AI Readiness Assessment: A 7-Point Checklist

An AI readiness assessment checks whether your organisation can support an AI project before you commit money to one. This seven-point checklist scores data quality, process documentation, technology, skills and culture, business case, governance and executive sponsorship from 0 to 3 each. A score of 16 or more out of 21 means you are ready to pick a first use case.

Why should you assess AI readiness before investing?

Gartner (February 2025) predicted that through 2026, organisations will abandon 60 per cent of AI projects unsupported by AI-ready data. Those failures trace back to organisations that started before they were ready, more than to vendors or models.

The organisations that do well with AI are often not the ones with the largest budgets. They assessed their readiness honestly first and fixed the gaps. Score yourself honestly on the seven points below and you will know where to spend time before you spend money. For an interactive version, try our AI readiness assessment tool.

What are the seven points of an AI readiness assessment?

1. 1Data qualityIs your data AI-ready?
2. 2Process docsDo you know how work flows?
3. 3InfrastructureCan your systems support AI?
4. 4Skills and cultureIs your team ready?
5. 5Business caseCan you invest properly?
6. 6GovernanceAre you compliant?
7. 7SponsorshipDoes leadership back this?

Rate each point from 0 (not started) to 3 (fully ready). The maximum is 21.

Point 1: Data quality and accessibility

AI is only as good as the data it uses. Informatica's CDO Insights survey (2025) found data quality and readiness was the most-cited obstacle to AI success. If data sits in disconnected spreadsheets, inconsistent CRMs or paper files across sites, no AI tool will fix that.

Questions to ask:

• Can you produce a single accurate customer list within 30 minutes?
• Are financial records in your ERP, Xero or MYOB reconciled within the month, across every entity?
• Does "revenue" mean the same thing to every department and branch?
• Is there at least 12 months of history for the process you want to automate?

Green flags: central data systems, regular cleaning routines, consistent naming, systems you can connect to.

Red flags: critical data in personal spreadsheets, duplicates across systems, no data owner, reliance on manual exports.

Score: 0 = data chaos | 1 = some structure, gaps | 2 = mostly central, minor issues | 3 = clean, accessible, governed

For a deeper look, see our data quality and AI readiness assessment.

Point 2: Process documentation

You cannot automate what you cannot describe. A frequent root cause in why AI projects fail in Australia is that nobody mapped the process first.

Questions to ask:

• Can you draw the end-to-end workflow for the target process?
• Are decision rules written down, or do they live in one person's head?
• Do you know the exception paths?
• Have you measured time per task, error rate and volume?

Green flags: written procedures, process maps, KPIs, several people who understand the process.

Red flags: knowledge held by one or two people, every branch doing it differently, no baseline metrics.

Score: 0 = none | 1 = informal knowledge | 2 = partial documentation | 3 = complete maps with metrics

Point 3: Technology infrastructure

AI has to connect to your finance system, CRM, job management platform or ERP. If those systems cannot integrate, you face significant work before the AI part begins.

Questions to ask:

• Do core systems offer APIs or other integration options?
• Is connectivity reliable across every site?
• Do you have internal or external IT capable of managing integrations?
• Are systems on current, supported versions?

Green flags: modern platforms, existing integrations, IT familiar with APIs.

Red flags: legacy software with no API, unsupported versions, manual file transfers between systems.

Score: 0 = legacy, no integration | 1 = some modern tools, disconnected | 2 = mostly integrated | 3 = modern, connected stack

Point 4: Skills and culture

Technology readiness means little if people are not ready.

Questions to ask:

• Have team members used AI tools such as Copilot or ChatGPT in their work, within policy?
• Is there openness to changing how work is done?
• Is there an internal champion for an AI project?
• Have you invested in digital skills training in the past 12 months?

Green flags: staff experimenting within policy, continuous improvement culture, a named champion.

Red flags: active resistance, no training, high turnover in target roles, fear around AI.

Score: 0 = no awareness or active resistance | 1 = curiosity, no action | 2 = some experimentation | 3 = trained team with a champion

Our guide on building an AI strategy covers how to plan skills alongside technology.

Point 5: Budget and business case

The cheapest option is rarely the best, and the build vs buy decision can change total cost of ownership substantially. Budget for implementation, integration, training and ongoing operation, beyond licences.

Questions to ask:

• Have you calculated the current cost of the process (staff time, errors, delays)?
• Is budget allocated, or are you still exploring?
• Have you defined success in dollar terms?
• Are you prepared to invest for 6 to 12 months before full return?

Green flags: documented case with baseline metrics, allocated budget, defined success criteria.

Red flags: no baseline, expecting immediate return, budget dependent on unconfirmed approval.

Score: 0 = no budget or case | 1 = rough estimates | 2 = documented case awaiting approval | 3 = approved budget with success metrics

Point 6: Governance and compliance

The Privacy Act 1988 applies to any AI processing personal information, and amendments passed in 2024 add automated decision transparency requirements from 10 December 2026. Australia's AI Ethics Principles and the Department of Industry, Science and Resources' Guidance for AI Adoption (2025) are increasingly referenced in procurement. In healthcare, financial services or government work, governance is a prerequisite.

Questions to ask:

• Does your privacy policy cover automated decision-making?
• Do you know where AI will process and store your data, and whether it stays in Australia?
• Do you understand how the AI Ethics Principles apply to your use case?
• Have you checked sector-specific requirements?

Green flags: updated privacy policy, clear residency position, legal review done.

Red flags: no privacy review since adopting digital tools, no view on where AI processes data, no legal input.

Score: 0 = not considered | 1 = aware, not actioned | 2 = policies in development | 3 = framework in place

Point 7: Executive sponsorship

Without genuine sponsorship, AI work gets deprioritised at the first budget review and abandoned at the first implementation problem. Across our consultants' careers on large data platform programs in BHP and Rio Tinto environments, the projects with active executive sponsors were the ones that survived the difficult middle phase.

Questions to ask:

• Is there a named executive sponsor?
• Do they understand the time, money and change management required?
• Will they remove blockers and make decisions?
• Is AI on the leadership or board agenda, beyond IT?

Green flags: named sponsor, leadership discussion, cross-functional support.

Red flags: AI treated as "an IT thing", no sponsor, results expected without involvement.

Score: 0 = no awareness | 1 = interest, no commitment | 2 = named sponsor, early engagement | 3 = active, invested sponsor

How do you interpret your AI readiness score?

Add the seven scores for a total between 0 and 21.

What is your total across all seven points?

• 16-21: Ready to moveFoundations in place. Select the right use case and partner, and aim to pilot within 60 days.
• 10-15: Nearly readySpend 4-8 weeks lifting your lowest areas before committing to a project.
• 5-9: Foundational work neededPrioritise data quality, process documentation and executive alignment before evaluating tools.
• 0-4: Not yet readyAI would be premature. Centralise data, document processes and build capability first.

	Not ready (0-9)	Ready (16-21)	Impact
Data	Spreadsheets and silos	Central and clean	Foundation
Processes	Held in people's heads	Documented with metrics	Clarity
Infrastructure	Legacy, disconnected	Modern, connected	Integration
Team	Resistant or unaware	Trained with a champion	Adoption
Budget	No business case	Approved with KPIs	Accountability
Governance	No policies	Framework in place	Compliance
Sponsorship	IT's problem	Executive-led	Longevity

What should you do next based on your score?

16-21: move to strategy. Build a targeted AI strategy that identifies the highest-value use case before you shortlist tools. A structured AI strategy engagement can shorten the path to a pilot.

10-15: close the gaps first. Lift your weakest points to at least 2. Process documentation and governance are the most common gaps at this stage.

Below 10: build digital foundations. Start with three actions:

1. Centralise data. Move critical data from spreadsheets into your ERP, CRM or job management system.
2. Document your top three processes. Map the workflows that consume the most staff time across sites.
3. Name an internal owner. Give one person the time and authority to drive readiness.

1. Weeks 1-2Self-assessmentComplete the checklist and identify the lowest-scoring areas.
2. Weeks 3-6Close gapsAddress data quality, process documentation or governance gaps.
3. Weeks 7-8StrategyDefine the target use case, build the business case, choose the approach.
4. Weeks 9-12PilotA focused pilot with clear success metrics, running alongside your team.

This week: book 45 minutes with your leadership team to complete the assessment, then assign owners to your two lowest-scoring areas.

How Solve8 can help

Our consultants run facilitated readiness assessments as the first phase of our AI strategy service, inspecting your systems and data and producing a prioritised plan, with a fixed fee agreed before we start. Where data is the gap, our data and reporting team fixes the foundations. Talk to us about where you scored.

Sources: Gartner, AI-ready data prediction (February 2025); Informatica CDO Insights (2025); OAIC and Privacy Act 1988 amendments (2024); Department of Industry, Science and Resources Guidance for AI Adoption (2025).