Is artificial intelligence (AI) an existential threat for software companies? For specific business models, yes, it is. But not for the entire industry. Far and away, AI is redirecting capital flows and generating new deal activity across emerging subsectors and business models.

Software-as-a-service (SaaS) business models tend to be most susceptible to disruption, particularly where technology was the sole differentiator without underlying data or workflow defensibility. However, middle-market software businesses in particular industries are producing robust deal activity as incumbents in this sector can lean on customer trust and distribution channels while integrating AI.

In light of recent market dynamics, portfolio companies are producing meaningful add-on activity as platform sponsors acquire AI capabilities to bolt into existing go-to-market efforts.

While headlines emphasize AI-driven disruption as an existential threat to the software industry, the reality is more nuanced. To successfully navigate the shifting landscape, sponsors need to:

• See how AI disruption accelerates deals
• Understand the spread between winners and losers
• Build a strategy to deal with zero-rate vintage assets
• Create an advantage with AI in industry incumbents, including SaaS companies
• View AI risks as a diligence problem, not a deal-stopper
• Consider how the software debt maturity wall is creating opportunity
• Commit to deliberate actions, not just market tailwinds, to drive results

Disruption Accelerates M&A; It Doesn’t Suppress It

Every significant technology displacement cycle in recent decades—from client-server models to cloud or on-premises software to SaaS—accelerated M&A activity, not suppressed it. Each disruption created waves of deal flow as new winners emerged and incumbents bolstered defensibility as maturing investment forced capital structures toward recapitalization or an exit.

While public market multiple compression affects valuation pricing, it does not invalidate deal theses. Middle-market software businesses continue to generate cash, renew customers, and attract sponsor interest. Simultaneously, specific sub-sectors are producing robust deal flows independent of the broader SaaS narrative

As new sub-sectors emerge from AI’s disruption, their capital needs vary significantly, presenting opportunities for investors. 

A new generation of technology businesses require growth capital, capital to consolidate, and eventually exit transactions. Emerging sub-sectors from AI disruption are driving deal flow development and the capital demands in each sub-sector—from infrastructure sectors that include power, energy, data center, and real estate to software applications in healthcare, customer experience, and cybersecurity—vary significantly.

The strongest deal flow is concentrated in highly regulated end-markets with high cost of error risk because regulatory moats reduce disruption risk and the complexity of compliance is a barrier to new market entrants. Two areas with high levels of deal interest are Healthcare and Fintech, with shifting payment models drawing investor attention. It’s also no surprise that these sectors are also complicated, highly regulated, and laden with proprietary data, all of which has increased the cost of getting it wrong. But, when done right, the software that serves that market has been a safer place to invest.

The Spread Between Winners and Losers Is Widening

Commoditized software companies that relied on technology as their sole differentiator, as well as those dependent on usage- or seat-based models, are coming under pressure.

Conversely, AI-native businesses and those with defensible moats—the company’s ability to maintain a competitive advantage through proprietary data, workflow integration, and other assets—are commanding valuation premiums.

These business structures are currently favored in the marketplace:

AI-Native Startups: These are companies founded over the past two years from a clean slate, being built on emerging AI from the first day

Moat-Protected Incumbents: These established players adapt their legacy technology with AI features and therefore carry built-in competitive advantages. Other moats include:

• Experience in highly regulated sectors, such as banking and healthcare
• Proprietary data, which presents a competitive advantage over competitors without access or ownership of such information
• Workflow complexity that leads to opportunities to address switching costs
• Accumulation of domain knowledge over the years

Disruption and price uncertainty historically creates the best entry points for experienced sponsors viewing these targeted acquisitions and platform combinations as portfolio strengthening opportunities.

Zero-Rate Vintage Assets Force Transactions, Regardless of Sentiment

A large group of PE-backed software businesses were underwritten at peak SaaS multiples during the zero-rate period, with leverage structures calibrated to 2021 growth rates. Those multiples are not returning as growth has not met expectations.

Decreasing valuations and extended hold periods have made value creation plans a profitability play, rather than a growth-at-all-cost strategy. 

As these companies mature and hold periods expire, sponsors must navigate a market where interest rates have risen, buyer-seller valuation gaps have widened, and AI has fundamentally reshaped growth assumptions. In many cases, sponsors are prioritizing refinancing and extending debt maturities beyond 2027 rather than pursuing traditional exits, creating a forced transaction environment driven by timing constraints rather than market sentiment.

Dealmakers must account for the widening gap between buyer and seller expectations due to higher rates and AI disruption. They must also truly understand that exit timelines will continue to extend as sponsors shift priority to refinancing and debt maturity management.

This leads to an important change in thinking that must occur among sponsors. Some assets simply cannot exit in the current environment and will need to be transformed within portfolios before sponsors can get value for them. Value creation becomes an important tool to sustain valuations since the exit horizon model relied upon in recent years is largely gone. The reality is that many sponsors who maintain assets with solid businesses would now trade at a significant discount to their valuation when they last transacted.

Incumbents Have Structural Advantage

The core attributes that made software attractive to PE investing—switching costs, embedded workflows, and contractual stickiness—remain intact.

While AI changes the competitive landscape, established platforms possess structural advantages that AI-native startups cannot replicate overnight. 

Primarily, incumbents bring customer contracts, trust and distribution models that startups cannot access immediately. They’re also primed to benefit from leveraging AI themselves to deepen their core product and cross-sell new AI-native capabilities.

They’re acquiring AI capabilities to bolt into existing go-to-market efforts. It provides faster deployment than building AI capabilities from scratch.

Established platforms also bring other competitive advantages beyond customer trust and distribution meddles that AI-native startups cannot replicate overnight: integration depth and contractual relationships.

The Software Debt Maturity Wall Is Creating Opportunity

Software businesses taken private at peak valuations and those that overinvested in development face a problem hiding as an opportunity. As these debt maturities approach, transactions are likely as stakeholders navigate capital structure deleveraging on fundamentally sound businesses.

Companies can respond to challenges by prioritizing performance improvement, operational and corporate restructuring, and resetting capital structure. 

Debt structures have been misaligned with the current growth and multiple environments. Examples include payment-in-kind notes and covenant-light facilities that were deployed at multiples that never materialized. In some cases, they led to sponsors handing back assets to lenders.

Many of these portfolio companies produce strong cash flows underpinned by stable recurring revenue and embedded customer relationships. Oftentimes, though, they are overleveraged relative to their ability to service debt at current cash generation.

Companies can respond to these challenges through prioritizing performance improvement and bringing discipline to their cash conversion cycles prior to discussions with lenders about resetting the capital structure.

As maturities approach in 2027 through 2029, investors should expect continued opportunities to pursue consensual and out-of-court liability management exercises. Stabilized businesses with recurring revenue and a clear go-to-market approach will find a wide variety of options to reset terms. Investors should expect material activity across performance improvement, restructuring, and transaction advisory as those negotiations play out.

AI Risk is a Diligence Problem, Not a Deal Stopper

Buyers are not walking away from software broadly. They are walking away from deals they cannot underwrite with confidence. 

Heightened buyer scrutiny means more focus on an asset’s sales efficiency, margin profile, customer resilience, and cash flow conversion.

In this environment, data represents a cornerstone of risk management in due diligence. Ensuring quality data can be more easily verified with AI diligence models but still must be validated by humans to prevent hallucinations from affecting evaluations during the process.

Integrated due diligence teams approach AI risk systematically across multiple dimensions, including product and technology assessments of AI threat at the product level and analyzing customer data, focusing on behavioral shifts and pricing pressure to determine the drivers behind customer expansion and contraction. Operationally, teams must identify AI-driven cost savings and additional value levers for the new owner.

Recently, companies have begun to reduce workforces as they anticipate or start to see productivity gains. However, companies should be careful in their financial due diligence to assess any real productivity observations before greenlighting deals. These may end up being phantom wages that will rematerialize during integration because AI has optimized work in unexpected ways and workers need to be rehired for the foreseeable future.

Other cautions will likely continue to arise as the market sees how AI integration is impacting operations in real life. In any due diligence case, however, deal viability rests on the underwriting competitive position, a company’s customer stickiness, and the value creation levers independent of AI’s disruption narrative.

Conclusion

AI in software is a repricing event, not an extinction event. While disruption is real and valuations are adjusting, the fundamental drivers of software M&A—scale needs, market consolidation, financial engineering, and LP distribution requirements—remain intact. Sophisticated sponsors recognize this as a buying moment. The due diligence challenge is not whether to do deals, but how to underwrite AI risk systematically as part of integrated transaction and operational assessment.

How A&M Can Help:

Alvarez & Marsal's integrated approach to technology, operations, and financial analysis positions us to help sponsors, buyers, and lenders navigate this complex landscape. Our Technology Industry Team within A&M’s Global Transaction Advisory Group (TAG) delivers comprehensive financial due diligence services across software, technology, and AI-enabled businesses. We bring a hands-on approach to target companies, providing deep insights into quality of earnings, quality of annual recurring revenue, cash investment requirements, customer retention, and the scalability of AI-driven business models. Leveraging extensive sector expertise, we help clients assess growth sustainability, evaluate key value drivers, identify transaction risks and opportunities, and execute successful investments and acquisitions

A&M’s PEPI Technology Industry Group practice helps PE funds and portfolio management translate AI from a technology theme into measurable business impact. Our work focuses on identifying where AI can drive cost efficiency, improve throughput, and enhance decision making across functions while also assessing AI readiness, execution risk, and value creation potential. We support clients through AI diligence, AI diagnostics, opportunity sizing, AI roadmap development, and practical implementation focused on EBITDA impact.