This article was originally published in Consumer Goods Technology.
The quality organization at a consumer goods company completed a promising AI pilot to increase operational efficiency and enable predictive insights. But once they tried to progress from the pilot, the initiative stalled. Across regions, a combination of legacy systems, varying data standards, and even paper-based processes prevented the ability to scale.
For global consumer goods manufacturers, this scenario highlights a critical reality: the path to tangible AI business value begins with modernizing digital and data foundations.
To understand how progress is unfolding, Veeva commissioned third-party research from April to October 2025 with 150 IT and quality leaders in consumer goods industries. The results, published in The State of AI in Consumer Goods, reveal an industry focused on unifying data and embedding governance to ensure AI becomes a trusted, scalable capability.
Unified data is the deciding factor
Success with AI begins and ends with high-quality, accessible, and interoperable data. Data silos and integration complexity remain the top obstacles to achieving scale and adoption.
Today, 62% of respondents report actively consolidating legacy systems into unified platforms. Simplifying and standardizing is foundational work. Unified data fuels current predictive analytics programs, and lays the groundwork for the responsible use of generative and agentic AI across the value chain.
Often the technical solution is ready, but the data foundation is not. For example, a global consumer goods company had to pause its plan to integrate a new supplier quality system because of 70,000 outdated, duplicated, and unclassified supplier records spread across regional spreadsheets.
Susanne Garcia-Schauermann, senior vice president of global quality, food safety and regulatory affairs at Mondelēz International, reinforces this point,
“It’s not like you can just throw any data into AI and get a magical solution. The quality needs to be right, and the governance has to be there. That’s a lot of work to get the foundation right before you can even start.”
Governance: The mechanism for trusted innovation
As AI use expands into regulated quality and manufacturing environments, any advances must be compliant. 60% of IT and quality leaders rank compliance and security as their top challenge.
Organizations are shifting perspectives accordingly. Instead of viewing governance as a constraint, it is the mechanism that enables focused innovation and scale: “AI is useless without governance and the structures around it,” says Amr Arafa, chief digital officer at Barry Callebaut.
Embedding traceability, auditability, and security directly into AI systems means validating data at the source, designing explainable models, and ensuring every AI-driven decision can be defended. For an industry built on consumer trust, a disciplined approach is required to achieve widespread adoption.
Predictive AI: The anchor of measurable value
Predictive analytics is ranked as the top priority for AI investment. This is where the industry is focusing its energy to deliver immediate, measurable business results.
For quality and IT teams, predictive analytics bridges data and decision-making. By turning manufacturing and quality data into real-time insights, organizations can anticipate issues, reduce downtime, and improve compliance outcomes. This represents a shift from experimentation and pilots to embedding intelligence into everyday work, setting the stage for more advanced capabilities to scale.
Agentic AI as the next frontier
As the investment prioritization for traditional and generative AI is stabilizing, leaders are twice as likely to prioritize agentic AI than they were six months ago. This reflects growing confidence that AI can move beyond offering insights and predictions to supporting, and even automating, decisions.
Berenice Vettore, global chief quality officer at The Estée Lauder Companies, reflects on this change: “We are maybe the last generation to have organizational charts with only humans. Now we will have org charts with AI agents, and agents managing agents. It’s the moment to dream. It’s the moment to really say, ‘If everything is possible, what could we do?’”
Strategy for IT and functional leaders
This transition requires pragmatism and patience. As Madhavi Purohit, global head of quality for home care at Unilever, explained:
“There is a legacy system today, but we are aiming toward the future. We make sure the quick wins are shared and recognized. At the same time, we know there will be a stage where old and new coexist — there will be overlap — and then we will transition fully to the other side.”
This dual focus on vision and transition underpins the strategy for leaders:
- Prioritize data: Standardize and unify legacy data environments before investing in complex AI models.
- Lead with governance: Embed trust, auditability, and compliance controls to ensure systems are defensible and scalable across global, regulated operations.
- Focus on value: Have clarity on the intended outcome, with well-defined ROI measures, and clear links to strategic objectives.
For IT and functional leaders shaping digital strategy, the findings and opportunities in The State of AI in Consumer Goods show that today’s decisions around data, governance, and technology will define tomorrow’s competitive advantage.
