CPG firms modernise data systems to unlock AI at scale

Consumer packaged goods companies are prioritising data and system modernisation as they plan for wider use of artificial intelligence, according to a new industry survey of more than 150 global CPG IT and functional leaders.

The State of AI in Consumer Goods Report found that 82% of respondents are consolidating legacy systems or moving from fragmented, best-of-breed tools to unified platforms. The research links those moves to the need for standardised data and processes before AI deployments scale across manufacturing and supply chain operations.

Platform shift

The survey points to a broad shift in technology strategy across the sector. Respondents described fragmented systems and inconsistent data as constraints on AI projects. The report frames platform consolidation as a prerequisite for more advanced analytics and automation.

The findings also highlight rising interest in agentic AI. A majority of respondents, 72%, said their organisations are using, preparing, or planning to adopt agentic AI in manufacturing operations. The report associates that interest with higher requirements for clean and standardised data, plus more consistent processes across sites and suppliers.

The survey results suggest companies see foundational work as a near-term focus, even as AI investment grows across industries. Many CPG organisations operate complex manufacturing and distribution networks. Those environments often rely on a mix of plant-specific systems, custom workflows, and manual controls for quality and compliance.

Barriers cited

Respondents identified several obstacles that slow deployment of AI and machine learning. The top challenges were compliance and security, cited by 60% of respondents, and high costs and resource constraints, also cited by 60%. Integration complexity with existing systems followed at 58%.

The emphasis on compliance and security reflects the regulated nature of many consumer goods categories, including food and beverage and chemicals. It also reflects the operational risk from model behaviour and data access across production environments, where systems record batch records, supplier information, and quality events.

Integration challenges also remain prominent. Many CPG companies run multiple enterprise systems across regions. They also run specialised manufacturing execution, laboratory, and quality management tools. The survey suggests that organisations continue to experience friction when connecting these systems and normalising data for analytics workflows.

Manual work

The report also indicates that manual processes remain common in quality and compliance management. A majority of respondents, 64%, said their organisations use a mix of digital and manual processes, or mostly manual processes with limited tools, to manage quality and compliance across the supply chain.

That reliance on manual processes can raise operational risk and increase workload during audits, investigations, and supplier incidents. It also complicates the creation of datasets that AI systems can use reliably, particularly when critical information sits in spreadsheets, emails, or local files.

Within the survey, respondents who prioritised advanced data integration and process automation said those areas matter for IT efficiency. They linked them to reductions in repetitive tasks and manual work. They also linked them to the removal of data silos.

Predictive analytics

When asked about the value of AI-powered predictive analytics, respondents pointed to quality and compliance. The top reason was "driving quality and compliance assurance", selected by 24% of respondents. The second was "improving decision making and data-driven insights", selected by 21%. The third was "delivering proactive issue detection and prevention", selected by 19%.

The report links that preference to demand for near real-time insight in quality control and manufacturing performance. It also points to the importance of domain-specific requirements in CPG production environments, where teams must manage deviations, complaints, recalls, and supplier quality performance.

Readiness priorities

The research also highlights organisational factors behind AI programmes. Respondents described people, process, and data as the key enablers for AI roll-outs. Comprehensive employee training programmes ranked highest, cited by 72% of respondents. High-quality data infrastructure followed at 66%. AI cyber security and compliance ranked next at 64%.

Those findings suggest CPG companies expect adoption to require changes in skills and operating models, not only technology investment. They also suggest that security and compliance teams play a central role in approvals and governance, particularly when AI tools interact with operational systems and regulated records.

Veeva published the report through its QualityOne business, which sells cloud software for quality management and related processes. Veeva operates across multiple regulated industries, including life sciences and consumer products.

"The State of AI in Consumer Goods Report shows that managing quality across numerous legacy systems is hindering AI-readiness. To use AI effectively, respondents are considering establishing a strong data foundation on a unified platform that can scale to realize clear value," said David Maher, Head of Strategy, Veeva QualityOne.

The report focuses on AI-readiness, challenges to AI adoption, and areas where IT teams see opportunities to change operations. The survey results indicate that CPG companies expect continued work on system consolidation, data standardisation, and process redesign as they move from pilots to broader AI deployments.