AI in financial services is not limited by data or models, but by the absence of a reasoning layer that connects domain knowledge, AI capability, and real world research workflows.
Across financial services, AI experimentation is near-universal. Deployment into production is not. Not because the technology isn't capable, but because something fundamental is missing from the architecture.
That something isn't better data. Clean, curated, governed data is the precondition — the foundation everything else depends on. But data alone doesn't produce trust. The missing piece is the reasoning layer that sits above it: the system that allows AI to operate on data with the logic, semantics, and domain understanding that macroeconomic analysis actually requires. That layer doesn't exist yet. Not in any form that connects the full chain from domain knowledge to AI capability to research workflow.
Closing that gap requires three things working together — not as adjacent workstreams, but as a single connected system:
Domain knowledge embedded into the reasoning layer itself. Structured context that AI can reason over: semantic encoding of what indicators mean, how they relate, where they conflict. Hard guardrails on what AI should not do, because preventing an analytically incorrect combination is as important as enabling a correct one.
AI capability designed around the specific demands of macroeconomic work — not general-purpose intelligence adapted after the fact.
Workflows that reflect how macro experts actually think, not how the system finds it convenient to present information.
Most organisations have one of these. Some have two. Almost nobody has connected all three — because doing so requires fluency across domains that rarely sit in the same room, let alone the same architecture.
And when that connection is missing, the failure mode is not just hallucination. It's something harder to catch: outputs that are plausible, well-formatted, and wrong in ways only a domain expert would recognise. In financial research, that is not a minor inefficiency. It is institutional risk. The foundation is already here. What's missing is the reasoning layer above it. Getting that right requires building alongside the full chain — the researchers who produce the analysis, the leaders who make decisions from it, and the teams who integrate it into the infrastructure of their organisation. Not a new way of working. A better version of the one they already have.
And that is the work I joined Macrobond to do.
