What Generative AI Does in Customer Service
Generative AI produces new text instead of retrieving or classifying existing text. In contact centers it is applied in four main ways.
- Post-interaction summarization. GenAI drafts a summary after the interaction ends, covering the customer's issue, what the agent did, and the resolution. In the deployments we have seen, this has been a dependable GenAI application in contact centers.
- Response drafting. In text channels, GenAI suggests complete draft responses for agents to review and send, which reduces cognitive load and response time. Accuracy is the critical variable.
- Knowledge base generation. GenAI can draft FAQ articles from interaction transcripts, surface knowledge gaps, and suggest updates to outdated content.
- Customer-facing autonomous response. GenAI handles complete customer interactions without a human agent. It is the most discussed application and the most problematic, because hallucination risk is highest here.
Where GenAI Delivers and Where It Does Not
- Post-interaction summarization for most interaction types
- Response drafting in low-stakes, high-volume text channels
- Knowledge article drafting for human review and publication
- Intent classification and routing augmentation
- Factually precise responses in regulated industries without RAG architecture and guardrails
- Complex multi-issue interactions where the reasoning chain becomes unreliable
- Autonomous resolution of anything involving financial transactions, medical information, or legal standing
- Emotional interactions that need empathy, judgment, and de-escalation
What Is Coming
Better retrieval-augmented generation (RAG). RAG architectures ground GenAI responses in verified organizational knowledge. As RAG improves, hallucination risk in structured domains will fall, which will make autonomous agents viable for a broader range of interaction types.
Better guardrails and output verification. The tooling for constraining GenAI output, meaning keeping it inside a defined domain, verifying factual claims, and flagging low-confidence responses, is improving quickly.
Smaller, specialized models. General-purpose large language models are being supplemented by smaller models fine-tuned on contact center interaction data, which are faster, cheaper to run, and more accurate for specific tasks.
Frequently Asked Questions
ChatGPT is one implementation of generative AI. Contact center vendors use proprietary models, fine-tuned models, and API access to models from OpenAI, Anthropic, Google, and others, and they do not use ChatGPT directly.
The main method is retrieval-augmented generation (RAG), which grounds model responses in verified organizational knowledge. Additional guardrails include output filtering, confidence thresholds, and human review for high-stakes responses.
For most organizations, buying from a CCaaS or specialist AI vendor is faster and lower-risk than building. Custom builds make sense for organizations with unique data assets or regulatory environments that require full control over model training.
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