Kay-Lee Franco

Agentic AI for IT service management Key Takeaways AI agents differ from endpoint agents (which collect device data) and automation bots (which execute workflows) by serving as an intelligent orchestration layer that understands natural language, reasons across context and coordinates existing automation. Successful agentic AI for IT requires: clean and current knowledge bases, real-time context

Why conversational AI is now an integration problem, not just a chatbot project Conversational AI integration in 2026 is no longer just about deploying a chatbot. A true conversational AI integration guide needs to go beyond responses and focus on how AI connects to your systems, executes workflows, and delivers measurable business outcomes. For many

AI for Service Delivers Business Value Across HR Operations AI for service in HR operations is helping global organisations manage growing employee support demands without increasing headcount. By embedding AI into workflows, Deutsche Bank has automated routine queries, improved service consistency, and gained insight into support trends – all while controlling costs. This use case

AI for Service Is Reshaping Customer Operations at Scale AI for Service in global banking, powered by platforms like Kore.ai,  is increasingly becoming essential for managing complex, high-volume customer environments. As customer expectations rise and interaction channels multiply, financial institutions require platforms capable of handling scale, precision, and multilingual complexity simultaneously. A global example from