by Akhila Gaddam
Artificial intelligence (AI) is increasingly being explored as a tool to support regulatory and quality functions in the dietary supplement and natural products industry.
While AI is often discussed in terms of efficiency or automation, its most practical value in regulated manufacturing environments lies in supporting information access, regulatory context and risk-based prioritization, while preserving professional judgment and accountability.
As with any emerging technology, it is important to distinguish between what AI can reliably support now and how its role may evolve over time. Making this distinction allows regulatory and quality professionals to adopt AI responsibly without weakening compliance expectations.
Where AI adds practical value
Within regulatory and quality programs, AI is most effective as a decision-support tool, particularly during early stages of analysis. Many AI-enabled platforms available to industry are designed to analyze text, organize information, and identify patterns across large volumes of data. They are not designed to make regulatory decisions.
One of the most useful applications is AI’s ability to review regulatory text and guidance and highlight relevant citations. For qualified individuals and compliance professionals, this can be especially helpful when developing a broad regulatory understanding of an issue. When reviewing procedures, deviations, audit observations, or potential gaps, AI tools can help identify applicable regulatory sections or guidance language, allowing teams to frame issues more efficiently and focus their expertise on interpretation and decision-making.
AI can also support document organization and trend analysis. For example, it may help cross-reference standard operating procedures, specifications and batch records, or identify recurring themes across deviations, complaints, or internal audit findings. In supplier qualification and supply-chain oversight, AI can assist by aggregating audit results, geographic risk indicators, and historical performance data to support risk-based prioritization.
In these applications, AI improves visibility and consistency by supporting early-stage analysis and issue framing, allowing regulatory and quality professionals to focus their expertise where it matters most.
Data integrity, reproducibility and regulatory judgment
AI does not change regulatory expectations. Responsibility for compliance remains with the manufacturer and its qualified personnel, and decisions related to hazard analysis, preventive control determination, deviation assessment, corrective actions and product disposition must remain human-led and defensible during regulatory inspections or third-party audits.
A key consideration is reproducibility. AI outputs depend on the quality, completeness and context of the information provided. Even when the same documents are used, differences in inputs or assumptions can lead to different results. From a regulatory perspective, this variability matters, as compliance decisions must be consistent, explainable and repeatable.
For this reason, AI-generated outputs should be treated as preliminary inputs, not conclusions. Qualified individuals must understand their specific manufacturing systems, processes and controls to determine whether AI-assisted observations are accurate, relevant and appropriate for their operation.
Governance and practical controls
Because AI systems rely entirely on the data they receive, organizations exploring AI should establish clear controls around how these tools are used. These include defining acceptable use cases within regulatory and quality functions, requiring documented human review and approval of AI-assisted outputs, ensuring traceability to underlying data sources and regulatory references, and integrating AI use into existing quality system documentation and training.
These measures help ensure AI supports inspection readiness rather than introducing new compliance risks.
A responsible path forward
AI can be a valuable tool for regulatory and quality professionals when its role is clearly defined and appropriately limited. Its strength lies in supporting regulatory awareness, organizing complex information, and enabling more efficient early-stage analysis, particularly when navigating complex regulatory frameworks or large volumes of documentation.
As policymakers and industry stakeholders continue to evaluate how emerging technologies fit within regulated manufacturing environments, it is important that AI adoption remains grounded in existing regulatory frameworks, data integrity principles and clear accountability structures. Organizations designing new quality management systems may have greater opportunity to integrate AI responsibly from the outset, rather than adding AI later to legacy systems with existing constraints.
By approaching AI with clear limits, strong governance and qualified oversight, the dietary supplement industry can explore innovation while maintaining the rigor needed to protect consumers and meet regulatory expectations.
Akhila Gaddam is Vice President of Technical Services at a U.S.-based dietary supplement manufacturing organization, where she focuses on regulatory compliance, food safety systems and operational readiness. Her work centers on translating regulatory requirements into practical manufacturing and quality programs that support consistent compliance and inspection readiness.
