AI Adoption Strategy Part II (Pharma)
In the first part, we introduced a proven, non-technical framework for adopting AI responsibly and effectively. In this second part, we’ll turn the framework into a clear, actionable, step-by-step guide using a simple, real-world example from the pharmaceutical industry to show how it works in practice.
1. Start with Purpose
AI projects often begin as experiments with no clear direction, which leads to scattered use cases that never grow into real value. But when AI is tied directly to business goals, it becomes a tool for tangible impact.
Define a Meaningful Objective
Pharma companies invest heavily in medical and marketing content: disease education emails, MOA slide decks, patient FAQ sheets, HCP outreach messages, and more. These assets take weeks to create and delay commercial activities.
Purpose statement
Accelerate medical content creation for HCP engagement campaigns to improve conversion rates and support earlier prescribing.
Goal-oriented questions:
By focusing on a high-impact area directly connected to commercial performance, the organization avoids “AI for the sake of AI” and moves toward AI as a growth driver.
2. Assess Readiness
Before building anything, evaluate whether your organization has the foundation to support AI. This means assessing both technology and people.
Checklist
AI relies heavily on structured medical information and clear regulatory guardrails. If these don’t exist, the generated content risks being inaccurate or non-compliant.
3. Use the Right Tool for the Right Job
Once the strategic goal is clear and readiness validated, it’s time to translate the strategic priority into a specific, measurable, low-risk pilot.
Pilot Use Case – AI Agent for Medical Content Generation
Problem:
Medical writers and marketing teams spend weeks drafting assets required for each HCP campaign. These delays slow down commercial rollout.
Solution:
An AI agent that generates first-draft medical materials using approved, controlled sources.
This can be handled by an AI agent that:
Faster content → earlier campaigns → earlier prescribing → → increased early uptake, especially in launch phases.
Even a 1% acceleration in prescribing for a €150M product can contribute around €1.5M in additional annual revenue.
4. Build Governance
Pilot success is not the finish line, it’s the starting point for safe enterprise-wide adoption. Without governance, AI quickly becomes Shadow AI: tools used inconsistently and without oversight.
What good AI governance looks like in pharma
Platforms like Microsoft Azure now embed responsible AI principles: safety filters, access management, data isolation, into their solutions. This gives organizations a safe starting foundation.
5. Scale with Structure
Once the pilot demonstrates value, expand adoption systematically.
How to scale AI safely and effectively
After proving success with content generation for one product or therapeutic area, expand to:
The pharmaceutical example in this guide shows that even a simple, well-chosen use case like accelerating medically accurate content creation can produce meaningful commercial impact while staying safe and compliant.
This is how organizations move from AI curiosity to AI capability. And eventually, to AI-powered business performance.
In the upcoming articles, we’ll walk through similar, easy-to-implement AI adoption examples in healthcare and education, showing how this framework applies across different sectors.
If your organization is exploring how to put AI into practice - not just experiment, but actually turn it into measurable value - our team is ready to support you in shaping the strategy, selecting the right use cases, and implementing solutions that work in the real world.
1. Start with Purpose
AI projects often begin as experiments with no clear direction, which leads to scattered use cases that never grow into real value. But when AI is tied directly to business goals, it becomes a tool for tangible impact.
Define a Meaningful Objective
Pharma companies invest heavily in medical and marketing content: disease education emails, MOA slide decks, patient FAQ sheets, HCP outreach messages, and more. These assets take weeks to create and delay commercial activities.
Purpose statement
Accelerate medical content creation for HCP engagement campaigns to improve conversion rates and support earlier prescribing.
Goal-oriented questions:
- Where are we losing time? Medical writers spend 40–70% of time drafting repetitive standard materials.
- What is the cost of delays? Slower campaigns = slower prescriber uptake = missed revenue.
- What outcome do we want? Faster content production, more campaigns released, higher HCP engagement.
By focusing on a high-impact area directly connected to commercial performance, the organization avoids “AI for the sake of AI” and moves toward AI as a growth driver.
2. Assess Readiness
Before building anything, evaluate whether your organization has the foundation to support AI. This means assessing both technology and people.
Checklist
- Technology: Cloud-ready IT environment, integrated CRM/medical information systems.
- Data: Approved product information, clinical summaries, safety statements, and compliance rules structured and accessible.
- Skills: Marketing, medical affairs, and compliance teams understand how to work with AI output.
- Governance: Documented medical review workflows, privacy rules, and access controls.
AI relies heavily on structured medical information and clear regulatory guardrails. If these don’t exist, the generated content risks being inaccurate or non-compliant.
3. Use the Right Tool for the Right Job
Once the strategic goal is clear and readiness validated, it’s time to translate the strategic priority into a specific, measurable, low-risk pilot.
Pilot Use Case – AI Agent for Medical Content Generation
Problem:
Medical writers and marketing teams spend weeks drafting assets required for each HCP campaign. These delays slow down commercial rollout.
Solution:
An AI agent that generates first-draft medical materials using approved, controlled sources.
This can be handled by an AI agent that:
- Pulls from approved product information
- Drafts HCP emails, educational summaries, MOA descriptions, and short messages
- Runs a compliance checks for mandatory disclaimers, off-label risk, and tone alignment
- It is non-critical and low-risk
- The data is structured and controlled
- It creates immediate, visible results
- It involves multiple teams early: medical, marketing, compliance, IT
- Reduction in content drafting time (target: 50–70%)
- Increase in number of campaign assets ready for review
- Faster campaign activation (measured in weeks saved)
- Higher HCP engagement (open rates, call-to-action responses)
Faster content → earlier campaigns → earlier prescribing → → increased early uptake, especially in launch phases.
Even a 1% acceleration in prescribing for a €150M product can contribute around €1.5M in additional annual revenue.
4. Build Governance
Pilot success is not the finish line, it’s the starting point for safe enterprise-wide adoption. Without governance, AI quickly becomes Shadow AI: tools used inconsistently and without oversight.
What good AI governance looks like in pharma
- Data usage rules: AI may only access approved, up-to-date medical content
- Human-in-the-loop controls: mandatory medical review for accuracy
- Regulatory guardrails: alignment with GDPR, HIPAA, EMA/FDA promotional guidelines
- Audit trails: Logs of generated content for internal and external audits
- Clear roles: who approves what, who monitors outputs, who updates the system
Platforms like Microsoft Azure now embed responsible AI principles: safety filters, access management, data isolation, into their solutions. This gives organizations a safe starting foundation.
5. Scale with Structure
Once the pilot demonstrates value, expand adoption systematically.
How to scale AI safely and effectively
- Train teams regularly on how to use AI tools and evaluate output
- Integrate AI into existing content approval systems, CRMs, or DAMs
- Establish standardized prompts and templates for quality and consistency
- Track usage and performance to see what’s working
- Update policies as tools and regulations evolve
After proving success with content generation for one product or therapeutic area, expand to:
- Patient support materials
- Literature review automation
- Personalized HCP communication at scale
- Medical information response drafting
- Request input – “Generate a disease overview and product MOA for upcoming HCP emails. Use only approved medical sources A, B, C.”
- Research in data, product labels, adverse event lists, citation library, etc.
- Produce content – emails, slide deck, messages
- Compliance check
- Review (human step)
- Go-live - launch timeline shortened by 2–4 weeks.
The pharmaceutical example in this guide shows that even a simple, well-chosen use case like accelerating medically accurate content creation can produce meaningful commercial impact while staying safe and compliant.
This is how organizations move from AI curiosity to AI capability. And eventually, to AI-powered business performance.
In the upcoming articles, we’ll walk through similar, easy-to-implement AI adoption examples in healthcare and education, showing how this framework applies across different sectors.
If your organization is exploring how to put AI into practice - not just experiment, but actually turn it into measurable value - our team is ready to support you in shaping the strategy, selecting the right use cases, and implementing solutions that work in the real world.
