAI. Best-practice prompts should guide GenAI to generate responses that align with business needs. A robust prompt management service acts like a supervisor for a new hire, ensuring that GenAI receives clear, actionable instructions.

Derail with no guard rails: GenAI tools must operate within defined and auditable business constraints. This involves setting up controls to prevent inappropriate use of knowledge, such as excluding compliance-heavy content from GenAI processing. Without fine-grained controls, it is impossible for GenAI to deliver trusted answers at scale.

The hallucination hazard: GenAI can sometimes produce incorrect or irrelevant outputs, a phenomenon known as “hallucination.” Without configurable and reliable quality assurance pipelines to verify GenAI responses in real-time, maintaining accuracy and relevance has emerged as a common pitfall in early GenAI projects. Even a few wrong answers are one too many in customer service!

Measure, manage, maximize: Without the ability to track and analyze GenAI interactions, assess prompt effectiveness, and leverage explicit and inferred user feedback, GenAI projects fail to improve user experience fast enough and get labeled as another failed chatbot. Make sure that your KM platform has comprehensive analytics capabilities to optimize GenAI effectiveness and business value.

Conclusion: Successful customer service automation with GenAI requires a strong foundation of integrated knowledge management. Investing in a modern knowledge hub to power GenAI projects will help you meet aggressive operational cost reduction and CX goals.

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