AI Erosion Hides Behind Healthy SaaS Metrics

For years, lenders relied on recurring revenue and high customer retention as reliable signs of a software company’s health. However, the rise of artificial intelligence is breaking that correlation. A SaaS borrower can still report strong growth and retention rates while its long-term competitive position quietly erodes due to AI-native competitors and platform bundling. This disconnect creates a significant blind spot in how credit quality is assessed, as the traditional metrics no longer tell the full story of a company’s future viability.

The stakes are high because private credit has substantial exposure to this sector. According to a March 2026 analysis by the Bank for International Settlements, outstanding private-credit loans to SaaS companies exceeded $500 billion by the end of 2025. This represents approximately 19% of all direct loans in the private credit market. Lenders are now forced to look beyond immediate financial pressure to determine whether a company’s product and pricing model are becoming structurally less defensible before these issues show up in standard credit reports.

Two Lenses Reveal Hidden Vulnerabilities

To address this gap, FTI Consulting analyzed 133 publicly traded enterprise software companies using a dual-lens approach. The first lens evaluates observable financial performance, including revenue growth, customer retention, profitability, cash generation, and leverage. The second lens assesses AI disruption risk, focusing on product substitution, revenue model resilience, AI delivery capability, and competitive defensibility. Each area is scored on a scale from one to four, with higher scores indicating greater risk. This method allows analysts to separate companies with operational challenges from those facing structural threats.

The resulting matrix groups companies into four distinct risk profiles. Some firms show weak financials but low AI risk, suggesting their issues are operational or cyclical rather than structural. Others remain financially healthy but exhibit elevated exposure to future pricing and retention pressure, a category labeled as latent AI risk. The most concerning group faces dual risk, where financial stress and AI exposure coincide, creating reinforcing pressure on growth, margins, and liquidity. This distinction is critical because it helps lenders prioritize action based on the nature of the threat rather than just its current visibility.

Defensibility Varies Across Software Sectors

AI risk is not distributed uniformly across the software industry. The analysis reveals that some sectors are significantly more vulnerable than others. Cybersecurity and vertical market software show lower average AI risk scores, often around 1.3 to 1.5, compared to higher scores for customer relationship management and infrastructure software. This gap reflects qualitative differences in defensibility, such as mission-critical workflows, specialized data requirements, and deep integration costs that make switching difficult for customers.

For example, in cybersecurity, a bundled AI feature from a competitor may not displace an incumbent provider if the customer requires proven performance, regulatory compliance, and rapid threat response. Similarly, vertical market software that serves as a core system of record for specific industries benefits from high switching costs. These factors create a buffer against AI-driven disruption, whereas products with lower barriers to entry and less specialized data are more likely to suffer from rapid substitution and pricing pressure.

Lenders Must Prioritize Structural Indicators

The key takeaway for investors and lenders is that financial stress and AI-driven structural impairment are distinct phenomena. A company can be financially stressed due to balance sheet issues while remaining structurally sound, or it can be financially healthy while facing severe structural erosion. The latter is more dangerous because the damage may not be visible in traditional credit metrics until it is too late. Lenders must therefore incorporate AI disruption risk into their underwriting processes to identify where competitive erosion is emerging.

As reported by GN technics/ai (en-US), this shift requires a change in how credit is evaluated. Instead of relying solely on historical performance, lenders need to assess the resilience of a company’s product and pricing model in an AI-driven market. By distinguishing between operational stress and structural impairment, lenders can better allocate capital and manage risk in a rapidly evolving software landscape.