Investment Research Spend Stalls Despite Rising AI Demand

What happened

New analysis from Substantive Research shows that spending on investment research has stagnated across asset managers and banks, even as demand grows for AI-powered research platforms, analytics and data tools. The finding points to a widening gap between how much firms want to use artificial intelligence in their research workflows and how much budget they are actually prepared to commit.

According to the research, buy-side and sell-side firms are increasingly exploring AI-driven research and data capabilities, yet overall research budgets have not grown to match that appetite. The result is a market where expectations for AI-enabled insight are rising faster than the financial commitment needed to deliver it.

Why it matters

For an industry built on information advantage, this is a telling signal about how AI adoption actually unfolds inside large, regulated organisations. Enthusiasm for AI tools is rarely the constraint — budget discipline, procurement cycles and legacy cost structures are. Asset managers and banks may want smarter, faster, more automated research, but existing spend is often locked into established providers, platforms and relationships that are slow to unwind.

This matters for anyone selling AI-enabled services into financial institutions, and for digital transformation leaders more broadly: pent-up demand for AI capability does not automatically translate into new budget. Firms are more likely to expect AI value to be absorbed within flat or shrinking cost envelopes than to fund it with fresh investment.

The Renascence take

The research-spend gap says less about AI's capability and more about how buying behaviour actually works inside large institutions.

Most commentary assumes AI adoption is gated by technology readiness. This story suggests the real gate is procurement psychology: once a budget line is fixed, decision-makers anchor to it regardless of new capability on offer, and "doing more with the same spend" becomes the default expectation rather than a negotiating position. Vendors and internal transformation teams selling AI-driven research tools should stop pitching incremental capability and start pitching budget-neutral substitution — showing exactly what existing spend gets cut or reallocated to fund the new tool. Anyone ignoring that anchoring effect will keep mistaking stated interest in AI for a genuine, fundable buying signal.