FAIR is Dead, Long Live FAIR – The Role of FAIR Principles in the AI Era
Author: Cedric Berger, Knowledge Management Lead
Category: Innovation & Technology
Format: Whitepaper
Estimated read time: ~12 min
Basel, Switzerland – December 9, 2025
In Life Sciences and beyond, FAIR principles were meant to free data from silos so humans and machines could finally (re)use it effectively. Yet many organisations have turned FAIR into a label for catalogues, storage projects, or IT system upgrades, rather than a genuine shift towards semantic, machine-actionable data.
The result is FAIR fatigue, where the original vision feels exhausted, just when AI needs data that machines can reliably interoperate and reuse the most.
Rethinking FAIR For Today’s AI Reality
In this new whitepaper, Cedric Berger, Knowledge Management Lead at MIGx, revisits what FAIR was supposed to achieve and why the story lost its way. He traces how FAIR moved from a bold vision of interoperable, self-describing data to a buzzword stretched across system-centric initiatives that often do little to help AI reason reliably over data.
This misunderstanding also shaped FAIRification, a demanding process that blends mindset, data governance, semantics, and technology.
A Practical View On Mindset, Metadata, And Technology
Cedric explains that FAIRification is not a technical upgrade but a cultural transformation built on three interconnected pillars. Each one must evolve for FAIR to work as intended:
- Mindset and Culture – Data as an Asset: Taking a data-centric mindset, rather than buying or rather than a system by product, with shared responsibility for clarity and reuse.
- Digital Resources – Metadata and Identifiers: Applying accurate metadata, persistent identifiers, and authoritative sources to give data consistent meaning.
- Technology – Fit-for-Purpose Semantic Infrastructure: Using Semantic Web standards such as URIs, vocabularies, and ontologies to enable machine readable interoperability.
Together, these pillars ensure data can be understood, linked, and reused by both humans and machines.
Why AI Revives the Need for FAIR
As organisations adopt AI and LLMs, the absence of semantics becomes more visible. AI systems cannot infer purpose or meaning from poorly described data. They require provenance, identifiers, and consistent metadata to reason accurately, integrate knowledge, and reduce hallucinations. FAIR provides the minimal semantic scaffolding needed for this.
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