Answer 6 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 60% complete
Incident Management — AI Foundation #1
How consistently are incidents categorised and owned across your organisation?
AI triage and automation depends entirely on consistent incident categorisation. Without it, AI inherits your inconsistency and scales it.
0
Not Ready
Incidents are categorised inconsistently or not at all. Different teams handle them differently with no shared standards.
1
Partially Ready
Some categorisation standards exist but they're not consistently applied. AI could use this data but results would be unreliable.
2
Mostly Ready
Categorisation is mostly consistent with clear ownership. AI could work with this data — some refinement would improve outcomes.
3
AI Ready
Consistent, owned and well-structured incident data. AI triage and automation would have a reliable foundation to work from.
Service Catalogue — AI Foundation #2
How well-defined and structured is your service catalogue?
AI request fulfilment, virtual agents and chatbots need a clear service catalogue to route and resolve requests. Without one, they can't function effectively.
0
Not Ready
No formal service catalogue. Services aren't defined, owned or structured in any consistent way.
1
Partially Ready
A partial catalogue exists — some services are defined but coverage is incomplete and ownership is unclear.
2
Mostly Ready
A catalogue exists with most services defined and owned. Some gaps remain but AI could route and fulfil most request types.
3
AI Ready
Complete, structured and actively maintained service catalogue. AI has everything it needs to route, fulfil and automate requests.
Knowledge Management — AI Foundation #3
How structured, accurate and current is your IT knowledge base?
AI knowledge retrieval is only as good as the knowledge it's trained on. Fragmented or outdated knowledge means AI surfaces wrong answers — confidently.
0
Not Ready
Knowledge is fragmented across emails, SharePoint, individuals' heads and nowhere formal. AI has nothing reliable to draw from.
1
Partially Ready
Some knowledge articles exist but they're inconsistently written, often outdated and not regularly reviewed.
2
Mostly Ready
A knowledge base exists with reasonable coverage. Articles are mostly current and structured — AI could use this with some risk of inaccuracy.
3
AI Ready
Structured, owned, regularly reviewed knowledge base. AI can confidently retrieve and surface accurate answers at point of need.
Data Quality & Measurement
How reliable and consistent is your ITSM data and reporting?
AI learns from your data. If your metrics are inconsistent, your reporting is distrusted or your data quality is poor, AI will learn and perpetuate those problems.
0
Not Ready
Data quality is poor and metrics are inconsistent. There's no reliable data foundation for AI to learn from or optimise against.
1
Partially Ready
Some metrics exist but they're not universally trusted or consistently calculated. AI insights would be unreliable.
2
Mostly Ready
Core metrics are defined and mostly consistent. AI could generate useful insights — some data quality improvement would enhance accuracy.
3
AI Ready
Consistent, trusted data with clear definitions and governance. AI has a reliable foundation for analytics, prediction and optimisation.
Process Consistency & Ownership
How consistently do your IT teams follow defined processes?
AI automation requires predictable, consistent processes. Where teams work around processes or apply them differently, AI automation breaks down at the exception points.
0
Not Ready
Processes are poorly defined and inconsistently followed. Teams work around them regularly. AI would automate workarounds rather than good practice.
1
Partially Ready
Processes exist but compliance varies. Some teams follow them well, others don't. AI would produce inconsistent results across the organisation.
2
Mostly Ready
Most teams follow defined processes most of the time. Some exceptions exist — AI would work well for standard scenarios.
3
AI Ready
Processes are consistently followed, owned and regularly reviewed. AI can automate with confidence that the underlying process is reliable.
Organisational Readiness
How prepared is your organisation to adopt and govern AI in IT operations?
AI tools require governance, change management and clear ownership of AI-driven decisions. Without this, adoption stalls or risks accelerate.
0
Not Ready
No AI strategy, governance or ownership for IT operations. AI is being discussed but no structured approach exists.
1
Partially Ready
Some AI interest and early conversations happening but no formal governance, strategy or accountable owner in place yet.
2
Mostly Ready
An AI strategy is forming with some governance thinking. Leadership is engaged and there's growing clarity on how AI will be adopted and governed.
3
AI Ready
Clear AI strategy, defined governance and named accountability for AI decisions in IT. The organisation is culturally and structurally ready to adopt AI responsibly.
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Your AI readiness results are ready
Enter your details to see how AI-ready your ITSM foundations are — and what to prioritise before your next AI investment.
Your ITSM AI Readiness
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Readiness by foundation area
Important: This scorecard gives you a directional view based on 6 high-level questions. A real AI Readiness Assessment examines each foundation practice in depth — with structured interviews, evidence-based scoring and a prioritised improvement roadmap. The difference between this and a full assessment is significant. But it's an honest starting point for the conversation.