As pharmaceutical companies seek to harness large volumes of data, build digital laboratories and meet regulatory needs, the integration of AI becomes more than an innovation driver — it becomes an essential component of data integrity, archive strategy and knowledge management.
The pharmaceutical industry is undergoing a profound transformation, largely driven by AI technologies. Deep learning, generative models and intelligent data analytics are no longer futuristic visions — they are already shaping how drugs are discovered, validated and brought to market.
Key areas of impact include:
• Accelerated compound screening, pattern recognition in biological data, and simulation of chemical processes.
• Optimisation of clinical trials, risk modelling and patient-centric therapies.
• The move towards generative AI solutions which do not only analyse data but propose new hypotheses, structures or processes.
In this podcast episode, the hosts highlight why archival strategy cannot remain passive: if data is to fuel AI, it must be accessible, structured, trustworthy and compliant.
In this episode, hosts Julian Weber, Business Development Consultant, and Dr. Philipp Krubasik, Head of Sales at biomedion, discuss several practical themes and highlight actionable challenges.
For AI initiatives to deliver value, several foundational conditions must be met — and they hinge on how well the data and archive strategy are implemented:
• Data integrity and traceability: AI models are only as good as the data they train on. If historic data resides in fragmented systems, unreadable formats or uncontrolled archives, the AI initiative risks flawed inputs.
• Long-term access: In highly regulated environments (e.g., GxP, GLP), data must remain accessible, auditable and trustworthy across years or decades. AI re-use of such data demands this level of fidelity.
• Structure, metadata & context: AI thrives on context-rich data-sets. Archived data that lacks metadata, context of collection or provenance will limit AI’s effectiveness.
• Vendor-neutrality and format stability: As addressed in previous episodes, legacy systems pose a risk. If data is locked in proprietary formats or inaccessible once software is retired, AI-driven analytics become hindered.
The podcast emphasises that building an AI-capable future lab is not just about installing algorithms — it is about preparing the archive so that data remains reliable, accessible and meaningful over time.
Based on the discussions in the episode and industry insights, here are strategic imperatives for pharma organizations:
1. Establish audit-proof, GxP-compliant archives: Ensure your archive platform (e.g., Watcher by biomedion) supports data integrity, long-term retention, traceability and vendor independence.
2. Enrich archived data with metadata and structure: Capture context of data generation (lab instrument, standard operating procedure, versioning) to enable downstream AI usage.
3. Break down silos and consolidate data: AI needs integrated, accessible data-pools rather than fragmented islands. This involves migrating legacy data, exporting open formats and consolidating archives.
4. Incorporate AI-use cases early: Identify where AI will deliver value (e.g., predictive maintenance of lab equipment, anomaly detection in test results, quality trend analysis) and ensure the archive supports those pathways.
5. Align with regulatory and ethical frameworks: With upcoming regulations (e.g., the EU AI Act) and heightened expectations around transparency, human oversight and risk categorisation in AI, archive systems must help organisations meet these demands.
By treating the archive not as a passive storage unit but as a strategic enabler of AI and lab transformation, pharmaceutical companies move from data-catchers to knowledge-builders.
• Conduct an archive-health audit: Review your existing systems, legacy dependencies, format risks and metadata gaps.
• Define the archive into AI-ready zone: Structure your archive environment into working systems, archive zones and AI-enabled layers.
• Choose a future-proof archive platform: Prioritise systems designed for long-term compliance, data usability and integration with AI solutions.
• Embed governance for AI-integration: Ensure that archived data flows into AI initiatives under controlled, documented processes — not ad-hoc or unmanaged.
• Build skills and culture: Shift mindset from “archive for compliance” to “archive for intelligence”. Encourage cross-functional collaboration between lab, IT, quality assurance and data science teams.
🎧 Listen now: DatenDokuDigital – Episode #2 "The Challenges of Data Archiving: Legacy Systems in Pharma"
Whether you work in quality management, research, regulatory affairs, or IT, DatenDokuDigital offers valuable insights for anyone driving digital transformation in the pharmaceutical industry.
Subscribe now: DatenDokuDigital – The Pharma Archive Podcast by biomedion
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Podcast Hosts:
🎙️ Julian Weber – Business Development Consultant, biomedion GmbH
🎙️ Dr. Philipp Krubasik – Head of Sales, biomedion GmbH
Produced by STUDIO VENEZIA – the podcast company.