top of page

AI Now Reads Between the Lines—And Its Reshaping Manager Discovery 

  • Brent Watters
  • Jun 24
  • 2 min read

A quiet revolution is underway—powered by AI—and it's transforming how institutional consultant database searches happen, who gets discovered, and what "visibility" really means. At the center of this shift is something surprisingly simple: plain-language search. 

From Checkboxes to Conversational Queries 


In the past, consultants had to navigate complex, structured interfaces to find managers—checking boxes for asset class, region, style, AUM, and more. The results were only as good as the filters and keywords selected. 


Now, thanks to natural language processing (NLP) and machine learning (ML), AI-enhanced platforms allow consultants to type or speak searches the way they naturally think. 

A consultant might now ask: “Show me global equity managers with a low drawdown profile and strong ESG integration.” And instead of a rigid keyword match, AI interprets the intent behind that query, scans multiple structured and unstructured data fields, and delivers a curated shortlist of relevant managers. 


How AI Interprets Plain-Language Search 

Let’s break down what’s really happening behind the scenes with that search example. 

  • “Global equity” → The AI doesn’t just look for that exact term. It pulls from strategy names, asset class tags, benchmarks, and even performance patterns to include strategies that fall under global equity—whether labeled that way or not. 


  • “Low drawdown profile” → This isn’t a checkbox. The AI analyzes submitted performance data for max drawdown, downside capture, standard deviation, and other risk-adjusted metrics to identify managers that fit the risk profile implied. 


  • “Strong ESG integration” → NLP tools analyze strategy descriptions, ESG policy fields, third-party ESG scores, and holdings exposure to determine the level of ESG incorporation. 

This isn’t about ticking boxes—it’s about interpreting data contextually and ranking managers based on alignment with the search’s full meaning. 



Real-World Impact for Managers - Here’s what it looks like in practice: 

Manager A doesn’t use the phrase “low drawdown” in their narrative, but: 

  • Submits detailed risk metrics with a 3-year max drawdown of -7% 

  • Has consistent downside capture below 80% 

  • Clearly articulates ESG integration in their process and portfolio composition 


Manager B, on the other hand: 

  • Submits incomplete risk metrics 

  • Has vague or jargon-heavy ESG language 

  • Updates their data infrequently 

In a plain-language search, Manager A surfaces higher—even if they don’t use the exact buzzwords—because the AI can read between the lines and interpret relevance. 

Staying visible in an AI-curated search environment  

  • You need to go beyond simply reporting performance. If you're still leading with performance data alone, you're likely invisible to today's AI-enhanced consultant databases. 

  • Use clear, precise language in your strategy descriptions 

  • Submit full datasets—especially around risk, ESG, and philosophy 

  • Update regularly to stay relevant in systems that prioritize fresh data 

  • Think like your audience—what would a consultant ask to find a manager like you? 

Bottom Line 

Plain-language search is the future of consultant database navigation. For asset managers, this means that clarity, consistency, and completeness are more critical than ever. If your data can't be understood by a machine trained to think like an investor, you risk being invisible—no matter how strong your performance is. 




Comments

bottom of page