ServiceNow and Atlassian Lead ITSM Market Toward AI Platforms Instead of Out-of-the-Box Solutions

The main question for ITSM in 2026 is no longer the presence of AI features as such, but the type of architecture they are built on. Over the past three years, the market has moved away from simple chatbots that merely try to guess ticket categories toward an AI layer embedded in the service platform itself. Now AI participates in request routing, predicts incidents, automatically closes standard requests, and gathers post-mortems after outages.

The practical effect has also become noticeable: companies with predictive ITSM practices recover from incidents roughly twice as fast as those who still rely on manual processing. Against this backdrop, the market has split into two approaches. The first is platform-based: the company gets an open AI layer where it can plug in different LLMs, build custom agents, set policies, limits, and auditing for each action.

This option is more complex and takes longer to implement, but it suits scenarios where AI should work across multiple departments, account for security requirements, and, if needed, run within a closed loop. The second is boxed: AI is already built into the product and provides quick starts with ready-made scenarios like ticket classification, operator suggestions, dialogue summarization, and a virtual assistant. This is simpler, but the room for customization and scaling is usually limited by the vendor's roadmap.

The platform approach today is best demonstrated by ServiceNow. Its AI layer unifies ITSM, HR, finance, and CRM, supports both first-party and third-party models, and allows building custom skills and agents. But the price for flexibility is high: advanced AI capabilities are licensed separately, and implementation can stretch over months.

Atlassian is betting on Rovo — a unified AI layer over Jira, Confluence, and Jira Service Management. The strength here is context: agents see connections between tasks, pages, messages, and external applications, meaning they can not only respond but also take action within existing processes. The limitation is straightforward: if an organization operates outside the Atlassian ecosystem, the effect of this approach becomes noticeably weaker.

The most balanced option appears to be BMC Helix, which combines platform architecture with a rich set of ready-made AI agents. HelixGPT can be deployed either in the cloud or on-premise, and the customer chooses the LLM provider themselves. This is particularly important for large companies that need independence from a single model and data control.

Freshservice, on the other hand, remains a model of the boxed approach: Freddy AI launches quickly, closes standard requests through Slack, Teams, and the service portal, and time-to-value is measured in weeks rather than quarters. But the limitations are typical for a box: AI mainly works with data inside the system itself and doesn't suit complex cross-functional scenarios. Ivanti is trying to occupy a middle position, combining ITSM, endpoint management, and security, but its agentic AI direction has not yet fully emerged from the stage of promises.

For the Russian market, this debate is particularly practical. Western cloud ecosystems, agreements with major LLM providers, and familiar enterprise scenarios are not accessible to everyone, while the tasks haven't gone away: businesses still need autonomous closure of standard tickets, search across corporate knowledge, automatic classification, and agents that take action rather than just write answers. Therefore, on-premise deployment, full action logs from the model, role-based access to AI features, and the ability to swap one model for another without rewriting processes are becoming increasingly important.

It is around these requirements that local platforms like SimpleOne are now being built, betting not on one 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, a boxed product can still be the best choice.

But if AI needs to work across multiple departments, handle sensitive data, pass audits, and evolve with the market of models, the platform approach becomes decisive. In 2026, the winners will not be vendors with more chatbots on their menu, but those who know how to give business control over every AI action.