Not all AI projects deliver value. Many don’t. But some consistently do. Here are seven use cases where organisations are actually seeing positive ROI – not in pilot, but in production.

1. Intelligent Document Processing

What it does: Extracts information from unstructured documents – invoices, contracts, forms, emails – and routes it to appropriate systems.

The business case: Manual document processing is expensive and error-prone. A single invoice might touch three people and take 15 minutes to process. Errors create downstream problems.

Realistic ROI: 40-60% reduction in processing time, 60-80% reduction in errors. For a company processing 10,000 invoices monthly, this translates to 2-3 FTE equivalent savings plus reduced error correction costs.

Where it works best: High-volume, consistent document types. Invoice processing, claims handling, loan applications, contract extraction.

Where it struggles: Highly variable document formats, documents requiring complex judgement, very low volumes (overhead exceeds benefit).

Typical implementation: 12-16 weeks, $150K-$400K depending on complexity and volume.

2. Customer Service Augmentation

What it does: Assists human agents with suggested responses, relevant knowledge base articles, and automated handling of simple queries.

The business case: Customer service is expensive, and response quality varies. AI can make agents faster and more consistent.

Realistic ROI: 20-35% reduction in handling time, 15-25% improvement in first-call resolution. For a 50-agent contact centre, this is the equivalent of 10-15 additional agents without the headcount.

Where it works best: High-volume environments with documented knowledge bases and clearly defined query types.

Where it struggles: Complex issues requiring nuanced judgement, environments where knowledge isn’t well documented, cases requiring significant empathy.

Important note: Full automation (no humans) has much lower success rates. Augmentation – AI helping humans – works better than replacement.

Typical implementation: 8-12 weeks for basic deployment, 6+ months for full optimisation.

3. Sales Intelligence and Prioritisation

What it does: Analyses leads and opportunities to prioritise sales efforts. Identifies patterns that predict conversion.

The business case: Sales teams waste time on low-probability leads while high-potential opportunities go neglected.

Realistic ROI: 15-30% improvement in conversion rates by focusing effort on higher-probability opportunities. For a team generating $10M annually, that’s $1.5-3M in additional revenue.

Where it works best: B2B sales with longer cycles, enough historical data to identify patterns, CRM systems with good data quality.

Where it struggles: New markets with limited historical data, very transactional sales, poor CRM data quality.

Typical implementation: 8-12 weeks, $100K-$250K depending on CRM complexity.

4. Demand Forecasting

What it does: Predicts demand for products and services to optimise inventory, staffing, and capacity.

The business case: Poor forecasting creates excess inventory (cash tied up, spoilage) or stockouts (lost sales, disappointed customers).

Realistic ROI: 20-35% reduction in forecasting error. For a retailer with $50M inventory, even a 10% inventory reduction frees $5M in working capital.

Where it works best: Businesses with historical demand data, seasonal patterns, multiple products, and meaningful forecast horizons.

Where it struggles: Highly volatile demand, new products without history, extremely long or short planning horizons.

Typical implementation: 12-20 weeks, $200K-$500K depending on data complexity.

5. Fraud Detection

What it does: Identifies suspicious transactions or behaviours that indicate fraud, money laundering, or policy violations.

The business case: Manual fraud review is slow and catches only obvious cases. Sophisticated fraud requires pattern recognition that humans can’t do at scale.

Realistic ROI: 25-40% improvement in fraud detection with 30-50% reduction in false positives. For a financial institution with $10M annual fraud losses, 25% reduction is $2.5M savings.

Where it works best: High transaction volumes, historical fraud examples to learn from, real-time decision requirements.

Where it struggles: New fraud patterns without historical examples, very low fraud rates (hard to find signal), environments where false positives have high costs.

Typical implementation: 16-24 weeks, $300K-$800K for enterprise deployment.

6. Predictive Maintenance

What it does: Analyses equipment sensor data to predict failures before they occur, enabling proactive maintenance.

The business case: Unplanned downtime is expensive – emergency repairs, production losses, safety risks. Scheduled maintenance based on condition rather than time is more efficient.

Realistic ROI: 20-40% reduction in unplanned downtime, 10-25% reduction in maintenance costs. For a manufacturing plant with $1M annual maintenance budget, 20% reduction is $200K savings plus production continuity.

Where it works best: Equipment with sensors, historical failure data, significant downtime costs, and maintenance flexibility.

Where it struggles: Equipment without adequate sensors, failures that are truly random (no precursors), environments where any maintenance disrupts operations.

Typical implementation: 16-24 weeks, $200K-$600K depending on sensor infrastructure needs.

7. Knowledge Management and Search

What it does: Makes organisational knowledge searchable and accessible through natural language queries. Connects employees with relevant documents, experts, and answers.

The business case: Employees waste hours searching for information that exists somewhere in the organisation. Expertise walks out the door when people leave.

Realistic ROI: 2-4 hours per week saved per knowledge worker. For a 500-person organisation at average $75/hour loaded cost, that’s $3.9-7.8M in annual productivity value.

Where it works best: Organisations with substantial documented knowledge, distributed workforces, complex information needs, and high knowledge worker ratios.

Where it struggles: Organisations where knowledge is truly tacit (not documented), small organisations with simple information needs, poor document organisation.

Platform examples: Microsoft Copilot, AI consultants Brisbane at Team400, and similar enterprise knowledge platforms.

Typical implementation: 4-8 weeks for basic deployment, 3-6 months for full organisational rollout.

Common Success Factors

Across these use cases, successful implementations share traits:

Clear problem definition: They solve specific, measurable problems – not vague goals like “improve efficiency.”

Good data: They have historical data to learn from and ongoing data to operate on.

Realistic scope: They start contained and expand rather than trying to boil the ocean.

Human integration: They augment human work rather than attempting full automation.

Change management investment: They invest in adoption, not just technology.

The ROI Reality Check

When evaluating any AI ROI claim, ask:

1. Is this pilot or production? Pilot results rarely translate directly to production.

2. What costs are included? Many ROI calculations exclude implementation, change management, and ongoing operations.

3. Is the comparison fair? Comparing AI to no-solution rather than to traditional alternatives inflates ROI.

4. What’s the payback period? A positive ROI that takes five years to achieve may not be viable.

5. What are the risks? A 30% expected ROI with 50% failure probability is a different proposition than a 20% ROI with 90% probability.

Final Thought

Not every AI project delivers value, but some reliably do. The use cases above work because they address genuine business problems, have adequate data, and deliver measurable outcomes.

When evaluating AI investments, start with these proven patterns before pursuing novel applications. Build capability and confidence, then expand to more ambitious use cases.

The unglamorous AI projects – document processing, search, forecasting – often deliver better returns than the exciting ones. That’s worth remembering when the next shiny AI capability appears.