How to make your data useful for AI while maintaining security standards.
Most firms using AI have seen gains in individual productivity, but far fewer have seen it improve the bottom line. In McKinsey’s 2026 global survey, 80% of respondents said AI had improved their own productivity, but only 37% said it had contributed to their organization’s earnings. The leading models are now capable enough for most tasks. What holds most firms back is the readiness of their data.
In our latest white paper, based on a recent webinar with Linedata AI experts Ashmita Gupta and David Boot, we outline what asset managers need to do to make their data useful for AI, and how to control what AI can see and do.
Key Highlights
- Today, context and data integrity matter more than which model you use. When AI tools generate insufficient or incomplete output, it’s typically because they didn’t have access to the right information.
- Shared definitions reduce errors. In one benchmark, accuracy on business questions rose from 16% to 54% when the data came with a description of what its terms meant.
- Documents can become trusted data, provided each extracted value records where it came from and whether a person has checked it.
- You probably do not need a new central data platform. AI can work across the systems where your data already lives.
- Give every AI agent its own identity and only the access or permissions it needs, and keep a person in the loop for every important decision.