Workspace AI- Natural Language Intelligence for Advisors

Overview

LSEG Workspace launched its first generative AI feature for external pilots — a major milestone in transforming how financial professionals interact with data. NL2Wealth introduces a natural language layer that allows users to discover, visualise, and share insights simply by asking questions in English.

As the Product Manager for NL2Wealth, I led the strategy, and cross-functional alignment behind this new capability, shaping how natural language fits into the broader Workspace ecosystem.

The Problem

Workspace offers rich financial data across multiple apps, screens, and navigation paths — but this power comes with complexity. Advisors often spend significant time locating relevant datasets, switching between tools, and piecing together insights manually.

At the same time, user expectations are shifting.
Traditional click-and-navigate workflows are being replaced by simple, conversational interactions powered by generative AI. Users now expect platforms to understand questions, capture context, and return the right insights instantly.

Natural Language to Wealth was created to bridge this gap.

Solution

NL2Wealth brings a conversational interface into Workspace, enabling users to query the existing Advisor Dashboard within workspace by:

• Ask financial questions in plain English

• Quickly surface relevant information without navigating multiple apps

• Discover insights that previously required multiple steps

• View results in clean, structured formats aligned with Workspace patterns

• Interact with data in a more intuitive, natural way

The experience feels simple on the surface, but it is grounded in thoughtful UX decisions around:

• Context awareness

• Safe, entitlement-aligned responses

• Clear disambiguation prompts

• Consistent output formatting

• Responsive follow-up guidance

My Role

I led the product direction for NL2Wealth across strategy, UX, and cross-team integration:

• Defining the end-to-end natural language experience

• Identifying high-value advisory use cases for initial pilots

• Aligning with AI, engineering, design, and entitlement teams

• Shaping intent structures, context behaviours, and user flows (without exposing model details)

• Ensuring the experience meets Workspace standards for accuracy, safety, and trust

• Partnering with leadership to position this as a cornerstone for future AI experiences

My focus: make natural language the simplest, fastest entry point to Workspace insights.

Impact

Improved data discoverability for advisors and validated the experience through ongoing testing with 500 pilot users - directly addressing one of Workspace’s most persistent customer needs.