AI

Autonomous Endpoint Management: Why IT Teams Are Moving Beyond Manual Fixes Most IT teams know the feeling. A device falls out of compliance. A patch gets missed. An incident sits unresolved for hours. Nobody notices until a user complains. This isn’t a resourcing problem. It’s a visibility and automation problem, and it’s exactly what autonomous […]

Everyone is investing in agentic AI to improve customer experience. Chatbots, virtual assistants, predictive campaigns – the ambition is genuine. So why does the experience still feel fragmented? The answer isn’t the AI. It’s what the AI has to work with. Most enterprise teams operate across a patchwork of platforms: CRM, CDP, contact centre software,

Kore.ai launched its Artemis platform in May 2026 with a ground-up redesign of how enterprises build and govern AI agents. At the centre of that redesign is Agent Blueprint Language (ABL), a compiled, declarative language that standardises how agents are defined, validated, and controlled before they ever reach production. The problem it solves Most organisations

Ivanti’s 2026 AI Maturity Report surveyed 3,900 employees across six countries and landed on a finding that should give every IT leader pause: the gap between how fast organisations are deploying AI and how well they are governing it is widening, not closing. More than half of organisations (56%) are now deploying AI broadly across

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