A report from Inside Retail Asia warns that artificial intelligence in retail is only as effective as the data it consumes, with the article stating, "The more powerful the technology becomes, the more brutally it exposes the quality of what you're feeding it." The piece argues that retailers rushing to adopt AI without first cleaning and structuring their data risk costly failures and unreliable insights.
Barcodes Philippines said that clean product identification data, including accurate GTINs and barcodes, is a foundational element for AI-driven retail systems, noting that many Philippine retailers and suppliers still struggle with inconsistent product data that undermines AI forecasting and personalization tools.
Data quality issues such as missing fields, inconsistent formats, and outdated records can lead AI models to generate skewed forecasts, misidentify customer preferences, or recommend poor inventory decisions. The report emphasizes that retailers must invest in data governance, standardization, and integration before deploying AI for demand forecasting, personalization, or supply chain optimization.
Industry experts cited in the article note that while AI tools have become more accessible, the focus on data hygiene has not kept pace. They recommend starting with small, well-defined projects that can validate data readiness, and then scaling AI initiatives only after establishing reliable data pipelines. Without this foundation, even the most sophisticated AI system will produce garbage-in, garbage-out results.