AI Ready Asset Management: Building the Right Data Foundation
AI is moving quickly across investment research, risk, operations, and client engagement. Yet many asset managers remain stuck in pilot mode. This whitepaper explains why the real barrier to scaling AI in asset management is not models, but data - and how firms are building AI-ready data foundations that enable trusted, enterprise adoption.
What You will Learn
- Why AI initiatives in asset management fail to scale beyond pilots
- What a practical data foundation for AI looks like
- How leading firms modernize their asset management data strategy
- How governance enables trusted and explainable AI
- A pragmatic path from experimentation to production
AI Ambition vs Data Reality
Most asset managers already have AI pilots in motion. The challenge is scaling them. Fragmented data, inconsistent definitions, and legacy architectures make AI difficult to trust, govern, and operationalize across the enterprise. Firms that succeed treat data as strategic infrastructure: the foundation for AI adoption.
What Changes with an AI-Ready Data Foundation
When data foundations are modernized:
- AI use cases move from isolated experiments to reusable capabilities
- Governance becomes embedded rather than reactive
- Models become explainable and auditable
- Time to deploy new AI use cases reduces significantly
This is the shift from AI experimentation to enterprise value.
Why DataArt
DataArt works with asset managers to align data platforms, governance, and AI adoption - enabling scalable, trusted AI in asset management rather than disconnected pilots.
