AI moves deeper into pharma and life sciences operations

Artificial intelligence is no longer a side conversation in pharma and life sciences. It's becoming part of the industry's operating model, from early discovery and clinical development to manufacturing, regulatory submissions, and safety monitoring. That's the central theme of a recent PharmaShots article by Rahul Mittal, and it lines up with a broader shift now visible in agency policy, company investment plans, and expert commentary across the sector. It also shows up in more applied reporting from PharmaShots, including an interview with Inocras CEO Jehee Suh, which describes how AI-driven bioinformatics and deep genomic analytics are being used to make whole-genome sequencing more clinically actionable in routine care rather than keeping it confined to research settings. (fda.gov)

The backdrop is a few years of rapid expansion in both traditional AI and generative AI. Industry analyses from McKinsey and Deloitte argue that life sciences companies are moving beyond experimentation and into scaled deployment, especially in R&D, manufacturing and supply chain, commercial operations, and medical affairs. Deloitte estimates that a top-10 biopharma company could capture $5 billion to $7 billion in peak value over five years by scaling AI, while its broader 2025 outlook says many executives planned to increase gen AI investment across the value chain. McKinsey has similarly argued that gen AI could unlock tens of billions of dollars annually across pharmaceutical and medical products. (deloitte.com)

Regulators are trying to catch up in a structured way. The FDA's revised discussion paper, originally issued in May 2023 and updated in February 2025, outlines current and potential uses of AI and machine learning in drug and biologic development. The agency then published draft guidance in January 2025 on the use of AI to support regulatory decision-making for drug and biological products. In announcing that draft, the FDA said it had already seen more than 500 drug and biologic submissions with AI components since 2016, and that its framework was informed by more than 800 public comments and expert workshop input. (fda.gov)

Europe is moving on a parallel track. The EMA says AI is now embedded in the European medicines regulatory network's 2025-2028 workplan, covering guidance, tools, collaboration, and experimentation across the medicine lifecycle. The agency also says its joint FDA-EMA principles for good AI practice apply from early research through manufacturing and safety monitoring, and explicitly states that those principles cover both human and veterinary medicines. That point is especially relevant for animal health companies and veterinary professionals, because it suggests the regulatory architecture being built around AI won't stop at human therapeutics. (ema.europa.eu)

There are also signs of practical progress, not just policy development. The EMA notes that in March 2025 its human medicines committee issued a qualification opinion on an AI-based methodology, describing it as the first time the agency would consider data generated with assistance from an AI-based tool to be scientifically valid in that context. And outside the regulatory sphere, companies are starting to show how AI may add value when paired with hard-to-interpret biological data. Inocras, for example, says it has refined its algorithms on thousands of real-world patient cases to help turn whole-genome sequencing into a standardized diagnostic workflow. Through platforms including CancerVision, RareVision, and MRDVision, the company combines deep genomic analytics, AI-driven bioinformatics, and clinical services such as genetic counseling and trial matching to support precision oncology, rare disease decision-making, and longitudinal disease monitoring. That kind of example matters because it moves the AI discussion from abstract productivity claims to a more practical question: can these tools make complex datasets usable within real clinical timelines and decisions? (ema.europa.eu)

At the same time, outside commentary remains measured. A 2025 review in Drug Target Review argued that AI remains a powerful early-stage discovery tool, but not a cure-all for the industry's persistent clinical attrition problem. A recent Springer Nature review likewise highlighted bottlenecks around high-quality datasets, explainability, privacy, intellectual property, and workforce impact. Those cautions also apply to genomics-heavy use cases: turning whole-genome data into clinically useful output depends not just on model performance, but on standardization, interpretability, workflow fit, and trust from clinicians who have to act on the results. (ema.europa.eu)

Why it matters: Veterinary professionals should read this trend less as a software story and more as an infrastructure story. AI is being woven into the upstream systems that affect product development timelines, manufacturing quality, regulatory evidence packages, pharmacovigilance workflows, and supply resilience. In practice, that could eventually influence which animal health products reach clinics, how diagnostic tools are validated, how adverse-event patterns are detected, and how manufacturers document quality decisions. The genomics example is relevant here too: one of AI's most credible near-term roles may be helping convert high-volume, hard-to-interpret data into standardized, clinically usable outputs. That has obvious implications for veterinary diagnostics and precision medicine if similar approaches mature in animal health. But the same concerns seen in human pharma, including bias, weak training data, poor transparency, and overreliance on automated outputs, could carry over to veterinary settings if governance lags adoption. (fda.gov)

For industry stakeholders, the message from experts is fairly consistent: the opportunity is real, but value depends on disciplined implementation. Deloitte says leaders need clear strategic priorities and the infrastructure to deploy models at scale, while McKinsey points to governance, talent, operating model design, change management, and risk as the main barriers to turning pilots into enterprise value. Inocras' account points to the same lesson from a different angle: adoption improves when AI is embedded in standardized workflows, aligned with clinical timelines, and supported by services that help translate outputs into decisions. In other words, AI may change the way medicines are brought to market, but only if companies can prove that these tools are credible, compliant, useful in regulated environments, and practical in day-to-day care. (deloitte.com)

What to watch: Expect the next phase to center on evidence. That means final FDA guidance, more detailed good-AI-practice expectations, additional EMA use cases, and closer scrutiny of whether AI improves measurable outcomes in discovery, development, manufacturing, safety, and clinical decision support, especially in regulated settings that affect both human and veterinary medicine. Also watch whether AI-enabled genomics platforms can show that they deliver standardized, cost-effective, clinically actionable insights at scale, not just technically impressive analyses. (fda.gov)