Hello everyone. Thank you for joining this session. Today we are going to look at a challenge that every contact center faces how to stay resilient when incidents strike, whether it's a sudden system outage, a spike in call volume, or an unexpected service disruption, these moments can quickly spiral and damage customer trust. Over the next few minutes, I'll share how AI automation and modern architectures can help us prepare for those high pressure situations and turn them into opportunities to deliver consistent, reliable support. My name is Ji Ku. And I'm with Copart. I'll walk you through how we can design customer support architectures that remain strong during pressure, and how intelligent automation and AI power decision making can help us maintain excellence even when critical incidents occur. In the world of contact centers, every single second matters when a system goes down. Or when demand suddenly surges, the disruption doesn't stay in one place. It cascades through the entire customer experience. For example, if a CRM system is unavailable, agents can't access order history or customer details. That leads to repeated questions longer hold times, and more dropped calls. From the customer's perspective, this feels like a lack of empathy, a lack of care, and it often spills onto social media. The end result is damage, trust and higher churn. The message is clear, incidents are inevitable, but how we response determines whether customers stay with us or leave. To handle this challenge, the foundation has to be cloud native. Support architecture. This begins with microservices, which isolate falls so that one failure doesn't bring the whole system down. It also means using even driven processing, which enables real time responsiveness to unexpected changes. And finally, auto-scaling infrastructure ensures that when traffic spikes, the system can adjust resources dynamically without sacrificing performance. This foundation is about resilience by design, not resilience by chance. On top of this foundation, we bring in ai, one of the first areas in AI powered intent detection. This technology uses natural language processing and historical data to understand what a customer is asking from the very first interaction. During incidents, accurate routing is essential. If calls are being misdirected or misclassified, it only adds friction. AI ensures that resources are allocated efficiently, even when the systems are under multi intent and emotional intelligence. Of course, customer conversations are layer simple. Many times a customer has more than one intent in the same interaction. Modern AI can detect both primary and secondary objectives, while also recognizing the emotional state of the customer. For instance, if someone says, I can't log in and I'm really frustrated, the system doesn't recognize a login issue. It also recognizes the urgency and the need for empathy. This allows us to prioritize the interaction correctly and provide the right support in the right way. Another major pillar in autonomous issue resolution is conversational ai. Conversational AI handles routine inquiries such as account updates, password resets, or basic troubleshooting. By taking care of these requests automatically, we create reserve capacity for human agents who are then free to handle more complex and emotionally charged issues during an incident. This reserve capacity can make the difference between chaos and control. Not all issues are equal in risk. AI helps by applying a risk-based escalation framework. Algorithms, analyze patterns, account histories and behaviors to flag potential fraud or security concerns. Each case is scored and the score determines priority. High risk situations are escalated immediately. While routine matters can follow the standard workflow. This dynamic prioritization ensures that tedious threats are never lost in the shuffle. Another way AI helps is through AI driven decision trees. Unlike static flowcharts, these trees adapt in real time. They learn from the past resolutions and adjust path automatically. For example, if one system is degraded during an incident, the decision tree can automatically reroute customers to an alternative solution. This flexibility makes the entire resolution process more resilient and far more efficient. Of course, some cases still require a human agent, but a handoff should never mean starting over. Dynamic handoff systems ensure that customer's context travels with them. This includes the interaction, history, intent, and emotional state. The agent receives a briefing so they can pick up immediately without asking the customer to repeat everything. This continuity becomes critical during outages where patients is already stretched thin. AI doesn't just serve customers. It also augments agents with real time assistance. Agents can see suggested responses, compliance guidance, and relevant knowledge articles right in front of them. AI monitoring ensures that regulatory requirements are consistently met even under stress. Speech analytics can even flag potential compliance violations in real time, allowing corrections before they become costly mistakes. Now, the best incident is the one that never happens. Predictive analytics provides an early warning system. Machine learning models analyze historical patterns, system metrics, and even external factors to anticipate potential problems. For example, if there's a certain increase in support tickets for password resets, the system can flag it as an early sign of a broader outage. This allows proactive communication with customers, often solving issues even before they ever become full incidents. Customer support is no longer limited to voice or text. Multimodal AI enables support through voice. Text, images, and even video. A customer can share a photo of an error message or a video of a malfunction, and AI can interpret it immediately. This dramatically reduces resolution time and during incidents. Clarity and speed are exactly what customers expect. Technology resilience is critical. We achieve this through a few mechanisms. Circuit breakers. Isolate degraded components so failures don't cascade. Intelligent load distribution balances. Workloads across available resources in real time and redundant systems ensure that even if a primary system goes down, there are still alternative parts available to keep operations running. To bring all this together, we use a four phase implementation approach. Phase one. It is establishing a cloud native foundation. Phase two is integrating AI for intent recognition and routing. Phase three is enhancing automation with predictive analytics and multimodal processing. Phase four is resilience optimization, fine tuning, load balancing, failover, and incident response procedures All together. Think of this as a roadmap rather than a one-time project. The future of incident ready contact centers is about convergence. When intelligent automation, predictive analytics, and human expertise come together, we achieve resilience at scale. It's not about eliminating incidents that's impossible. It's about being ready so that when an incident happens, the customer still experience excellence. Thank you all for taking the time to join this session. I hope you've taken away some clear ideas about how AI and automation can transform the resilience of your customer support operations. By building incident ready systems today, we can ensure that customer trust is preserved tomorrow. Thank you and have a good day.