A new open-access industry survey provides one of the clearest recent pictures of how digital and AI-enabled technologies are actually being used in pharmaceutical development and manufacturing.
Published online in July 2026 in the International Journal of Pharmaceutics, the study was conducted by the Digital CMC Centre of Excellence in Regulatory Science and Innovation (CERSI), led by the University of Strathclyde and supported by the UK Medicines and Healthcare products Regulatory Agency (MHRA).
The results reveal an important gap between interest in AI and its actual deployment in regulated pharmaceutical environments.
While many respondents reported experience with AI-enabled tools, only 6% of responses indicated current AI use in cGMP/GDP environments. At the same time, 69% indicated that such use was being considered.
This suggests that pharma interest in AI is already substantial, but companies remain cautious about moving AI from development or exploratory applications into regulated manufacturing and quality operations.
Regulatory uncertainty appears to be a major barrier
One of the most interesting findings concerns regulation.
According to the study, 69% of respondents somewhat or strongly agreed that the current pharmaceutical regulatory environment represents a barrier to implementation of AI tools.
The authors identify several areas where uncertainty remains particularly important: explainability, validation, model drift, lifecycle management and the evidence regulators may expect during submissions or inspections.
The study also found a major difference between internal implementation and regulatory use: fewer than 15% of reported digital and AI-enabled tools had been included in regulatory submissions.
The authors appropriately caution that not every internally used digital tool would be expected to appear in a regulatory submission. Nevertheless, the difference indicates that translating useful internal models into regulator-facing applications remains difficult.
Why this matters for pharmaceutical companies
The findings challenge a common assumption that the main obstacle to pharmaceutical AI adoption is technology.
The models may already be capable of performing useful tasks. The bigger challenge may increasingly be demonstrating that they can operate within a controlled pharmaceutical lifecycle.
For an AI system influencing CMC or GMP activities, companies may need to demonstrate:
- a clearly defined intended use and context of use;
- data quality and data-governance controls;
- appropriate validation or qualification;
- performance acceptance criteria;
- explainability appropriate to the risk and intended use;
- management of model drift and performance deterioration;
- human oversight and accountability;
- change control for models, configuration and data;
- ongoing lifecycle monitoring;
- evidence suitable for regulatory submissions and inspections.
The study therefore supports an important distinction between AI that works and AI that is regulatory-ready.
A model may provide impressive technical performance and still be difficult to deploy in GMP if the company cannot demonstrate its validation status, data provenance, lifecycle controls and behaviour under foreseeable failure conditions.
Quality by Digital Design
The paper also discusses the emerging concept of Quality by Digital Design (QbDD), building on the established Quality by Design philosophy.
This may become an important direction for pharmaceutical digital transformation. Instead of adding digital tools or AI after a process has already been designed, digital models, data architecture and control strategies could increasingly become part of process and product development from the beginning.
Such an approach could make regulatory justification easier because model purpose, data requirements, risk controls and lifecycle expectations could be designed together with the pharmaceutical process rather than added retrospectively.
Practical takeaway
The survey suggests that pharma does not primarily lack interest in AI. It lacks sufficient regulatory certainty and practical implementation experience for higher-impact regulated applications.
For companies considering AI in manufacturing or quality systems, the most valuable next step may therefore not be another AI proof-of-concept.
It may be to take one well-defined use case and demonstrate the complete regulated lifecycle: intended use, risk assessment, data governance, challenge testing, qualification, implementation, monitoring, change control and requalification.
That experience may ultimately be more valuable than operating numerous disconnected AI pilots that never progress into regulated use.
Source
Cook G. et al. “Digital and AI-enabled models in pharmaceutical development and manufacturing: a regulatory-focused industry survey.” International Journal of Pharmaceutics, 2026. Open access.
https://www.scienced … ii/S0378517326006253