FactSet enabling auditable AI workflows
SVP of Technology & Product Strategy at FactSet on driving trust through auditability
AI is turning premium financial analysis from a seat based product into a workflow that can be rented by the question. For smaller wealth firms, RIAs, and emerging managers, that matters because they no longer need a full team trained on complex terminal menus to screen companies, pull comps, draft commentary, or search filings. They can ask in plain language, get source linked outputs, and use APIs or packaged data inside their own tools.
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FactSet has been building toward this with Mercury, Conversational API, GenAI Data Packages, AWS Data Exchange, Snowflake delivery, and the Open:FactSet Marketplace. The common thread is simpler access to the same underlying data and analytics without forcing every user into the full workstation workflow.
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The competitive shift is from who owns the terminal to who can turn proprietary data into auditable answers inside everyday workflows. AlphaSense pushes further into document search and insight extraction, while FactSet remains strongest where structured estimates, models, market data, and Excel or PowerPoint output matter.
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This widens the addressable market below the largest institutions. A junior advisor, boutique bank, or lean buy side team can now do first pass research, build draft materials, and search internal notes with much less labor, which lowers both software friction and staffing requirements.
Over the next few years, the winners in investment data will be the platforms that package trusted data for both humans and AI agents. That pushes FactSet toward becoming an infrastructure layer for wealth, banking, and buy side workflows, with value moving from raw access toward embedded, auditable decision support inside the tools firms already use.