AI in Pharma: Foundations, Use Cases and Implementation in Europe
- Learn how to identify, prioritise and implement AI use cases
- Professional education provider with more than 45 years of experience
- Officially certified according to ISO 9001 and ISO 21001
Online-Seminar
From
1.790,00 €
plus VAT
Services & Process
Veranstaltung - 1.790,-€ zzgl. MwSt.
The participation fee includes documentation for download, a certificate, access to the Learning Space and technical support including PreMeeting.
Booking details
Online-Seminar
From
1.790,00 €
plus VAT
Services & Process
Veranstaltung - 1.790,-€ zzgl. MwSt.
The participation fee includes documentation for download, a certificate, access to the Learning Space and technical support including PreMeeting.
Learn how artificial intelligence is transforming the pharmaceutical industry in Europe. This foundation course covers AI concepts, key use cases, EU AI Act and GDPR essentials, GxP implications and practical steps to implement AI safely and at scale.
Your speakers
Maik Lange
Bayer AG, Berlin, GERMANY
Generative AI Transformation Lead & Mission Master, Visionary Strategist Marketing & Healthcare Innovation Enthusiast
Your speaker
Maik Lange
Bayer AG, Berlin, GERMANY
Generative AI Transformation Lead & Mission Master, Visionary Strategist Marketing & Healthcare Innovation Enthusiast
Oliver Patel
AstraZeneca
Head of Enterprise AI Governance
Oliver Patel is
Head of Global Enterprise AI Governance at AstraZeneca, overseeing responsible AI across a 80,000+ employee organization and a complex AI…
Your speaker
Oliver Patel
AstraZeneca
Head of Enterprise AI Governance
Oliver Patel is
Head of Global Enterprise AI Governance at AstraZeneca, overseeing responsible AI across a 80,000+ employee organization and a complex AI ecosystem.
He is a former academic and UK Government policy adviser, experienced public speaker and commentator (BBC, Sky News, CNN) and trusted translator between technical and non-technical stakeholders.
Alex Turpin
AstraZeneca, Spain
Head AI Governance R&D
Alex Turpin is Head of AI Governance, R&D at AstraZeneca, where he leads the design and evolution of governance frameworks for advanced AI systems across…
Your speaker
Alex Turpin
AstraZeneca, Spain
Head AI Governance R&D
Alex Turpin is Head of AI Governance, R&D at AstraZeneca, where he leads the design and evolution of governance frameworks for advanced AI systems across research and development.
With a PhD in physics and more than 15 years of experience across deep tech, healthcare, and AI-driven innovation, he combines deep technical understanding with a strong systems and innovation mindset. His background spans academia, neuroscience-related research, health-tech startups, and senior AI leadership roles, including Technical Director positions in fast-growing environments.
At AstraZeneca, Alex focuses on one of the most critical challenges facing the industry today: how can we make sure that governance of technology (including AI) is not a limiting control layer, but a strategic capability that enables speed, trust, and responsible innovation at scale.
He is also a co-founder of a deep-tech AI startup in remote health and ambient monitoring, and is actively involved in shaping thought leadership at the intersection of AI, governance, and real-world clinical impact.
Wei Wannhoff
Merck Healthcare KGaA, Darmstadt, GERMANY
Associate Director Technology and Data Analytics;
After completing her bioinformatics studies in Germany, Wei gained hands-on experience in data management and…
Your speaker
Wei Wannhoff
Merck Healthcare KGaA, Darmstadt, GERMANY
Associate Director Technology and Data Analytics;
After completing her bioinformatics studies in Germany, Wei gained hands-on experience in data management and SAE management in clinical research. She is an experienced professional with 15 years of industry experience and a strong focus on pharmacovigilance and drug safety management.
Throughout her career, she has combined excellence, innovation, and compliance within drug safety and safety data analytics. With a comprehensive understanding of regulatory requirements and a proven track record in data analysis, software tool development, and compliance management, Wei has demonstrated expertise in leading cross-functional teams and driving process improvements. As an AI Champion, she leads internal AI use cases in the drug safety domain.
What to Expect
- Core AI concepts for pharmaceutical companies
- EU perspective: AI Act and GDPR essentials for AI projects
- Key AI use cases across the pharmaceutical value chain
- Roles and responsibilities for the "human in the loop" in AI-supported GxP processes
- AI as a strategic differentiator in pharma: from proofs of concept and pilots to scaled solutions
- Systematic selection and evaluation of AI use cases
- Governance and risk management for AI projects in pharmaceutical organisations
- Practical implementation and evaluation of an AI use case, including change management
Who Should Attend
This course is designed for professionals and leaders in the pharmaceutical industry who already have a basic understanding of artificial intelligence and now want to deepen their skills for practical application.
Basic understanding means you have heard of common AI tools such as ChatGPT or Gemini and can roughly distinguish between the terms AI, machine learning and large language models (LLMs).
By attending, you will significantly deepen your understanding of AI in the pharmaceutical context and strengthen your competence in identifying, evaluating and implementing AI use cases in line with European requirements.
Objective of the Event
Artificial intelligence is reshaping core processes in the pharmaceutical industry at remarkable speed. At the same time, the EU AI Act, GDPR and GxP requirements set a demanding framework for governance, data quality and documentation.
This two-day foundation course provides a structured overview of key AI concepts, concrete use cases along the pharmaceutical value chain and proven approaches for implementation in European organisations. Experienced speakers from leading pharmaceutical companies (including AstraZeneca, Merck and Bayer) share how to move AI initiatives from first pilots to scalable solutions.
This course is the core module of the qualification course "AI Management in the Pharmaceutical Industry". You can find the full programme of the qualification course under webcode 60012205.
The course can alternatively be booked as a stand-alone module.
Your Benefit
After this course, you will be able to
- speak confidently about core AI concepts in a pharma specific way
distinguish between different types of AI systems and understand where they can add value in your organisation
recognise high risk versus lower risk AI use cases from both a regulatory and a business perspective
understand the key elements of the EU AI Act and GDPR that affect AI initiatives in pharmaceutical companies
- identify and evaluate promising AI use cases using clear criteria
anticipate typical pitfalls that prevent AI pilots from scaling and know how to address them
- structure the implementation of an AI use case, including change management and governance aspects
Programme
Day 1: 09:00 - 17:00 CET
Day 2: 09:00 - 14:30 CET
Day 1
09:15
Álex Turpin
- Definitions and terminology: Artificial Intelligence, Machine Learning, Deep Learning
- Types of AI systems and where they typically show up in pharma organisations
- Machine Learning and Neural Networks
- Generative AI - Foundation Models, LLMs and Diffusion Models
10:45
11:00
Oliver Patel
- AI Act & High risk AI in pharma practice
- GDPR and data for AI: essentials for non lawyers
12:45
14:00
Wei Wannhoff
- Overview of AI use cases across key functions: R&D and clinical development, regulatory affairs, pharmacovigilance, medical affairs etc.
- Distinguishing lower risk vs. higher risk AI use cases from a business perspective
15:30
15:45
Wei Wannhoff
- Specific challenges in GxP regulated environments when introducing AI:
- Data integrity and data quality in GxP systems
- Traceability, audit trails and documentation requirements
- Roles and responsibilities for the "human in the loop" in AI supported GxP processes
- How inspectors and quality units are likely to look at AI supported GxP processes
17:00
09:15
What is Artificial Intelligence? Core concepts for pharma
What is Artificial Intelligence? Core concepts for pharma
Álex Turpin
- Definitions and terminology: Artificial Intelligence, Machine Learning, Deep Learning
- Types of AI systems and where they typically show up in pharma organisations
- Machine Learning and Neural Networks
- Generative AI - Foundation Models, LLMs and Diffusion Models
Break
11:00
Legal & Governance Essentials for AI in Pharma in Europe
Legal & Governance Essentials for AI in Pharma in Europe
Oliver Patel
- AI Act & High risk AI in pharma practice
- GDPR and data for AI: essentials for non lawyers
Lunch break
14:00
AI in Pharma: key use cases across core functions
AI in Pharma: key use cases across core functions
Wei Wannhoff
- Overview of AI use cases across key functions: R&D and clinical development, regulatory affairs, pharmacovigilance, medical affairs etc.
- Distinguishing lower risk vs. higher risk AI use cases from a business perspective
Break
15:45
AI across the pharmaceutical value chain - with a focus on GxP data and documentation
AI across the pharmaceutical value chain - with a focus on GxP data and documentation
Wei Wannhoff
- Specific challenges in GxP regulated environments when introducing AI:
- Data integrity and data quality in GxP systems
- Traceability, audit trails and documentation requirements
- Roles and responsibilities for the "human in the loop" in AI supported GxP processes
- How inspectors and quality units are likely to look at AI supported GxP processes
End of Day 1
Day 2
09:00
Maik Lange
- Why AI matters for competitiveness in pharma
- Typical patterns: proof of concepts, pilots and the "pilot purgatory" problem
- What distinguishes successful, scaled initiatives from never ending pilots
09:30
Álex Turpin
- How to find good AI use cases in your organisation
- Evaluation criteria:§§§Business value and impact
- Technical feasibility and data readiness
- Multi country feasibility in European organisations
- Typical reasons why pilots do not scale - and how to address them
- Collaboration with external partners - when does it make sense?
11:00
11:15
Álex Turpin
- Why AI in pharma needs governance - Being clear about the purpose ("intended use")
- What the AI system is supposed to do and in which context (which data, which process)
- What it is not allowed to do (boundaries)
- Working with vendors - what to ask and responsibilities
12:15
13:15
Wei Wannhoff
- What needs to be considered when moving into implementation?
- What are the requirements for effective change management?
14:30
09:00
AI as a strategic differentiator - from pilots to scale
AI as a strategic differentiator - from pilots to scale
Maik Lange
- Why AI matters for competitiveness in pharma
- Typical patterns: proof of concepts, pilots and the "pilot purgatory" problem
- What distinguishes successful, scaled initiatives from never ending pilots
09:30
Selecting and evaluating AI use cases
Selecting and evaluating AI use cases
Álex Turpin
- How to find good AI use cases in your organisation
- Evaluation criteria:§§§Business value and impact
- Technical feasibility and data readiness
- Multi country feasibility in European organisations
- Typical reasons why pilots do not scale - and how to address them
- Collaboration with external partners - when does it make sense?
Break
11:15
Keeping AI projects safe and under control
Keeping AI projects safe and under control
Álex Turpin
- Why AI in pharma needs governance - Being clear about the purpose ("intended use")
- What the AI system is supposed to do and in which context (which data, which process)
- What it is not allowed to do (boundaries)
- Working with vendors - what to ask and responsibilities
Lunch break
13:15
Implementation and evaluation of an AI Use Case together with the participants
Implementation and evaluation of an AI Use Case together with the participants
Wei Wannhoff
- What needs to be considered when moving into implementation?
- What are the requirements for effective change management?
End of day 2
Downloads
Request the documents using our form.
Brochure
Market Access: Basic Concepts and Processes
Weitere Informationen
Qualification Course "AI Management in the Pharmaceutical Industry"
This seminar is part of the qualification course "AI Management in the Pharmaceutical Industry". Further information is available under webcode 60012205.
Technical requirements
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Further Information
Your Contact Person
We are happy to advise you personally and individually.
Leila Dörfler
Team Leader
+49 6221 500-695
l.doerfler@forum-institut.de