What challenges are we seeing as life sciences organizations expand AI adoption?
Artificial intelligence (AI) is increasingly embedded in compliance areas like medical information, pharmacovigilance, clinical operations, manufacturing, quality systems, and other regulated processes.
We are finding, however, that in many organizations, adoption has moved faster than the quality framework needed to govern it.
The most common concern is not the AI technology itself. It is the absence of clearly defined ownership, intended use, validation, human oversight, data controls, and lifecycle monitoring.
Questions to ask about AI-enabled systems:
- Can the tool recognize potential adverse events and product complaints?
- Is the original patient or reporter language retained?
- Is information routed promptly to the appropriate safety or quality function?
- Are duplicate detection, follow-up, and reconciliation controls established?
- Has the end-to-end process been tested—not merely the chatbot interface?
- Can the organization demonstrate that no safety information was lost?
Can Your Organization Answer These Questions?
- Where is AI currently being used?
- Who owns each AI use case?
- What decision or regulated process does it influence?
- What could happen if the output is incorrect, incomplete, or delayed?
- What data does the tool receive, retrieve, generate, and retain?
- How was the system validated for its specific intended use?
- Where is qualified human review required?
- How are model, prompt, and configuration changes controlled?
- How will performance degradation or model drift be detected?
- Can each AI-assisted decision be reconstructed during an inspection?
An inability to answer these questions is itself an AI governance finding.
What an AI Compliance Assessment Should Examine
- Governance and accountability: AI policy, inventory, ownership, approval pathways, roles, training and AI literacy.
- GxP use-case risk: Intended use, context of use, patient impact, product-quality impact and regulatory significance.
- Validation and model credibility: Requirements, testing strategy, data suitability, performance metrics, limitations and acceptance criteria.
- Data integrity and traceability: Data provenance, ALCOA+ controls, audit trails, record retention and reproducibility.
- Human oversight: Reviewer qualifications, decision authority, escalation, overrides and automation bias.
- Patient safety and quality interfaces: Adverse-event intake, product complaints, medical information, signal detection and regulatory reporting.
- Supplier and technology oversight: Vendor qualification, contractual controls, cloud hosting, model changes, subcontractors and third-party data.
- Lifecycle controls: Change management, periodic review, drift monitoring, incident management, CAPA and decommissioning.
AI readiness is not a one-time validation exercise
Effective AI oversight requires a lifecycle approach that connects Quality, Regulatory, Pharmacovigilance, Medical, Clinical, IT, Data Science, Privacy and Cybersecurity.
Organizations do not need to eliminate AI risk. They need to demonstrate that the risks are understood, controlled, monitored, and appropriately escalated.
How ProPharma Supports Compliant AI Adoption
ProPharma’s QA/AI & ML Compliance Services’ Quality, CSV, and Data Integrity experts help organizations implement AI in a way that is both innovative and inspection ready.
Our capabilities include:
- Development of risk-based AI governance frameworks
- AI system selection and suitability assessments for GxP use
- Qualification and validation aligned with regulatory expectations
- Design and implementation of monitoring and lifecycle management programs
- Establishment of governance, change control, and oversight SOPs
Whether you’re building internally or selecting a vendor solution, here are the core controls QA teams should evaluate and embed early: