How Bloomberg is Using Agentic AI for Complex Workflows

2026-04-21 · Source: AI Magazine · Field: Finance & Economics — Capital Markets & Investment Management, FinTech & Digital Financial Services · Depth: Intermediate, short

Summary

Bloomberg has updated its ASKB conversational AI interface on the Bloomberg Terminal, transforming it into an "agentic AI engine" for institutional intelligence. This enhancement, announced at the AI in Finance Summit, integrates Bloomberg's extensive market data with a firm's proprietary knowledge to optimize investment processes. The system now allows clients to apply AI agents directly within existing financial analysis and portfolio management tools, automating workflows and uncovering deeper insights. Key integrations include Portfolio & Risk Analytics ({PORT}), Research Management Solutions (RMS Enterprise), Alternative Data ({ALTD}), and Expert Intelligence, enabling users to analyze security lists, create research content, synthesize alternative data signals, and access expert transcripts. ASKB Workflows also allow for automation of multi-step research tasks and sharing of BQL formulas, supporting various asset classes and use cases across the investment process.

Key takeaway

For AI Product Managers developing financial tools, your focus should be on creating agentic AI systems that seamlessly integrate external market intelligence with internal proprietary data. This approach, exemplified by Bloomberg's ASKB, allows for automated workflows, deeper insights, and enhanced decision-making, ultimately driving greater value from existing data subscriptions and improving user experience across diverse financial functions.

Key insights

Bloomberg's agentic AI integrates market data with proprietary firm knowledge to enhance financial analysis and investment workflows.

Principles

• Synthesize external market trends with internal data.
• Automate multi-step research tasks for efficiency.

Method

ASKB transitions from a search tool to an integrated agentic AI engine, coordinating Bloomberg's AI agents to synthesize market data with proprietary firm knowledge across various Terminal functions.

In practice

• Query personal security lists for news and data analysis.
• Analyze team research content and proprietary financial models.
• Automate customized morning briefs and share workflows.

Topics

• Agentic AI
• Bloomberg Terminal
• ASKB
• Investment Process Optimization
• Financial Market Data

Best for: Executive, AI Product Manager, Data Scientist, Consultant, Domain Expert

Related on AIssential

• How We Built a Finance AI Agent That Processes 2,000 Invoices a Day (Architecture Inside) — A multi-agent LLM architecture can automate high-volume financial document processing with high accuracy and speed.
• The Bloomberg Terminal Is Getting an AI Makeover, Like It or Not — The Bloomberg Terminal's complex interface fosters user devotion and signifies professional expertise in finance.
• Introducing Ask Ad Manager, the AI agent that will help you get more done — Ask Ad Manager, an AI agent built with Gemini, streamlines ad operations for publishers through conversational troubleshooting and reporting.
• What A Time For Legal AI! + Legal Innovators California — The legal AI market is rapidly expanding with new agentic AI solutions enhancing drafting, workflow, and contract management.
• Improve Your Agentic AI Trading With a Great Data Pipeline — High-quality, diverse data pipelines are crucial for successful agentic AI trading, outweighing model sophistication.
• Alibaba Unveils Enterprise Agent Tool — Alibaba's Accio Work offers SMBs a no-code, agentic AI team to automate complex international trade processes.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by AI Magazine.