Beyond generic AI: Why private markets deserve industry-specific intelligence
By Kumar Ujjwal, Founder and CEO
Most organizations know they need AI. But very few know how to evaluate it.
That’s not a technology problem – it’s a framework problem.
Procurement teams are still approaching AI like it's traditional software, using RFPs built for static tools: cloud specs, interface features, programming language support.
Those questions might help you buy an accounting system. They won’t help you assess intelligence.
Because AI isn’t just infrastructure. It’s adaptive, contextual, and probabilistic. It doesn’t just run your processes – it can learn from them, refine them, and eventually own them.
That requires a different kind of evaluation – one that moves beyond checklists and into capability.
This article offers a modern framework for fund administrators and operational leaders: How to evaluate AI not by what it is, but by what it does – and how far it can take your firm.
1. Domain-specific knowledge
Generic AI can’t reliably interpret fund workflows. It may hallucinate regulatory logic, misclassify financial documents, or misunderstand terms like "LP onboarding" or "NAV." That’s more than inconvenient it’s a liability.
Ask: Is this system trained specifically for fund administration workflows and language?
2. Multi-model orchestration
There is no single LLM that does everything well. The most effective AI systems use a multi-model approach – leveraging best-in-class models for specific tasks: one for classification, one for generation, another for complex calculations.
Ask: Does this platform orchestrate multiple models to handle distinct fund admin functions – or is it confined to one vendor ecosystem?
3. Agentic workflows
A chatbot is not a strategy. True ROI in fund administration comes from autonomous agents that execute full workflows: preparing reports, tracking compliance, generating audit-ready documentation – without constant human input.
Ask: Can this AI run complex, multi-step processes end-to-end, not just respond to prompts?
4. Continuous learning
Traditional software is static – what you buy is what you get. AI should be different. It should improve with usage, user feedback, and data exposure. Otherwise, it's just legacy software with a neural net.
Ask: What’s the system’s approach to learning over time? How does it improve with each workflow and interaction?
5. Implementation methodology
AI isn’t plug-and-play. It must be tailored to the firm's workflows, processes, and goals. A good vendor doesn’t just deploy code – they embed intelligence into your operations.
Ask: What’s your onboarding and tuning process? How long until this AI is fully operational and generating ROI?
From features to capabilities
The firms unlocking real value from AI aren’t asking if it integrates with a chatbot. They’re asking how it helps scale their operations without adding headcount. They’re asking what it can automate today – and how it will learn to handle tomorrow’s tasks.
In short, they’re not buying AI. They’re deploying intelligent systems designed to transform how work gets done.
Let’s evolve the RFP
If your team is evaluating AI this year, now’s the time to shift your mindset.
- Move from static feature checklists to dynamic intelligence assessments.
- Stop evaluating interfaces. Start evaluating outcomes.
- Look beyond vendor demos – ask how the system adapts, scales, and learns.
The firms gaining AI Alpha are the ones who ask the right questions – because they’re not looking for software. They’re investing in capability.