AI-Driven Retail

Unlock the potential of data and Artificial Intelligence in the Retail sector

We are advancing knowledge-driven and automated Retail

Comprehensive support for the retail industry

Modern retail demands speed, precision, and flexibility. That’s why we help our clients implement AI-powered intelligent solutions – from assortment optimization and seasonal product management to the automation of decision-making processes.

Our solutions support companies in effective planning, adapting their offerings, and responding efficiently to rapidly changing market conditions. This enables businesses to forecast purchasing trends, personalize customer communication, optimize pricing, and reduce inventory losses – all by fully harnessing the potential of the data they already possess.

Our solutions drive the growth of modern retail, enabling faster response to trends, streamlining processes, and building a competitive advantage powered by data and Artificial Intelligence.

AI-powered Retail

Modern retail powered by technology and algorithms

Artificial Intelligence is becoming a key driver in the development of the retail industry. Leveraging data and algorithms enables companies to respond more quickly to market changes, tailor their offerings more effectively to customer expectations, and make accurate operational decisions.

Modern retail demands rapid decision-making, precise planning, and operational agility. By leveraging advanced algorithms and real-time data analytics, companies can forecast purchasing trends, personalize their offerings, optimize pricing, and minimize inventory losses. This is not just technology – it is a new standard in managing sales and enhancing the customer experience.

A woman with curly hair, dressed in a black leather jacket and a light blue shirt, is shopping in a store. In her left hand, she holds a green basket filled with leafy vegetables and other products, while her right hand reaches for a package.

How does Artificial Intelligence support companies in the retail sector?

Client

Merchandising

Understanding

Micro-segmentation of customers and recipients

Trip Missions

Customer churn prediction

Omnichannel analytics

Customer Lifetime Value

Return analysis

Planning

Demand forecasting

New product introduction

Assortment optimization

Product segmentation

Availability

Inventory allocation

Replenishment optimization

Localization

Optymalizacja zasięgu

Macro space optimization

Micro space optimization

Store segmentation

Pricing and promotions

Markdown optimization

Base price optimization

Promotion effectiveness

Supply chain

Marketing

Supplier

Optimal sourcing

Product cost modeling

Supplier performance

Network

Supply chain control

Network design and optimization

Chain structure

Inventory

Product flow optimizer

Multi-level inventory optimization

Execution

Marketing mix optimization

Campaign analytics

Digital

Community segmentation

Next Best Experience

They have trusted us

Retail

E-commerce Retail

Retail

Retail

E-commerce Retail

Retail

Changing the approach in Retail industry

Intelligent transformation of key processes in the Retail industry

Most retail and manufacturing companies aim to integrate demand forecasting, order planning, and inventory management processes into a single, cohesive ecosystem.

By leveraging historical data, real-time analytics, and advanced Artificial Intelligence algorithms, it becomes possible to automatically forecast demand, generate orders, and align them with current sales dynamics. This enables holistic inventory management across the entire organization. Such an approach allows for more accurate forecasts, faster responses to market changes, and the elimination of data silos.

Benefits

More accurate demand and sales forecasting
Faster and automatic order generation and modification
Consistent and centralized data management from multiple sources
Just-in-time response to changes and anomalies

Use case group	Traditional approach	Data-driven approach
Forecasting method	Forecasts based on intuition The lack of tools for sales and demand forecasting forces business decisions to rely solely on historical report results.	Forecasts based on advanced algorithms Advanced Artificial Intelligence algorithms generate dedicated sales forecasts for specific products in individual stores, as well as demand forecasts for specific product groups in warehouses.
Data analysis method	Manual analyses The need to prepare time-consuming analyses based on data collected from various sources, often in Excel, and to make manual adjustments.	Automated analyses Advanced Artificial Intelligence enables the analysis of large datasets in real time.
Order generation method	Manual order generation Manual generation of orders for suppliers and advanced recommendations for stores.	Automated order generation Automated generation of orders for suppliers and replenishment instructions for stores.
Method of modifying orders and replenishment instructions	Manual modification of orders and replenishment instructions Daily manual modification of orders and replenishment instructions to adjust quantities to current sales dynamics.	Automated modification of orders and replenishment instructions Automatic adjustment of orders and replenishment instructions to the current sales dynamics.
Data collection method	Data silos Data dispersed across different departments and various IT systems, hindering comprehensive analysis and extraction of valuable business insights.	Holistic data analysis A cross-departmental approach to collecting, processing, and managing data, enabling the aggregation and analysis of information from across the entire organization.
Method of responding to anomalies	Reactive actions Inability to respond just-in-time to changes in demand and unforeseen business situations.	Real-time operations Detection of anomalies and real-time reporting, allowing the prevention of losses through just-in-time responses tailored to a specific product and store.

AI for Retail

The 3 AI solutions that are becoming increasingly popular

Demand
forecasting

Predicting product demand based on historical data and external factors (such as weather, holidays, or promotions) optimizes inventory replenishment, minimizes markdowns, prevents stockouts, and improves product availability on shelves.

Dynamic
pricing

Pricing models analyze demand fluctuations, inventory levels, competitor prices, and customer behavior in real time to optimize pricing.Retailers can optimize pricing strategies by adjusting prices according to market conditions. This approach increases both business performance and customer satisfaction.

Process
automation

AI agents streamline back-office operations by handling repetitive and time-consuming tasks such as data entry, document processing, and compliance checks. They quickly classify and process documents, reducing manual effort and improving efficiency.By automating workflows, accuracy increases, processes accelerate, and teams can focus on higher-value tasks.

“Based on our long-standing cooperation, we can confirm that 3Soft is a professional partner in the architecting, development, and implementation of advanced IT solutions that leverage the power of artificial intelligence, positively impact our operations, and enable business scaling through the use of cutting-edge cloud computing capabilities.”

Marcin Moroz I Michał Nowaczyk

IT Director at Lidl Poland I CFO at Lidl Poland

The future driven by Artificial Intelligence

Generative AI as a pillar of Retail
industry transformation

62%

retail companies plan significant investments in GenAI within the next 2 years – up to 63% more than today*.

It is crucial, however, that these investments are directed toward areas that truly boost sales, enhance customer shopping experiences, and optimize operational processes. At 3Soft, this is exactly where we focus our efforts.

*Source: NTT Data Global GenAI Report, 2025

Kobieta w jasnobeżowej koszulce i dżinsach pakuje książkę w papier w księgarni, a w tle znajdują się półki pełne książek.Ask ChatGPT

How does GenAI support
companies in the Retail industry?

01: Intelligent automation of operational processes
Automatic management of tasks such as inventory replenishment, order processing, returns handling, or supplier documentation analysis, reducing operation time and lowering costs.
02: Advanced customer behavior analysis
AI enables the analysis of purchase history, preferences, and shopping paths to identify trends, segment audiences, and predict future needs.
03: Supply chain optimization
Artificial Intelligence supports demand forecasting, optimizes delivery routes, monitors inventory levels, and identifies supply risks to ensure smooth operations and minimize losses.
04: Personalization of shopping experiences
AI systems create personalized product recommendations across online and offline channels, tailoring the offering to individual customer preferences, thereby increasing conversion and loyalty.
05: Dynamic pricing management
Machine Learning algorithms help set optimal real-time prices, considering demand, seasonality, competitor actions, and inventory levels.