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What Mature Asset Managers Are Actually Doing with Data, AI, and Digital Strategy (And What That Says About You)

Picture of Jason Montague By   ·  2 minute read

We talk to an enormous variety of asset managers every week—alternatives, long-only, boutiques, global giants—and here’s the truth: 

  • Everyone’s “modernizing their data strategy.” 
  • Everyone’s “exploring AI.” 
  • And most have Snowflake… somewhere. 

But only a subset of firms are pulling it all together—in a way that’s meaningful, aligned to how they make money, and built to scale.  Here’s what those mature firms are actually doing, broken down by firm type. 


Small to Mid-Sized Managers (<$50B AUM) 

Mostly focused strategies: SMAs, mutual funds, ETFs, or private credit. 

1. Digital strategy = automation + leverage. 

They aren’t building massive data science teams—they’re automating lower value tasks: 

  • Fund reporting automation (PDF parsing, Excel-to-database ingestion) 
  • AI-assisted analyst workflows (e.g., memo summarization, news classification) 
  • CRM and client data unification to personalize outreach 
2. Their Snowflake use case is focused. 

These firms treat Snowflake like a clean, central data lakehouse, not a pet project. 

  • EDM-lite: Reference data + position data + benchmark data = golden records 
  • Quick reporting wins for investor ops and compliance 
  • Plugged into Sigma or Power BI for end-user access 
3. They buy TIME, not tools. 

No boiling the ocean. They pick 1–2 domains (like client reporting or investment research) and go deep with a partner who brings the talent and IP to move fast. 


Mid to Large Managers ($50B–$500B AUM) 

Multi-strategy, global footprint, more complex operations, and investor types. 

1. Data is treated like a product. 

They’ve moved from “building a data platform” to managing data as a set of products: 

  • Exposure analytics 
  • ESG data aggregation 
  • Client servicing data marts

Each with a domain owner, SLA, and roadmap. 

2. AI is embedded into real workflows. 

Not just pilots—in-production tools built around: 

  • Deal memo review for PE/credit 
  • Portfolio monitoring (entity risk, market sentiment) 
  • Auto-tagging and enrichment of documents and emails
  • NLP over call transcripts for sentiment trends 
3. Snowflake is the platform, not a component. 

This is where firms: 

  • Centralize security master, position, risk, and client data 
  • Build data contracts across domains 
  • Extend into AI/ML with Snowpark or external LLMs that come to the data 
  • They industrialize self-service. 

Tools like Sigma sit on top of governed models, enabling PMs, client teams, and compliance users to get what they need—without ticket queues. 

 

Alternatives Powerhouses / Complex Multi-Manager Models 

Think PE, private credit, hedge fund platforms, and fund-of-funds. 

  1. Intelligence is a competitive advantage.
    These firms live and die by: 
  • Proprietary deal flow and the speed of underwriting 
  • Real-time risk and performance visibility 
  • Sophisticated client mandates and reporting 

Their digital strategy is surgical: 

  • AI-powered diligence (summarizing CIMs, scraping web/private data, entity linking) 
  • Real-time dashboards from portfolio companies or underlying funds 
  • Deep integrations with external vendors (e.g., iLevel, Burgiss, DealCloud) and unstructured data ingestion pipelines 

2. Snowflake becomes an intelligence engine.

It’s the staging ground for:

  • Entity resolution and tagging across sources 
  • Real-time KPI rollups across illiquid portfolios 
  • Secure, governed collaboration between ops, IR, and deal teams 

  1. Digital strategy is owned at the C-level.
These firms often have a Chief Data Officer or Chief Digital Officer with direct accountability to the CEO or CIO—because data isn’t back-office, it’s strategic. 

Common Traits Across the Best Firms 

No matter the firm size or strategy, the leaders share a few key traits:   

  1. They start with business workflows. Not “data for data’s sake.” 
  2. They staff lean, but with high-leverage roles. Data product managers, not armies of analysts. 
  3. They don’t chase perfect—they deliver repeatability. MVPs that scale. 
  4. They treat AI seriously— they have been learning by doing for some time. 
  5. They demand ROI in months or quarters, not years. 

Where Are You? 

Ask yourself: 

  • Do we have a clear inventory of our core data products? 
  • Can our PMs or IR teams answer complex questions without sending an email to IT? 
  • Are our AI efforts plugged into real workflows—or just experiments? 
  • Is our Snowflake instance generating actual business leverage? 

If not, that’s a signal—not a failure. But it’s time to move from adopting technology to executing strategy.  That’s the real difference between “exploring” and “maturing.”