I don’t often write case studies because they tend toward either uncritical success stories or unverifiable claims. This one is different – I was directly involved, and the client has allowed me to share specifics (with company details anonymised).

This is how AI demand forecasting actually works in practice, including the parts that vendors don’t mention.

The Context

The company is a mid-sized Australian retailer with around 200 stores nationally. They sell seasonal products where demand forecasting directly impacts inventory costs and stockout rates.

Their existing forecasting system was Excel-based, maintained by a small planning team. It worked, sort of. But error rates were high, the team was overwhelmed, and the business was growing faster than the manual process could scale.

The Initial Ambition

The project started with ambitious goals:

• Reduce forecast error by 50%
• Cut inventory carrying costs by 20%
• Eliminate stockouts for top-selling items
• Free the planning team for strategic work

These goals came from a consulting engagement that recommended AI forecasting. The recommendations looked compelling. Reality would prove more complicated.

What We Actually Built

After several months of work, the implemented system included:

Data integration: Connections to POS systems, inventory databases, marketing calendars, and external data (weather, economic indicators, competitor pricing).

Feature engineering: Transformation of raw data into features useful for prediction – seasonality indicators, promotional flags, trend calculations, cross-product relationships.

Model ensemble: Multiple forecasting models (gradient boosting, neural networks, traditional statistical) combined through weighted averaging.

Automated pipeline: Daily data refresh, model retraining, forecast generation, and output delivery to planning systems.

Monitoring dashboard: Tracking forecast accuracy, identifying systematic errors, alerting on unusual patterns.

What Worked

Accuracy improved significantly. Forecast error dropped from around 25% to around 15% – a 40% improvement. Not the 50% initially targeted, but meaningful.

Scale became manageable. The system handles thousands of SKU-location combinations that the manual process couldn’t address. Coverage expanded dramatically.

Planning team redeployed. Instead of maintaining spreadsheets, the team now focuses on exception handling and strategic planning. Better use of human expertise.

Some categories performed exceptionally. Stable, high-volume products saw error rates below 10%. The model learned these patterns very effectively.

What Didn’t Work

New product forecasting remained difficult. Without historical data, the models had little to work with. Cold-start items still required human judgment.

Promotional forecasting was unreliable. The model struggled with promotions, especially novel ones. The relationship between promotion type and demand uplift was too variable.

External disruptions broke everything. During unexpected supply chain disruptions, the model produced forecasts based on patterns that no longer applied. Human override became necessary.

Some categories resisted prediction. Fashion-sensitive products with short lifecycles and trend-driven demand didn’t respond well to historical-pattern models.

The Costs

Let me be specific about costs:

Initial implementation: $450,000 over 8 months. This included data engineering, model development, integration, and change management.

Annual operating cost: $120,000. Cloud infrastructure, model maintenance, data pipeline support, and monitoring.

Hidden costs: Approximately $150,000 in internal staff time for data preparation, testing, and transition. This wasn’t originally budgeted.

Total first-year cost: Around $720,000.

The Returns

Measuring returns required careful baseline work:

Inventory reduction: 12% reduction in average inventory levels while maintaining service levels. Worth approximately $800,000 in freed working capital.

Stockout reduction: 35% fewer stockout incidents for forecast-covered products. Worth approximately $400,000 in recovered sales and customer retention.

Labor efficiency: Planning team redirected to higher-value work. Difficult to quantify but estimated at $200,000 in effective capacity.

First-year ROI: Approximately 95% based on conservative estimates. Payback in around 12 months.

Lessons Learned

Start with clean data expectations. We spent more time on data issues than model development. Budget accordingly.

Don’t over-promise accuracy. Our initial 50% error reduction target was unrealistic. Setting expectations appropriately would have avoided some stakeholder frustration.

Plan for human-AI collaboration. The model handles routine forecasting; humans handle exceptions. This division of labour wasn’t initially designed but emerged as necessary.

Build monitoring from day one. Knowing when the model is wrong is as important as the model being right. Our monitoring investment paid off repeatedly.

Category-specific approaches work better. A single model for all products performed worse than category-specific models. The added complexity was worth it.

What I’d Do Differently

Phase the rollout more gradually. We tried to cover too many products too quickly. A slower expansion would have allowed more learning and adjustment.

Invest more in change management. Some planning team members resisted the new system. Better engagement earlier would have smoothed adoption.

Build exception handling first. We focused on the model and added exception handling later. Those processes should have been designed together.

Set realistic expectations with executives. The board expected transformation; they got meaningful improvement. Both are good, but the gap created unnecessary tension.

The Takeaway

This project delivered genuine value: meaningful accuracy improvements, significant cost savings, and better allocation of human expertise. It was a success.

But it was also harder, slower, and more expensive than initially projected. The vendors and consultants who pitched the project were optimistic about the easy parts and silent about the hard parts.

That’s the reality of enterprise AI. The technology works, but implementing it in real organisations with real data and real people is challenging. Success requires realistic expectations, adequate investment, and patience.

If you’re considering similar initiatives, plan for the messy reality, not the polished vendor demo.