The buy-side trading desk is evolving toward a more unified, multi-asset model. As workflows converge across equities, FX and fixed income, firms are increasingly moving away from siloed systems toward connected, front-to-back architectures that support consistency across the trade lifecycle.
This shift is creating the conditions for AI to play a more meaningful role in trading workflows. Rather than operating in isolated use cases, AI can now be applied more effectively across execution, where aligned infrastructure enables it to work across products and processes.
A key enabler is the emergence of an agentic layer — an AI capability that can interpret trader intent and translate it into structured workflows. By navigating asset-class-specific processes on behalf of the user, AI can help streamline execution and reduce complexity, while keeping the trader in control.
As firms look to modernise, reducing operational fragmentation is becoming a priority. Maintaining separate teams, tools and protocols across asset classes introduces inefficiencies and limits the ability to scale AI capabilities. Moving toward shared workflows and a more consistent architecture helps address this challenge.
The paper explores how trading desks are evolving, including:
- The shift toward asset-agnostic, multi-asset workflows
- The growing role of AI within execution
- How firms are reducing complexity by moving away from siloed architectures
- How workflows are aligning across idea generation, pre-trade, execution and post-trade
How we help across front-to-back workflows
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