Answer 10 quick questions about the ITSM practices AI depends on most — and find out whether your foundations are ready to support AI investment, or whether there's work to do first.
Takes 3 minutes · Free · Based on real ITSM assessment methodology
Question 1 of 100% complete
Incident Management
How consistent and well-structured is your incident data?
AI tools that support incident triage and automation depend entirely on clean, consistently categorised incident data. We're looking at data quality, not just process maturity.
0
Not Ready
Incident data is inconsistent and unreliable. Categorisation varies by person, making it unsuitable as an input for any AI tool.
1
Early Stage
Some consistency exists but significant variation in categorisation, priority assignment and resolution data quality limits AI usefulness.
2
Developing
Incident data is reasonably consistent. Most records are complete and categorised correctly, though gaps exist that would affect AI accuracy.
3
Mostly Ready
Incident data is clean, consistently structured and well-maintained. It would provide reliable inputs for AI triage and pattern recognition tools.
4
AI Ready
Incident data is comprehensive, consistently categorised and actively governed. It is a high-quality input ready to drive AI-powered automation and analytics.
Service Catalogue
How complete and accurate is your service catalogue?
AI self-service and virtual agents depend on a well-structured, accurate service catalogue. Without it, AI cannot route requests or provide reliable self-service.
0
Not Ready
No service catalogue exists. AI self-service is not viable without a structured catalogue to work from.
1
Early Stage
A basic catalogue exists but it's incomplete, out of date or not structured in a way that AI tools can reliably consume.
2
Developing
A service catalogue exists covering most services. It's reasonably accurate but gaps and inconsistencies would limit AI self-service effectiveness.
3
Mostly Ready
The catalogue is comprehensive, accurate and actively maintained. It would support AI-powered request routing and self-service with minor gaps to address.
4
AI Ready
The service catalogue is complete, consistently structured and regularly reviewed. It is ready to serve as the foundation for AI self-service and virtual agent deployments.
Knowledge Management
How well-maintained and accessible is your IT knowledge base?
AI knowledge tools and virtual agents surface answers from your existing knowledge base. If that knowledge is outdated, incomplete or inconsistent, AI will produce confident wrong answers.
0
Not Ready
No structured knowledge base exists. AI knowledge tools would have nothing reliable to work with.
1
Early Stage
Some knowledge articles exist but they are outdated, inconsistently structured and not actively maintained — making AI knowledge tools unreliable.
2
Developing
A knowledge base exists and is partially maintained. Coverage is incomplete and article quality varies, which would affect AI accuracy in some areas.
3
Mostly Ready
The knowledge base is well-maintained, consistently structured and covers most common scenarios. It would support AI knowledge tools effectively with some gaps to address.
4
AI Ready
Knowledge is comprehensive, consistently authored, regularly reviewed and actively governed. It is ready to power AI knowledge surfacing and virtual agent responses reliably.
Data Quality & Measurement
How reliable and well-governed is your ITSM data overall?
AI analytics and predictive tools are only as good as the data they analyse. We're looking at data completeness, consistency and whether there's active governance over data quality.
0
Not Ready
ITSM data is unreliable and ungoverned. There is no foundation for AI analytics or predictive tooling.
1
Early Stage
Data exists but is inconsistently captured and not governed. Significant cleansing would be required before AI analytics could produce trustworthy outputs.
2
Developing
Data quality is improving but gaps and inconsistencies remain. AI analytics would produce some useful insights but with significant noise.
3
Mostly Ready
Data is reasonably complete and consistently captured. Active governance exists and AI analytics would produce reliable outputs in most areas.
4
AI Ready
ITSM data is comprehensive, consistently structured and actively governed with clear ownership. It provides a high-quality foundation for AI-powered analytics and prediction.
Process Consistency
How consistently are your ITSM processes followed across teams?
AI inherits your process inconsistencies and amplifies them. If the same type of request is handled five different ways by five different people, AI automation will produce five different — and unpredictable — outcomes.
0
Not Ready
Processes are not consistently followed. Each team or individual handles work differently, making AI automation unpredictable and unreliable.
1
Early Stage
Processes exist but compliance is inconsistent. Variation across teams would cause AI automation to produce unreliable results.
2
Developing
Processes are mostly followed but exceptions are common. AI automation could work in well-defined areas but inconsistency would limit broader deployment.
3
Mostly Ready
Processes are consistently followed across the organisation. AI automation would produce reliable, predictable outcomes in most practice areas.
4
AI Ready
Process compliance is high and actively monitored. Exceptions are rare and well-governed. AI automation would operate reliably and consistently across all practice areas.
Change Management Maturity
How well-controlled is your approach to managing changes, including AI tool deployments?
Deploying AI tools is itself a significant change. Organisations with weak change management often introduce AI in ways that cause incidents, erode user trust and are difficult to reverse.
0
Not Ready
No change management process exists. AI deployments would happen without risk assessment, testing or rollback planning.
1
Early Stage
A change process exists but is poorly followed. AI tool deployments would likely bypass proper assessment and approval.
2
Developing
Change management is in place and mostly followed. AI deployments would go through a process, though post-deployment review and outcome tracking are inconsistent.
3
Mostly Ready
Change is well-governed and consistently applied. AI tool deployments would be properly risk-assessed, tested and reviewed.
4
AI Ready
Change management is mature and data-driven. AI deployments would be managed with full risk assessment, staged rollout, outcome measurement and continuous improvement.
Data Governance
How clearly defined is ownership, classification and governance of your IT data?
AI tools process and learn from your data. Without clear data ownership, classification and retention policies, AI deployments can create compliance, privacy and security risks.
0
Not Ready
No data governance exists. Data ownership is unclear and there are no classification or retention policies — creating significant risk in any AI deployment.
1
Early Stage
Some data policies exist on paper but ownership is unclear and governance is not actively enforced. AI deployments would carry significant data risk.
2
Developing
Data governance exists for key datasets but coverage is incomplete. Some AI deployments would be feasible but data risk would need careful management.
3
Mostly Ready
Data ownership is defined and governance is actively maintained for most datasets. AI deployments could proceed with manageable data risk in most areas.
4
AI Ready
Data governance is comprehensive, actively enforced and aligned with compliance requirements. AI can be deployed confidently with clear data ownership and policy coverage.
Integration & API Readiness
How well can your existing systems and tools integrate with new AI capabilities?
AI tools don't operate in isolation — they need to connect to your ITSM platform, data sources and other systems. Poor integration capability is a practical blocker to AI adoption.
0
Not Ready
Systems are largely siloed with no API capabilities. Integrating AI tools would require significant technical investment before any value could be realised.
1
Early Stage
Some integration capability exists but it's limited and inconsistent. AI tool integration would be complex, costly and require significant technical effort.
2
Developing
Core systems have basic API capabilities. AI integration is feasible but would require development effort and careful management of technical debt.
3
Mostly Ready
Systems are well-integrated with documented APIs. AI tools could be connected to core platforms with manageable effort and clear technical pathways.
4
AI Ready
Integration capability is mature with well-documented, actively maintained APIs across core systems. AI tools can be connected quickly and reliably with low technical risk.
Organisational Readiness
How prepared are your people and culture for AI adoption in IT service management?
Technology readiness and cultural readiness are different things. Organisations that deploy AI without preparing their people typically see low adoption, workarounds and eroded trust in the tools.
0
Not Ready
There is no awareness or appetite for AI adoption. People are not prepared and there is no programme to build readiness.
1
Early Stage
Awareness of AI exists but understanding is low and there are concerns about job impact. No structured readiness programme is in place.
2
Developing
Some teams are open to AI adoption and basic awareness programmes exist. Readiness is uneven across the organisation and more structured preparation is needed.
3
Mostly Ready
Most teams understand the AI adoption roadmap and have been involved in planning. Concerns have been addressed and adoption programmes are in place.
4
AI Ready
The organisation is actively engaged in AI adoption. Teams are trained, concerns have been addressed and there is genuine enthusiasm for AI-enabled improvement.
Leadership & Vision
How clear and committed is your leadership's AI strategy for IT service management?
AI initiatives without executive sponsorship and a clear strategic vision consistently underdeliver. We're looking at whether there's genuine leadership commitment and a realistic plan — not just aspiration.
0
Not Ready
No AI strategy exists for ITSM. Leadership has not committed to AI adoption and there is no budget, sponsorship or roadmap.
1
Early Stage
There is interest in AI at leadership level but no clear strategy, defined use cases or committed investment. AI discussions are aspirational rather than planned.
2
Developing
A high-level AI vision exists and leadership is supportive. Use cases have been identified but the roadmap, business case and investment commitment are still developing.
3
Mostly Ready
Leadership has committed to an AI strategy with defined use cases, a roadmap and allocated budget. Sponsorship is clear and the programme is moving forward.
4
AI Ready
Leadership has a clear, well-resourced AI strategy with executive sponsorship, defined outcomes and active governance. The organisation is executing against a realistic, evidence-based AI roadmap.
Almost there
Your results are ready
Enter your details below to see your personalised AI readiness score and analysis.
Your AI Readiness Assessment
—
—
Readiness scores by area (0–4 scale)
Important context: This scorecard gives you a directional view of your AI readiness across 10 key areas. A full AI readiness assessment goes deeper — covering all 34 ITIL 4 practice areas, structured interviews with your leadership and frontline teams, and a detailed gap analysis with a prioritised improvement roadmap. The gap between this and a full assessment is significant. But it's a useful starting point.
We use cookies to understand how visitors use our site (Lead Forensics) and to process enquiry form submissions. No advertising cookies are used.
Privacy & Cookie Policy