"Machine Learning Use Cases by Industry: What's Working in 2025"
"What are other companies in our industry doing with machine learning?"
It's the question we hear most often. Business leaders want to know what's actually working—not theoretical possibilities, but real implementations delivering measurable results.
Here's a practical tour of machine learning use cases by industry, based on what we've seen deployed and working.
Healthcare
Healthcare has unique constraints—regulation, privacy, clinical validation—but also significant opportunities.
What's Working
Medical image analysis: ML models identify anomalies in radiology, pathology, and dermatology images. Not replacing clinicians—flagging cases that need attention.
Results we've seen: 30% reduction in missed findings. 40% faster preliminary reads.
Clinical documentation: ML extracts structured data from clinical notes, auto-generates documentation, and codes for billing.
Results: 50% reduction in documentation time. Improved coding accuracy.
Appointment optimisation: Predicting no-shows, optimising scheduling, managing capacity.
Results: 15-20% reduction in no-shows. Better capacity utilisation.
Patient flow prediction: Forecasting admissions, length of stay, discharge readiness.
Results: Improved bed management. Reduced wait times.
What's Not Ready
Autonomous diagnostic decisions remain appropriately limited. ML assists clinicians; it doesn't replace clinical judgment. This is by design—regulatory and liability frameworks require human oversight.
Learn more about how AI consultants Brisbane help healthcare organisations implement these solutions.
Financial Services
Financial services was early to ML adoption. The sector has mature use cases with proven results.
What's Working
Fraud detection: Pattern recognition across transactions to identify suspicious activity.
Results: 40-60% improvement in fraud detection. Significant false positive reduction through model tuning.
Credit decisioning: ML models assess creditworthiness using broader data signals than traditional scoring.
Results: 20-30% improvement in prediction accuracy. Faster decisioning.
Document processing: Automating extraction from loan applications, KYC documents, contracts.
Results: 70-80% automation on standard document types. Days reduced to hours for processing.
Customer service automation: Handling routine enquiries, gathering information for complex cases.
Results: 50-65% enquiry automation. Improved response times.
Anti-money laundering: Pattern detection across transactions and customer behaviour.
Results: Better detection rates. Reduced false positives in investigation queues.
Regulatory Reality
Financial services ML operates under significant oversight. Explainability requirements mean some advanced techniques aren't suitable. Models need to be auditable and defensible.
Learn more about how AI consultants Sydney help financial services firms implement ML solutions.
Manufacturing
Manufacturing ML often focuses on operational efficiency—optimising processes that run continuously.
What's Working
Predictive maintenance: ML predicts equipment failures before they happen, enabling proactive maintenance.
Results: 20-40% reduction in unplanned downtime. 10-25% reduction in maintenance costs.
Quality control: Computer vision identifies defects faster and more consistently than human inspection.
Results: 30-50% improvement in defect detection. Faster inspection throughput.
Demand forecasting: Better predictions of product demand to optimise production planning.
Results: 15-25% reduction in inventory costs. Improved fill rates.
Process optimisation: ML identifies optimal process parameters for quality and efficiency.
Results: 5-15% efficiency improvements. Reduced waste.
Supply chain visibility: Predicting disruptions, optimising supplier selection, managing logistics.
Results: Better risk management. Cost optimisation.
Integration Challenge
Manufacturing ML often requires integration with operational technology (OT) systems—PLCs, SCADA, historians. This is different from typical IT integration and needs specialised expertise.
Learn more about how AI consultants Melbourne help manufacturers adopt ML.
Retail
Retail ML has matured significantly, with clear use cases across the customer journey.
What's Working
Personalisation: Product recommendations, personalised marketing, dynamic pricing.
Results: 10-20% increase in conversion. 15-30% increase in average order value.
Inventory optimisation: Predicting demand by SKU and location, optimising stock levels.
Results: 20-30% reduction in stockouts. 15-25% reduction in overstock.
Customer service: Chatbots handling order enquiries, returns, product questions.
Results: 40-60% enquiry automation. 24/7 availability.
Visual search: Customers search by image rather than text.
Results: Improved product discovery. Higher conversion on visual-first products.
Loss prevention: Identifying theft patterns, optimising security resources.
Results: 10-20% reduction in shrinkage.
Data Advantage
Retailers with strong data infrastructure see better results. The ML is only as good as the data feeding it.
Learn more about how AI consultants Brisbane help retailers implement ML solutions.
Insurance
Insurance has natural ML applications—the industry has always been about predicting risk.
What's Working
Claims automation: Document extraction, initial assessment, fraud detection.
Results: 30-50% faster claims processing. Improved fraud detection.
Underwriting support: ML models assess risk factors, suggest pricing, flag unusual applications.
Results: Faster underwriting. More consistent decisioning.
Customer service: Policy enquiries, claims status, document requests.
Results: 50-70% enquiry automation. Improved customer satisfaction.
Fraud detection: Pattern recognition across claims to identify suspicious activity.
Results: 40-60% improvement in fraud identification.
Risk modelling: Better predictions of loss frequency and severity.
Results: Improved pricing accuracy. Better portfolio management.
Regulatory Considerations
Insurance ML faces similar explainability requirements to financial services. Models affecting pricing or coverage decisions need to be defensible and fair.
Learn more about how AI consultants Sydney help insurers implement ML solutions.
Logistics and Supply Chain
Logistics involves optimisation problems that ML handles well—routing, scheduling, forecasting.
What's Working
Route optimisation: ML optimises delivery routes considering traffic, constraints, and priorities.
Results: 10-20% reduction in delivery costs. Improved on-time delivery.
Demand forecasting: Better predictions enable better inventory positioning and capacity planning.
Results: Improved fill rates. Reduced expedited shipping.
Warehouse optimisation: ML optimises picking routes, slotting, and workforce scheduling.
Results: 15-25% improvement in warehouse productivity.
Predictive maintenance: Fleet maintenance prediction reduces breakdowns.
Results: Reduced vehicle downtime. Lower maintenance costs.
Document processing: Automating bills of lading, customs documents, proof of delivery.
Results: Faster processing. Reduced errors.
Learn more about how AI consultants Melbourne help logistics companies implement ML.
Professional Services
Professional services ML is often about making knowledge workers more productive.
What's Working
Document analysis: ML reviews contracts, identifies key terms, flags issues.
Results: 60-80% reduction in initial review time. Improved consistency.
Research acceleration: ML surfaces relevant precedents, regulations, and information.
Results: Faster research. Broader coverage.
Knowledge management: Natural language access to firm knowledge.
Results: Reduced time searching. Better knowledge sharing.
Client insights: Analysing client interactions to identify opportunities and risks.
Results: Improved client retention. Better cross-selling.
Quality Bar
Professional services firms have high quality standards. ML that's "pretty good" often isn't good enough. Implementation requires careful validation and appropriate human oversight.
Learn more about how AI consultants Sydney help professional services firms implement ML.
Cross-Industry Patterns
Looking across industries, common patterns emerge:
Document processing works everywhere: Every industry has documents that need to be read and data that needs to be extracted.
Customer service automation is mature: The technology and practices are well-established.
Prediction improves with data: Better data infrastructure leads to better ML results.
Integration is the challenge: The ML model is often the easy part; connecting to existing systems is hard.
Human oversight remains essential: Even in mature use cases, human review is part of the process.
Getting Started in Your Industry
If you're exploring ML for your industry:
Look at peer implementations: What are similar companies actually doing (not announcing)?
Start with proven use cases: Document processing, customer service, and forecasting work across industries.
Assess your data: ML requires data. What do you have? What state is it in?
Plan for integration: How will ML solutions connect to your existing systems?
Consider regulation: What constraints apply in your industry?
As AI consultants Brisbane, we work with businesses across multiple industries on practical ML implementations. Happy to discuss what we're seeing in your sector.
Our team of AI consultants Brisbane helps businesses identify and implement the right ML solutions for their industry. Get in touch to explore your industry's ML opportunities.