I spent last month visiting six distribution centres across Victoria and New South Wales. The goal was simple: see what AI applications are actually working in warehouse operations, not what vendors promise in glossy presentations.

The gap between marketing and reality is significant. But there are genuine success stories worth understanding.

The applications that deliver results

Inventory optimisation

This is where I saw the most mature AI deployments. Predicting demand, optimising stock levels, reducing both stockouts and excess inventory.

A building supplies distributor in Sydney had implemented demand forecasting AI that reduced their stockouts by 34% while simultaneously cutting inventory holding costs by 22%. The system analyses historical sales, seasonality, supplier lead times, and even local building permit data.

“We used to carry way too much of slow-moving items and constantly run out of fast movers,” their operations manager explained. “The AI finds patterns we’d never spot manually.”

The key to their success was integrating the AI with their ERP and purchasing systems. Recommendations automatically generate purchase orders, with humans reviewing exceptions.

Pick path optimisation

For warehouses with complex picking operations, AI can significantly reduce travel time.

One grocery distributor I visited reduced picker travel time by 28% by dynamically optimising pick sequences based on current inventory locations, order priorities, and picker positions.

The interesting part: the system learns from each shift. If a picker consistently takes longer in certain aisles (maybe those shelves are harder to reach), the algorithm adapts.

Annual savings for this operation: roughly $180,000 in labour costs, plus faster order fulfilment.

Quality and receiving inspection

Computer vision systems checking incoming goods and identifying quality issues before they enter inventory.

A pharmaceutical distributor uses cameras to verify shipments match invoices—checking quantities, reading barcodes, flagging discrepancies. What used to take three people now runs automatically, with one person handling exceptions.

Predictive equipment maintenance

Warehouses have equipment too. Forklifts, conveyors, automated systems. Predictive maintenance applies here just as it does in factories.

One large distribution centre had reduced forklift breakdowns by 65% through condition monitoring and predictive algorithms.

What’s not working as well

Fully autonomous mobile robots

Several sites had experimented with autonomous mobile robots (AMRs) for picking and transport. Results were mixed.

The technology works in very controlled environments—uniform floors, wide aisles, consistent lighting. Real warehouses are messier. Pallets get left in the wrong spots. Lighting varies. Floors have patches and slopes.

One site spent $400,000 on AMRs that achieved about 60% of promised productivity. They’re still using them, but expectations have been adjusted.

Voice-to-AI picking

Natural language interfaces for pickers—speaking to an AI assistant rather than using RF devices or paper.

The concept is appealing. In practice, background noise in warehouses makes speech recognition unreliable. And pickers found talking to a computer awkward.

I spoke with one site that tried this for six months before returning to traditional RF devices.

Computer vision for everything

Some vendors promise computer vision systems that track every item, every movement, every person in the warehouse. Continuous real-time visibility.

The sites I visited that attempted this found it expensive and complex. Camera coverage, processing power, integration with WMS—the costs add up fast. And the value proposition wasn’t always clear.

Targeted vision applications (receiving inspection, specific quality checks) delivered better ROI than warehouse-wide surveillance.

What makes AI succeed in warehouses

Clean data is foundational

Every successful implementation I saw had invested in data quality first. Accurate inventory records. Correct item masters. Reliable location tracking.

AI amplifies your data. If your data is bad, AI makes bad decisions faster.

Start with a specific problem

The failed implementations typically tried to do too much. “Smart warehouse” initiatives that attempted to transform everything at once.

The successes started with a single, measurable problem. Too many stockouts. Excessive picking time. High receiving error rates. Solve one thing, prove value, then expand.

Integration matters more than algorithms

The most sophisticated algorithm is useless if it can’t connect with your WMS, ERP, and operational systems.

Several sites had invested in beautiful AI platforms that couldn’t easily exchange data with their existing systems. They became expensive dashboards rather than operational tools.

People need to trust the system

Warehouse workers are practical. They’ll ignore recommendations from a system they don’t trust.

Building trust requires demonstrating that AI suggestions work. Early quick wins matter. So does explaining why the system makes certain recommendations.

The cost question

What does warehouse AI actually cost?

The range is enormous, from $50,000 pilots to multi-million dollar implementations. Here’s what I observed:

Inventory optimisation: $50,000-$200,000 for mid-size operations. Ongoing costs for SaaS platforms or maintenance.

Pick optimisation: Similar range, though integration with WMS can add cost.

Computer vision (targeted applications): $30,000-$100,000 per application area.

Full warehouse automation with AI: Multiple millions. Not practical for most Australian operations.

ROI varies dramatically based on warehouse size, order volumes, and current efficiency. The building supplies distributor I mentioned earlier saw payback in about eight months. Others took longer.

Getting started

If you’re considering warehouse AI, here’s what the successful sites did:

Assess your data: Is your inventory accurate? Are locations reliable? Can you extract historical data for analysis? If not, fix this first.

Identify your biggest pain point: Not the trendiest technology, but your actual operational problem.

Talk to people who’ve done it: Site visits matter. Vendors will tell you what works; operators will tell you what doesn’t.

Start small: Pilot in one area or one product category. Prove value before expanding.

Plan for integration: Budget for connecting AI to your existing systems. This is often underestimated.

If you need help evaluating options, firms like Team400 work with distribution and logistics operations on AI implementation. Getting objective advice before committing to vendors can save significant time and money.

The broader trend

Australian distribution is under pressure. E-commerce expectations. Labour costs. Speed requirements. Customer demands.

AI won’t solve all these problems. But for specific applications—inventory optimisation, pick efficiency, quality checking—it’s delivering real results.

The sites that succeed treat AI as a tool for specific problems, not a magic transformation. They invest in data and integration. They start small and prove value.

That’s not as exciting as marketing presentations promise. But it’s what actually works.

When you’re ready to explore options, having AI consultants Sydney or similar specialists assess your operation can help identify where AI will genuinely add value versus where it’s not worth the investment.