Retail & AI: Why Ground Truth Data Determines Who Actually Wins

AI has become the backbone of modern retail, powering inventory automation, personalised shopping, predictive supply chains, and omnichannel engagement. Retailers like Walmart, Carrefour, ASOS, and H&M are already reporting double-digit improvements from AI deployments. But beneath the hype sits a blunt reality most leaders avoid: AI only works if the behavioural data feeding it reflects what customers actually do. And for most retailers, it doesn’t.

Sensors misclassify behaviour. Transaction logs capture purchases but miss intent. Computer vision sees movement but misses hesitation, comparison, and micro-interactions. As a result, retailers deploy “smart” systems trained on incomplete or distorted ground data, then wonder why performance plateaus.

AI’s Impact Is Real… When the Data Is Real

According to Engipulse (2025), leading retailers demonstrate what’s possible when AI has high-quality inputs:

• Walmart’s Intelligent Retail Lab reported a 16% reduction in stockouts via computer vision and forecasting algorithms.
• Carrefour’s AI-powered smart shelves deliver real-time inventory accuracy, improving customer satisfaction and reducing operational blind spots.
• In eCommerce, recommendation engines drive up to 10% conversion lifts, 15% revenue gains, and 73% higher customer lifetime value.
• ASOS achieved a 253% increase in profits, driven by AI-enhanced fit tools and personalised styling.

These results prove AI’s upside. But they also reveal the bottleneck: every success story depends on accurate behavioural data.

Supermarket Aisle, Source: https://staffinc.co/wawasan/industri/apa-itu-retail-dan-jenisnya

The Bottleneck: Flawed Ground Data

Most retailers try to scale AI before fixing the foundation. The cost? Weak predictions, misfiring personalisation, slow payback periods, and inflated AI expectations.

Here’s the uncomfortable truth:
AI fails not because the algorithms are bad, but because the data is blind.

• Computer vision can detect objects, but not customer intent.
• Footfall counters track movement but not decision-making.
• POS logs can’t explain why a product was ignored or abandoned.
• IoT sensors infer behaviour but don’t verify it.

This is the gap that quietly erodes ROI in almost every retail AI deployment.

Why Ground Truth Is the Missing Layer

Ground truth verified behavioural data from real shoppers in real stores is what turns AI from guesswork into operational intelligence. When retailers validate what customers actually do, not what sensors assume, the entire AI stack improves:

• Forecasting engines predict demand more accurately.
• Personalisation systems recommend what people truly want.
• Inventory systems reduce stockouts based on real browsing patterns.
• Store operations shift from reactive to predictive.

Without ground truth, AI is just an expensive pattern-matching exercise. With it, AI becomes a competitive advantage.

Where Tictag Fits

Tictag delivers the one thing every AI system depends on but retailers rarely have: verified human-labelled behavioural data from physical stores.

The platform gives retailers a way to:

• In-store customer behaviour analysis
• Uncover hidden friction and lost opportunities
• Reveal what drives or blocks sales
• Get visual insights & actionable recommendations

The value is tangible: with verified ground truth data, retailers improve forecast accuracy, convert more in-store intent into sales, and unlock the revenue gains their AI platforms were designed to deliver.

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📋 Sources

• Puleri-Standish, J. (2024). How Generative AI is Transforming the Future of Retail. LinkedIn
• Engipulse. (2025). Retail AI Revolution: Case Studies Driving 2025 Success.
• Feedcast AI. (2024). AI Personalization Trends in eCommerce.
• Insider. (2024). AI Retail Trends.
• Market Data Forecast. (2024). Asia Pacific Artificial Intelligence Retail Market.
• Acropolium. (2024). AI in Retail: Use Cases from Personalization to Smart Inventory Management.
• IOPEX. (2024). Omnichannel in Retail.