The private market AI readiness audit: How prepared is your underwriting team?

Modernizing your underwriting process requires shifting philosophy away from analyst-powered workflows to analyst-verified outputs. 

By 2026, firms require fully agentic underwriting workflows capable of reasoning over complex financial logic with the rigor of an experienced VP. This readiness audit diagnoses your bottlenecks and maps a 90-day trajectory toward capturing a competitive advantage: deep, defensible investment judgment at scale.

The AI underwriting readiness audit: 5 key workflow questions

Assess your current posture across these five critical dimensions to determine where your firm sits on the AI maturity curve.

1. Document ingestion: How do you handle an incoming VDR from an applicant?

Your firm receives a ZIP file containing 200+ mismatched PDFs, scanned tax returns, and broken Excel models. 

• Manual: Analysts manually rename, sort, and triage data room files, often renaming company_model_v2.6(1)(copy) to Management Operating Model FY22-27 just to organize the directory.
• Assisted: Basic OCR can scrape text from templated documents like consumer tax returns, but for unstructured private market deals, analysts have to spend hours verifying layouts and moving files into internal folders.
• Agentic: An intelligent ingestion engine automatically classifies every document by content and context, instantly distinguishing between final audits, draft interim statements, and missing checklists.

2. Financial spreading: How is data mapped into your proprietary in-house model?

You need to normalize unique borrower line items into your firm’s standardized chart of accounts to perform portfolio-wide analysis.

• Manual: Data is hand-keyed from PDFs into Excel, taking up to 75% of an analyst's time and leaving room for error.
• Static extraction: Extraction tools pull numbers into CSVs, but they are formula blind and fail to understand how cells are mathematically linked.
• Deterministic: Specialized agents trace formula chains and reconstruct global cash flows, ensuring every output is mathematically sound, linked, and auditable.

3. Investment Committee (IC) preparation: What happens to the memo when the MD requests a sensitivity change?

The bridge between your Excel model and your Word document is often the step most prone to version-control errors.

• Static: Analysts copy-paste screenshots of tables from Excel into Word, restarting the process every time a borrower sends a revised model.
• Templated: Standard Word templates are used, but data must still be manually updated, and citations are handled via manual footnotes.
• Dynamic: IC memos are live extensions of the spread; when a normalization mapping changes, every chart, table, and narrative in the memo updates automatically with linked citations.

4. Risk identification: How do analysts uncover hidden risks in footnotes of a credit agreement?

Critical risks, such as tenant rollover spikes or change-of-control clauses, are often buried in dense, non-financial documents.

• Selective: Analysts skim long documents for red flags, often missing nuances in deep footnotes due to time constraints.
• Keyword search: Control+F is the primary tool for finding terms, but it does not account for context or implied risks.
• Contextual: AI reads every line of every contract to surface risk-relevant details (e.g., tenant rollover, change-of-control) and links them back to specific clauses. With an agentic platform, you can assess risks and investigate them in real time against dynamic market data.

5. Institutional memory: How do you reference EBITDA adjustments used in a similar deal last year?

Deal history is often siloed in old memos and personal hard drives, making benchmarking nearly impossible.

• Siloed: Deal history is scattered across old memos, making it impossible for analysts to compare new borrowers against past performance.
• Centralized storage: Deals are stored on a shared drive, but finding comparable covenant sets or EBITDA adjustments remains manual.
• Indexed: The platform indexes the firm’s entire corpus of historical work, allowing for instant benchmarking of new deals against past portfolio successes.

Reflecting on your audit results

After completing the underwriting workflow audit questions in our previous guide, it is time to perform a gap analysis. This isn't just about identifying what you lack; it’s about understanding the specific risk your firm carries by maintaining the status quo.

Your workflows are still being executed manually

• What it means: Your analysts are likely spending 75% of their time on low-leverage data entry. This creates a blind spot, as the team is too exhausted by the spreading process to conduct any detailed second-order analysis.
• Strategic risk: You are at high risk of losing bids. In 2026, if it takes multiple days to issue a term sheet while a competitor using AI for their analysis takes 24 hours, you’ll lose out on the best deals.

Analysts are being assisted with modern templated tools

• What it means: You are likely using OCR or basic templates, but your workflow is still fragmented. You likely export data to a CSV file, then manually transfer it into your proprietary model.
• Strategic risk: Analysts still spend hours manually verifying AI outputs because the system isn't deterministic. You have speed, but you lack the source traceability required to defend a deal at an IC meeting.

Your firm uses agentic AI to accelerate underwriting decisions

• What it means: You have decoupled deal volume from headcount. Your team is likely acting as verifiers or reviewers rather than executing the tedious underwriting tasks themselves. 
• Strategic advantage: Your firm can maintain a living model of the portfolio. Because your private credit underwriting process is dynamic, you can run sensitivity scenarios across your entire portfolio instantly if macro conditions change.

How a fully agentic platform elevates the role of your analysts

A critical value-add of agentic systems is to define where the AI's "agency" ends, and an analyst's judgment begins.

F2’s 90-day implementation roadmap

Transitioning to an AI workflow is an iterative process. With F2, a successful rollout typically occurs over three months:

Day 1–30: Identifying workflows and training on precedent materials 

• Pre-rollout (t-minus 2 weeks): Establish a cross-functional team (IT, Legal, Sponsors) to authorize API connections and define governance policies.
• Preference population: F2 ingests your firm’s historical IC memos to replicate your internal tone, risk priorities, and formatting guardrails.
• User kickoff: Week 1 focuses on basic ingestion and report generation; Week 2 moves to building models using the agentic reasoning engine.

Day 31–60: End-to-end workflow integration and collaboration

• Model building: Analysts move from validating spreads to generating long-form memos that directly reference their live data.
• Collaboration: Teams use shared workspaces to audit outputs and ask the AI agent to investigate specific management claims in real-time.

Day 61–90+: Strategic optimization

• Customer check-ins: Ongoing "office hours" help analysts customize org-level prompts for bespoke deal types.
• Adoption benchmarking: Senior leaders review utilization data to ensure the firm is capturing material time reduction and improved accuracy in underwriting workflows.

Operationalizing AI: The path forward

Operationalizing AI isn't just about faster spreads; it's about building the institutional intelligence required to lead the next cycle of your firm’s growth. Firms that embrace this agentic future today are not just saving hours — they are empowering their teams to focus on the high-impact judgment that defines a top-tier investor.