🛍️ AI in Retail Planning: Faster Forecasts, Better Decisions

Описание: AI in Retail Planning: Faster Forecasting, Better Data Quality & Smarter Decisions

Retail leaders are under pressure to introduce AI into planning processes—but many struggle with where to begin.

In this video, Tom Owen a retail planning and transformation expert shares practical examples of how AI is already creating measurable value across retail organizations. From incorporating macroeconomic data into forecasting to automating manual planning activities and improving data quality, AI is helping teams make faster, more informed decisions.

Rather than replacing merchandise planners, AI supports them by reducing repetitive work, identifying data gaps, and improving forecast accuracy. The result is a more efficient planning process, better visibility into business performance, and stronger decision-making across retail operations.

What You'll Learn
How AI improves retail forecasting with macroeconomic data
Ways AI accelerates planning and decision-making processes
Why merchandise planners remain essential in AI-enabled workflows
How AI identifies data quality issues and missing information
The difference between AI-powered planning tools and generative AI applications
Practical retail use cases delivering value today

Key Benefits / Key Takeaways
Create more accurate forecasts by combining internal and external data sources
Reduce manual effort in planning and forecasting activities
Improve decision speed without sacrificing human oversight
Identify sales, product, and operational data gaps automatically
Strengthen retail planning with higher-quality data foundations
Apply AI in practical business scenarios beyond chatbot technology

Mini FAQ
Why is AI important for retail planning?
AI helps retailers improve forecasting accuracy, automate repetitive tasks, and uncover insights hidden within large datasets.

How does AI support merchandise planners?
AI handles time-consuming analysis and data processing, allowing planners to focus on reviewing outputs and making strategic decisions.

What types of retail data can AI improve?
AI can identify gaps in sales data, product attributes, forecasting inputs, and other critical planning datasets.

Growing retail organizations are increasingly adopting AI to improve forecasting, automation, and data quality across their planning processes.

If your retail planning still relies on manual processes or fragmented data, it may be time to explore how AI can support more scalable decision-making. Subscribe for more retail and planning insights, or get in touch to continue the conversation: https://bdg.io/uk/about-us/contact/

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