Why Pharma Needs a Dedicated AI Strategy
Many pharmaceutical companies have launched dozens of AI pilots — but few have scaled them into production. The missing ingredient is usually not technology but strategy: a clear articulation of where AI creates the most value, how it fits into the operating model, and what governance frameworks ensure responsible deployment in a regulated industry.
The ANG Approach to AI Strategy
Our framework starts with business-back prioritization: mapping AI use cases to specific value pools across R&D, Commercial, Manufacturing, and Corporate IT. Each use case is scored on value potential, feasibility (data readiness, technical complexity), and regulatory risk. The output is a sequenced roadmap with quick wins, medium-term initiatives, and strategic bets.
Key Components
- AI use case portfolio mapping across the pharma value chain
- Data maturity assessment and remediation plan
- AI operating model design: centralized CoE vs. federated vs. hybrid
- Governance framework aligned with EU AI Act and Swiss nDSG requirements
- Talent and capability building strategy
- Vendor and technology stack evaluation
The best AI strategies start with business problems, not technology solutions. The question is never "how do we use AI?" but "where does AI create measurable value?"
Common Pitfalls
We frequently see pharma organizations over-invest in horizontal AI platforms before validating specific use cases, or under-invest in data quality and governance. A good strategy balances ambition with pragmatism, ensuring each initiative has a clear path from pilot to production.