SimpleOne: AI in ITSM has become the corporate standard for support and service desk

SimpleOne, Ainergy, and T1 Integration described how artificial intelligence has moved from pilot launches into the operational ITSM workflow. The main question for companies is no longer whether AI is needed at all, but how to embed it in support to process requests faster, meet SLA, and maintain control over data.

Why AI for Support

Service teams have long had a practical request for automation. Business needs to respond faster, reduce errors, handle growth in requests, and work equally well across different channels — from email and self-service portals to telephony and corporate messengers. As a company grows, manual request processing begins to slow down both the team and users.

Against this backdrop, AI becomes not a fashionable add-on, but a way to maintain service quality without constantly expanding headcount. The practical value here revolves around several tasks. First, AI assistants and RAG search help provide answers not "from memory," but based on internal regulations, knowledge bases, and already-solved cases.

Second, automation of routine operations relieves part of the load on the first line. Third, standard answers and unified request processing logic reduce variance in quality. As a result, the support service gains an advantage across three metrics at once: speed, cost, and stability.

"AI in ITSM is not a separate feature, but an accelerator for the entire service model," noted a representative of

Ainergy.

Where AI Makes an Impact

In the scenario shown, AI is embedded directly into the Service Desk workflow, not isolated in a separate experimental module. If the request is simple, the system can itself recognize its topic, assess the impact on the service, determine the needed service, and either immediately send the user an instruction or assign the request to the correct group. If the case is more complex, AI suggests the next diagnostic steps to the engineer, finds similar incidents, and helps draft a response without excessive manual searching through systems.

• automatic classification and routing of requests from email, portal, telephony, and messengers;
• zero-level support with quick answers from knowledge base and ready-made scenarios;
• neural search through documentation, regulations, and incident history;
• chat-bot that can either answer itself or create a ticket if data is insufficient;
• generation of draft knowledge base articles from closed requests.

Special emphasis is placed on the end-to-end processing funnel. Up to 80% of routine requests can be closed by AI agents at the zero level, with the remaining 10–20% going to engineers already with prepared context. This changes the very economics of support: specialists spend less time on routine work and more time on non-standard cases. In parallel, the system replenishes the knowledge base, so each resolved incident increases the chances of automatic resolution of the next similar request.

Another important scenario is Problem Management. AI analyzes closed requests, groups similar incidents, and can automatically signal when a pattern has accumulated that indicates a systemic problem. For managers this is a shift from reactive model to managing causes of failures. Instead of processing similar requests one by one, the team sees bottlenecks, risks of delays, and potential mass incidents earlier.

How the Security Perimeter is Organized

Rapid AI implementation in support runs into obstacles not only in answer accuracy but also in architecture. Public LLMs are convenient for experiments but poorly suited for working with corporate files, personal data, trade secrets, and access keys. The risk here is not theoretical: the same tools that help automate useful processes also simplify harmful scenarios — from phishing to malware generation. That's why for company workflows, organizations increasingly look toward controlled APIs, private clouds, and local models.

The material particularly emphasizes that local deployment provides the clearest security perimeter: the security team sees what happens to data, and the probability of a leak is lower than when sending sensitive information to external services. On top of this, application-level measures are needed: role-based access model, content filtering before generating answers, masking of personal data in hybrid scenarios, and full logging of all operations with the neural network. Such a set makes AI not a "black box" but a controlled tool within the corporate process.

What This Means

The story with AI in ITSM is quickly moving beyond the pilot stage. For large service teams, this is no longer an experiment for innovation's sake, but a way to relieve the first line, speed up request processing, and turn the knowledge base into a constantly growing asset that works together with support, rather than lying separate from it.