Young female researcher conducting experiments and analysis with advanced laboratory equipment in a high-tech facility

AI in pharma R&D: how to achieve scalable value capture

Authors

22 minute read 19 Jun 2026

The pharma industry can unlock real AI value by shifting from fragmented tools to integrated architecture-first operating models.

In brief
  • Pharma’s AI gap stems from fragmented systems and misaligned workflows.
  • Integrated platforms and data backbones enable scalable AI value.
  • Embedding AI into execution paths transforms R&D outcomes.

The pharmaceutical industry stands at a critical juncture. While AI promises to accelerate drug discovery and reduce the $2b, 10-year journey to market, most organizations find themselves trapped in perpetual experimentation mode.1-2 The gap between AI’s theoretical potential and operational reality stems not from a lack of models or computational power but from a fundamental misalignment between how AI systems work and how pharmaceutical R&D organizations are structured.

How EY can help

Organizations now face a pivotal decision: to use AI not incrementally, but to fundamentally transform the way medicines are discovered, developed, and delivered to patients. By shifting from viewing AI as an ornamental add-on to treating it as the core operating fabric of the organization, biopharma can transition from fragmented experimentation to predictable, scalable value capture.

The current state: why pharma R&D isn’t capturing AI value at scale

An industry-wide challenge

Despite significant investments in AI technologies and partnerships, most organizations are not capturing value at the pace or scale they envisioned. While approximately 50 new drugs receive approval annually, the fundamental economics — $2b and a decade per drug — remain stubbornly unchanged for most programs.3-4

The challenge is not a lack of AI capability. Models can now predict protein structures, assess biophysical properties, generate novel molecules and evaluate everything from safety profiles to manufacturability constraints. Yet these powerful tools remain largely disconnected from the daily workflows of discovery scientists, underused in decision-making processes and absent from the critical path of drug development programs.

The root cause: architecture before AI

Most pharmaceutical companies function on what amounts to patchwork infrastructures: disconnected software systems, fragile connections between platforms, and improvised processes that have layered upon each other across multiple decades.

This fragmentation creates three critical failures. First, AI systems cannot reason reliably without semantic stability; they need consistent definitions and relationships between entities (e.g., AI does not know that the “PD marker” in Discovery and “Biomarker” in Translational is the same real-world thing unless this is manually mapped). Second, the feedback loops that make AI systems smarter over time are broken; insights gained from one program rarely inform another. Third, the cognitive burden on scientists is immense, forcing them to serve as human integrators rather than focusing on novel scientific questions.

The cost of disconnected systems

Many organizations have pursued point solutions, deploying specialized AI tools for protein design here, molecule generation there, clinical trial optimization somewhere else. While individually impressive, these tools compound rather than solve the fragmentation problem.

Consider a typical scenario: A discovery organization deploys an AI-powered molecule generation tool that produces promising small molecule candidates optimized for target binding. However, to evaluate these candidates, medicinal chemists must manually export the structures, reformat them and upload them into a separate absorption, distribution, metabolism and excretion (ADME) prediction platform. The ADME predictions then need to be copied into spreadsheets and cross-referenced with synthetic accessibility scores from yet another tool. Meanwhile, historical specific absorption rate (SAR) data showing why similar scaffolds failed three years ago sits in a presentation deck that only senior scientists remember exists. The AI tools each work beautifully in isolation, but scientists spend hours serving as human integration middleware, copying, reformatting, contextualizing and reconciling data across disconnected systems rather than unlocking the iterative chemical refinement potential AI models offer.

Another common pattern: An organization implements an AI platform for clinical trial site selection that predicts enrollment rates with impressive accuracy. Simultaneously, a different team deploys a patient stratification tool using genomic biomarkers. However, these systems don’t communicate; the site selection model has no visibility into which sites have the laboratory capabilities for the genomic assays, and the stratification tool cannot inform enrollment projections. Clinical operations teams manually create bridging spreadsheets, introducing errors and delays while the sophisticated AI predictions sit unused because they cannot be operationalized together.

The more sophisticated the individual tools become, the more critical the integrating architecture becomes. No AI system, regardless of its sophistication, can compensate for fundamental architectural fragmentation. AI amplifies the strengths of an organization’s architecture, but it amplifies weaknesses even faster:

From experimentation to execution

The organizations that will succeed in the next phase of pharmaceutical R&D are those that reconceive AI not as a collection of tools but as the operational fabric of discovery itself. This requires a fundamental shift in thinking: AI must be embedded directly into execution paths that affect program decisions, experiment selection, resource allocation and regulatory filings.

The differentiator in the current market is operationality - the degree to which AI systems are embedded into workflows that run parts of the business. Organizations are moving from treating AI as a feature to be evaluated, to treating it as part of the operating fabric itself. 

Scientist pipetting DNA samples into microcentrifuge tubes during an experiment in the laboratory with the DNA profile on the monitor screen.
1

Pillar 1

Platform-centric architecture and technical standards

Platform development involves creating a shared, consistent R&D model for enhancement and AI integration

At the heart of this vision lies a properly architected platform, not as a collection of tools but as a multilayer system through which an organization encodes its essential abstractions: schema, taxonomy, ontology and workflow logic. Platform development is not about selecting vendor software. It is about creating a shared, semantically consistent model of the R&D domain that can be built upon, extended and enhanced, including by AI systems that rely on semantic clarity to make reliable inferences. This requires a rigorous focus on technical architecture and standards:

Industry patterns show that organizations pursuing platform transformations typically face 12 to 24 months of challenging workflow redesign before AI value begins to accelerate. Those that maintain discipline through this period report 2x–3x improvements in AI model performance and adoption rates compared to organizations that layer AI onto fragmented infrastructure. The most successful organizational transformations integrate the AI technologies seamlessly into the lab workflows.5

Scientist analyzing sample in a microbiological lab
2

Pillar 2

Data enablement backbone

Organizations that prepare their data for AI find that investments enhance both AI performance and human productivity

In production AI environments, most failures trace back to data issues rather than model limitations. Stale records, fragmented sources, missing context and inconsistent ontologies silently degrade performance. Historical data exists but is unstructured, poorly annotated or locked in formats no longer supported. AI is forcing a mandatory data maturity upgrade across the industry:

Where humans once compensated for data fragmentation through institutional memory and contextual understanding, AI agents cannot. Organizations that succeed in making their data AI ready report that the investment pays dividends not just in AI performance but in human productivity; scientists spend less time searching for information and more time generating insights.

Scientist holding a DNA sample with the results on a computer sceeen in a laboratory
3

Pillar 3

AI-native workflows and talent

To achieve success, R&D teams must redesign workflows to be inherently AI native instead of treating AI as an additional component

Even with infrastructure in place, many organizations struggle to identify where AI will create the most value and how to integrate it into scientific decision-making. Teams build impressive models that generate predictions no one acts upon because they’re not embedded in actual workflows or because scientists don’t trust predictions they can’t understand or validate. Ultimately, no model will ever be able to fully replace running experiments to see how a molecule behaves in a lab, but AI-identified targets can drastically speed up the process. However, model predictions are only as good as the data fed into them. Success requires redesigning workflows to be AI native rather than an add-on:

Rather than beginning with broad experimental screening and progressively narrowing, AI-native approaches begin with computational prediction of the most promising candidates across a much larger virtual space, followed by targeted experimental validation of only the highest-confidence predictions. These experiments generate high-quality data that continuously improves the AI models, creating a virtuous cycle of prediction, validation and learning.

Successful implementations provide not just predictions but confidence intervals, alternative scenarios and the underlying data that informed the prediction. They allow scientists and clinicians to override or refine AI suggestions and capture that feedback to improve future predictions. They surface historical context and suggest improvements based on what worked in similar situations previously.

UK, Buckinghamshire, High Wycombe, Man examining graph on monitor
4

Pillar 4

Governance, risk, compliance and security

As pharma R&D shifts AI systems from exploration to implementation, new oversight obstacles emerge

As AI systems move from experimentation to execution, organizations face new governance challenges. How do you ensure AI-driven decisions are auditable? How do you manage the risk of model failures? How do you maintain compliance with regulatory expectations that are still evolving? How do you secure proprietary data while enabling AI innovation?

As AI becomes more operational, regulatory scrutiny will intensify. Organizations building robust governance frameworks proactively are positioning themselves to work collaboratively with regulators rather than reactively responding to concerns. The ability to provide complete documentation of how models were trained, validated, deployed and monitored will become a competitive advantage in regulatory interactions.

high angle view of vials in centrifuge at process lab
5

Pillar 5

Programmatic value capture and PMO rigor

Organizations need to shift from investigation and exploration to a structured approach to create real value

To drive actual value, organizations must move away from “experimentation mode” and toward a programmatic approach:

Organizations are increasingly deploying AI solutions across the full spectrum of R&D activities: from target identification and validation, through molecular design and optimization, into preclinical development, clinical trial design, manufacturing optimization and regulatory strategy. The most mature organizations have AI embedded in 50% or more of their core R&D workflows.6

Scientist examining DNA model in laboratory
6

Conclusion

The imperative for architectural thinking

For the pharma industry, AI value in R&D will arise not from having advanced models or large data science teams but instead from realizing that system design is a crucial strategic decision

Conclusion: the imperative for architectural thinking

The pharmaceutical industry’s AI challenge is ultimately an architectural challenge. Organizations that will capture sustainable value from AI are not necessarily those with the most sophisticated models or the largest data science teams. Instead, they are the organizations that recognize that the system design decision represents the organization’s most critical strategic determination.

The path forward is knowable and, while demanding, is eminently achievable. It requires systematic investment across five interconnected pillars: platforms that provide semantic consistency; data enablement and technical backbone that make information accessible, trustworthy and integrated; AI solutions and talent that embed intelligence into both digital and lab workflows; governance that makes it all auditable and secure; and a PMO approach that enforces rigor and enables programmatic value capture.

The industry is at an inflection point. A growing cohort of organizations is moving beyond experimentation to embed AI directly into execution paths that affect revenue, compliance and operations. The differentiator is no longer whether an organization has AI capabilities, but whether those capabilities are operational — embedded in workflows that actually run a complex business, such as pharma R&D.

Organizations that make their decades of historical data AI-friendly will be unlocking the knowledge and patterns of thousands of failed molecules currently locked in data silos. The predictions from these organizations will be enhanced by years’ worth of experimental findings that currently sit unused, allowing companies to more effectively screen out unsafe and ineffective drug candidates in models and at the bench. Ultimately, creating an AI-ready data architecture and leveraging enhanced predictive models will lead to poor candidates failing faster, improving safety and efficacy beginning at the preclinical stage.

For pharmaceutical R&D organizations willing to make this journey, the prize is substantial: dramatically faster time to market, higher probability of success, lower cost per program and, ultimately, more medicines reaching patients who desperately need them. The frontier is uncharted, but the compass points are clear. The question is not whether to transform but whether to lead or follow in the transformation that is already underway.

AI transformation framework for pharma R&D

Strategic sequencing of AI initiatives based on complexity, data requirements and organizational readiness

1. Efficiency plays

2. Complex cognition

3. Workflow simplification

Key characteristics

  • Document-/literature-heavy tasks
  • Limited expert judgment required
  • Clear success metrics
  • Low risk, high repetition

Key characteristics

  • Multiple data modalities
  • Significant domain expertise needed
  • Hypothesis generation and validation
  • Medium- to high-risk decisions

Key characteristics

  • Transactional and process metadata
  • Orchestration and routing tasks
  • Requires process standardization
  • Low- to medium-risk efficiency gain

Discovery

  • Literature mining for target-disease associations
  • Competitor intelligence synthesis
  • Prior art and patent searches

Clinical

  • Protocol template generation
  • Regulatory guidance compilation
  • Meeting minutes and action tracking

Regulatory

  • CSR first draft generation
  • Cross-reference table creation
  • Label comparison analysis

Discovery

  • Multi-omics target prioritization
  • AI-driven molecular design (MPO)
  • ADME/tox prediction models

Preclinical

  • Cross-species translation modeling
  • Safety biomarker discovery
  • PK/PD modeling integration

Clinical

  • Patient stratification strategy
  • Trial simulation and optimization
  • Safety signal detection

Discovery

  • Hit-to-lead workflow orchestration
  • Compound nomination workflows
  • Design-make-test-analyze automation

Clinical

  • eTMF management and filing
  • Clinical supply chain optimization
  • Change order management

Manufacturing

  • Batch record management
  • Deviation workflow automation
  • Tech transfer workflows

Time to value

3–9 months

Time to value

12–24 months

Time to value

6–18 months

Team of Research Scientists Working On Computer
1

Appendix 1

Talent development

Perhaps the most critical, and most overlooked, element of successful AI deployment is building the right talent ecosystem. Organizations cannot simply hire technologists and expect them to ask the pivotal questions in drug development, nor can traditional scientists immediately adopt AI-native mindsets. Success requires a deliberate, multifaceted talent strategy.

Cross-training and upskilling programs

Scientists and data fluency: Research scientists need foundational training in data science principles, statistical thinking, AI model capabilities and limitations, and how to critically evaluate AI predictions. They should understand how models learn, what makes training data high quality, how to apply AI tools to their own research questions, and when to trust or question predictions. Models make predictions based on previously observed trends and known chemical rules, so it is up to the scientists to interpret these predictions and translate them into actionable, informative experiments.

Technologists and domain expertise: Data scientists and AI engineers need deep immersion in biology, chemistry and drug development. They should understand disease biology, medicinal chemistry principles, what makes a good drug candidate, regulatory requirements and the clinical development process. Organizations achieve this through both rotational programs where AI engineers embed with discovery teams and joint projects pairing data scientists with therapeutic area experts. This knowledge will allow them to build better models by understanding what kinds of inputs and biological trends are valuable and therefore should be more heavily weighted in an algorithm. Ultimately, training technologists on the science will enable them to build more informative and scientifically sound models.

Hybrid role creation: The most successful organizations create new hybrid roles that bridge the gap. One emerging role is that of an AI product manager: an individual who understands both the technology and the science, serving as a translator and prioritizing AI initiatives based on scientific and business value.

Strategic talent sourcing

Building this hybrid workforce requires a multi-pronged approach to talent acquisition and development.

Leveraging global talent ecosystems: Establish centers of excellence in locations with strong concentrations of both life sciences and AI talent. Build distributed teams where domain-heavy roles (medicinal chemists, clinical scientists) concentrate in biopharma hubs, while AI and data-heavy roles (ML engineers, data engineers, platform developers) leverage cost-effective talent markets with deep technical expertise. This approach allows organizations to scale AI capabilities without proportionally scaling costs.

Offshore and nearshore talent for specialized functions: For specific AI operations functions (such as model training and optimization, data engineering pipelines, MLOps infrastructure, quality assurance for AI systems) leverage offshore technical talent in India, Eastern Europe and Latin America. This enables 24/7 development cycles and provides cost advantages for computationally intensive but less domain-dependent work. However, maintain strategic domain-AI integration roles on-site with R&D teams so AI solutions remain grounded in scientific reality and translatable to the bench.

Communities of practice

Create cross-functional communities where scientists and technologists learn from each other:

  • Regular seminars where scientists present challenging problems and technologists propose AI approaches
  • Showcases where successful AI applications are demonstrated and lessons shared
  • Internal conferences bringing together AI practitioners across therapeutic areas to share methodologies

Incentive alignment

Adjust performance metrics and incentives to reward cross-functional collaboration and AI adoption:

  • Recognize scientists for proposing and implementing AI solutions, not just traditional experimental work
  • Evaluate data scientists on scientific impact and adoption, not just model performance metrics
  • Reward teams for sharing learnings and enabling others to succeed with AI
  • Encourage rapid translation of AI output to lab experiments

The talent challenge cannot be overstated. Even the most sophisticated AI infrastructure will fail without people who can bridge the gap between what AI can do and what drug development needs. Organizations that invest as heavily in talent development as they do in technology infrastructure position themselves to capture sustained competitive advantage.

Modern Medical Research Laboratory: Two Scientists Use Computer with Screen Showing DNA Gene Analysis, Specialists Discuss Innovative Technology. Advanced Scientific Lab for Medicine, Biotechnology
2

Appendix 2

Leadership action plan

From insight to implementation: a dual-track approach

Critical success factors


Summary

Pharma can capture scalable AI value by redesigning architecture, data, workflows and governance into an integrated operating model.

About this article

Authors

Related topics
Life Sciences

Related articles

Four futures of AI: Life sciences

What could a life sciences industry reshaped by artificial intelligence (AI) look like in 2030? We’ve identified four distinct scenarios – learn more.

Explore the four futures of AI by industry

See what the future of AI means for your industry. Learn more.

EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients.