A new analysis from consulting firm AlixPartners suggests the AI-driven “SaaSpocalypse” gripping enterprise software is less a cyclical slowdown and more a structural reset — one that could reshape private-equity portfolios in painful ways.
The firm examined 500 software companies across 12 private-equity portfolios and developed an “AI Disruption Score” to assess which businesses are most exposed to AI and which are relatively insulated. Rather than naming specific companies, AlixPartners ranked subsectors and business models based on two main factors: data and vertical specialization.
Those two “moats,” the firm argued, are emerging as the clearest predictors of resilience as AI threatens to commoditize the traditional software stack. AlixPartners is well-placed to assess such risks because it spotted the potential threat of AI to the software industry a year ago, well before most investors and analysts.
AI Disruption Score
The AlixPartners AI Disruption Score places software companies on a spectrum from 1 to 7, with higher scores indicating greater exposure to AI disruption. Businesses with strong data and vertical industry moats fall into the lowest-risk category, while those lacking both are clustered in the highest-risk tier, where business models face “structural pressure” and potential consolidation.
“Protection from AI disruption is far higher when companies own proprietary data, systems of context, ecosystem leverage, embedded workflows, and operate in regulated or critical domains,” the firm wrote in an exclusive presentation prepared for Business Insider.
The results are stark. Only about 14% of companies analyzed had strong moats across both dimensions, while roughly a quarter had weak defenses on both fronts, leaving them highly vulnerable as AI-native competitors scale rapidly.
Winners and losers
The distinction often comes down to the nature of the software itself.
Highly exposed categories include marketing automation, horizontal productivity tools, CRM add-ons, and analytics platforms — areas where AI can easily replicate features like summarization, reporting, and customer engagement workflows. These are typically “point solutions” with low switching costs and limited proprietary data, making them easier for AI agents to displace.
By contrast, software embedded in regulated or high-stakes environments, such as payments, financial operations, healthcare systems, and cybersecurity, tends to score much lower on disruption risk. These companies benefit from compliance requirements, mission-critical workflows, and years of accumulated proprietary data, all of which create barriers to entry.
“If you’re a large financial institution, fraud detection software is mission-critical, and it’s not something you have any tolerance for error on,” said Jordan Berger, SVP of TMT Market Intelligence at AlixPartners. “These companies are much less likely in these contexts to let agentic AI native challengers run rampant throughout their enterprise ecosystem. So that type of barrier to entry we see as very durable.”
There’s a middle ground, too. Companies in this category include systems-of-record providers across subsectors such as enterprise resource planning, customer relationship management, and IT service management. (A system of record is the main place where a company keeps the official, trusted version of its important data).
Other subsectors in this group include industry-specific SaaS platforms with limited regulation, such as construction software, as well as offerings focused on finance, sales operations, and procurement.
The firm’s framework highlights that not all “systems of record” are equally safe. As the findings show, even enterprise resource planning (ERP) systems, long considered defensible, are only rated as having medium-strength moats. AI agents may reduce the number of human users, and therefore software licenses, while stripping away higher-margin add-ons and interfaces, according to AlixPartners’ Milicevic.
A looming debt wall
There’s a mountain of debt-fueled software investment that could be impacted if generative AI ends up deeply disrupting SaaS business models. These companies had steady subscription-based revenue, which attracted many private-equity buyouts in recent years. Now, there’s a $40 billion debt wall in 2028 that will need to be refinanced, just as AI impacts take hold.
After discussing this looming situation with lenders, AlixPartners expects most lenders will charge PE-backed software companies slightly higher interest rates when it’s time to refinance, maybe 50 basis points more, according to Nenad Milicevic, a partner and managing director at AlixPartners.
If lenders charge indebted software companies true rates that reflect AI disruption risks, that might cause more problems because the higher interest costs could overwhelm some of these businesses, he added.
“If they bring them up to market level, they would probably ask for 400 basis points more, and then it’s over,” Milicevic said in an interview with Business Insider. “And that has a contagion effect on the whole market, so they will be very careful.”
Falling revenue
The broader backdrop amplifies the risk. SaaS companies are simultaneously grappling with a shift away from seat-based pricing toward usage- and outcome-based models, as well as a surge in AI-native competition.
AlixPartners estimates SaaS revenues could decline by up to 15% over the next year and by 25% to 35% over three years in some segments.
The firm ultimately groups companies into four categories: “fortress” businesses with strong moats; “survivors” that must quickly build or acquire AI capabilities; firms likely to be sold to AI-native buyers; and those facing potential wind-down.
The findings drive home a tough message: The era of growth-at-any-price SaaS is ending, and AI is accelerating a divide between a small group of defensible platforms and a much larger pool of exposed assets.
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