How to Use Your Business Data for AI

Most Australian businesses have more useful data than they realise — and less ready data than they think. The gap between the data you have and the data AI can work with is often significant, but almost always bridgeable with the right preparation.

What Data AI Can Work With

Modern AI can work with almost any type of business data:

• Structured data: Transaction records, customer databases, financial data, inventory, sensor readings — typically stored in databases or spreadsheets. AI's strongest input.
• Documents: Invoices, contracts, reports, emails, forms — AI can extract, classify and analyse at scale.
• Communications: Email threads, chat logs, customer service records — valuable for pattern analysis and response automation.
• Images and video: Product photos, site inspections, quality control images — AI vision can analyse these for defects, classifications, measurements.
• Audio: Call recordings, meetings, voice messages — AI transcription and analysis is mature and effective.

The Most Valuable Data Assets Most Businesses Have

• Historical transaction data (what your customers bought, when, at what price)
• Customer service interactions (emails, support tickets, call records)
• Operational data (production records, maintenance logs, time tracking)
• Financial records (invoices, expenses, cash flow data)
• Document archives (contracts, proposals, specifications)

Getting Your Data Ready

The four key steps to making your data AI-ready:

1. Inventory: Map what data you have, where it lives and what format it's in.
2. Assess quality: Identify gaps, inconsistencies and errors. Data quality directly determines AI quality.
3. Connect sources: Set up data pipelines to bring relevant data together in a form AI can access.
4. Establish governance: Define who can access what data and what privacy obligations apply before building AI on top of it.