Smarter market access with AI: A playbook with tools & tips

By Dr Stefan Walzer, expert-trainer of The AI for Pharma Market Access Course 
 

AI is beginning to change how pharma teams handle pricing, HTA submissions, and reimbursement planning. However, using it effectively requires the right tools and a clear understanding of the risks and limitations.

This compact guide introduces practical AI tools, prompt strategies, and key compliance considerations for professionals working in market access.
 

What You’ll Learn:

✅ How AI Supports Market Access Tasks

Get an overview of common use cases where AI supports tasks such as literature review, code identification, trial evaluation, dossier drafting, and payer engagement preparation.

✅ How to Use Prompts More Effectively

See examples of structured prompts that help guide AI tools in specific tasks like evidence extraction, pricing comparisons, and HTA documentation.

✅ Ethical and Regulatory Essentials

Understand the most relevant compliance considerations, including data privacy, transparency, and the use of AI in regulated environments.

📝 This guide offers a starting point. For deeper application and expert instruction, we recommend our full training course.
 

Section 1: How AI Is Being Used in Pharma Market Access

AI is already supporting many core activities across the market access lifecycle. Below is a practical overview of the key functions AI tools are being used for, whether embedded in platforms or built with large language models and domain-specific algorithms.

⚠️ Note: These AI use cases are not a full replacement for expert review or regulatory compliance. Instead, they serve as productivity boosters, helping teams work faster and more consistently - particularly when paired with proper human oversight.

💡 Pro Tip: For sensitive or unpublished data, always use secure GPTs hosted within the EU, with encryption and no external training or data sharing. Avoid public tools for anything involving internal or regulatory content.

💡 Pro Tip: When working with AI in a regulatory setting, always document where automation was used, ensure outputs are validated by domain experts, and confirm that tools meet data security standards (e.g. no external training, EU-hosted infrastructure).
 

Section 2: Prompting Tips for Market Access Tasks

The quality of your prompt directly affects the accuracy and usefulness of AI outputs. Follow these practical tips to reduce errors, improve relevance, and align results with regulatory and scientific expectations:

1. Instruct the AI to only use the uploaded references and not access any other sources.

2. Prompt the GPT or AI tool you use to write with scientific accuracy and objectivity.

3. Be very clear and precise about what the AI should focus on (e.g. endpoints, comparators, outcomes).

4. Avoid vague or polite language, it reduces the precision of the response.

❌ Avoid vague prompts like: “Can you help me write a dossier?”
Always define the task, specify data sources, and indicate the intended audience or region specifically and in detail.
 

Section 3: Ethical and Regulatory Considerations

As AI becomes more integrated into market access workflows, ethical and regulatory responsibility must remain a top priority. GenAI offers real benefits in terms of efficiency and insight but without proper safeguards, it also poses risks related to data privacy, bias, and compliance.

Below are core principles to guide the responsible use of AI in pharma market access:
 

🔐 Ensure Data Privacy and Security

1. Use encrypted systems and environments not using your data for training purposes to process sensitive data.

2. Avoid uploading protected health information to commercial LLMs. Only use safe GPTs which are ideally closed-systems.

3. Recognise that complete de-identification cannot fully eliminate re-identification risk.

4. Prefer federated learning or differential privacy when applicable.
    

⚖️ Stay Compliant with Regulatory Frameworks

1. Follow GDPR, the EU AI Act, and (if applicable) evolving EMA, NICE, IQWiG, and other institution’s guidance.

2. Monitor the legal status of AI tools and ensure compliance before integrating them into HTA, pricing, or reimbursement workflows.
    

💎 Maintain Transparency and Explainability

1. Document where and how AI is used (e.g. in literature reviews, health economic modelling, or dossier writing)

2. Be prepared to justify AI-generated outputs to internal and external stakeholders. Hence, critically review, adjust and approve AI-generated content.
    

👤 Embed Human Oversight

1. Never rely on AI-generated content alone for regulatory or payer-facing submissions.

2. Ensure that all outputs are reviewed by qualified experts to validate relevance, accuracy, and appropriateness in context.

3. Use AI to assist, not replace, human judgement.
    

🤝 Promote Fairness and Mitigate Bias

1. Be aware that foundation models can reinforce systemic bias if underlying data lacks representation.

2. When using GenAI to model populations or treatment effects, evaluate whether data inputs reflect diversity across demographics and geographies.

3. Implement fairness checks where feasible, especially in pricing or clinical justification outputs.
    

⚖️ Balance Innovation with Regulation

1. Rapid AI development requires ongoing collaboration between pharma companies and regulators.

2. Align internal processes with external expectations from the start.

This framework reflects emerging consensus across agencies and industry experts, but it’s only a starting point. Ethical use of AI in pharma access will require continuous adaptation, internal governance, and transparent dialogue across all stakeholders.
 

Ready to Go Deeper?

This guide is only the beginning…