Which functions actually sit in this training?

Medical affairs, regulatory affairs, pharmacovigilance, quality, learning, commercial, digital, legal, and compliance teams across pharma, biotech, and life-sciences organizations. Cohorts group by function, so each team spends its time on the document types and review boundaries that are theirs.

Is a data-science or technical background required?

Coding or machine-learning experience is not assumed. What matters more is familiarity with the documents, evidence, review steps, or communication workflows a participant already owns.

How are the sessions arranged?

About 13 hours across two days, run onsite or live online, splitting easily into half-day blocks with specific deep dives available to just the functions that need them.

Does the syllabus actually reflect our products and document lifecycle?

Before the training we map your functions, document types, tool boundaries, and review paths. The exercise material is then built to that shape and fabricated outright, so we never need your real product claims, confidential information, or identifiable safety and health data to make it realistic.

Does completing this training certify anything regulatory or compliance-related?

No. This is skills training, not a regulatory assessment, legal or medical advice, or a compliance certification. It builds safer drafting, checking, and escalation habits, and your accountable medical, safety, regulatory, quality, legal, and compliance functions keep every one of their existing responsibilities.

Does any of this let AI make a clinical, regulatory or pharmacovigilance decision?

No. This training does not teach anyone to hand case assessment, signal evaluation, clinical judgement, regulatory strategy, approval, or submission decisions to a model. AI can support intake, organization, summarization, drafting, and triage work, but a qualified person still reviews the evidence and makes the call.

Which specific tools do people practice on?

The core skills transfer across mainstream and organization-approved enterprise tools alike. If your organization already runs a validated or controlled internal environment, exercises can move onto its permitted capabilities and data rules, though the training itself does not validate any tool.

Does any confidential, patient, trial, or safety-case data enter the room?

None. The material is fabricated or fully anonymized, so nothing confidential, patient-identifying, or trial-specific has to travel. One module covers special-category data, tool approval, minimization, and escalation. Patient-facing clinical workflows sit outside this program and are covered in our [AI training for healthcare](/ai-training/healthcare). Here the focus stays on organizational document lifecycles and life-sciences functions.

AI Training · Pharma & Life Sciences

AI Training for Pharma & Life Sciences

Scientific literature, medical content, regulatory documents, safety cases, SOPs, and field materials all depend on precise text. Pharma and life-sciences teams use this hands-on training to draft, summarize, and triage that material with generative AI.

Evidence checks, approved claims, and qualified human review stay built into every workflow, from the first draft to the final sign-off.

modules: 6
hours: 13

Some of the 500+ brands we've worked with

Selected brands we’ve worked with across industries.

See all references

The common assumption is that a language model reading a clinical paper is basically doing what a scientist does, just faster. It isn't. A model can summarize fluently and still invent a citation or drop a study limitation. It may also drift past an approved claim without any signal that something is wrong.

AI speeds up reading, structuring, and first drafts across the evidence chain — literature to briefs, regulatory drafts, safety intake to reporting — but never replaces scientific, medical, safety, or regulatory judgment. It never predicts molecular properties, runs bioinformatics, or produces a scientific conclusion; a scientist verifies every finding. In pharmacovigilance, AI only organizes or flags — a professional decides each case; in regulatory work, specialists alone own validation, approval, and submission. See the AI training catalog.

The reading queue never empties

Journal articles, congress materials, and evidence updates create a reading queue that never empties — AI can summarize and organize that material, but every scientific statement in the output still needs to be traced back to its source.

The same information gets rewritten into a new format constantly

Medical, regulatory, quality, and training teams turn one piece of approved information into a dozen different documents — AI-assisted drafting speeds up that first version while the document owner keeps control of review and approval.

One inconsistent term causes more rework than a slow draft

Medical responses, training material, SOP support, and field content all need the same terminology no matter which team wrote them — structured prompts and a controlled source pack make that consistency achievable without a slower process.

This data carries more risk than marketing copy

Safety narratives, research records, and health information can carry confidential or special-category data without anyone noticing at first glance — teams need a plain rule for what's allowed into which tool before anyone experiments.

Verification is the skill

A fluent answer can misstate a study or invent a citation and read exactly as confidently either way — organizations that get value teach people to check sources and escalate uncertainty instead of trusting output at face value.

01
Generative AI foundations for pharma and life sciences
120 minBeginner
What large language models do well, where they quietly fail, and why scientific, medical, regulatory, and safety work needs tighter controls than most industries. Teams build a common vocabulary here and learn to tell drafting support apart from an actual clinical or regulatory decision.
What a language model is doing when it drafts an answer
Citation errors and hallucinations behind unsupported claims
Task lines: where drafting support ends and an expert decision begins
Where AI already shows up in the office tools everyone already has
A first experiment worth trying because the risk is genuinely low
A model for reviewing and escalating anything higher-stakes
02
Prompt craft grounded in sources and validation
120 minBeginner
A repeatable structure for life-sciences documents keeps every draft traceable to its basis, with purpose, audience, approved sources, task, format, and checks all made explicit before participants verify each material statement and move it forward.
A prompt template built for scientific and operational documents
Supplying approved context without exposing anything confidential
A source-linked summary, always
Checking a quotation, reference, figure, or study detail against its source
Telling a source fact apart from a model suggestion and a reviewer's conclusion
Building a controlled prompt and source library as a team
03
Scientific literature and medical affairs content
150 minIntermediate
Literature monitoring and scientific summarization support medical affairs content workflows under one rule: everything stays traceable. AI screens, structures, and drafts, while a qualified reviewer still decides relevance, scientific accuracy, balance, and whether something is fit to use.
Summarizing a publication with every claim linked to its source passage
Literature monitoring with relevance tags and a working review queue
Evidence tables, briefing notes
A first draft of medical information or educational content
Reshaping one approved evidence base for several professional audiences
Scientific review for missing context or overstatement, including invented citations
04
Regulatory, quality, SOP, and training documents
150 minIntermediate
Regulatory affairs, quality, and learning all produce document-heavy work: structured first drafts, controlled rewrites, comparisons, and consistency checks. AI supports the document preparation here, while accountable specialists keep authorship, validation, approval, and submission decisions.
Drafting a regulatory-document section from approved structured input
Comparing versions and flagging what's missing or inconsistent
Turning an approved procedure into SOP support and training material
Learning aids and FAQs
Keeping defined terminology the same across an entire document set
Ownership, validation, approval, and an audit trail for every review gate
05
Pharmacovigilance support within responsible data boundaries
120 minIntermediate
Clear boundaries for confidential, personal, health, and special-category data sit alongside a careful pharmacovigilance use case here. Participants learn where AI can help organize or prioritize information. A qualified safety professional still reviews and assesses every case before deciding it.
Naming confidential, personal, health, and special-category data correctly
What separates a consumer tool from an enterprise one, prompt by prompt
De-identification and minimization through synthetic examples in exercises
Pharmacovigilance intake and triage support that stops short of a case decision
Qualified human review with escalation and a documented rationale
Logging AI-assisted work and flagging uncertain output
06
Approved commercial content and governed rollout
120 minAdvanced
Commercial and field-team content gets created here inside approved claims, followed by a practical adoption plan. Teams design controlled pilots, review paths, reusable assets, and measurement that rewards reliable use over raw output volume.
Drafting field and commercial content from an approved claim library
Adjusting tone and format without expanding or changing the claim
Medical, legal, regulatory checkpoints
Choosing a pilot workflow by value and sensitivity against review effort
A champions model spanning medical, regulatory, safety, quality, and commercial teams
A 90-day rollout plan with named owners and a feedback loop

What you will learn

Summarize scientific text, source-linked, then verify against the original
A literature flow, reviewer-ready
Draft medical, regulatory, SOP, or training content from approved inputs
Create commercial and field-team drafts that never leave the approved claim or its review path
Use AI for pharmacovigilance intake and triage support, while safety assessment and case decisions stay with a qualified person
Handle confidential, personal, health, or special-category data inside clear boundaries
Catch errors before they ship
Plan a governed path from pilot to rollout, with named owners, review gates, and reusable prompt assets

Who should attend

Medical affairs and medical information teams
Regulatory affairs and submissions teams
Pharmacovigilance and drug-safety teams
Quality and learning teams responsible for SOPs
Commercial excellence and marketing teams supporting the field force
Digital, data, legal, compliance, and transformation leaders

Life-sciences functions share two days, usually split into half-day sessions so scientific, safety, regulatory, and commercial reviewers can each join the workshops most relevant to them. Onsite and live-online formats cover the same syllabus.

Format: Onsite or live online
Duration: 2 days (about 13 hours, can be split into half-day sessions)
Group size: Up to 20 participants per group
Language: English or Turkish
Materials: Prompt library, validation checklists, document templates, and exercise workbook
Certificate: Certificate of completion

AI for Healthcare Teams6 modules · 13 hoursHealthcare teams learn to draft clinical documentation, patient communication, and admin text with generative AI, then check it against GDPR and KVKK before anything goes out.View training

AI for Chemical Industry Teams6 modules · 12 hoursChemical industry documentation runs on SOPs, batch records, SDS revisions, and regulatory dossiers, produced at a scale and density most industries don't match. This training builds a faster, reviewed way to draft and organize that text for process-safety and quality teams, with product stewardship included.View training

AI Training for Veterinary Teams6 modules · 10 hoursA veterinary practice writes consultation notes and discharge instructions every day. Owner messages add to the load. This program trains clinical and support staff to draft that text with AI, built around clinician review, and never touches diagnosis, prescribing, or treatment planning.View training

Preparing Teams for the EU AI Act6 modules · 12 hoursEU AI Act readiness training for compliance, legal, risk, HR, IT, procurement, and business teams: map AI use, build literacy, ask sharper vendor questions, and leave with an accountable action plan.View training

Tell us which medical and regulatory teams need this, along with safety, quality, commercial or digital teams. Name the document workflows that matter most. Those answers shape the modules and examples, with review boundaries built around your organization.

Two colleagues updating a workflow board and checking a process change

Which functions actually sit in this training?
Medical affairs, regulatory affairs, pharmacovigilance, quality, learning, commercial, digital, legal, and compliance teams across pharma, biotech, and life-sciences organizations. Cohorts group by function, so each team spends its time on the document types and review boundaries that are theirs.
Is a data-science or technical background required?
Coding or machine-learning experience is not assumed. What matters more is familiarity with the documents, evidence, review steps, or communication workflows a participant already owns.
How are the sessions arranged?
About 13 hours across two days, run onsite or live online, splitting easily into half-day blocks with specific deep dives available to just the functions that need them.
Does the syllabus actually reflect our products and document lifecycle?
Before the training we map your functions, document types, tool boundaries, and review paths. The exercise material is then built to that shape and fabricated outright, so we never need your real product claims, confidential information, or identifiable safety and health data to make it realistic.
Does completing this training certify anything regulatory or compliance-related?
No. This is skills training, not a regulatory assessment, legal or medical advice, or a compliance certification. It builds safer drafting, checking, and escalation habits, and your accountable medical, safety, regulatory, quality, legal, and compliance functions keep every one of their existing responsibilities.
Does any of this let AI make a clinical, regulatory or pharmacovigilance decision?
No. This training does not teach anyone to hand case assessment, signal evaluation, clinical judgement, regulatory strategy, approval, or submission decisions to a model. AI can support intake, organization, summarization, drafting, and triage work, but a qualified person still reviews the evidence and makes the call.
Which specific tools do people practice on?
The core skills transfer across mainstream and organization-approved enterprise tools alike. If your organization already runs a validated or controlled internal environment, exercises can move onto its permitted capabilities and data rules, though the training itself does not validate any tool.
Does any confidential, patient, trial, or safety-case data enter the room?
None. The material is fabricated or fully anonymized, so nothing confidential, patient-identifying, or trial-specific has to travel. One module covers special-category data, tool approval, minimization, and escalation. Patient-facing clinical workflows sit outside this program and are covered in our AI training for healthcare. Here the focus stays on organizational document lifecycles and life-sciences functions.

By team

By industry

By tool

AI Training for Pharma & Life Sciences