The Light of Helios:
Stories, Insights, and Thought Leadership

What makes it an "agent" A chatbot responds. An agent resolves. The practical markers of a real customer service agent are: It acts, not just answers. When resolving a contact requires doing something, looking up an order, processing a return, updating an account, it performs that action through your connected systems. It handles a real conversation. It holds context across a multi-turn exchange and adapts, rather than matching one question to one canned reply. It works from

The global ITSM market is worth roughly $12-15 billion today and is growing at about 14-16% a year toward $30-37 billion by the early 2030s. Below is a triangulated set of figures across the major research firms, plus the AI adoption and resolution numbers that matter most for 2026. Cite the specific firm and year whenever you use a single number, scope and methodology differ between reports. How big is the ITSM market? Estimates cluster tightly. The spread comes from scope,

AI ITSM is the use of AI agents inside IT service management, to resolve requests, not just route them. The term covers a wide range, from a chatbot bolted onto an old ticketing tool to an agent that runs the service desk end to end. The distinction matters, because those are very different things wearing the same label. This post explains what AI ITSM means in plain terms, what it actually changes, what a resolved request looks like end to end, and the one architectural dist

Gartner predicts that by 2029, agentic AI will autonomously resolve about 80% of common service issues without human intervention, cutting operational costs roughly 30%. Everyone quotes the number. Fewer talk about how you actually get there, and that is the part that matters. The honest, operator's view: 80% is not a date you wait for, it is an outcome you engineer, and the ceiling on how fast you reach it is not the model. It is execution. The part most people skip That 80%

Here's a number that should bother every IT leader evaluating an ITSM platform: licensing is typically 25% of your first-year cost. The other 75% is implementation, consulting, training, and governance infrastructure. That ratio comes from third-party analyses of enterprise ITSM deployments, not from competitors trying to make the incumbents look expensive. It's the documented reality of deploying a platform that was designed for maximum configurability rather than maximum ti

IT automation is a broad term, and that breadth is why conversations about it often talk past each other. It covers everything from a shell script that restarts a service to an AI agent that resolves a support request end to end. They are all automation, but they solve different problems and fail in different ways. This post lays out the landscape, where AI agents fit in it, and how to decide what to automate with what. What IT automation covers Most IT automation falls on a

Most IT teams are good at closing incidents and bad at stopping them from coming back. That is the gap problem management is meant to fill: not fixing the ticket in front of you, but finding and removing the underlying cause so the same incident stops recurring. It is also the discipline that gets skipped, because the team is too busy fighting the incidents to investigate them. AI changes that math by doing the correlation and pattern work that problem management depends on.

Most companies do not have a knowledge problem. They have a findability problem. The answer to almost any internal question already exists, in a wiki, a doc, a policy, a past ticket, but it is scattered, out of date in places, and faster to ask a colleague than to find. An internal knowledge base AI fixes the retrieval, not the writing: it answers employees' questions from your existing content, in the tools they already use, so the knowledge you already have actually gets us