Аналитика и экспертиза

Analysis of the Global Applied AI Agent Market in 2026: Capitalization Dynamics, Growth Leaders, and the Architecture of Enterprise-Scale Deployment

Retail Is Entering the Age of Autonomous Decision-Making

The Rise of the Agentic Enterprise and the End of Passive Growth Systems

Pricing New Products: The Decision That Quietly Determines Everything

A short look at how retailers can improve pricing decisions using AI.

The End of Passive Strategy: How AI Is Reshaping Business Decisions in 2026

The Future of AI Pricing: From Automation to Autonomy

How the development of AI agents is changing the approach to price management in retail. From simple rules to self-learning systems. ‍The future of AI pricing isn’t a bigger model. It’s a better operating system: agents that plan, test, learn, and explain—inside the pricing principles retailers already trust

KVI strategy 2025: what has changed in the approach to key products

Retail has returned to a timeless truth: shoppers judge an entire chain through a small set of remembered prices. What’s different in 2025 is which prices they remember—and how strongly “felt inflation” can diverge from measured inflation. That divergence is forcing a shift from static KVI lists to a continuously managed portfolio of price-image anchors.

The Hidden Risks of Isolated SKU‑Level Price Optimization

In an era of advanced analytics, retailers can tweak prices for every SKU in each local market with unprecedented precision. This hyper-local optimization promises margin gains and competitive agility. Yet when SKU-level price changes are made in isolation – without a cohesive pricing architecture – they can do more harm than good.

Pricing AI Without Demand Forecasting: A Practical Playbook for Retail & E‑commerce

Retail pricing has always balanced two traditions: commercial discipline (margin, price image, roles in the assortment) and local market reality (competition, seasonality, inventory pressure). Demand forecasting helps, but it is not the only way to reach good price decisions—especially when the market changes faster than forecasts can keep up.

Competitive pricing beyond monitoring

Competitive pricing has long been a discipline of rigor: maintain a trusted price image, defend key value items (KVIs), and react quickly when a rival moves. For years, that “monitor → compare → adjust” loop worked because competitor behavior was relatively stable and pricing cadence was measured in weeks.

10 Common Mistakes in Dynamic Pricing and How to Avoid Them

We analyze typical pitfalls in implementing dynamic pricing systems based on experience from 50+ projects.

Behavioral Pricing: Crowdsourced Value Discovery in Modern Price Optimization

Classical pricing models often treat price as the output of a demand curve – find the elasticity, plug it in, and the optimal price falls out. But real customers don’t live on smooth curves; they react to prices in ways that economics 101 can’t fully capture. Behavioral pricing flips the script by recognizing that price isn’t just a point on a demand function – it’s a perception in the customer’s mind.