A technical guide for CTOs and product leads covering how conversational AI works end-to-end, from NLU and intent classification to dialogue state tracking and response generation. Breaks down the four types of conversational AI (rule-based chatbots, AI chatbots, voice assistants, autonomous agents), compares conversational AI vs. generative AI, and covers ROI benchmarks (up to 80% cost reduction per interaction). Includes a vendor evaluation scorecard with criteria like intent classification F1 scores, multi-turn context support, compliance requirements, and on-premises vs. cloud deployment tradeoffs. Also addresses common failure modes such as hallucination risks, PII exposure, model drift, and anthropomorphism disclosure obligations.
Table of contents
What conversational AI actually is (and isn't)How conversational AI works: End-to-end architectureTypes of conversational AI: Chatbots to autonomous agentsConversational AI vs. generative AI: Key differencesConversational AI use cases across industriesConversational AI use cases across industriesROI of conversational AI: Costs, deflection, and revenueHow to evaluate a conversational AI platform: ScorecardChallenges, risks, and how to mitigate themFrequently asked questions about conversational AIBuild or buy? how Netguru accelerates your first deploymentSort: