Investment Research Budgets Flatline Despite AI Push

What happened

Research and market data analytics firm Substantive Research has published new survey findings showing that investment research budgets remain largely static even as asset managers and banks face mounting pressure to fund AI-driven research tools and data requirements. The survey indicates that spending on investment research has effectively flatlined, despite the sector's growing appetite for AI capabilities that promise to reshape how research is sourced, synthesised and priced.

According to Substantive Research, this stagnation in spend sits awkwardly alongside rising demand from research consumers for AI-enabled discovery and analytics tools — creating a gap between what buy-side and sell-side firms want to do with AI and what their existing budgets will actually support.

Why it matters

The finding points to a wider tension now facing financial services and other data-intensive industries: AI adoption is accelerating expectations faster than budgets are adjusting to pay for it. Firms are being asked to absorb new AI-driven research and data tools within cost structures built for a pre-AI research model, forcing hard choices about which providers, formats or legacy subscriptions to cut.

For technology and transformation leaders, this is a cautionary signal rather than a straightforward AI success story. It suggests that AI's promised efficiency gains in research and analytics are not yet translating into expanded willingness to spend — and that providers of research, market data and analytics services will need to demonstrate clear, measurable value from AI features if they want budgets to move at all.

The Renascence take

Flat spend alongside rising AI expectation is a classic value-perception problem, not simply a cost one.

Budgets rarely expand just because a capability becomes technically possible — they expand when buyers can see, in their own workflow, that a new tool measurably saves time or improves a decision. Research providers racing to bolt AI features onto existing products risk adding cost without shifting perceived value, which is exactly how spend stays flat while expectations rise. The firms that break this stalemate won't be the ones with the most AI features; they'll be the ones that redesign the research experience end-to-end so the AI's value is immediately legible to the analyst or portfolio manager actually using it — and priced accordingly.