The reality today is that most contact centers or customer support functions handle millions of annual calls, face high call abandonment rates, and have wait times that drive customers away. Conversational AI agents for customer support address these breakdowns with agentic systems that handle complex requests autonomously.
What makes conversational AI agents different is their ability to understand intent, pull data from enterprise systems, and execute multi-step tasks like order changes and policy updates without human intervention. These AI agents use large language models and agentic architectures to reason, plan, and act, not just match keywords.
But agentic AI deployment without clear use cases leads to failed projects. Research shows that by 2027, more than 40% of enterprise AI rollouts will be cancelled. Studies also show that 75% of customers expect help within five minutes, and 60% will switch brands after a single bad support experience. Support leaders need to identify where conversational AI solves specific operational problems: call abandonment, agent turnover, resolution speed, and customer retention.
Download this e-book to explore four conversational AI use cases and determine where to apply the technology in your contact center.
How conversational AI addresses customer support challenges
Discover how conversational AI customer support tackles problems contact centers can’t solve with headcount alone: overwhelming call volumes, IVR abandonment rates, high agent attrition, and churn drivers hidden in millions of call transcripts.
Conversational AI agents handle requests end-to-end
Early chatbots matched keywords and followed rigid scripts. Modern conversational AI agents built on multimodal LLMs, such as Vertex AI Search for Commerce and agentic commerce, interpret voice, images, and documents through visual search and OCR, execute tasks like order placement across enterprise systems, and maintain context across channels.
- 50% to 1% call abandonment: A tier-1 home improvement retailer reduced abandonment across 125 million store calls and 20 million contact center calls annually.
- 95% faster response times: An aftermarket autoparts retailer automated 100% of routine queries across 1,000 stores, enabling 24/7 order completion through WhatsApp.
- 27-second handling time: Average call duration dropped from 98 seconds through self-service automation and intelligent routing.
- Multilingual support: Communicate across languages and regions without translation barriers.
AI agent assistants provide real-time guidance during calls
Complex issues require human judgment, but agents need instant access to information. AI agent assistants, available through Google Cloud Gemini Enterprise for Customer Experience, surface knowledge through custom Copilot widgets integrated with contact center platforms, suggest LLM-generated responses aligned with brand tone, and automate routine steps through API integrations.
- AI knowledge assist: Cuts lookup time with information surfaced instantly during calls.
- SOP-guided workflows: Offers step-by-step guidance based on standard operating procedures to ensure compliance.
- Smart reply: Suggests context-aware responses to help agents reply faster and more consistently.
- Live translation: Enables agents to serve customers in multiple languages without switching tools.
AI onboarding and coaching builds proficiency faster
Contact centers face industry-high turnover rates that trigger costly recruiting and training cycles. AI-driven onboarding uses virtual AI customers that mirror real-world scenarios, dynamic training scenario generation from actual conversation transcripts, and automated conversation evaluation for tone, accuracy, and process adherence.
- 6,808 chats in two months: Mattress Firm’s SleepExpert.AI launched interactive AI agents that adapt training sessions in real time, helping Sleep Experts® rehearse sales techniques during quieter store hours.
- Reduced supervisor workload: Automated feedback eliminates the need for constant manual oversight.
- Faster proficiency: New hires practice with lifelike AI-powered customers before handling live calls.
Conversational analytics extracts patterns from millions of interactions
Most reporting tools track call duration and resolution time while overlooking insights hidden in unstructured conversation data. AI-powered conversational analytics uses topic modeling, sentiment analysis, and LSTM-based sequential survival modeling to identify churn drivers and measure their impact over time.
- 15% more high-risk customers identified: A major telecom provider extracted churn topics like pricing and service quality from call transcripts, improving prediction accuracy by 8%.
- 12,000 additional customers retained monthly: Sequential survival modeling optimized intervention timing, with 33% of churners detected through call transcript analysis.
- 30,000+ personalized retention offers: LTV integration connected churn risk to customer value for smarter prioritization.
Your conversational AI customer support questions, answered
