Overcoming AI Roadblocks by Fixing the Data Foundations
30 October 2025
AI can sharpen risk forecasting, reduce repetitive work, speed up collaboration, and tighten compliance. But none of that lands without solid data. Right now, poor data quality is the biggest brake on progress - 72% of insurers say it's holding them back.
AI is only as reliable as the information you feed it. Incomplete, outdated, or inaccurate data will derail even the best AI projects. The fix starts at the foundation: build a modern archive that delivers clean, current, and provable data in real time.
Data quality debt is real - and expensive
Insurers sit on massive, valuable datasets. That scale gives AI an edge - if the inputs are sound. If they aren't, the outcomes can skew pricing, distort underwriting, and trigger regulatory scrutiny.
Expect more oversight, not less. You'll need to explain how a model reached a recommendation, and prove the inputs were accurate, appropriate, and handled lawfully. Frameworks like the NIST AI Risk Management Framework make that expectation clear.
Five steps to a stronger data foundation
Your goal: feed AI with compliant, current, and context-rich data. Here's a practical blueprint that uses intelligent archiving to get there.
1) Visibility and lineage
Know where every dataset came from, who owns it, and how it has changed. Track lineage from source to model input so you can trust what goes in - and explain what comes out.
- Inventory sources across policy, claims, billing, email, chat, and call recordings.
- Capture metadata on origin, ownership, and transformation steps.
- Preserve original source documents alongside derived versions.
2) Authenticity and chain of custody
Data must be exactly what was captured - provable and tamper-evident. That's your evidence when auditors ask, "How do you know this record wasn't altered?"
- Store objects in native formats with cryptographic hashes.
- Maintain an immutable audit trail for access and changes.
- Use write-once storage and time-based retention where required.
3) Accurate classification across data types
Insurers manage structured, semi-structured, and unstructured data. Treat each class appropriately instead of forcing everything into one rigid model.
- Apply schemas that fit the data: policy tables, documents, images, voice, and chat.
- Auto-detect sensitive fields (PII, PHI, financial identifiers) on ingest.
- Tag records with lifecycle, jurisdiction, and purpose-of-use metadata.
4) Consistency and normalization
Standard definitions make analytics and AI dependable. Without them, two systems will read the same data and disagree.
- Adopt common dictionaries for entities, dates, coverage terms, and currencies.
- Map variants into a unified view; keep raw data accessible for traceability.
- De-duplicate, resolve entities, and reconcile IDs across systems.
5) Granular access and entitlements
Protect confidentiality while enabling work. Access should be precise - down to the field when needed.
- Grant least-privilege access by role, purpose, and jurisdiction.
- Mask sensitive fields for most users; reveal on approved workflows.
- Log every read, write, export, and model-consumption event.
Turn your archive into a live data asset
Stop "store it and forget." Treat the archive as the trusted system of record that feeds AI. That means real-time ingestion, automated classification, policy-based retention, legal holds, and APIs that deliver governed data to your models and analytics tools.
- Stream updates from core and edge systems to keep AI inputs fresh.
- Apply governance at ingest so data is usable on day one.
- Serve model-ready datasets with context, lineage, and consent flags attached.
Looking ahead
Fixing data quality debt isn't glamorous, but it's the difference between AI that helps underwriting and claims - and AI that creates risk. Build visibility, prove authenticity, classify correctly, standardize, and control access. Do that, and AI becomes reliable instead of unpredictable.
If you're upskilling teams on AI data practices and governance, explore curated learning paths by role here: Complete AI Training - Courses by Job.
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