Is my business data ready for AI implementation?

One of the most common concerns Australian business owners have about AI is whether their data is ready. The good news is that most businesses with 12+ months of operational history have sufficient data for meaningful AI implementation — the question is whether that data is accessible, consistent, and relevant to the use case. **The Data Readiness Assessment** PresciaIQ's data readiness assessment evaluates four dimensions. Volume: do you have enough historical examples for the AI to learn from? For most use cases, 12–24 months of transaction history is sufficient. For high-frequency decisions (hourly demand forecasting, real-time fraud detection), more data is required. Quality: is the data accurate, consistent, and complete? Missing values, inconsistent formats, and duplicate records reduce model accuracy. PresciaIQ's data preparation process addresses these issues as part of every implementation. Relevance: does the data capture the signals that predict the outcome you care about? A churn prediction model needs data about customer engagement, not just transaction history. Accessibility: can the data be extracted from your systems in a usable format? Data locked in legacy systems or paper records requires additional extraction work before AI can be applied. **Common Data Scenarios** Small businesses with Xero and a basic CRM typically have sufficient financial and customer data for revenue forecasting and churn prediction. Manufacturers with a SCADA system and maintenance records have sufficient data for predictive maintenance. Retailers with a POS system and 2+ years of sales history have sufficient data for demand forecasting. Professional services firms with a time-tracking system and client history have sufficient data for revenue forecasting and churn prediction. **When Data Is Insufficient** If your business has less than 12 months of relevant data, or if data is stored in inconsistent formats across multiple systems, PresciaIQ recommends a data foundation phase before AI implementation. This phase establishes data collection processes, integrates data sources, and builds the historical dataset required for model training. A 3–6 month data foundation phase typically costs $5,000–$15,000 and creates the foundation for all future AI implementations. **The Intelligence Audit** PresciaIQ's free 15-minute Intelligence Audit includes a data readiness assessment that identifies the specific data you have, the data gaps that need to be addressed, and the AI use cases that can be implemented immediately versus those that require a data foundation phase. Book at presciaiq.com.au or call 0400 457 006.