Is Your Health Service Ready for AI? A Practical Assessment Framework

Every healthcare executive I meet wants to know: “Are we ready for AI?”

The honest answer is usually “it depends.” Ready for what? AI documentation tools have different requirements than diagnostic AI. Some organisations are ready for pilots but not production deployment. Others have technical capability but lack governance structures.

Here’s a framework for assessing AI readiness that I’ve developed over numerous engagements. It’s not exhaustive, but it covers the dimensions that matter most.

Dimension 1: Data Readiness

AI runs on data. If your data isn’t ready, your AI won’t work.

Data availability. Can you access the clinical data AI needs? This includes:

  • Electronic medical records
  • Diagnostic imaging (for radiology AI)
  • Pathology results
  • Vital signs and observations
  • Medication records

Many organisations have data in systems that are hard to access or export. That’s a barrier.

Data quality. Is the data accurate, complete, and consistent? Common issues:

  • Inconsistent coding practices
  • Missing data fields
  • Duplicate records
  • Data entry errors

Poor data quality undermines AI performance. Cleaning data is possible but expensive.

Data integration. Can you bring data together from multiple sources? Clinical AI often needs data from multiple systems. If your systems don’t talk to each other, that’s a problem.

Data governance. Do you have policies and processes for managing clinical data? Who can access what? How is data protected? What consent frameworks apply?

Assessment questions:

  • Can you extract a complete patient record across systems?
  • What’s your estimated data completeness rate?
  • When was your last data quality audit?
  • Do you have documented data governance policies?

Dimension 2: Technical Infrastructure

AI needs infrastructure to run.

Network capability. Is your network fast and reliable enough for AI workloads? Cloud-based AI needs good connectivity. Large file transfers (imaging, for example) need bandwidth.

Computing resources. Do you have the computing power for AI? Some AI runs in the cloud (vendor-managed), some runs on-premises (you manage). Know your approach and its requirements.

Integration capability. Can you connect AI systems to clinical systems? API availability, integration standards (HL7, FHIR), and integration expertise all matter.

IT team capacity. Does your IT team have capacity for AI projects? Implementation, integration, and ongoing support require IT resources.

Assessment questions:

  • What’s your network latency and bandwidth to major cloud providers?
  • Do your clinical systems have APIs for data access?
  • What’s your IT team’s experience with AI integration?
  • Do you have budget for infrastructure investment?

Dimension 3: Clinical Governance

AI creates new governance requirements.

Governance structures. Do you have governance structures for clinical AI? This includes:

  • AI clinical governance committee (or equivalent)
  • Clear roles and responsibilities
  • Decision-making processes for AI initiatives

Performance monitoring. Can you monitor AI performance over time? This requires:

  • Defined metrics and thresholds
  • Data collection processes
  • Regular review cadence

Incident management. How would you handle AI-related clinical incidents? Integration with existing incident management, specific AI considerations, and escalation pathways matter.

Regulatory compliance. Do you understand TGA requirements for clinical AI? Registration requirements, post-market monitoring, and change management all apply.

Assessment questions:

  • Who is responsible for clinical AI governance?
  • What AI performance metrics would you track?
  • How would an AI-related incident be reported and managed?
  • Have you consulted with TGA on your AI plans?

Dimension 4: Organisational Culture

Technology is easy; culture is hard.

Executive support. Is there genuine executive commitment to AI? Not just interest—willingness to invest, accept risk, and sustain effort through challenges.

Clinical engagement. Are clinicians open to AI? Enthusiasm, scepticism, or resistance? Who would champion AI adoption?

Change management capability. Can your organisation manage change effectively? AI implementation is organisational change, not just technology deployment.

Learning orientation. Is your organisation willing to experiment, fail, learn, and iterate? AI adoption involves uncertainty; rigid organisations struggle.

Assessment questions:

  • Has the executive team discussed AI strategy?
  • Which clinical leaders would support AI initiatives?
  • What’s your organisation’s track record with technology change?
  • How does your organisation respond to pilot failures?

Dimension 5: Strategic Alignment

AI should serve strategic objectives.

Clear objectives. What are you trying to achieve with AI? Specific, measurable objectives enable evaluation. Vague aspirations don’t.

Problem focus. Are you starting with genuine problems to solve? Or looking for problems to apply AI to?

Resource commitment. Are you willing to invest appropriately? AI isn’t free—implementation, integration, ongoing costs, and staff time all require funding.

Realistic expectations. Do you understand what AI can and can’t do? Unrealistic expectations lead to disappointment.

Assessment questions:

  • What clinical or operational problems would AI address?
  • What outcomes would indicate AI success?
  • What budget is available for AI initiatives?
  • What’s your timeline for AI value realisation?

Scoring Your Readiness

For each dimension, rate yourself:

1 - Not ready: Significant gaps, major work required before AI adoption 2 - Emerging: Some foundations in place, specific gaps to address 3 - Developing: Reasonable readiness, targeted improvements needed 4 - Ready: Strong position for AI adoption 5 - Advanced: Leading practice, positioned for sophisticated AI use

No organisation I’ve assessed scores 5 across all dimensions. Most have strengths in some areas and gaps in others.

The assessment isn’t about getting high scores—it’s about understanding where you are and what needs attention.

Using the Assessment

Once you’ve assessed readiness:

Identify critical gaps. Which gaps would most significantly impede AI success? Prioritise these.

Match AI ambition to readiness. If you’re not ready for complex diagnostic AI, start with simpler applications. Build capability progressively.

Develop a readiness roadmap. What investments and changes would improve readiness? Plan and execute these.

Reassess regularly. Readiness evolves. Reassess annually or when planning major AI initiatives.

Common Patterns

Patterns I see frequently:

Strong technology, weak governance. IT-led organisations often have technical capability but haven’t developed clinical governance structures.

Strong clinical engagement, weak data. Clinical teams enthusiastic about AI discover their data isn’t usable.

Executive interest without investment. Leaders want AI outcomes but haven’t committed resources.

Pilot success, scaling failure. Organisations that did well with pilots struggle to scale because readiness requirements increase.

Recognising these patterns helps address them proactively.

The Bottom Line

Most healthcare organisations aren’t fully ready for AI—and that’s okay. Readiness can be built. What matters is understanding your current position and developing realistic plans.

The organisations that succeed with AI aren’t necessarily the ones that started readiest. They’re the ones that assessed honestly, addressed gaps systematically, and built capability over time.

Dr. Rebecca Liu is a health informatics specialist and former Chief Clinical Information Officer. She advises healthcare organisations on clinical AI strategy and implementation.