Private Credit Software Diligence: Testing AI Risk

For nearly a decade, private credit funds treated enterprise software as the gold standard of recurring-revenue lending. Predictable subscription renewals, high gross margins, and customer switching costs allowed direct lenders to stretch leverage multiples and accept recurring-revenue covenants with minimal near-term cash generation. Rapid advances in generative AI and workflow automation have fundamentally disrupted that underwriting logic, transforming software portfolios into one of private debt's primary exposure zones.

Direct, non-bank lending to software-as-a-service (SaaS) companies has ballooned from less than $10 billion in 2015 to more than $500 billion in 2025. Business development companies account for one slice of that total: the Bank for International Settlements (BIS) reports that BDCs, which originate about one fifth of all US direct loans, have lent around $115 billion to software firms, roughly a fifth of their total lending and over 80% of their technology portfolios. As seat-based pricing models face disruption and autonomous agents compress engineering cycles, enterprise software borrowers face severe terminal value questions, margin compression, and refinancing bottlenecks.

This concentration has intensified credit scrutiny across direct lenders and Business Development Companies (BDCs). Morgan Stanley warned that default rates in private credit direct lending could climb to around 8%, approaching pandemic-era peaks, with pressure concentrated in sectors vulnerable to AI disruption such as software. Credit committees can no longer rely on top-line Annual Recurring Revenue (ARR) growth alone when underwriting private credit software risk; they must rigorously test debt service durability under aggressive disruption scenarios.

• Massive capital concentration: Software borrowers account for more than $500 billion of total direct lending volume, creating systemic vulnerability to tech sector repricing.
• Default rate expansion: Direct lending defaults are projected by Morgan Stanley to approach 8% as legacy software business models face disruption.
• Refinancing wall: Loans underwritten at peak 2021-2022 valuations face maturity walls where takeout capital depends on proven AI defensibility.

Traditional software credit underwriting relied heavily on ARR run-rate metrics and Net Revenue Retention (NRR) benchmarks. Under AI disruption, these top-line metrics often obscure underlying cash-flow erosion. Direct lenders must implement a multi-layered financial framework that bridges contractually reported ARR to realized cash generation and audits EBITDA durability.

A key vulnerability in software credit is the divergence between contracted ARR and GAAP revenue. When software borrowers face increased discounting, shorter contract commitments, or seat utilization contraction, top-line ARR figures frequently overstate debtor health. Lenders must evaluate the durability of the revenue base by isolating expansion driven by genuine usage from defensive discounting designed to mask churn.

Furthermore, lenders must scrutinize the translation of gross margins to cash flow. AI-driven features often introduce substantial third-party inference compute costs and API fees that sit in cost of goods sold (COGS) rather than in fixed research and development. Benchmark work on AI economics puts compute-heavy AI gross margins at roughly 50% to 60%, a full band below the 60% to 80% or higher standard that defined classic software businesses. Simultaneously, private equity sponsors frequently utilize aggressive pro-forma EBITDA add-backs, such as software capitalization, restructuring adjustments, and speculative go-to-market cost synergies, to inflate covenant headroom.

• ARR to GAAP Bridge: Reconcile reported ARR against deferred revenue, billing schedules, and actual cash receipts to identify unearned or uncollectible commitments.
• Gross Margin Stress-Testing: Quantify the ongoing compute, model inference, and infrastructure costs required to sustain AI capabilities within product lines.
• EBITDA Add-Back Scrutiny: Eliminate non-recurring add-backs, capitalized research and development expenses, and aggressive synergy credits to reveal true unadjusted debt-service capability.
• Cohorted Net Retention Audits: Separate seat expansion from price increases, evaluating logo retention across historical enterprise cohorts to detect latent contract down-sizing.

By establishing a rigorous bridge between top-line ARR durability and unadjusted free cash flow, credit teams can separate genuinely sticky workflow platforms from commoditized software wrappers.

When underwriting new software facilities or evaluating portfolio amendments, direct lenders and credit committees focus their diligence on covenant package design, downside liquidity runways, and structural trigger mechanisms. The objective is to secure early-warning governance rights long before enterprise value erodes past the debt attachment point.

A primary area of negotiation is the covenant transition mechanism, commonly known as the covenant flip. In growth-oriented software buyouts, loans frequently originate with recurring-revenue covenants (such as minimum ARR thresholds or maximum debt-to-ARR ratios). Lenders are increasingly shortening the recurring-revenue covenant runway, requiring borrowers to transition to strict debt-to-EBITDA or minimum fixed-charge coverage ratios within 18 to 24 months.

Lenders are also closely monitoring shadow defaults. Rather than experiencing sudden covenant breaches, distressed software borrowers frequently exhibit gradual liquidity degradation, prompting sponsors to negotiate maturity extensions, covenant holidays, or payment-in-kind (PIK) interest elections. PIK elections allow borrowers to preserve cash by capitalizing interest into the principal balance, but they also signal underlying operational stress and inflate overall debt burden.

• Liquidity runway under zero-growth scenarios: Testing how many quarters of fixed debt service the borrower can withstand if net new ARR drops to zero and customer renewals deteriorate materially.
• Covenant flip timing and headroom: Setting tighter step-downs on debt-to-ARR covenants and establishing hard operational EBITDA hurdles before allowing transition.
• PIK toggle restrictions: Restricting sponsor options to elect PIK interest by tying capitalization rights to minimum liquidity floors and mandatory sponsor equity cures.
• Minimum cash and liquidity triggers: Implementing absolute dollar liquidity covenants that trigger board observation rights and mandatory weekly cash forecasting upon breach.

Direct lenders, BDC managers, and credit committees now demand far deeper operational disclosures from software borrowers and their financial sponsors. An analysis of 155 public and private BDC portfolios revealed that software companies comprise almost 30% of investment cost and fair value, highlighting the critical importance of transparent portfolio monitoring.

To secure favorable debt terms or covenant relief, borrowers must clearly demonstrate structural defensibility against AI displacement. Credit analysts categorize software assets along a clear spectrum: vulnerable horizontal point solutions (such as basic copy generation, commoditized analytics, or generic CRM add-ons) versus deeply embedded, regulated vertical SaaS systems of record that manage mission-critical operational workflows and proprietary institutional data.

Borrowers are also required to substantiate the depth of private equity sponsor commitment. Direct lenders assess whether the financial sponsor maintains adequate dry powder in the specific fund vehicle and whether the sponsor has demonstrated a willingness to provide defensive equity injections or subordinate debt to support the borrower through multi-year technology transitions.

• Workflow embeddedness proof: Verification that the software serves as the primary system of record with deep two-way API integrations into core enterprise architecture.
• Proprietary domain data moats: Demonstration of unique historical data assets that enable differentiated AI automation without reliance on commoditized public foundation models.
• Sponsor support track record: Formal documentation of fund lifecycle reserves, cross-portfolio equity cure commitments, and historical capital support in distressed scenarios.
• Detailed pipeline conversion metrics: Granular quarterly pipeline tracking showing win/loss ratios against both legacy software competitors and emerging AI-native startups.

Credit underwriters and restructuring analysts should utilize a standardized matrix to identify early signals of technology obsolescence, revenue decay, and liquidity strain. Tracking these indicators allows deal teams to intervene before enterprise value falls below debt recovery thresholds.

Regular tracking of these indicators enables credit teams to maintain dynamic portfolio risk scores and automate warning alerts across their entire direct lending exposure.

Conducting thorough software covenant due diligence requires access to structured operational and technical documentation within the virtual data room. Lenders should not rely solely on summary management presentations; they must review underlying operational data to verify contract durability and technology defensibility.

A comprehensive credit review combines source code evaluation, customer cohort reconciliations, vendor cost tracking, and internal governance audits. Integrating a detailed technical due diligence process ensures credit committees quantify both commercial viability and underlying technical liability.

• Full Customer Contract Schedule: Master services agreements, renewal schedules, termination-for-convenience clauses, and minimum volume commitments across the top 50 accounts.
• Cohort Churn & Downsell Logs: Historical gross and net retention tables segmented by customer size, vertical industry, and product tier over a minimum 12-quarter horizon.
• Third-Party Infrastructure & Model Agreements: Detailed hosting, cloud infrastructure, and AI model API billing records to audit gross margin durability and vendor lock-in.
• Software Architecture Documentation: Independent code audit reports, security review findings, technical debt logs, and multi-year product roadmap milestone tracking.
• Cash Flow & Working Capital Bridge: Monthly reconciliations connecting billed ARR, unearned revenue balances, capitalized R&D expenditures, and cash debt service payments.
• Sponsor Fund Financial Health: Fund vintage, remaining uncalled capital commitments, cross-guarantee provisions, and co-lender syndication terms.

Private credit funds, restructuring advisors, and BDC investment committees must modernize their underwriting and monitoring playbooks to protect portfolio returns against accelerating software disruption. Evaluating software borrowers requires continuous cross-referencing between financial models, customer contracts, and technology architectures.

Credit teams should integrate standardized disruption stress-tests into their quarterly credit committee reviews. By modeling severe contract down-sizing scenarios and auditing gross margins against rising compute costs, direct lenders can proactively structure bespoke covenant cushions, establish clear equity cure parameters, and protect recovery values.

How Plausity supports the workflow

Plausity provides an end-to-end AI-powered due diligence platform designed specifically for private equity deal teams, direct lenders, and M&A advisory professionals. Rather than manually parsing disparate data rooms, credit teams use the platform to extract critical risk signals and accelerate decision-making.

Using automated data room ingestion, investment professionals can process thousands of complex documents, including credit agreements, customer master service agreements, and technical reports, within minutes. The core AI analysis engine cross-references contractual terms with financial model projections to detect discrepancies, hidden termination rights, and unhedged compute cost escalations.

Through automated risk scanning, lenders surface, categorize, and score credit risks based on materiality and debt-service impact. The findings and risk intelligence layer then maintains complete source traceability, allowing investment committees to trace every risk finding and covenant trigger directly back to source documents in the virtual data room.

Plausity is an AI-native due diligence and deal intelligence platform. For private credit teams underwriting software borrowers under AI disruption pressure, Plausity helps convert scattered ARR schedules, cohort retention data, covenant models, and sponsor-support letters into a structured, source-backed evidence base that stays traceable through the credit committee.

Lenders can use AI-powered diligence analysis to cross-reference revenue durability, churn sensitivity, and EBITDA quality against the sponsor case, then organize downside triggers and covenant-design risks with findings and risk intelligence before amending or extending debt. Plausity is a document-and-workflow layer, not a substitute for professional judgement: it does not independently provide legal, financial, tax, commercial, or technical advice, and it does not guarantee investor decisions, valuations, or diligence outcomes.

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