A new industry survey reveals that consumer packaged goods (CPG) companies are prioritizing data and system modernization as they prepare for wider adoption of artificial intelligence. The report, based on insights from more than 150 global CPG IT and operations leaders, underscores the importance of consolidating legacy systems and standardizing data to support scalable AI initiatives across manufacturing and supply chain functions.
The “State of AI in Consumer Goods Report” highlights a significant shift in technology strategy within the sector. A striking 82% of respondents are moving away from fragmented, best-of-breed tools towards unified platforms. This move aims to create a more consistent data environment and streamline processes, which are seen as essential prerequisites for advancing analytics and automation efforts.
The survey also points to a rising interest in agentic AI—systems capable of observing, deciding, and executing actions autonomously. About 72% of respondents indicated their organizations are already using, preparing, or planning to adopt agentic AI in manufacturing operations. This growing enthusiasm is tied to the necessity of clean, standardized data and more uniform processes across sites and suppliers.
Despite the optimism, respondents identified several barriers to AI deployment. Compliance and security concerns topped the list, with 60% citing these as significant challenges, reflecting the heavily regulated nature of sectors like food, beverage, and chemicals. High costs, resource constraints, and the complexity of integrating existing systems—many of which are region-specific or specialized—also hinder progress. Many companies still rely heavily on manual processes for quality and compliance management, which can pose operational risks and complicate data collection, especially when critical information is stored in spreadsheets or emails.
The report emphasizes that manual workflows—used by 64% of respondents—continue to dominate quality and compliance activities across the supply chain. This reliance not only increases workload but also affects the reliability of datasets that AI systems depend on for accurate decision-making.
When it comes to predictive analytics, the primary focus is on ensuring quality and compliance, with 24% of respondents highlighting this as the main benefit. Improving decision-making and gaining data-driven insights followed, with 21%, while 19% emphasized proactive issue detection and prevention. These capabilities are increasingly vital for managing deviations, recalls, and supplier performance in real-time.
Organizational readiness factors also play a critical role. Respondents identified comprehensive employee training, high-quality data infrastructure, and AI-specific cybersecurity and compliance measures as key enablers for successful AI deployment. The findings suggest that technological investments alone are insufficient; successful adoption requires changes in skills, operational models, and governance—especially involving security and compliance teams.
Veeva, which published the report through its QualityOne division—focused on cloud-based quality management solutions—notes that the ongoing focus on system consolidation and data standardization will be essential as companies transition from pilot projects to enterprise-wide AI implementations.
David Maher, Head of Strategy at Veeva QualityOne, summarized the findings: “Managing quality across numerous legacy systems is hindering AI readiness. To harness AI’s full potential, companies are considering establishing a strong data foundation on unified platforms that can scale and deliver measurable value.” The report underscores that continued efforts in system integration, data standardization, and process redesign are vital for CPG companies seeking to realize the benefits of AI at scale.
