5 Examples of How Companies Use AI to Reduce Costs and Inventory

Artificial intelligence (AI) has become one of the most frequently used—and often misused—buzzwords in business presentations. In executive boardrooms, the question is no longer whether AI will transform business, but something far more practical: can it improve margins, reduce capital tied up in inventory, or accelerate processes?

This is where the distinction between a technological trend and real business value becomes clear. Today, leadership teams no longer need warnings about “missing the train.” They need concrete examples and measurable outcomes: how much a process can be shortened, how much inventory can be reduced, or how much additional revenue can be generated through improved product availability.

Experience from projects in Slovenia and the broader region reveals a consistent pattern. The greatest impact does not occur where companies start with technology, but where they begin with a clear business question: which decision or process do they want to improve?

Such questions most often arise in operational processes, where companies make a large number of similar decisions every day—about ordering, inventory, production, or customer communication. These are precisely the areas where data analytics and predictive models can deliver the fastest measurable results.

Real-world examples show that the greatest financial impact often comes from decisions related to ordering and inventory management, where companies must align sales, logistics, and supplier conditions. Let’s look at a few examples from different industries.

Retail and Distribution: From Three People and Three Days to Three Hours

In retail, pharmaceuticals, medical equipment distribution, fashion, and pet care, ordering is still often a mix of experience, spreadsheets, and intuition. This approach works when the number of locations is small and the assortment is limited. However, once a company manages dozens of outlets, thousands of SKUs, diverse supplier contracts, promotions, expiration dates, and safety stock requirements, manual decision-making becomes a bottleneck.

At one of the larger regional retail companies, three employees spent nearly three working days per week preparing order proposals. They had to review sales by location, inventory levels, and seasonal trends, while also considering promotions, minimum order quantities, supplier incentives, and warehouse constraints. The process was complex and heavily dependent on individual expertise.

The first step was integrating data from ERP, sales systems, and logistics, and defining business rules—from contractual constraints to safety stock levels and special storage requirements. The process was then enhanced with demand forecasting models. Today, the system automatically generates order proposals by location, optimizes transfers between stores, and accounts for logistical and contractual constraints.

A process that previously required nearly three days of work from three employees is now completed in just a few hours. The system automatically generates and places orders for more than 85% of items. Stockouts have been reduced by up to 90%, directly resulting in higher revenues. At the same time, inventory levels of slow-moving items have decreased, reducing capital tied up in stock.

The system is not 100% autonomous and does not replace responsible personnel. It acts as an assistant, automating data-intensive decisions. More complex items are still reviewed by users. The key difference? The algorithm doesn’t forget to place an order.

Logistics: Margin No Longer Lost in the Truck Bed

Companies typically monitor logistics costs very closely, as they are directly visible on invoices. Less obvious, however, are the consequences of suboptimal ordering decisions—capital tied up in inventory, overfilled warehouses, and slow inventory turnover—which only become visible later in financial statements.

At several companies, we found that orders were placed “to fill a truck,” regardless of actual sales dynamics. The result was inventory sitting for months and frozen capital.

By optimizing load sizes and ordering frequency based on sales forecasts, lead times, and contractual conditions, results became visible quickly. Transport costs decreased by 5–15%, while capital tied up in inventory was reduced by more than 20% on average—while maintaining or even improving product availability. In some categories, reductions ranged from 25% to 65%.

The system takes into account seasonality, minimum order quantities, supplier incentives, and warehouse constraints, and proposes an optimal distribution of orders throughout the year. The algorithm can also calculate whether achieving supplier bonuses makes financial sense given the cost of additional inventory and capital turnover. This is not a technological experiment, but an optimization of a key financial decision.

Manufacturing: Lower Inventory and Faster Response to Orders

In manufacturing companies, inefficiencies are often the result of disconnected departments. Sales promises delivery, production optimizes capacity, and procurement minimizes risk. When these perspectives are not aligned, delays or excess inventory occur.

We connect sales forecasts with actual production capabilities. The model includes bill of materials, technological processes, machine capacities, and raw material lead times. Instead of separate plans, the system calculates a scenario that is both capacity-feasible and financially sound.

In make-to-stock production, this results in 10–20% better product availability and up to 30% fewer emergency plan adjustments. In make-to-order production, response times have been reduced by 15–25%. Inventory of raw materials and semi-finished goods has decreased by 10–20% without negatively affecting service levels.

The biggest change, however, is organizational. Sales, production, and procurement begin making decisions based on the same data. There are fewer internal escalations, greater transparency, and more predictable cash flow.

B2C Industries: Personalization Starts with Data

In hospitality, banking, insurance, and other B2C industries, AI is often associated with personalization. In practice, however, the biggest improvement often begins with data cleansing.

At one client, we first deduplicated the guest database. Removing duplicate records increased email open rates by more than 20% and reduced unsubscribe rates. Only then did we introduce behavioral segmentation and prediction of optimal contact timing. Campaign effectiveness increased by 15–30%.

In the automation of inquiry handling, the system now automatically classifies messages and prepares response suggestions. Processing time has been reduced by approximately 40%, and communication has become more consistent.

A generic AI assistant that is not integrated with ERP and CRM systems typically does not deliver measurable results. Impact is only achieved when the solution is embedded into the core of business operations.

Finance and Accounting: Automation Where It’s Least Expected

A similar approach proves effective in finance. At one industrial company, we developed a model that automatically suggests the account and tax code upon receipt of an e-invoice. The accountant reviews and confirms the suggestion, significantly reducing processing time.

The same approach can be applied to account closing, reconciliation of open items, and other repetitive processes. Anything based on rules and historical patterns can be optimized.

The Difference Lies in the Fundamentals, Not the Technology

Successful projects do not start with technology, but with a concrete business decision, a well-defined use case, and a clearly identified problem. The most important factors are connected and high-quality data, as well as collaboration between business units and IT. Generic solutions do not work—adaptation to the specifics of each company is essential.

Artificial intelligence is not a magic wand, but it is an extremely effective lever where decisions are repetitive and directly impact costs, revenues, or capital. Most companies in Slovenia and the region already have the necessary data in their systems. The key question is no longer whether to implement AI, but which business process to improve first.

When AI becomes part of the core business, results are clearly reflected in margins, inventory levels, cash flow, and customer satisfaction. At that point, we are no longer talking about a technology trend, but about a competitive advantage.