2026-09-22

Pharma has never had the luxury of treating compliance as a checkbox. For Indian pharmaceutical companies, every product, process, and decision must withstand scrutiny not only from the Central Drugs Standard Control Organisation (CDSCO) but also, often, from global regulators such as the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). The ability to reconstruct what hap pened, who approved it, and why has long been the foundation of regulatory confidence.

Artificial Intelligence (AI) is now becoming part of those very decisions, helping identify safety signals, support quality investigations, and accelerate research. Yet, as AI begins to influence decisions that affect patient safety, product quality, and regulatory outcomes, a more fundamental question is emerging: how do organisations establish the same level of trust and accountability they have traditionally expected of human decision makers?

The opportunity is significant. McKinsey estimates that generative AI could unlock between USD 60 billion and USD 110 billion in annual value for the pharmaceutical and medical products industry, with the biggest gains expected across research, manufacturing, and commercial operations.

Unlike a Subject Matter Expert (SME), an AI system does not automatically preserve the reasoning behind its recommendations. Unless that capability is deliberately built in, organisations may know what decision was made but be unable to demonstrate why. In an industry where evidence matters as much as outcomes, that gap cannot be overlooked. Every AI-assisted decision needs to leave a footprint.

AI is Expanding Pharma’s Compliance Perimeter

Much of the conversation around AI governance today focuses on algorithms. In reality, the governance challenge runs much deeper. Every AI initiative quietly expands an organisation’s compliance perimeter. Consider a pharmaceutical company deploying an AI platform to support pharmacovigilance. On the surface, it appears to be onboarding a single application, but in practice, that application may rely on foundation models, cloud infrastructure, third-party datasets, and external services.

That ecosystem rarely ends with direct vendors. A Contract Research Organisation (CRO) or Contract Development and Manufacturing Organisation (CDMO) may rely on third-party vendors, like cloud providers, external quality systems, and infrastructure partners. Every new dependency creates an other potential point of failure. The AI model may perform as expected, while the compliance risk emerges elsewhere, in infrastructure that the organisation neither owns nor directly controls. As AI becomes embedded in regulated workflows, pharmaceutical companies are no longer managing individual technologies but increasingly complex digital ecosystems.

Growth is Increasingly Constrained by Invisible Dependencies

Today, almost every strategic initiative comes bundled with an invisible compliance project. Whether introducing AI into quality management, pharmacovigilance, or manufacturing through a new CDMO, the technology itself is only one part of the equation. Before they move forward, organisations must navigate security assessments, validation requirements, data residency considerations, and extensive third-party due diligence.

Innovation slows, not because organisations lack ambition, but because trust does not scale at the same pace as technology. Many organisations now face an uncomfortable choice: accelerate AI adoption while accepting governance blind spots, or rely on exhaustive manual verification processes that delay innovation by weeks or even months. Neither is sustainable.

Pharma has Solved this Problem Before

The good news is that pharmaceutical companies have solved a remarkably similar problem before. For decades, pharmaceutical manufacturing has been built on the principle that quality cannot be inspected into a product. It must be designed into every process. That philosophy transformed manufacturing, and the same principle now needs to shape AI governance.

Trust cannot be established after AI has influenced a clinical, manufacturing, or regulatory decision. It has to be embedded throughout the lifecycle of that system, from vendor onboarding and model selection to data governance, continuous monitoring and audit trails. The real challenge is not explaining one AI-assisted decision after the fact, it is creating a system where every AI-assisted decision automatically carries the context and traceability needed to explain itself whenever regulators, customers, or auditors ask.

Compliance has to Become Continuous

Traditional governance was designed for a world where technology changed at a manageable pace. Annual audits and periodic vendor reviews worked because technology changed relatively slowly. AI changes that assumption. Models evolve, infrastructure changes, new integrations appear, and third-party relationships expand. By the next review, the environment may already be very different.

That is why continuous assurance is becoming just as important as continuous manufacturing. Evidence cannot wait for regulators, vendor reviews, or audits. As AI becomes embedded across regulated processes, governance itself has to evolve into a continuously operating capability rather than a periodic exercise.

Across the pharmaceutical industry, organisations are begin ning to recognise that compliance can no longer operate as a series of standalone projects tied to individual audits or regu
latory milestones. As AI becomes embedded across research, manufacturing, quality, and commercial operations, governance has to evolve into a living system that adapts alongside technology.

That means building controls that can absorb new AI use cases, evolving regulations, and changing vendor ecosystems without forcing organisations to redesign their compliance programs every time something changes. Otherwise, as AI dependencies continue to multiply, organisations risk accumulating significant operational overhead, leaving governance teams trapped in a reactive cycle of identifying and mitigating AI-related risks instead of enabling innovation. The objective is not simply to meet today’s regulatory requirements, but to build governance that can adapt as technology and regulation evolve.

The Shift Pharma Needs to Make

Preparing for AI is not about investing in another point solution labeled “AI compliance.” It is about extending the same discipline that pharmaceutical companies already apply to GxP, quality management, and data privacy to a new category of decision-maker: the algorithm. Readiness begins by treating AI as an extension of existing governance rather than a separate compliance challenge.

In practice, organisations need visibility into where AI is being used, including third-party tools and services, while extending existing governance processes to cover AI rather than creating parallel compliance programs. AI-assisted decisions should carry the same expectations for traceability, account ability, and documentation as any GxP-critical human decision. The objective is not to create parallel governance for AI, but to embed it into the compliance disciplines that pharmaceutical organisations already trust.

Pharma has never had the luxury of treating compliance as an afterthought, and AI does not change that. It simply introduces a new decision-maker into an industry where every important decision has always had to stand up to scrutiny.

The organisations that lead the next phase of pharmaceutical innovation will not necessarily be the ones deploying AI the fastest. They will be the ones that can explain every AI-assisted decision with the same confidence they explain every human one. In pharmaceuticals, trust has never been built on outcomes alone, it has always been built on evidence. Every AI-assisted decision already leaves a footprint. The real question is whether organisations have built the capability to follow it.