AI UAG Roundtable 2026: Insights & Takeaways
Allocators, OCIOs, multi-manager platforms, investment consultancies, and asset administrators compared notes on where AI is actively streamlining operational and investment due diligence, and where the broader questions around governance, cost optimization, and institutional memory still sit.
DiligenceVault hosts the AI User Advisory Group to give allocators and diligence teams a working space to compare notes on how they are applying AI inside their actual workflows. This latest session made one thing clear: the conversation has moved from surface-level feature adoption, such as ad hoc chatbots and basic document summarization, toward structural workflow redesign. As the volume of unstructured manager data continues to multiply, including PPMs, Form ADVs, LPAs, audited financial statements, and ongoing monitoring questionnaires, diligence teams face a persistent constraint: managing expanding coverage mandates without a linear increase in headcount.
AI in diligence is no longer about features. It is about turning firm-specific judgment into governed, scalable operating infrastructure, while keeping humans accountable for risk.
Allocators and diligence teams are moving past ad hoc experimentation. They are capturing implicit human judgment and turning it into repeatable, governed operating models: structured playbooks that codify firm-specific reasoning, automated clerical data extraction, and analytical bandwidth reserved exclusively for high-judgment risk evaluation.
These takeaways are shared as a peer-to-peer resource for participants and the broader institutional community. Where a broader conclusion or forward-looking synthesis is drawn rather than reporting the direct consensus of the room, it is explicitly noted.
- Codify judgment, not just prompts. Allocators are capturing implicit firm playbooks and scaling institutional reasoning into governed digital workflows, not relying on one-off chat interactions.
- Automate the clerical layer, protect the analytical layer. Document extraction and DDQ first-draft generation that historically took hours is being compressed to minutes, shifting analyst time from copy/paste to verification and risk evaluation.
- Hybrid AI architecture is the enterprise standard. Many allocators now have enterprise LLM platforms and are pairing them with vertical AI platforms for structured diligence workflows, audit-ready extraction, and questionnaire automation.
- Source-linked governance builds institutional trust. Because models are probabilistic, every AI-assisted output requires granular audit trails linking back to source text, combined with two-tier human review: one pass for factual accuracy, a second for qualitative risk.
- Data architecture, not model selection, is the bottleneck. As model capabilities converge, the competitive advantage shifts to managing secure connectors, private cloud hosting, enterprise integration, and intelligent cost-aware model routing.
Section 01
Using AI to codify institutional knowledge into digital twins
The shift is straightforward: stop using AI for one-off tasks and start encoding how your firm actually runs diligence into structured, reusable playbooks. A practical example: one participating firm codified its proprietary red-flag criteria and evaluation frameworks into a prompt library, so every DDQ review automatically applies the same standards, regardless of which analyst opens the file. The result is a digital twin of the team's collective expertise, one that preserves institutional memory even as staff turns over.
Webinar Poll Results
Participant perspectives from the roundtable
Three live polls during the session gave the room a chance to see where peers actually stand, and the results reinforced a theme that came up throughout the conversation: most diligence teams are already doing something with AI, but getting from early experiments to a scaled, embedded workflow is harder than it sounds.
Over half the room (56%) said they came specifically to hear how peers are using AI day-to-day, which speaks to a real gap that many teams feel: they know AI can help, but they do not have enough visibility into what good looks like in practice. When asked about the single biggest barrier to deeper adoption, the top answer (35%) was not distrust or governance red tape. It was that teams are already moving and just trying to refine. The next two barriers, team bandwidth (19%) and not knowing which use case to start with (18%), reflect a challenge that comes through clearly in every UAG session: diligence teams are stretched thin, and adding a new tool to a workflow that already runs on tight deadlines requires more than enthusiasm.
The third poll asked what participants would fix first in their workflow if they could. About a third (32%) were not yet sure, which underscores the value of sessions like this one for helping teams frame the right starting point. Among those with a specific answer, first-draft DDQ responses (19%) and extracting insight from dense source documents (16%) topped the list. These are not edge cases. They are the exact bottlenecks that consume the most analyst time and offer the clearest path to measurable time savings.
Poll 1: Q. What brings you to this session today?
Poll 2: Q. What's the biggest thing standing between you and using AI more in diligence?
Poll 3: Q. If you could wave a wand and fix one part of your workflow tomorrow, what would it be?
Section 02
Compressing manual questionnaire intake from hours to minutes
A primary operational challenge raised by allocators and administrators is the sheer volume of qualitative data required for thorough manager reviews. Standard onboarding and monitoring questionnaires often contain over 150 questions, requiring manual cross-referencing across Form ADVs, offering memorandums, compliance manuals, and audited financial statements.
Participants shared that leveraging document intelligence to pre-fill standard frameworks dramatically alters the economics of due diligence.
Manual document searches and data entry that historically required 3 to 5 hours per evaluation are routinely compressed to 15 to 20 minutes.
AI-driven document extraction generates output covering 60% to 75% of standard questionnaires from core offering materials before a human reviewer opens the file.
Analysts start with a structured, sourced draft. Bandwidth shifts toward verifying responses, addressing exceptions, and analyzing qualitative risk.
Compressing this initial intake also allows teams to expand their capacity. Rather than limiting reviews due to time constraints, teams can manage higher volumes and support growing client onboarding pipelines without needing to linearly add headcount.
Section 03
Operationalizing the hybrid enterprise AI model
A recurring theme in the session was how allocators navigate AI deployment across competing corporate priorities. Diligence teams from asset management and OCIO platforms described an emerging hybrid approach to technology architecture: pairing internal enterprise LLM resources with external vertical AI tools such as DV Assist. The goal is straightforward: make AI accessible to every team and embedded into every core process.
Many allocators deploy broad, LLM-agnostic internal platforms, often custom-built or hosted within private cloud environments, to handle generic administrative tasks, draft routine emails, generate call summaries, and suggest background check candidates.
Specialized platforms such as DiligenceVault are used for structured diligence workflows, analyst evaluations, manager questionnaires, custom prompt execution, and audit-ready document extraction.
This dual-track strategy ensures that highly regulated, complex investment data remains structured within a specialized system of record, while broader enterprise AI tools handle general productivity.
Section 04
Institutional governance, two-tier review, and source lineage
As AI tools become more embedded in investment decision-making, governance frameworks need to move from high-level policy statements into active daily practices. The room agreed that AI should function as a capable junior analyst, not an authoritative decision-maker.
Key governance principles highlighted during the session include the following.
Audit trails require every AI-extracted data point to link directly back to the exact page, paragraph, and line of the source document.
A primary analyst verifies factual accuracy and completeness of the AI-generated draft, followed by a secondary reviewer who evaluates qualitative risk and subtle nuances.
External models are hosted within private cloud environments, with contractual guarantees that client data is never used to train underlying models.
Section 05
Data architecture, research management integration, and ongoing monitoring
As model capabilities homogenize across major foundation providers, the critical operational constraint is shifting from model performance to data architecture and workflow integration.
Participants noted that useful diligence insights often remain fragmented across disparate systems, including manager portals, internal drives, third-party research management systems, and portfolio accounting tools. The next frontier involves connecting these environments.
Exporting structured evaluation reports directly into enterprise research management systems, or linking request forms to automated platform workflows.
Using specialized tools to monitor continuous filings, such as annual Form ADV brochure updates, and alert teams to material changes between formal review cycles.
Leveraging secure MCP connectors to allow AI systems to query data across internal drives and diligence repositories simultaneously.
Section 06
Optimizing AI costs through architectural model routing
While initial institutional focus centered on feasibility and speed, participants raised important questions regarding the long-term economics of AI adoption. As usage scales across thousands of colleagues, relying exclusively on expensive frontier models for every basic administrative task becomes cost-prohibitive.
Future-proof technology platforms are designing governance layers that dynamically route tasks based on complexity.
Basic data extraction, document classification, and formatting are routed to lightweight or open-source models hosted in secure private cloud environments.
In-depth qualitative reasoning, discrepancy detection, and complex risk evaluation are reserved for advanced frontier models.
This architectural optimization ensures that institutions capture maximum efficiency gains without facing exponential token costs as usage matures.
DiligenceVault Perspective
AI is not a standalone feature. It is operational infrastructure for scaling institutional expertise.
The primary challenge facing investment teams is rarely a lack of data. It is the fragmentation of data across inconsistent formats and isolated systems. True operational alpha is achieved when structured data, firm-specific playbooks, and secure governance converge.
DV Assist is built to eliminate the clerical burden of manual document reviews and memo generation by extracting data from complex legal and financial filings, including PPMs, LPAs, Form ADVs, and audited financial statements, and pre-filling due diligence questionnaires. Every output remains traceable to its source text to satisfy strict compliance standards.
Prompt libraries are designed to capture your firm's unique evaluation criteria and institutional memory. By standardizing prompts, custom templates, and workflow rules at the firm level, DiligenceVault helps ensure that your team's collective expertise remains permanent, reviewable, and scalable.
Connected architecture is where the industry is heading. Our roadmap emphasizes secure MCP connectors, advanced risk and review agents, and automated workflow triggers, designed to let your team query data across platforms while maintaining control over data privacy and security.
For Allocators
Where to start in the next diligence cycle
- Isolate clerical bottlenecks. Map your team's current diligence lifecycle to separate repetitive data mapping, such as initial DDQ pre-filling and financial statement extraction, from high-judgment risk analysis. Focus initial AI deployment strictly on clerical tasks.
- Establish firm-wide prompt libraries. Audit how individual analysts currently use AI. Transition successful individual prompts into centralized, firm-approved prompt libraries to prevent institutional knowledge loss when staff turns over.
- Enforce source-linked verification. Require all AI-assisted write-ups and evaluation memos to include direct citations back to source documents before entering peer review or investment committees.
For Asset Managers
What allocators may start asking about your own AI governance
- Centralize data repositories. Ensure all core fund documentation, including PPMs, LPAs, audited financial statements, and compliance manuals, is structured and centralized. AI document extraction is only as effective as the underlying document foundation.
- Offer pre-filled questionnaires to speed onboarding. Administrators and investor relations teams can accelerate subadvisor or manager onboarding by leveraging document intelligence to pre-fill standard questionnaires for clients, cutting completion times significantly.
- Prepare for governance-driven diligence. Expect allocators to ask detailed questions regarding how your firm governs AI usage, data permissioning, and security within your own investment and operational processes.
Open Threads
Questions left open
- The analyst pipeline and talent development. If AI absorbs the foundational clerical work historically performed by junior analysts, such as reading dense documents and mapping data, how will institutions intentionally design junior talent development to ensure the next generation builds necessary domain judgment?
- Measuring technology budgets versus headcount. As research capacity scales through technology, how will finance and operational leaders balance traditional personnel compensation budgets against software, infrastructure, and model usage costs over a 3 to 5 year horizon?
- Standardizing AI diligence frameworks. What specific diligence framework should allocators apply when evaluating asset managers who deploy autonomous AI agents or machine learning models directly within their investment strategies?
- Public versus private model deployment. As open-source models approach frontier capabilities, how quickly will institutional security teams transition from private cloud-hosted vendor APIs to fully self-hosted, domain-specific models?
Join the next AI User Advisory Group
Our User Advisory Group and industry roundtables bring together allocators, diligence teams, and asset administrators to compare notes and shape the future of digital diligence.
- Next AI User Advisory Group session: Fall 2026. View event details and register your interest on the DiligenceVault Events page.
- Request a guided trial: Experience how DV Assist can run your firm's actual DDQ templates against dense manager filings to demonstrate verified time savings. Contact us for a demo.
For inquiries, platform roadmaps, or to participate in upcoming design partner programs, contact the DiligenceVault team at ask@diligencevault.com.