How AI is reshaping pharma and life sciences

Artificial intelligence is becoming embedded across the pharmaceutical and life sciences value chain, and the change is no longer limited to early discovery. In a recent PharmaShots publication, Rahul Mittal argues that AI is altering how medicines are discovered, developed, manufactured, approved, supplied, and brought to market. External reporting and regulatory activity suggest that assessment is directionally right: AI is now showing up in R&D, clinical development, manufacturing, supply chain operations, pharmacovigilance, and regulatory submissions, with life sciences companies under pressure to prove that these systems deliver measurable value without compromising compliance. (fda.gov)

What led to this moment is a mix of scientific progress, commercial pressure, and regulatory catch-up. Tools such as protein-structure prediction platforms, large language models, and AI-assisted genomics have expanded what companies can do in target discovery, molecule design, and data interpretation. At the same time, the economics of drug development remain punishing. Deloitte reports that the average cost of bringing a drug to market now exceeds $2 billion, and 41% of biopharma executives in its 2026 outlook said improving R&D productivity is their top cost-management priority. That helps explain why AI has become less of a side bet and more of a board-level operating question. (deloitte.com)

Regulators are responding. FDA said in its draft guidance announcement that use of AI in drug development and submissions has increased exponentially since 2016, and that the agency has experience with more than 500 drug and biological product submissions containing AI components. The draft guidance focuses on model credibility, asking sponsors to define the model’s context of use, assess risk, and generate evidence that the output is trustworthy for the intended regulatory purpose. Importantly for animal health, FDA said the guidance is relevant to both human and animal drug development. That matters because veterinary drug sponsors using AI in formulation, toxicology modeling, manufacturing, or clinical development should expect growing scrutiny around validation and documentation. (fda.gov)

The industry is also getting more specific about where AI is creating value and where it is stalling. McKinsey estimates generative AI could unlock $60 billion to $110 billion annually across pharmaceutical and medical products, but its January 2025 survey found only 5% of life sciences organizations had turned gen AI into a true competitive differentiator with consistent, significant financial value. The gap, according to McKinsey, is less about lack of pilots and more about weak strategy, governance, talent planning, change management, and risk controls. ISPE is making a similar argument in 2026, saying AI is already influencing development, manufacturing, supply chain, and post-market surveillance, but that durable adoption in regulated settings depends on validation strategies, risk-based classification, human oversight, accountability, and cybersecurity. (mckinsey.com)

There are also signs of where the next wave is heading. In genomics, a separate PharmaShots interview with Jehee Suh of Inocras offers a more concrete example of how AI is being operationalized in clinical settings. Suh said the company has been working to move whole-genome sequencing from a research tool into an everyday diagnostic workflow by refining its algorithms on thousands of real-world patient cases. The company’s platforms — CancerVision, RareVision, and MRDVision — combine deep genomic analytics with AI-driven bioinformatics and clinical services such as genetic counseling and trial matching, with the aim of making whole-genome data more usable in oncology and rare disease decision-making. Inocras also described its strategy as reducing the historic barriers of cost, complexity, and clinical integration by standardizing workflows around real clinical timelines, not just research use. That doesn't directly translate into veterinary practice, but it does show how AI is being paired with sequencing, bioinformatics, and clinically actionable reporting, a model that could eventually shape companion animal oncology, inherited disease testing, and precision therapeutics. In parallel, industry discussion is shifting from narrow machine learning tools toward agentic AI systems and integrated platforms that support formulation, documentation, and laboratory workflows. (inocras.com)

Why it matters: For veterinary professionals, the practical takeaway is that AI’s impact on animal health will likely arrive through the products, diagnostics, and quality systems that reach clinics, not just through client-facing software. If pharmaceutical and life sciences companies use AI to shorten discovery timelines, improve manufacturing reliability, strengthen pharmacovigilance, or make genomics more clinically usable, those changes could affect the speed, cost, evidence base, and monitoring of future veterinary therapeutics. The genomics example is especially relevant because it shows how companies are trying to turn high-dimensional data into routine clinical decision support, not just research output. But the same forces also raise familiar concerns: data quality, explainability, bias, cybersecurity, validation, and regulatory defensibility. In other words, AI may accelerate innovation, but in regulated medicine, speed only matters if the outputs are credible and auditable. (fda.gov)

The bigger industry lesson is that adoption is maturing. Early enthusiasm focused on what AI might do; the current phase is about whether companies can operationalize it responsibly. That includes defining where AI belongs in GMP and GxP environments, deciding when human review is mandatory, and proving that models remain fit for purpose over time. For veterinary drug developers and the clinicians who eventually use those products, that governance layer may be as important as the algorithms themselves. (ispe.org)

What to watch: Expect more detailed FDA expectations, more public examples of AI-backed submissions and partnerships, and more discussion about governance frameworks as companies try to move from isolated pilots to enterprise-scale, regulator-ready deployment in both human and animal health. Also watch for more efforts to standardize AI-supported genomics workflows and package them with clinically useful services, because that is one of the clearest examples of how advanced analytics may move from specialized labs into everyday care. (fda.gov)