ServiceNow and Atlassian push the ITSM market toward AI platforms over out-of-the-box tools

The key question for ITSM in 2026 is no longer whether a product has AI features, but what architecture they run on. Over the past three years, the market has moved away from simple chatbots that merely try to guess a ticket's category toward an AI layer built into the service platform itself. AI now takes part in routing requests, predicts incidents, automatically closes routine requests and compiles postmortems after outages. The practical effect is visible too: companies with predictive ITSM practices recover from incidents roughly twice as fast as those that still rely on manual handling.

Against this backdrop, the market has split into two approaches. The first is the platform approach: the company gets an open AI layer where it can plug in different LLMs, build its own agents, and set policies, limits and audit for every action. This option is harder and slower to deploy, but it suits scenarios where AI must work across several departments at once, meet security requirements and, if necessary, run inside a closed perimeter.

The second is the out-of-the-box approach: AI is already built into the product and offers a fast start with ready-made scenarios such as ticket classification, agent suggestions, dialogue summarization and a virtual assistant. This is simpler, but room for customization and scaling is usually limited by the vendor's roadmap.

Today ServiceNow shows the platform approach at its best. Its AI layer unites ITSM, HR, finance and CRM, supports both its own and third-party models, and lets teams build custom skills and agents. But flexibility comes at a high price: advanced AI capabilities are licensed separately, and deployment can stretch over months.

Atlassian is betting on Rovo, a shared AI layer across Jira, Confluence and Jira Service Management. Its strength is context: agents see the links between tasks, pages, messages and external apps, so they can not only answer but also take actions within existing processes. The limitation is simple: if an organization lives outside the Atlassian ecosystem, the benefit of this approach becomes noticeably weaker.

BMC Helix looks like the most balanced option, combining a platform architecture with a rich set of ready-made AI agents. HelixGPT can be deployed both in the cloud and on-premise, and the customer chooses the LLM provider. This matters especially for large companies that need independence from a single model and control over their data.

Freshservice, by contrast, remains the model of the out-of-the-box approach: Freddy AI launches quickly, handles routine requests through Slack, Teams and the service portal, and time-to-value is measured in weeks, not quarters. But the limitations are typical of a boxed product: the AI works mostly with data inside the system itself and is poorly suited to complex cross-functional scenarios.

Ivanti is trying to take a middle position, combining ITSM, endpoint management and security, but its agentic AI direction has not yet fully moved beyond the stage of promises.

For the Russian market, this debate is especially practical. Western cloud ecosystems, agreements with major LLM providers and familiar enterprise scenarios are not available to everyone, yet the needs have not gone away: businesses still need autonomous closing of routine tickets, search across corporate knowledge, automatic classification, and agents that act rather than just write replies. That is why on-premise deployment, a full log of model actions, role-based access to AI functions and the ability to swap one model for another without rewriting processes matter more and more. Local platforms such as SimpleOne are being built around exactly these requirements, betting not on a single ready-made button but on managed AI infrastructure.

The conclusion is simple: the AI-ready ITSM market is moving from a set of flashy features to infrastructure with clear governance. If a company needs quick results for a small service team, an out-of-the-box product can still be the best choice. But if AI must work across several departments, handle sensitive data, pass audits and evolve with the model market, the platform approach becomes decisive. In 2026, the winners will not be vendors with more chatbots in the menu, but those who can give the business control over every AI action.