PLAYBOOK ATLASPlaybook Atlas

Pharma & Biotech

AI in pharma & biotech: explore 53 applications.

Make research, trial and scientific-review workflows more reproducible and inspectable.

Explore literature review, trial matching, protocol feasibility, scientific computing and discovery support for research and development teams.

Plan your first AI move See the 53 applications

Where the 53 applications sit

Explore the domains
  1. 28Research and Development
  2. 13Regulatory and Compliance
  3. 5Sales and Marketing
  4. 4Manufacturing
  5. 4Support Functions

01Why AI now

Why pharma & biotech is moving on AI, in three figures.

Each figure links to its source.

  1. AI drug discovery market: $4B by 2027

    MarketsandMarkets forecast growth from $0.6 billion in 2022 to $4 billion in 2027, representing a 45.7% compound annual growth rate.

    Source: Drug Discovery Online

  2. AI-native firms: about 15 clinical assets

    The Nature analysis found about 15 assets in clinical development across the reconstructed pipelines of 20 AI-native drug-discovery companies.

    Source: Nature Reviews Drug Discovery

  3. R&D: 30–40% of AI value opportunity

    Deloitte said AI applied to novel-drug identification and accelerated development could create both cost savings and revenue uplift, with R&D representing 30–40% of the overall value opportunity.

    Source: Deloitte

How to evaluate a first project

Define the scientific question, dataset and accepted output before choosing a method. Preserve source provenance, protocol versions and reproducible calculations. Assess computational predictions against appropriate reference or experimental evidence; producing a molecule, score or trial shortlist does not establish clinical efficacy or eligibility.

02Capability map

Where pharma & biotech puts AI to work, domain by domain.

Pick a domain to see its applications. Each one opens a full report.

Inside Research and Development

Applications in Research and Development

What the use cases ask AI to do

Share of the pharma & biotech use cases, by the kind of work the AI does.

  1. Reasoning

    Analyzes, predicts and decides: scoring, forecasting, classifying.

    73%

  2. Generation

    Creates: text, images, code and other content.

    23%

  3. Perception

    Sees, hears and reads: documents, images, audio and video.

    4%

Under 1% each: optimization and agentic.

03Workflow ideas

Ways AI could change the work, in pharma & biotech.

Atlas groups these applications by the changes they propose to a workflow. These preliminary classifications help you explore ideas; each application still needs a review of its sources and operating requirements.

Workflow categories in this catalog

Share of the 47 applications assigned a category.

How to evaluate an application

  1. Start with a bounded task

    Identify the inputs, the person responsible, and the decision or output the workflow must produce.

  2. Check the support for each method

    Look for source-backed details about the specific implementation, including its limits and human review.

  3. Measure the pilot

    Record the current baseline, agree on acceptance criteria, and test on representative cases before expanding.

Explore applications

04What gets built

The approaches pharma & biotech uses most, and when to skip them.

Knowing when not to use one matters as much as knowing when to.

  1. 01 · 8 applications

    Workflow Automation

    Use AI inside orchestrated workflows to automate multi-step business processes.

    Use it when

    • When you have repetitive, rule-heavy workflows that still require human judgment or document understanding.
    • When the process spans multiple systems (ERP, CRM, ticketing, email) and manual coordination is slow or error-prone.
    • When large volumes of semi-structured or unstructured data (emails, PDFs, forms) must be processed consistently.

    Skip it when

    • When the process is highly creative, exploratory, or non-repeatable, with no stable sequence of steps.
    • When decisions are extremely high-stakes (e.g., life-critical medical decisions, major legal judgments) and regulations require full human control.
    • When you lack reliable access to the necessary systems or data sources to complete the workflow end-to-end.
    Workflow Automation, in depth

  2. 02 · 6 applications

    RAG-Standard

    LLM + retrieval layer that injects relevant documents into the prompt at query time.

    Use it when

    • You need an LLM to answer questions based on proprietary or private documents that are not part of its pretraining data.
    • Your knowledge changes frequently (e.g., policies, product docs, pricing) and you want updates to be reflected without re-training the model.
    • You have a large corpus of unstructured or semi-structured text (wikis, PDFs, tickets, emails, manuals) that users struggle to search effectively.

    Skip it when

    • Your task is primarily structured data querying or analytics (e.g., reporting over a relational database) where SQL or BI tools are more accurate and efficient.
    • You have very small, static knowledge (e.g., a short FAQ) that can fit directly into a prompt or be encoded as rules without a retrieval layer.
    • You require strict, deterministic behavior with no tolerance for probabilistic errors (e.g., certain financial calculations, safety-critical controls).
    RAG-Standard, in depth

  3. 03 · 5 applications

    Clinical-Workflow-Intelligence

    Embed AI directly into clinician-facing workflow and decision-support loops.

    Clinical-Workflow-Intelligence, in depth

05The applications

53 pharma & biotech applications, each with a full report.

The widest first, by the number of use cases each covers. Every report shows what changed, how it works, who has done it and what can go wrong.

  1. Computational Drug DiscoveryThis application area focuses on using computational models to accelerate and de‑risk the discovery and early development of drugs and biologics. It spans target identification, hit and lead discovery, protein and antibody engineering, and early safety/efficacy prediction. By learning from omics data, chemical and biological assays, literature, and historical trial outcomes, these systems prioritize promising targets, propose or optimize molecules, and predict key properties such as potency, toxicity, and developability. It matters because traditional pharma and biotech R&D is slow, costly, and characterized by very high failure rates, especially in late‑stage trials. Computational drug discovery shortens experimental cycles, reduces the number of wet‑lab and structural biology experiments required, and helps select better candidates and trial designs earlier. This not only cuts time and cost but also expands the search space of possible molecules and protein variants, increasing the chances of finding first‑in‑class or best‑in‑class therapies and enabling more scalable precision medicine. Under this umbrella are specific capabilities like protein structure and interaction prediction, structure‑aware protein language models, virtual screening of small molecules, clinical trial design optimization, and cloud platforms that integrate sequencing with automated analytics. Benchmarks such as CASP and dedicated evaluation centers help the ecosystem compare and improve algorithms, driving continual performance gains that feed back into faster, more reliable R&D decisions.Analog → Twin25 use cases
  2. Protocol Feasibility Risk ScoringForecasts protocol risk before launch so teams can reduce avoidable trial failures Evidence basis: A Scientific Reports analysis of 420k+ trials showed interpretable ML can estimate early termination risk from design features; a separate 2000+ trial operations study showed recruitment and duration efficiency can be predicted from protocol characteristicsExpert → Platform24 use cases
  3. Adaptive Trial Design Intelligence HubAdaptive Trial Design Intelligence uses advanced AI to design, simulate, and optimize clinical trial protocols in real time across decentralized, adaptive, and externally controlled designs. It integrates real‑world data, trial evidence, and discovery insights to refine eligibility criteria, dosing strategies, and sample sizes as new data emerge. Sponsors gain faster time to statistical readouts, higher trial success probabilities, and more capital‑efficient drug development programs.Expert → AI14 use cases
  4. Genomic Precision Platform HubThis AI solution covers AI platforms that analyze genomic and multi-omics data to link genotype to phenotype and inform precision medicine, target discovery, and product development. By automating large-scale genomic analytics and integrating clinical, pharmacological, and cosmetic data, these systems accelerate R&D, improve hit quality, and enable more personalized therapies and products, reducing time and cost to market.Silo → Int11 use cases
  5. PharmaSegment IQUnifies cross-channel video performance data for pharmaceutical brand teams to compare audience response, improve customer segmentation, and optimize media ROI across fragmented marketing channels.Opaque → Trans8 use cases
  6. Protocol Deviation Early-Warning AnalyticsFlags rising deviation risk at site and study level before it escalates into major findings Evidence basis: Centralized statistical monitoring methods detect atypical center behavior early using quantitative tests; FDA RBM recommendations support predefined KRIs and adaptive follow-up that fit AI-assisted deviation warningsExpert → AI7 use cases
All 53 applications, A to Z

Work with us

The catalog shows what is possible. A sprint decides what you should fund.

Reading every report still leaves the hardest question open: which move is worth funding first, here, given your constraints. That is the sprint: two weeks, fixed scope, and a candid answer either way.

Book a 30-minute callHow the sprint works

Two weeks Fixed scope A candid fit-or-no-fit answer

  1. 01

    Three ranked opportunities

    A decision-ready shortlist scored against value, feasibility, evidence, risk, and time to proof.

  2. 02

    One pilot-ready workflow

    A bounded operating design with actors, handoffs, data needs, controls, and success measures.

  3. 03

    A defensible 90-day plan

    Build-versus-buy guidance, ROI assumptions, risk treatment, sequencing, and executive decisions.

06Rules & standards

Rules and standards to check, with their scope and sources.

Selected US and EU references for these workflows. Applicability depends on jurisdiction, organization, data and intended use. The planning notes are Atlas editorial guidance; they are not official implementation-time estimates or an exhaustive list of obligations.

  1. FDA draft guidance on AI in drug-development evidence

    Draft guidance

    United States · AI-generated information supporting drug regulatory decisions

    FDA’s 2025 draft discusses evaluating AI used to produce evidence for drug and biological-product regulatory decisions. It is draft guidance, not a universal AI approval requirement or a fixed review timetable.

    FDA: Artificial Intelligence for Drug Development

    Source checked

    Project planning note

    Define the regulatory question and context of use, then document the evidence needed to assess model credibility.

  2. EMA reflection paper on AI in medicines

    Agency guidance

    European Union · medicinal-product lifecycle

    The adopted reflection paper sets out principles for AI across the medicines lifecycle. Read it alongside the legal requirements for the product and intended use; it is not a standalone AI authorization.

    EMA: AI in the medicinal-product lifecycle

    Source checked

    Project planning note

    Identify where AI affects the medicine lifecycle and align the review with the relevant product and data requirements.

07The AI graveyard

One failure, and the lesson it paid for.

  1. BenevolentAI Clinical Failures

    2023 · Source

    Stock down 80%, trials failed

    What went wrong

    AI-identified drug candidates for atopic dermatitis failed clinical trials. AI prediction of efficacy did not translate to human outcomes.

    The lesson

    AI-discovered drugs still face biological complexity that algorithms cannot fully predict

Your next step

Find your first move in pharma & biotech.

Ask the workspace which move fits your company. Add your size, systems and constraints, and it works from these 53 applications to a plan your team can act on.

Plan your first AI move

Rather talk it through? Book a 30-minute call