Technology Report
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Executive Summary
This article is part of Bain’s Technology Report 2026 If software investing has delivered anything to private equity investors so far in 2026, it is the disquieting certainty that the world has changed—probably forever. Revenue growth that was running around 20% annually is now trending at half that. Net revenue retention (NRR) has dropped about 9 points since 2021 (see Figure 1). Dealmaking is at a crawl. Aging portfolios are suddenly a thing in tech investing. And looming over everything else is the specter of artificial intelligence, which, at best, threatens the once-unassailable SaaS value proposition and, at worst, raises fears that some software use cases are veering toward obsolescence.
Figure 1
Perhaps most ominous is the building evidence that software is losing some of the predictability that made it a preferred asset class for the buyout community, including reliable margin profiles, common expansion mechanics, and similar diligence frameworks. The software assets that are transacting in the private markets continue to attract high prices, largely because sponsors have focused their efforts on selling their A-plus companies. Yet deals completed after the Covid-19 pandemic, including more seasoned investments from 2020 to 2022, are, to date, generating returns below pre-Covid averages (see Figure 2). While many of these investments are not yet fully realized, and performance could improve over time, that will be a challenge given the high prices sponsors paid for assets and the sector's slowing growth.
Figure 2
Notes: Includes fully realized deals and deals that have realized at least 25% of their returns; includes all deal sizes; all figures in USD Source: SPI by StepStone (April 2026)What comes next?Patterns are emerging that suggest a number of practical actions private equity investors can take right now to keep pressing forward. We already know enough about AI's disruptive power, in fact, to start making no-regrets shifts in every phase of the PE value proposition. Proactive general partners (GPs) are taking steps to:
The new due diligence imperativeFor more than a decade, software value creation has centered on maximizing predictable, recurring revenue. SaaS companies grew by adding seats, adding premium features, and cross-selling products. High margins and sticky customer relationships—reinforced by embedded workflows, user habits, and accumulated data—created durable competitive moats. AI is changing all that. Beyond enabling AI-native challengers to leapfrog established software providers, it fundamentally changes software economics, eroding some of the industry’s structural advantages. The compute required to power AI adds substantial costs to every query, shifting revenue models from seat-based subscriptions toward usage and outcomes. Margins are lower, and future growth is harder to predict. That shift makes traditional SaaS metrics less reliable in due diligence. Annual recurring revenue (ARR), NRR, and revenue multiples were reliable proxies for value when software moats were durable and margins consistently high. Today, as the link between growth and valuation weakens, investors need new ways to assess which companies can create lasting value in an AI-driven market. Diligence in this new environment has to start with an assessment of two things: How much can AI impact the user workflows the software supports, and how much risk is there that AI could displace the software altogether within those workflows? Stress-testing requires specificity, since product moats, workflow moats, and data moats each come under different degrees of pressure from AI. Strong diligence will also capture upside: How AI-ready is the company itself? Is it rapidly deploying AI internally to improve efficiency? Is it gaining traction with AI on the product side, either by adding features or by launching new products that customers value? The key here is evidence: The market is already bifurcating between companies that can demonstrate measurable AI traction and those that are still spinning a narrative without numbers behind it. In diligence, the key question is no longer "do they have an AI strategy?" It’s "can they show me the proof points?" AI transformation during ownershipUsing AI internally to improve efficiency and transform workflows is rapidly becoming table stakes for any company in your portfolio, software or otherwise. The opportunity to truly inflect performance still involves some experimentation and faith in the technology, but standing still isn't an option. The more complex question is how to reshape the product roadmap to generate AI-based revenue—and how to scale meaningful innovation at speed. That can be a tall order for an incumbent software company accustomed to optimizing for a traditional seat-based platform, especially given all the daily requirements of managing the core business. The companies seeing the most success tend to have a light-bulb moment when they recognize that simply helping humans do tasks faster with incremental product enhancements is increasingly missing the point in an AI world. If an agentic system, for instance, can actually manage a workflow end to end, the goal should be enabling a step change in measurable outcomes, not just user efficiency. That means turning the traditional product development approach on its head. Instead of focusing solely on how customers use discrete products within workflows, the most innovative companies are looking to connect the dots across the entire process. They are going deep on the customer's broad objective and how their solution can be rebuilt to achieve that outcome—sometimes freeing up workers for more productive pursuits, sometimes replacing them altogether. The lesson here is that AI is not a game of incrementalism. Capturing the opportunity starts with zero-basing product assumptions and reimagining what's possible. The answer won't always be a complete rebuild. But you need to decide how you can take customer outcomes to a new level by matching AI technology to a deeper understanding of the relevant workflows. And you'll need to assess what that will require in terms of adding talent and making organizational changes to support rapid execution. Proving itIt's no surprise that GPs and their portfolio company management teams are expending massive bandwidth to develop new AI features and products. But they're probably not spending enough energy to develop the means to track progress and measure impact with concrete data. That helps explain the wide gap in value expectations we're often seeing between buyers and sellers. Because the traditional SaaS KPI stack (ARR, NRR, and gross margin) was built for a different value proposition (a world of near-zero marginal cost, seat-driven expansion, and predictable retention), it fails to capture AI impact reliably. ARR, for instance, is a great measure of the predictable revenue derived from subscription seats. But AI is priced on actual usage and outcomes, which can be bursty and unreliable. Understanding the true performance of these products requires separately tracking at least three distinct revenue buckets: traditional AI and machine learning (predictive models, risk scores), AI add-ons (copilots, assistant features), and agentic products (workflow automation, autonomous execution). Each has a distinct margin profile, growth trajectory, and competitive dynamics. The cost side, too, is very different. AI products have significant variable costs per use that don't exist for traditional SaaS solutions. A full understanding of what you're spending requires tracking hosting and infrastructure costs, third-party model costs, and the fully loaded cost of each employee devoted in full or in part to AI-related R&D. The bottom line is that if you don't change your metrics, you can't evaluate AI impact. The revenues and costs directly attributable to AI first need to be separated from the core business and then broken down into their constituent parts. Without that, it's impossible to answer with precision the three essential questions on everyone's mind: Is AI driving incremental revenue? Is AI changing cost structures? And are AI-related products scaling efficiently? Armed with firm answers, portfolio companies can design the best product roadmap and allocate resources accordingly, and GPs can start building the kind of evidence-based exit story buyers are demanding. Twenty twenty-six will likely be remembered as the year AI truly redefined the software industry—the kind of no-turning-back moment that challenges all previous assumptions. Nothing about that is easy. But the chaos won't last forever. The leaders coming out of this transformation are already hard at work rethinking how to underwrite risk, inflect portfolio company performance, and measure results in a world upended by AI. One thing is clear: There's no time to waste. More from the report
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