Australian businesses spent an estimated $4.2 billion on AI initiatives in 2025. Analysts estimate that more than half delivered negligible returns. The problem isn't the technology — it's the selection criteria.
The ROI Framework That Actually Works
Before evaluating any AI project, apply three filters: volume, variability, and value. High-volume tasks with low variability and clear value output are where AI delivers. Everything else is experimentation at best.
What Delivers
- Process automation with clear inputs and outputs — accounts payable, document processing, data entry. ROI is measurable within 90 days.
- Customer service AI with defined scope — FAQ handling, appointment booking, tier-1 support. Works when the problem space is bounded.
- Predictive analytics on existing data — demand forecasting, churn prediction, maintenance scheduling. Requires clean data but delivers compounding returns.
What Doesn't
- Chatbots deployed without use case definition — "let's add a chatbot to the website" without defining what problem it solves.
- AI tools without data infrastructure — the model is only as good as the data feeding it. Deploying AI on dirty, incomplete, or siloed data produces unreliable outputs.
- Generative AI for compliance-sensitive outputs — legal documents, medical summaries, financial advice. The hallucination risk isn't worth it without heavy human oversight.
How to Evaluate Any AI Opportunity
Before committing budget, answer four questions:
- What is the current cost of the process (time × people × frequency)?
- What is the realistic automation rate (not vendor claims — independent benchmarks)?
- What are the failure modes and their downstream costs?
- What does the first 90 days look like before full deployment?
If you can't answer all four clearly, you're not ready to commit budget. You're ready for a scoping engagement.
The Australian Context
Australian businesses face specific constraints that influence ROI: smaller data volumes than US counterparts, privacy obligations under the Australian Privacy Act, and a smaller talent pool for implementation. These factors favour buying over building, and favour narrow implementations over broad AI platforms.
The businesses generating real returns in 2026 aren't the ones with the biggest AI budgets. They're the ones with the most disciplined selection criteria.