How Retail Businesses Can Avoid AI Pitfalls: Whitepaper

How retail businesses can dodge AI’s hidden pitfalls

AI is transforming how retailers make decisions, manage inventory, and engage customers. Yet many initiatives stall due to messy data, overly complex systems, or unrealistic expectations. This whitepaper explores the common pitfalls — and shows how to design AI programs that deliver measurable impact.

AI doesn’t transform retail — data does. Without the right foundation, even the smartest models are just expensive experiments.

Petyo Dimitrov

Director of Data and AI

1 How Retail Businesses Can Dodge AIs Hidden Pitfalls - Avenga

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Specifically Developed for

Retail and eCommerce Leaders

Get practical steps to improve personalization and loyalty without overcomplicating technology.

Supply Chain and Operations Managers

See how AI can help with accurate forecasting, leaner inventory, and smoother workflows.

Data and IT Professionals

Learn how to create data pipelines and systems that are simple, reliable, and built for scale.

Business Strategists and Executives

Understand how to tie AI efforts to bigger business goals, not just isolated experiments.

Key Insights in the Whitepaper

Where AI Projects Stumble

The overlooked challenges that slow down or derail retail AI adoption.

How to Get It Right

Clear recommendations for building a strategy that combines data, people, and technology.

Examples That Work

Stories of retailers who avoided pitfalls and turned AI into a driver of revenue.

What’s Next for Retail AI

Emerging trends that are shaping how retailers invest in and apply AI.

Download Whitepaper

FAQ

Absolutely. Even small-scale AI projects can deliver value when built on a foundation of clean data and clear goals. Many retailers begin with use cases such as demand forecasting, personalized recommendations, or loyalty optimization — areas where quick wins are most achievable. These early projects generate momentum, build cross-team trust, and lay the foundation for scaling AI into more complex operations. This whitepaper offers a roadmap for identifying the best entry points.

Most AI projects fail not because of the technology itself, but because of the environment surrounding it. Common pitfalls include outdated processes, siloed or incomplete data, and weak team adoption. Gartner estimates that roughly 85% of AI projects fail — most often due to data quality issues. In retail, that can translate into irrelevant recommendations, inaccurate stock levels, or rising customer complaints. This whitepaper outlines how strategies such as data governance, accelerators, and Centers of Excellence can help prevent these challenges.

Not anymore. Thanks to cloud-native platforms, accelerators, and pre-built templates, AI is more accessible than ever. Mid-sized retailers can now use the same kinds of tools that were once reserved for global corporations. The real difference isn’t the size of the budget but how effectively it’s applied. For example, using a feature store or AutoML template can cut development time by more than 50% and deliver results much faster. The whitepaper highlights cost-effective ways to adopt AI without overspending.

Avenga helps retailers avoid costly missteps by focusing on the fundamentals: clean and unified data, lean technology stacks, and strong governance. Our solutions connect supply chains, stores, and digital channels, turning raw signals into actionable insights. That includes cloud-native data platforms, accelerators that speed up model deployment, and smart store solutions like digital shelf labels and contextual commerce. With over 30 years of experience and 6,000+ experts, Avenga ensures AI doesn’t just launch — it scales, adapts, and delivers measurable results.