How MedTech leaders turn mergers and acquisitions into an operational advantage

MES provides the operational context that AI needs to produce trustworthy and compliant outputs. While many pharmaceutical companies have digitised batch records and manufacturing processes, AI still struggles when data lacks shared meaning across systems. MES connects batches, recipes, materials, equipment, operators, and procedures into a coherent operational context, enabling AI to understand not just what happened, but why it happened. This makes AI recommendations more reliable and defensible in regulated environments.

The primary challenge is not the AI models themselves, but the lack of harmonised and contextualised manufacturing data. Many AI pilots demonstrate value in isolated use cases, yet fail to scale because information remains fragmented across systems. Without a shared semantic foundation that connects process, quality, equipment, and batch data, AI cannot consistently support decision-making across enterprise operations.

MES serves as the system of record for manufacturing intent. It captures and links critical operational elements such as batch genealogy, recipe execution, material lots, equipment usage, operator actions, and quality events. By providing this context, MES transforms raw manufacturing data into operational knowledge that AI agents can interpret, reason over, and use to support investigations, recommendations, and workflow coordination.

AI agents can assist with tasks that traditionally require significant manual effort, such as deviation investigations, batch review, evidence gathering, root-cause analysis, and workflow orchestration. By leveraging MES context and connected operational data, AI agents can automatically assemble relevant information, recommend next-best actions, and accelerate decision-making while remaining aligned with GxP requirements and established procedures.

Organisations should focus on strengthening their operational data foundation before investing heavily in advanced AI capabilities. This includes using MES as the trusted source of manufacturing context, establishing semantic and reusable information models, connecting data across quality and manufacturing systems, and ensuring robust governance and validation practises. A strong contextual foundation enables AI to operate with greater accuracy, transparency, and regulatory confidence.