Solutions

AI-based
Operations

Make operations faster and more consistent using AI, safely.

AI in operations should not be a "black box." IKC helps you apply AI where it creates real value. Triage, prioritisation, knowledge assistance, and operational insights with governance, auditability, and human-in-the-loop controls built in.

Improve speed and consistency with AI-assisted triage and decision support
Reduce operational load with knowledge and response assistance (with controls)
Build trust with data quality, security, governance, and measurable outcomes
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If these issues show up,
AI-based operations can help incrementally

Too many tickets/alerts, and prioritisation depends on individuals

Teams spend time searching for context across tools, messages, and documents

Repeated incidents and manual triage create backlog and slow resolution

Operational data exists, but insights don't translate into action

You want to adopt AI, but need a safe approach aligned to security and compliance

Practical AI use cases
designed for operations

AI-assisted Triage & Prioritisation

Recommend category, assignment group, priority, and next best action based on operational context

Context Enrichment

Pull relevant signals (related incidents, changes, assets, known issues) to reduce manual investigation

Knowledge & Response Assistance

Suggest resolutions, standard steps, and runbook guidance while keeping humans in control

Operational Insights & Pattern Detection

Identify repeat patterns, common failure modes, and improvement candidates for problem prevention

Quality & Governance Foundations

Data definitions, access control, audit trails, and evaluation criteria to keep AI safe and trustworthy

AI-based operations is most effective when paired with strong ITSM/ITOM/ITAM + Integration, because AI relies on reliable operational signals.

Common Use Cases

01

AI-assisted categorisation and routing

Automatically suggest ticket category and assignment group based on content and historical patterns

02

Suggested resolution steps

Provide resolution recommendations from your knowledge base and prior cases

03

Faster context gathering

Surface related incidents, changes, and asset information for analysts

01

Alert noise reduction

Recommendations for grouping, correlation, and suppression of duplicate alerts

02

Priority suggestions

AI-recommended priority based on service impact signals (where available)

03

Guided response prompts

Context-aware runbook suggestions aligned with operational procedures

01

Recovery action recommendations

Suggest recovery actions and compliance checks for offboarding flows

02

Anomaly detection

Flag anomalies in entitlement/licence usage patterns (where feasible)

01

Prevention backlog creation

Convert recurring tickets/alerts into a prioritised prevention backlog

02

Automation candidates

Recommend top candidates for automation and process standardisation

Start small. Prove value. Scale safely.

01

Discover

  • Identify high-impact, low-risk AI candidates (triage, assistance, insights)
  • Assess data readiness (quality, completeness, ownership, access constraints)
02

Design

  • Define operating model: who approves, who monitors, what's automated vs. suggested
  • Define guardrails: permissions, audit logging, escalation rules, and exception handling
  • Establish success metrics and evaluation approach
03

Build & Integrate

  • Implement workflows and integrations needed to supply reliable context
  • Configure assistance and recommendation experiences with human review where required
04

Validate & Adopt

  • Test against real scenarios and edge cases
  • Train operators and document safe-use procedures
  • Roll out in phases (pilot → controlled expansion)
05

Operate & Improve

  • Monitor quality and usage, capture feedback, and improve continuously
  • Expand scope only when accuracy, safety, and governance are proven

Typical Deliverables

AI opportunity backlog

(prioritised by impact, risk, readiness)

Data readiness assessment

and improvement plan

Governance model

(roles, permissions, auditability, human-in-the-loop rules)

Implemented AI-assisted workflows

(triage, enrichment, knowledge assistance)

Operational runbooks

for monitoring, review, escalation, and safe use

Success metrics dashboard

and phased roadmap for expansion

Platforms

Solutions define outcomes. Platforms enable execution.
AI-based operations can leverage capabilities available within your environment, such as:

ServiceNow

Align AI-assisted experiences with IT workflows, governance, and operational data

Workato

Orchestrate cross-system signals and actions to support AI-assisted operations

Hybrid Approaches

Flexible configuration where required by your security, data, or architecture constraints

Frequently Asked Questions (FAQ)

A. Not necessarily. Many high-value use cases start as recommendations and assistance with human approval. This creates value while keeping control in the hands of the business. Automation can be introduced gradually once the process is proven safe and reliable. This staged approach reduces risk and builds trust.

A. Safety comes from governance and controls: data access control, audit trails, defined escalation paths, and measurable evaluation. It also comes from phased rollout so the organisation can learn safely before scaling. We design these elements before expanding AI into critical operational processes.

A. No, but you do need sufficiently reliable signals for the use case you choose. We assess data readiness, select use cases that can deliver value within current constraints, and improve the data foundation over time. This avoids waiting indefinitely while still keeping AI adoption responsible.

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