AI Powered ITSM

Autonomous incident classificationAI reads incoming tickets and assigns priority, category, and affected service without human intervention.

Intelligent routingIncidents are matched to the right team or individual based on skills, current load, and historical resolution patterns.

AI powered diagnosisThe platform correlates the incident with CMDB data, recent changes, and similar past incidents to surface probable root cause immediately.

Auto escalation with SLA awarenessEscalation rules fire automatically when breach thresholds approach, notifying the right stakeholders before the clock runs out.

War room summariesFor major incidents, AI generates live status briefs for executive comms without your engineers having to stop and write updates.

Conversational request intakeUsers describe what they need in plain language; AI maps it to the right service and prepopulates the form.

Automated approval workflowsMulti stage approvals are routed dynamically based on request type, cost threshold, and organisational hierarchy.

Fulfilment automationStandard requests (software installs, access provisioning, hardware orders) are fulfilled automatically via integrations with your provisioning and IAM tools.

Real time status visibilityRequesters see live progress updates without having to chase the service desk.

Continuous deflectionThe AI suggests self service alternatives and knowledge articles before the request is submitted, reducing catalogue volume over time.

AI assisted catalogue curationThe platform identifies gaps, duplicates, and stale entries and suggests corrections automatically.

Natural language searchUsers find services by describing what they need, not by knowing the exact catalogue name.

Rich service recordsEach service includes SLA commitments, ownership, dependencies, cost, and linked knowledge articles in one place.

Subscription and notification modelUsers subscribe to services they depend on and receive proactive updates when availability or terms change.

Business service mappingEach IT service is linked to the business capabilities it supports, giving IT leaders a clear line of sight to business impact.

Auto discovery and reconciliationAgents continuously scan your infrastructure and update CI records without manual intervention.

Relationship mappingDependencies between CIs (servers, applications, services, databases) are detected and visualised automatically.

Change impact analysisBefore any change is approved, the CMDB shows every downstream CI and service that could be affected.

Drift detectionWhen the actual state of infrastructure diverges from the recorded state, the platform raises an alert and proposes a correction.

Federated data modelPulls CI data from cloud providers, virtualisation layers, network scanners, and agent based tools into a single normalised record.

Trend analysisAI continuously analyses incident data to identify recurring failures that indicate an underlying problem.

Automatic problem record creationWhen a pattern meets defined thresholds, a problem record is raised and linked to all associated incidents automatically.

Root cause investigation supportThe platform prepopulates problem records with correlated CMDB data, change history, and similar resolved problems to accelerate investigation.

Known error database (KEDB)Confirmed root causes and workarounds are published automatically to the knowledge base, reducing repeat incident resolution time.

Resolution trackingProblem records stay open and visible until a permanent fix is implemented and verified, preventing premature closure.

AI risk scoringEvery change request receives an automatic risk score based on CI impact, change frequency, implementation window, and historical outcomes for similar changes.

Dynamic approval routingStandard changes are auto approved; normal and emergency changes are routed to the right CAB members based on risk and domain.

Conflict detectionThe platform flags scheduling conflicts between simultaneous changes and prevents deployment windows from overlapping on shared infrastructure.

CI/CD integrationChange records can be raised, updated, and closed automatically by your deployment pipeline, eliminating manual handoffs.

Post implementation reviewAI compares pre and post change metrics to confirm success and detect regression, closing the feedback loop on every change.

Multi tier SLA/OLA/UC supportDefine service level targets at every tier: external SLAs with customers, internal OLAs between teams, and underpinning contracts with vendors.

Real time breach predictionAI monitors remaining time versus current resolution velocity and flags at risk tickets with enough lead time to intervene.

Automated escalation and reassignmentWhen breach risk is detected, the platform can automatically escalate, reassign, or notify without waiting for a manager to notice.

SLA calendar awarenessMaintenance windows, business hours, and holiday calendars are respected automatically when calculating time to respond and time to resolve.

SLA analytics and trendingDashboards surface which service categories, teams, and CI types are consistently near breach, enabling systemic improvement rather than firefighting.

AI article generationResolution notes from closed incidents and problems are automatically converted into structured knowledge articles for review and publication.

Semantic searchUsers describe their issue in plain language and the AI surfaces the most relevant article, even when the wording doesn't match the article's exact terminology.

Contextual surfacingKnowledge articles are proactively presented to agents while they are working a ticket, based on the incident's category, symptoms, and affected CI.

Article lifecycle managementArticles are tracked for usage, flagged when they haven't been accessed in 90 days, and automatically sent for review when linked incidents increase.

Confidence scoringAI rates article quality based on how often it successfully deflects or resolves the issues it's linked to, surfacing low confidence articles for remediation.

Three pipeline viewThe portfolio is organised into the service pipeline (in development), service catalogue (live), and retired services, with clear governance for transitions between stages.

Business value mappingEach service is linked to the business capabilities and processes it enables, making the cost to value ratio visible at a glance.

Cost and capacity dataActual run costs, resource consumption, and capacity utilisation are pulled from financial and infrastructure systems and surfaced per service.

Service health scoringA composite score aggregates availability, SLA performance, incident volume, and user satisfaction into a single service health indicator.

Rationalisation recommendationsAI identifies redundant, underutilised, or high cost services and flags them for review, supporting IT rationalisation and cost optimisation programmes.

Release planning and schedulingRelease records aggregate all constituent changes, assign owners, and establish deployment windows with automated conflict detection.

Environment managementThe platform tracks the state of dev, test, staging, and production environments and ensures releases progress through the correct gates.

Go/no go decision supportPrerelease checklists are completed and verified automatically; AI summarises outstanding risks for the release manager before sign off.

Rollback orchestrationRollback plans are captured at release creation time and can be triggered with a single action if post deployment monitoring detects a regression.

Release analyticsTrack release frequency, lead time, change failure rate, and mean time to restore (the four DORA metrics) to measure and improve delivery performance over time.