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The Financial Times recently asked whether AI could help identify skill in fund managers. The deeper opportunity, however, is not just identifying skill but embedding intelligence across the entire multi-manager lifecycle — from research to pricing, from portfolio construction to performance attribution.

Key Points

1. From Research to Evidence-Based Pricing

In a multi-manager environment, research teams drown in unstructured data: manager notes, due diligence reports, market context, and historical performance metrics.

AI can standardise this toolchain — extracting key factors from past reports, summarising narratives, and running elasticity simulations to test fee sensitivity.

→ Outcome: faster, more consistent research output, and pricing anchored in data rather than negotiation intuition.

2. AI as a Meta-Analyst

Beyond automating notes, AI can learn what signals historically preceded outperformance — qualitative (team stability, philosophy coherence) and quantitative (alpha persistence, drawdown patterns).

This turns the research platform into a meta-analyst that detects emerging skill or risk decay earlier than periodic reviews.

3. Feedback Loops Across the Lifecycle

Each phase — research, selection, monitoring, and pricing — generates intelligence that improves the next.

By closing the loop, AI shifts due diligence from a static report into a living system of skill inference.

→ Example: a manager flagged for inconsistent factor exposures automatically triggers a note update and repricing scenario.

4. Amplifying Human Judgment

AI doesn’t replace manager selection. It gives human committees augmented memory, speed, and analytical reach — ensuring that intuition operates on complete, standardised evidence.

5. Tangible Metrics of Success

In the Gemmo model, success looks like:

  • 2× faster note drafting
  • A single source of truth for the manager pipeline
  • Pricing decisions supported by elasticity simulations and benchmark data
  • Continuous AI-driven skill scoring from live performance feeds

Closing Thought

Where the FT article sees AI as a diagnostic of fund-manager skill, the multi-manager world can go further: AI as an orchestration layer that connects research, selection, and pricing into one adaptive system.
That’s not about predicting bubbles — it’s about building resilience and evidence into how asset owners allocate capital in the first place.

 

 

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