Executive Summary
- Pharma AI spending is climbing fast, but only 9% of executives report real returns.
- The bottleneck is data, not models — 68% of tech leaders blame poor data governance for AI failures.
- Digital transformation means connecting and standardizing scientific information so AI works from trusted evidence.
- Companies that did it are shipping faster: Insilico reached preclinical candidates in 12–18 months vs. 2.5–4 years.
- The barrier is expertise; outsourcing curation keeps researchers on science.
Artificial intelligence (AI) is rapidly reshaping pharmaceutical R&D, with the pharma AI market projected to grow from $6.16 billion in 2026 to $34.99 billion by 2031, nearly sixfold over the next five years. Deloitte's 2026 Life Sciences Outlook found that 78% of biopharma and medtech executives expect AI to play a central role in driving major change, while 41% identified improving R&D productivity as their top priority. However, translating AI investment into measurable business value remains a significant challenge. Only 22% of biopharma and medtech executives report having successfully scaled AI initiatives, and just 9% report achieving significant returns in 2025.
As AI becomes embedded across pharmaceutical R&D, organizations must ensure models generate outputs they can trust to deliver measurable value. Yet, fragmented and unreliable scientific data limit what AI can achieve, regardless of how advanced the underlying models have become. Gartner found that 63% of organizations either lack, or are unsure whether they have, the data management practices needed to support AI at scale. That's where digital transformation comes into play.
This article covers how digital transformation determines whether AI investment in pharma actually pays off, what changes when organizations get it right, and how to build it without diverting scientific talent from the science itself.
Digital transformation: the catalyst for AI-ready data
In pharmaceutical R&D, AI supports high-stakes work, including target identification, lead optimization, and clinical trial design. Each draws on evidence spread across biology, chemistry, clinical research, and regulatory affairs. When that evidence is split across systems that don't talk to each other, AI has no way to reconcile the gaps. The result may appear confident but still be incomplete, built on only part of the available evidence. In drug discovery, that kind of blind spot can slow timelines, weaken competitive position, and delay treatments.
Digital transformation is the ongoing work of structuring and connecting scientific information across the organization, bringing consistency to formats and terminologies so every team, and every model, can draw on the same trusted evidence. ZS's 2026 CDIO Research found that 68% of pharma and biotech technology leaders cite neglecting data quality and governance early as the primary reason AI initiatives fail. Digital transformation closes that gap by helping organizations unlock legacy scientific information while improving how new information is captured, standardized, and governed. The result is a connected, AI-ready data ecosystem that enables models to operate on trusted, complete, and consistent evidence.
How AI-ready data drives measurable R&D value in pharma
With an AI-ready data foundation in place, AI delivers more value across the drug development lifecycle. Operating on connected, trusted information instead of fragmented datasets, models evaluate more evidence, spot promising targets faster, optimize candidates more precisely, and produce more reliable insights.
These capabilities are already delivering measurable results:
- Insilico Medicine: Using a unified platform built on structured scientific information, generative AI identified both the biological target and the drug molecule for rentosertib, the first AI-discovered and AI-designed drug to reach clinical proof-of-concept, in a Phase IIa trial for idiopathic pulmonary fibrosis. Across Insilico's broader portfolio, this approach has taken 12 to 18 months on average to reach a nominated preclinical candidate, using roughly 60 to 200 molecules per project — versus 2.5 to 4 years in traditional drug discovery.
- Absci: Working from the same foundation — structured, consistent antibody design data instead of fragmented legacy records — Absci's generative AI platform designed the antibody sequences behind ABS-101, a candidate for inflammatory bowel disease, moving from target selection to a submitted IND in about 24 months.
Beyond speed: cleaner data, better outcomes
A December 2024 Nature study found that integrating real-world clinical data with AI meaningfully improved cancer outcome prediction across nearly 25,000 patients. Separately, IQVIA's Global R&D Trends 2026 report found AI-enabled R&D programs at emerging biopharma companies are achieving stronger early clinical success rates than comparable non-AI-enabled programs.
Why digital transformation in the pharma industry is hard to get right
Building an AI-ready data foundation sounds straightforward. The true differentiator, however, lies in curating information by organizing decades of scientific literature and internal research, applying consistent nomenclatures and terminologies across disciplines, and connecting fragmented knowledge into a trusted, searchable resource. Most pharmaceutical R&D organizations lack the specialized expertise needed to perform this work at scale.
49% of pharmaceutical professionals cite a lack of specialized skills and talent as the biggest barrier to digital transformation, and nearly half of organizations still consider their workforce unprepared for the work involved. Without a dedicated team to manage data, the responsibility often falls to the chemists, biologists, and clinical researchers. For pharma researchers already stretched across their core research, taking on data curation, standardization, and governance represents a real trade-off: every hour spent organizing scientific information is an hour not spent advancing discovery. Under that pressure, data management can be rushed or skipped, leaving AI models to operate on fragmented or unrepresentative scientific information, which reduces confidence in AI-supported R&D decisions.
Closing the expertise gap to scale digital transformation
The lack of a dedicated scientific information capability plays out across the industry: 67% of R&D leaders are dissatisfied with the pace of AI adoption, with unreliable and unstructured information cited among the most common obstacles. However, pharma organizations do not have to build every aspect of an AI-ready data ecosystem alone. Many complement their R&D and informatics teams with specialized expertise that helps establish the scientific data foundation AI depends on without shifting curation and governance responsibilities onto researchers.
CAS Custom ServicesSM combines domain expertise, scientific data curation, and AI and algorithm expertise to help organizations transform fragmented information into connected, AI-ready data. Whether you are training AI models, integrating new scientific information, deploying agentic AI, or implementing retrieval-augmented generation (RAG), CAS helps strengthen the data foundation that underpins digital transformation. The result is more reliable AI-supported R&D, greater confidence in AI-driven decisions, and reclaimed time for researchers to focus on drug discovery.
Successful pharma AI investment starts with digital transformation
Digital transformation provides AI with the connected, trustworthy scientific information it depends on to deliver value across pharmaceutical R&D. For pharma organizations with that foundation in place, AI is already accelerating drug discovery, improving prediction accuracy, and supporting more informed decision-making throughout the drug development lifecycle.
Organizations that treat digital transformation as a deliberate, well-resourced capability create the conditions for AI and researchers to succeed rather than forcing a trade-off between scientific discovery and scientific information management. That is what separates AI initiatives that remain stuck in pilot programs from those delivering measurable value across pharmaceutical R&D.
