AI in pharma: use cases, risks, and what actually works

AI in pharma has moved from experimentation to active deployment across R&D, clinical development, manufacturing, and commercial functions.

But there is a clear gap between investment and measurable value.

Pharmaceutical companies have invested heavily in AI over the past few years, yet many programs remain stuck in pilot stages or fail to scale. The issue is not the lack of capable technology. It is how AI is applied.

Most unsuccessful initiatives are framed at a broad level: “AI for drug discovery” or “AI for clinical trials,” without defining the specific task, data, and workflow required to make them work in practice.

If you’re evaluating AI in pharma, the key is not what AI can do in theory.

It is which specific applications are working today, where they are being deployed at scale, and what it takes to make them operational.

In this guide, we’ll break down how AI is actually used across pharma, where it delivers measurable value, the challenges organizations face, and what it takes to implement it effectively.

What is AI in pharma?

AI in pharma refers to the use of machine learning, natural language processing, generative AI, and related technologies to perform specific tasks across the pharmaceutical value chain.

These tasks can be:

• Drug discovery and preclinical research
• Clinical trial design and operations
• Regulatory submission and compliance
• Manufacturing and supply chain
• Commercial strategy and medical affairs

However, each function uses AI differently, with different levels of maturity, data requirements, and regulatory constraints.

This is why a more accurate definition would be: AI in pharma is a set of application-specific technologies used to improve decision-making, efficiency, and scalability in defined workflows across the pharmaceutical value chain.

Because the success of an AI initiative depends less on the underlying technology and more on how precisely the application is defined: what task it performs, what data it uses, how it is validated, and how it fits into existing workflows. 

6 applications of AI in pharma

AI in pharma is applied across the full value chain, but the maturity and reliability of applications vary significantly by function.

To understand where AI is actually working, you need to look at specific use cases within each function.

Drug Discovery and R&D

In early-stage research, AI is used across a few well-defined areas:

• Target identification from multi-omics data
• Molecular property prediction (including ADMET)
• Compound screening prioritization
• Protein structure prediction

What you’ll notice here is that AI improves prioritization, not outcomes on its own. It helps narrow down which compounds or targets are worth testing, reducing wasted experimental effort.

At the same time, it hasn’t changed the most expensive part of the process. Clinical attrition remains largely unaffected, and predicting success in Phase II and III trials is still a major limitation.

Clinical Development

In clinical trials, AI is applied to improve efficiency across recruitment, site selection, safety monitoring, and biomarker discovery.

The most established applications include:

• Patient recruitment using NLP on EHR data
• Site selection based on historical performance
• Safety signal detection from clinical datasets
• Biomarker discovery for patient stratification

Where it works best is in areas with clear operational friction. You can see this in patient recruitment, where NLP models have reduced enrollment timelines by 30–50% in certain studies.

The closer the application is to a measurable bottleneck, the easier it is to see real impact.

Regulatory Affairs

Regulatory workflows are heavily document-driven, which makes them a natural fit for AI-assisted review.

AI is typically used for consistency checks, labeling analysis, and literature review across large submission packages. The value comes from improving accuracy and review efficiency at scale, not from replacing human decision-making.

You still need expert validation at every stage. Regulatory environments require traceability and accountability, which means AI remains a support layer rather than a decision-maker.

Manufacturing and Supply Chain

Manufacturing is one of the most mature areas for AI in pharma, even though it gets less attention.

Applications such as process optimization, predictive maintenance, quality control, and demand forecasting are already deployed in production environments.

The advantage here is clarity. You can directly measure outcomes like batch failure, yield variation, and downtime, which makes it easier to evaluate performance and ROI.

This is one reason manufacturing AI applications have reached higher maturity compared to R&D or clinical functions.

Commercial and Medical Affairs

In commercial functions, AI is used to optimize how resources are allocated and how healthcare professionals are engaged.

You’ll see this in territory optimization systems, next-best-action platforms, market access and patient journey analytics

In medical affairs, AI supports literature monitoring, content organization, and field insight synthesis. It helps structure and scale communication, but accuracy and compliance still depend on human review.

Across all these functions, you get the most reliable value from AI when it is applied to specific, well-defined tasks with clear success metrics, not broad transformation initiatives.

Pharma Communications

AI is increasingly used to support how scientific, clinical, and commercial information is structured and presented across pharma teams.

This includes:

• Drafting and structuring scientific content for different audiences
• Summarizing literature and clinical data
• Organizing insights from multiple sources into clear narratives
• Supporting pharma presentation creation for KOL meetings, internal reviews, and regulatory discussions
• Creating accurate, compliant content for pharmaceutical marketing

What changes here is not the source of information, but how efficiently it is turned into usable communication.

You can move from raw data or scattered inputs to structured, audience-ready content faster, especially in workflows that involve repeated communication across teams.

At the same time, this is a high-risk area if not handled correctly. Scientific accuracy, regulatory compliance, and consistency remain critical, which means AI outputs must be reviewed and validated before use.

In practice, AI supports structuring and scaling pharma communication, while domain experts remain responsible for accuracy and final approval.

Maturity of AI in pharma: where each function stands

After looking at applications, the next step is understanding how mature those applications actually are in practice.

Not all AI use cases in pharma are at the same stage. Some are already deployed at scale, while others are still in pilot phases or early experimentation.

To evaluate this properly, you need to look at three things: whether the application is in production, whether it has documented outcomes and what level of governance it requires.

Maturity can be broadly understood as:

• High maturity: Deployed at scale with consistent, measurable outcomes
• Moderate maturity: Proven in pilots but not yet consistently scaled
• Early maturity: Strong concept, limited real-world deployment

Check this table to understand AI maturity across different pharma functions: 

6 challenges of AI in pharma

The challenges related to AI adoption in pharma are structural, regulatory, and organizational. Most programs struggle not because the technology doesn’t work, but because the conditions required to make it work consistently are not in place.

• Data quality and fragmentation: Pharma data is distributed across clinical, research, manufacturing, and commercial systems, often with inconsistent formats and standards. This makes integration difficult and limits model performance in real-world settings
• Gap between pilot and production: Many AI systems perform well in controlled environments but fail to scale across real-world workflows due to variability in data, systems, and operational processes
• Regulatory and compliance constraints: AI systems must meet strict requirements for validation, auditability, and traceability. Frameworks such as FDA guidance and 21 CFR Part 11 limit how AI can be deployed, especially in high-risk environments
• Accuracy risks in generative AI: Generative AI can produce plausible but incorrect scientific content. In regulated contexts like medical affairs or regulatory writing, this creates compliance and credibility risks without proper expert review
• Talent and skill gaps: Effective AI implementation requires both domain expertise and technical capability. This hybrid skill set is difficult to build, making it hard to scale AI initiatives across teams
• Governance and oversight complexity: AI requires structured governance for validation, monitoring, and review. Overly rigid processes slow adoption, while insufficient oversight creates regulatory exposure

AI in pharma case studies

To understand where AI is actually delivering value in pharma, you need to look at real deployments tied to specific outcomes, not just use cases.

These case studies show how AI is being applied in practice across different parts of the pharmaceutical value chain.

AstraZeneca: AI in Drug Discovery and Clinical Development

AstraZeneca has integrated AI across both research and clinical workflows, particularly in collaboration with AI platforms like BenevolentAI.

AI is used to:

• Predict which molecules to design and prioritize in medicinal chemistry
• Identify drug targets from complex biological datasets
• Improve clinical trial design and patient matching

In practice, this means AI is helping researchers decide what molecules to create next and how to design them, rather than replacing lab work entirely.

The impact is clear in early-stage research speed and decision-making, but like most pharma AI programs, it does not eliminate the need for experimental validation or reduce clinical attrition.

Pfizer: Predictive Quality and Manufacturing Optimization

Pfizer has implemented AI in manufacturing to improve quality and reduce production risk.

AI models analyze manufacturing data in real time to:

• Detect deviations in production processes
• Predict quality issues before batch release
• Improve consistency across manufacturing runs

This shifts quality management from reactive (after failure) to predictive (before failure).

The value here is operational. Reduced batch failures, improved compliance, and better process control directly translate into cost savings and reliability improvements. 

Berg Health: AI-Driven Drug Discovery (US-Based Biotech)

Berg Health uses AI to identify drug targets and develop candidate therapies based on biological data.

One of its AI-driven programs led to the development of a drug candidate (BPM31510) for pancreatic cancer, which progressed into clinical trials.

The approach combines deep learning models trained on biological and clinical data and experimental validation of AI-generated hypotheses

What stands out here is not full automation, but how AI helps generate non-obvious biological insights that guide research direction. 

How Prezent AI supports pharmaceutical teams using AI

Across pharma functions, including R&D, clinical, medical affairs, regulatory, and commercial, the same constraint shows up repeatedly.

Teams are expected to produce high-quality, scientifically accurate, and clearly structured content, often under tight timelines and for very different audiences.

An MSL preparing for a KOL discussion needs to present clinical evidence with precision and credibility. A commercial leader needs to translate complex analytics into a market access narrative that executives can evaluate quickly. A medical affairs team needs to synthesize field insights into something leadership can act on.

These tasks are recurring, high-stakes deliverables that directly impact how decisions are made and how teams are evaluated.

Prezent is built for this context.

It helps pharmaceutical teams turn validated data, analysis, and insights into well-structured, visually coherent, and audience-ready presentations without starting from scratch each time, with features like:

• Astrid AI to transform raw inputs such as clinical data, analysis outputs, and notes into structured narratives tailored to different audiences
• Synthesis and story structuring to organize complex information into clear, logical flows that are easier to present and evaluate
• Slide Library and standardized templates designed for scientific and business communication, ensuring consistency and reducing formatting effort
• Story frameworks that guide how clinical, regulatory, and commercial information should be presented across different contexts
• Expert-led presentation support including overnight services and fixed-scope projects to help teams deliver high-quality outputs when timelines are tight

Book a demo or start a free trial to see how you can improve pharma communications with Prezent AI.

Frequently asked questions about AI in pharma

1. How are pharmaceutical companies using AI?

Pharma companies use AI across R&D, clinical trials, manufacturing, commercial, and medical affairs. Applications include target identification, patient recruitment, process optimization, and scientific content structuring, with maturity varying by function.

2. What are the main AI use cases in pharma?

High-maturity use cases include manufacturing process optimization, NLP-based patient recruitment, and commercial territory optimization. Moderate use cases include ADMET prediction and safety monitoring, while areas like generative AI without validation remain early-stage.

3. What is generative AI and how is it used in pharma?

Generative AI creates new content from learned patterns. In pharma, it is used for molecule design, literature synthesis, and content drafting. However, in regulated contexts, outputs must be reviewed to ensure scientific accuracy and compliance.

4. What are the biggest challenges of AI adoption in pharma?

Key challenges include fragmented data infrastructure, lack of domain-AI talent, governance complexity, and unrealistic expectations. Most failures come from poor implementation, not lack of technology capability.

5. How is AI used in pharmaceutical manufacturing?

AI is used for process optimization, predictive maintenance, and demand forecasting. These applications improve yield, reduce downtime, and prevent supply shortages, making manufacturing one of the most mature AI areas in pharma.

6. Which pharmaceutical companies are leading in AI adoption?

Companies like Roche, AstraZeneca, and Sanofi have invested heavily in AI across R&D and clinical development. AI-native firms like Recursion Pharmaceuticals and Insilico Medicine are also advancing AI-designed drugs into clinical trials.