Pharma & Life Sciences

Reimagine trial coordination, pharmacovigilance, and regulatory submissions.

Roles and workflows

Three roles in pharma ops, where people and AI work together

Pattern A

Clinical trial coordination

Clinical trial coordinator

Responsibilities to review

  • Read the protocol and prior deviations
  • Review site monitoring notes
  • Draft the deviation note with classification
  • Draft the corrective action plan

Illustrative workflow responsibilities. Confirm the tools, data, human decisions and review criteria for your work.

AI reads the protocol, prior deviations, site monitoring notes. Drafts the deviation note with classification (major/minor) and the corrective action plan. Coordinator reviews against the SOP and signs.

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Pattern B

Pharmacovigilance case processing

PV analyst

Responsibilities to review

  • Read the adverse event report
  • Code MedDRA terms
  • Assess causality
  • Draft the seriousness call

Illustrative workflow responsibilities. Confirm the tools, data, human decisions and review criteria for your work.

AI reads the adverse event report, codes MedDRA, assesses causality, drafts the seriousness call. PV analyst reviews the flagged cases with full evidence chain surfaced.

See the role detail →
Pattern C

Regulatory submission

Regulatory affairs manager

Responsibilities to review

  • Read the source data packages
  • Review prior submissions and agency guidance
  • Draft the submission section with cross-references
  • Build traceability and cross-reference index

Illustrative workflow responsibilities. Confirm the tools, data, human decisions and review criteria for your work.

AI reads the source data packages, prior submissions, agency guidance. Drafts the submission section with cross-references and traceability. Regulatory manager reviews against the agency's expected dossier.

See the role detail →
12,385
Real job postings in this industry
512
Companies in this industry
10,159
Tasks mapped to industry roles
943
Source examples classified for AI support

Prepare your workforce for the redesigned work.

Your workforce brings people and AI agents together. Workflows define how they share the work. Define what agents do, who reviews their output, and when they hand work back. Use Task Intelligence to understand the tasks within each step, then prepare your people to build and run the new version through simulations, hands-on projects and skill validation. Revisit affected responsibilities and readiness when the work changes.

Task evidence within pharma workflows

These research inputs include vendor and source-reported software capabilities, product features and descriptions of work. The stored classifications in this selection contain 943 source examples for AI support and 631 for automation. They are not validated workflow outcomes or proof of agent performance. Task Intelligence helps examine the work in context; review each example against your responsibilities, controls and exceptions before using it in a redesign. The first 5 AI-support examples are below.

Augment
AlphaBind predicts binding affinity from protein sequence across candidate variants
Source context: AI-Guided Antibody Engineering for Biologics and Molecular Glues · A-Alpha Bio
Augment
Experimental validation via AlphaSeq confirms top predicted binders
Source context: AI-Guided Antibody Engineering for Biologics and Molecular Glues · A-Alpha Bio
Augment
Iterative design-predict-validate cycle converges on optimized candidates
Source context: AI-Guided Antibody Engineering for Biologics and Molecular Glues · A-Alpha Bio
Augment
Massive sequence space explored around parental protein
Source context: High-Throughput Protein-Protein Interaction Measurement (AlphaSeq) · A-Alpha Bio
Augment
Cross-reactivity predictions across hundreds of diverse viral variants
Source context: High-Throughput Protein-Protein Interaction Measurement (AlphaSeq) · A-Alpha Bio
631 source examples classified for automation›
Automate
AbCellera runs high-throughput immune screening against target
Source context: AI-Powered Antibody Discovery for Difficult Targets · AbCellera
Automate
Computational tools analyze thousands of antibody candidates
Source context: AI-Powered Antibody Discovery for Difficult Targets · AbCellera
Automate
Deep Agent receives task specification (e.g., 'process these claims documents')
Source context: Healthcare Administrative Workflow Automation · Abacus.AI
Automate
Agent extracts structured data from unstructured healthcare documents
Source context: Healthcare Administrative Workflow Automation · Abacus.AI
Automate
Multi-system coordination across connected platforms
Source context: Healthcare Administrative Workflow Automation · Abacus.AI
Automate
Results delivered in structured format or logged to target systems
Source context: Healthcare Administrative Workflow Automation · Abacus.AI
Automate
Scheduled automation for recurring tasks
Source context: Healthcare Administrative Workflow Automation · Abacus.AI
Automate
Physician activates Abridge during or before patient encounter
Source context: Ambient Clinical Documentation · Abridge
Automate
AI captures and transcribes the conversation in real time
Source context: Ambient Clinical Documentation · Abridge
Automate
Conversation captured during clinical visit
Source context: Patient Encounter Summarization · Abridge
Automate
AI identifies key patient instructions, diagnoses, and follow-up actions
Source context: Patient Encounter Summarization · Abridge
Automate
Member accesses symptom checker within insurer or provider's app
Source context: Enterprise Health Navigation for Payers and Providers · Ada Health
Automate
Population-level data aggregated for health management insights
Source context: Enterprise Health Navigation for Payers and Providers · Ada Health
Automate
Generate module applies AI to create synthetic data from real datasets
Source context: Privacy-Preserving Synthetic Data Generation · Aetion
Automate
Synthetic dataset used for analysis across regulatory contexts
Source context: Privacy-Preserving Synthetic Data Generation · Aetion
Automate
Normal/insignificantly abnormal results auto-processed per protocol
Source context: Automated Lab Result Inbox Processing · Affineon Health
Automate
Customizable patient messaging sent automatically per protocol
Source context: Automated Lab Result Inbox Processing · Affineon Health
Automate
All actions logged with full audit trail
Source context: Automated Lab Result Inbox Processing · Affineon Health
Automate
CT scan completed and auto-routed to Aidoc's aiOS platform
Source context: AI-Powered Stroke Detection and Care Team Activation · Aidoc
Automate
AI analyzes images for stroke, hemorrhage, LVO, and brain aneurysm findings
Source context: AI-Powered Stroke Detection and Care Team Activation · Aidoc
Automate
High-confidence findings trigger immediate care team notification
Source context: AI-Powered Stroke Detection and Care Team Activation · Aidoc
Automate
aiOS pushes alert with AI-annotated images to radiologist and stroke team
Source context: AI-Powered Stroke Detection and Care Team Activation · Aidoc
Automate
Care team activates stroke protocol with contextual clinical data
Source context: AI-Powered Stroke Detection and Care Team Activation · Aidoc
Automate
CTPA images analyzed by Aidoc PE algorithm
Source context: Pulmonary Embolism AI Triage and Follow-up · Aidoc
Automate
PE findings detected and severity scored
Source context: Pulmonary Embolism AI Triage and Follow-up · Aidoc
Automate
Care team automatically notified with AI-annotated scan
Source context: Pulmonary Embolism AI Triage and Follow-up · Aidoc
Automate
Patient management workflow activated
Source context: Pulmonary Embolism AI Triage and Follow-up · Aidoc
Automate
99% of eligible AA patients scheduled for long-term follow-up via coordination tools
Source context: Pulmonary Embolism AI Triage and Follow-up · Aidoc
Automate
Nurse activates Aiva via voice command or wake word
Source context: Voice-Powered Clinical Documentation and EHR Control · Aiva Health
Automate
Voice commands processed and routed to appropriate system
Source context: Voice-Powered Clinical Documentation and EHR Control · Aiva Health

How we work with pharma orgs

Co-sponsored model. Functional head (Head of Clinical Ops, Head of PV, VP Regulatory) plus your transformation office in the room. The 6 to 8 week AI Bootcamp prepares people for agreed workflow responsibilities through simulations and assessments. Transformation Engagement scales across, same eTMF, same PV system, same GxP envelope.

AI Bootcamp
6 to 8 weeks
One functional area, workflow practice and assessment
Transformation Engagement
Custom
Operating-model redesign across clinical, PV, regulatory, manufacturing ops
Implementation
Same GxP stack
Same staff, same validated systems, AI cowork layer on top with audit trail

Want a pharma-specific walkthrough?

20 minutes. We pull your top three task patterns from the dataset and show you the redesign live, with your role mix and your regulatory frame.

Book a time