As Head of Sales at Raylu I had the opportunity of attending the The Private Markets AI Summit and here are some of my thoughts, insights, and quotes from the event.

From Documents to Intelligence: Private Markets’ AI Shift

Across sessions, one theme kept surfacing: AI is only as valuable as the workflow it supports. Firms are moving from managing documents to building intelligence systems that slot into real processes - with trust, auditability, and outcomes front-and-center.

1) The Challenge Isn’t Building AI - It’s Knowing Where to Use It

The hardest problems are organizational, not technical: picking the right use cases, comparing crowded toolsets, and fitting them into existing processes.

• Evaluation is shifting from feature checklists to workflow fit and trust in outputs.

• Top-down mandates often miss the day-to-day context where AI helps most; bottom-up experiments led by analysts/associates are sticking.

• Mid-level operators who understand both investment and data are becoming change catalysts (and de facto AI trainers).

“Our evaluation criteria begin with mapping data flows and seeing how tools fit our workflow.” - Sean Dobson, Wafra

2) Managing Expectations - What AI Can (and Can’t) Do Today

No one-click IC memo. Helpful automation emerges when firms wire AI into micro-tasks - extraction, summarization, pattern detection - while humans own synthesis and judgment.

• “Prompt engineering” is an operational skill, not a fad.

• Tools aren’t magic; trained users + structured inputs = usable outputs.

“Decision makers need to stop expecting a single button for an IC memo; build the workflows that make lives easier.” - Summit discussion

3) Customization & Process Understanding Are Critical

Generic tools fall short. Every firm’s process has unique data flows, permissions, and decision gates.

• LLMs aren’t trained to think like investors out of the box - contextual input and investment logic must be encoded.

• Winning teams start small (CIM aggregation, pre-populating memos, surfacing diligence blind spots) and customize step-by-step.

• Trust requires access controls, provenance, and audit trails.

“You can’t expect an out-of-the-box tool. Customize it to your investment process - or it stays superficial.” - Summit discussion

4) The Path Forward: AI as Analyst, Not Oracle

Treat AI like a sharp junior analyst: generate hypotheses, summarize evidence, highlight inconsistencies - don’t expect full automation of diligence or sourcing.

• Build systems that simulate the thought process behind an investment.

• Ask AI to identify what’s missing, not to render the verdict.

“Task tools like an analyst - aggregate CIM data, run checks, and add steps until it consistently solves a specific problem.” - Hebbia AI

5) Looking Ahead - From Reading Memos to Creating Super Agents

Routine document review will keep automating. Opinions diverge on headcount impact, but most agree productivity (and expectations) will rise.

• Institutional memory (decades of memos/notes/deals) is an untapped dataset - as it’s digitized and contextualized, it becomes a new form of alpha.

• Agents will evolve from summarizers into context-aware co-workers, maintaining “living documents” that adapt as deals evolve.

“AI can add context to a living, breathing document - and learn from your process.” - Summit discussion
“Earning client trust means showing credible sources; transparency matters as much as answers.” - Sree, Octus

How Firms Are Using AI to Compete, Operate & Win

6) Learning & Adaptation as Core Competencies

The durable advantage isn’t a single tool - it’s tighter feedback loops between data, decision, and outcome.

• Teams are blending human judgment with machine recalibration and hiring engineers into investment teams.

• Infrastructure that compounds learnings from every sourcing and diligence cycle becomes a flywheel.

“Data’s power isn’t just insight - it’s feedback. Make the loop tighter and you don’t get left behind.” - Simon, Founder of WovenLight

7) Guardrails, Change Management & Data Quality

AI without structure is chaos.

• Build guardrails: transparency, data lineage, auditability.

• Garbage in → garbage out; invest in data quality and cultural adoption.

• Senior engagement rises when tools provide clear, verifiable outcomes.

“There’s no silver bullet - real performance comes from the hard yards of change management.” - Summit discussion

8) The “GP Factory” - Automation Meets Trust

Think modern manufacturing: robotics + real-time visibility.

• Expect transparency and hyper-personalization in GP/LP interactions.

• CFOs will want “pizza-tracker” visibility into quarter-end closes - real-time, auditable workflows.

• Consolidation and automation are accelerating across the back office.

“Re-think the factory: outsourcing the wrong foundations undermines trust; make core processes visible and auditable.” - Summit discussion

AI Infrastructure for Platform Needs

Break silos, integrate systems, and make data auditable. Scalable architectures pair stochastic reasoning (LLMs) with deterministic logic (coded workflows), with humans in the loop.

• Partially autonomous UX lets teams imprint firm-specific reasoning.

• Buy vs. build: adopt modular components, then invest in customization to fit your workflow reality.

Closing Takeaways

AI isn’t a product; it’s an evolving capability. Winning firms:

1. Map processes before tools.

2. Embed feedback loops that learn from every decision.

3. Treat data infrastructure as a strategic asset.

4. Start small, iterate fast, and train people alongside models.

5. Customize for your investment logic; don’t expect generic tools to think like your team.

Raylu’s POV: What “Workflow-First AI” Looks Like

At Raylu, we’ve built our platform around these principles:

• Workflow-native agents: treat AI as a junior analyst that reads everything, extracts 200+ standardized signals per company, and flags blind spots - while preserving data lineage.

• Source-grounded answers: every output links back to its sources for trust and auditability.

• Your process, encoded: we customize to your thesis, scoring rubrics, and diligence checkpoints so the system thinks in your firm’s language.

• Living market maps: as new theses emerge, Raylu updates targets, enriches intelligence, and keeps outreach-ready lists current.

If you’re exploring how to turn AI from a demo into durable process advantage, book a demo with Raylu today.