AI Consulting for Brisbane Industries: Mining to Finance
Queensland's economy isn't like Sydney or Melbourne. Mining drives billions in revenue. Agriculture spans cattle stations the size of small countries. Healthcare serves populations spread across vast distances. Tourism ebbs and flows with seasons.
These differences create unique AI opportunities—and unique challenges. Generic AI consulting advice doesn't always apply.
After working with Brisbane businesses on AI projects, here's what actually works for Queensland's key industries.
Mining and Resources
Queensland's resources sector is worth over $90 billion annually. AI opportunities are significant but require understanding of the environment.
What Works
Predictive maintenance: Mining equipment is expensive and downtime is costly. AI that predicts failures before they happen has clear ROI.
One mining services client reduced unplanned downtime by 23% using sensors and AI prediction. Payback was under 6 months.
Safety analytics: Analysing incident reports, near-misses, and environmental data to predict and prevent safety issues.
Exploration optimisation: AI analysis of geological data to improve targeting and reduce drilling costs.
Autonomous operations: Not just haul trucks—AI for drilling, blasting optimisation, and process control.
The Challenges
Connectivity: Remote sites often have limited or satellite-only connectivity. AI solutions need to work at the edge, not just in the cloud.
Harsh conditions: Equipment operates in dust, heat, and 24/7 cycles. Hardware needs to be ruggedised. Maintenance windows are tight.
Change resistance: Mining has strong safety culture (good) that can resist change (challenging). Involve workers early and often.
Integration complexity: Mining operations often run SCADA systems, proprietary software, and legacy databases. Integration is non-trivial.
Getting Started
Start with maintenance data. Most mining operations have years of equipment sensor data that's never been properly analysed. A pilot project analysing existing data can prove value without requiring new infrastructure.
Agriculture and Agribusiness
Queensland agriculture is diverse: cattle, sugar, cotton, grains, horticulture. AI applications vary by sector but common patterns emerge.
What Works
Yield prediction: Combining weather data, soil data, and historical yields to improve planning and marketing decisions.
Livestock monitoring: AI analysis of drone footage, sensor data, or satellite imagery to monitor herd health and location.
Supply chain optimisation: Coordinating harvest, processing, and transport logistics. Especially valuable for perishables.
Quality grading: Automated grading of produce using computer vision. Faster and more consistent than manual inspection.
The Challenges
Connectivity (again): Rural Queensland has connectivity gaps. Solutions need offline capability.
Data scarcity: Many farms have limited historical data in digital form. You might need to start data collection before AI.
Seasonality: Agriculture is seasonal. ROI calculations need to account for annual cycles, not monthly.
Price sensitivity: Smaller operations are cost-conscious. Solutions need to be right-sized.
Getting Started
Weather and yield data are often the best starting point. Weather data is readily available. Yield data exists (even if in spreadsheets). Correlating these can provide actionable insights without major investment.
Healthcare
Queensland Health is one of the state's largest employers. Private healthcare is significant too. The opportunity is large but so are the constraints.
What Works
Administrative automation: Document processing, coding, billing, scheduling. High volume, pattern-based, clear ROI.
Diagnostic support: AI that helps clinicians by surfacing relevant information, flagging anomalies, or prioritising reviews. Not replacing clinicians—augmenting them.
Remote patient monitoring: AI analysis of patient data from wearables or home devices. Particularly valuable for Queensland's dispersed population.
Resource optimisation: Theatre scheduling, bed management, staff rostering. Complex optimisation problems suited to AI.
The Challenges
Regulation: TGA requirements for software as medical device. Privacy Act and health records legislation. Clinical governance requirements.
Integration: Healthcare systems are notoriously difficult to integrate. HL7, FHIR, and proprietary formats abound.
Risk tolerance: Healthcare is conservative about new technology—appropriately so. Evidence requirements are high.
Procurement: Public healthcare procurement is complex and slow. Private healthcare has different dynamics.
Getting Started
Administrative functions are lower risk than clinical. Start with document processing, scheduling, or back-office automation. Build trust and evidence before moving to patient-facing applications.
Financial Services
Brisbane hosts regional headquarters for major banks and insurers plus a growing fintech sector.
What Works
Customer service automation: Handling routine enquiries, processing simple requests, triaging complex issues. We've built AI customer service systems that handle 70%+ of enquiries.
Document processing: Applications, claims, compliance documents. High volume, structured data extraction.
Risk assessment: Augmenting underwriting and credit decisions with AI analysis. Humans remain in the loop for final decisions.
Compliance monitoring: Scanning transactions, documents, and communications for compliance issues.
The Challenges
APRA: Prudential standards for AI in regulated entities. CPS 234 (information security), CPS 220 (risk management), and forthcoming AI-specific guidance.
Explainability: Financial decisions need to be explainable. Black-box AI is problematic for credit decisions.
Fairness: AI must not discriminate on protected attributes. This requires careful model design and testing.
Legacy systems: Core banking systems are often decades old. Integration is challenging.
Getting Started
Customer service is often the best starting point—high volume, lower risk, clear metrics. Build capability and confidence before moving to regulated functions like credit or underwriting.
Tourism and Hospitality
Tourism is Queensland's fourth-largest export earner. Seasonal and cyclical, but significant.
What Works
Dynamic pricing: AI-optimised pricing for accommodation, experiences, and packages. Responds to demand signals in real-time.
Customer service: Multilingual chatbots for international visitors. 24/7 availability during peak seasons.
Demand forecasting: Predicting booking patterns to optimise staffing, inventory, and marketing.
Personalisation: Recommendation engines for experiences, restaurants, and activities.
The Challenges
Seasonality: Extreme peaks and troughs make ROI calculation tricky.
Fragmentation: Many small operators. Hard to achieve scale for AI investment individually.
Multilingual requirements: International visitors need multilingual support.
Real-time requirements: Tourism decisions are often made in-the-moment.
Getting Started
Regional tourism organisations might aggregate demand to make AI investment viable for small operators. Individual operators should start with pricing optimisation—it can be applied quickly with clear ROI.
Finding the Right Partner
For Brisbane businesses exploring AI:
Industry experience matters: Generic AI expertise isn't enough. Look for partners who understand your sector's specific challenges.
Local presence helps: Understanding Queensland's regulatory environment, connectivity challenges, and business culture matters.
Start pragmatic: Pilot projects that prove value quickly build momentum for bigger investments.
Plan for integration: Legacy systems aren't going away. Budget for the integration work.
We work with Brisbane businesses across industries. Happy to discuss your specific situation.