Aisera in 2026

Before 15 December 2025, buyers evaluating enterprise AI ITSM had two clear leaders: Moveworks and Aisera. After the ServiceNow acquisition of Moveworks, the independent tier collapsed to one credible option: Aisera. This is not Aisera marketing; it is a structural consequence of the consolidation.

Enterprises running Jira Service Management as their primary ITSM and evaluating agentic AI overlay no longer have a compelling reason to choose Moveworks (now tied to ServiceNow's roadmap). Aisera integrates deeply with Jira, Slack, Teams, Okta, and Entra ID while remaining independent of any single ITSM vendor. For orgs that want AI capability without locking their ITSM into ServiceNow's orbit, Aisera is the default first evaluation.

Aisera's UniversalGPT engine is a domain-specific LLM fine-tuned on IT and HR support patterns rather than a general-purpose model with ITSM prompting. This matters for intent classification accuracy: domain-specific training produces higher accuracy on the tail of unusual or compound requests that matter most in enterprise environments.

Aisera does not publish pricing. It is custom-quoted based on user count, use-case scope, integration complexity, and desired automation depth. For reference, Aisera occupies a similar price bracket to pre-acquisition Moveworks. Third-party procurement data indicates typical annual contract values of $150K-$500K for mid-to-large enterprises, with per-employee annual cost of $80-$200 depending on scope and the number of integrated systems.

Implementation services are required and add to the total cost. Aisera certified partners charge $15K-$50K for a standard deployment. Multi-system action framework configuration (for password reset execution, ticket creation across ITSM, provisioning) adds further. Budget $250K-$700K total year-one cost for a 2,000-5,000 employee deployment including software, services, and internal resource time.

Aisera's platform in April 2026 includes: the UniversalGPT conversational AI engine (fine-tuned for IT and HR support), proactive incident detection and root-cause clustering, auto-remediation workflows for L1 (password reset, access provisioning, software installation), and strong integration with Slack, Teams, Jira, ServiceNow, Freshservice, Zendesk, Okta, Entra ID, and Ping Identity.

The knowledge layer supports Confluence, SharePoint, Google Drive, ServiceNow KB, and custom web crawl. Aisera's KB governance tooling is one of the strongest in the category: it surfaces stale articles, identifies KB gaps based on unresolved ticket patterns, and recommends article creation or update actions. This is relevant because KB hygiene is the single biggest driver of deflection accuracy.

Domain-specific LLM fine-tuning is available for organisations with large volumes of proprietary tickets and runbook data. This differentiates Aisera from vendors using vanilla GPT-4 with system prompting: domain fine-tuning produces meaningfully higher intent-classification accuracy on unusual or compound requests.

Aisera partners consistently report 3-6 months for enterprise implementations. This is longer than Moveworks' pre-acquisition average of 2 months and significantly longer than Freshservice's 2-8 weeks. The longer timeline reflects the depth of configuration: intent library build-out for 200+ ticket types, action framework setup for multi-system execution, KB connector validation, and staged rollout across departments.

The implementation investment is correlated with deflection outcome. Aisera deployments that invest in full intent library configuration and action framework depth consistently reach 50-65% deflection. Deployments that take shortcuts on intent configuration or run with a fragmented KB consistently underperform at 25-35%.

Aisera requires dedicated internal technical resources throughout the implementation: at minimum, one ITSM admin, one identity provider contact, and executive IT sponsorship. Organisations that assign these roles clearly and commit the internal time consistently achieve faster go-live and higher deflection outcomes. See implementation guide for the full phase breakdown.