Data Infrastructure
How would you describe your organisation's data environment?
Think about your core business data: customer records, transactions, operational data.
Data is mostly in spreadsheets, email, and paper files. No central system.
We have core systems (CRM, ERP) but data is siloed. Manual exports to combine.
Key systems are connected. We have some automated reporting but gaps remain.
Well-integrated systems with a data warehouse or lake. Clean, accessible data.
Mature data platform with APIs, real-time pipelines, and governance in place.
Process Maturity
How standardised are your core business processes?
Consider the processes that drive the most value or consume the most time.
Processes are ad hoc. Different people do things differently every time.
Some processes are documented but not consistently followed.
Core processes are standardised and documented. Some basic automation exists.
Well-defined processes with KPIs. Some RPA or workflow automation in place.
Fully optimised processes with continuous improvement. Automation is embedded.
Team Capability
What AI/automation expertise exists in your organisation?
Consider both technical talent and business understanding of AI.
No dedicated AI or data team. Limited technical skills beyond basic IT.
IT team with general skills but no AI-specific expertise. Interested but learning.
1-2 data analysts or engineers. Some experience with automation tools.
Dedicated data team with ML/AI skills. Have run AI pilots or small projects.
Strong AI team with production experience. Clear AI strategy and governance.
Strategic Alignment
How does leadership view AI automation?
Consider the C-suite and board perspective on AI investment.
AI is not on the leadership agenda. No budget or interest.
Leadership is curious about AI but no concrete plans or budget allocated.
AI is in the strategic plan. Some budget allocated. Looking for the right entry point.
Active executive sponsor. Budget approved. Clear use cases identified.
AI is a board-level priority with dedicated funding and clear success metrics.
Compliance Readiness
How prepared are you for AI governance and compliance?
Consider industry regulations (APRA, Privacy Act) and internal policies.
Haven't considered AI-specific compliance or governance requirements.
Aware of regulations but no AI-specific policies or frameworks in place.
Basic data governance exists. Starting to consider AI-specific requirements.
Data governance framework in place. AI ethics guidelines being developed.
Comprehensive AI governance: ethics framework, risk management, audit trails.
Automation Opportunity
How much manual, repetitive work exists in your operations?
Think about data entry, report generation, document processing, approvals.
Significant: teams spend 50%+ of time on repetitive tasks. Huge automation potential.
Substantial: many manual processes that could clearly be automated.
Moderate: some repetitive work but some automation already in place.
Limited: most obvious automation has been done. Gains would be incremental.
Minimal: operations are highly automated already.
Investment Readiness
What budget range could you allocate to an AI automation initiative?
Consider the first project, not a multi-year program.
Under $5,000 — exploring with minimal investment
$5,000–$15,000 — ready for an assessment or small pilot
$15,000–$50,000 — ready for a focused automation project
$50,000–$150,000 — ready for a serious multi-use-case program
$150,000+ — enterprise-scale AI transformation
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