Why Businesses Are Turning Away From Traditional SaaS — And What’s Replacing It

In early 2026, amid waves of investor caution and accelerating technological innovation, a compelling narrative has taken hold across the enterprise software landscape: many companies are beginning to shift away from the traditional software-as-a-service (SaaS) model in favor of custom, AI-enhanced tools and internal platforms. What once seemed like a stable cornerstone of enterprise IT — the recurring revenue SaaS subscription — is being reevaluated as emerging AI capabilities reshape how software is built, deployed and consumed.

That shift isn’t simply theoretical. Data from enterprise development platform Retool shows that a significant portion of organizations have already replaced at least one SaaS tool with internally built software, with most planning to continue doing so throughout 2026. Companies report that these custom tools are crafted specifically to their workflows — often at a fraction of the expense of expensive SaaS licenses — and can better adapt as business needs evolve.

The Emergence of AI-Built Software

The transformative spark behind this shift is artificial intelligence. Modern generative AI tools go beyond simple automation or chatbot interfaces; they can help generate code, design workflows and build applications quickly. In Retool’s findings, more than half of developers building internal tools said they’ve already built at least one app using AI, frequently leveraging major models like ChatGPT, Google’s Gemini or Anthropic’s Claude.

The appeal is simple: AI shortens development time and simplifies functionality that previously required experienced software engineers and lengthy vendor integrations. As companies gain confidence in AI-generated code, it becomes feasible to create tools that replace SaaS offerings for workflow automation, internal business processes, customer relationship management and analytics — all without the recurring subscription costs.

A broader industry frame for this shift is the emerging concept of “vibe coding,” where AI assists developers by writing and debugging code based on natural language prompts. Although still imperfect — around one-fifth of builders report encountering inaccurate outputs or “hallucinations” from AI tools — the capability is maturing rapidly and proving useful for a growing range of business applications.

Wall Street’s Concerns and the “SaaSpocalypse” Narrative

Investor sentiment reflects this technological pivot. Major software stocks, long thought to be reliable growth drivers, have seen increased volatility as markets reassess future earnings potential. A recent wave of new AI releases — such as advanced models from Anthropic — helped intensify these concerns by fueling speculation that AI agents could eventually perform tasks once delivered via SaaS platforms.

This dynamic has even spawned the term “SaaSpocalypse” among some analysts, referring to the potential decline of the traditional SaaS model as enterprises adopt AI-centric alternatives. While some observers see this as an overstatement, the conversation reflects a broader anxiety about how flexible and efficient AI-generated tools could disrupt incumbent vendors.

In addition to revenue risk, emerging AI systems may compress traditional SaaS pricing structures. Rather than paying for dozens of individual user licenses — the so-called “per-seat” model — companies might instead consume AI-generated outcomes, such as automated reports or workflow completions, under usage-based or outcome-based pricing, which could be dramatically cheaper and more aligned with actual business value.

Cost Pressures and Customized Platforms

The financial incentive for internal software development is significant. Companies widely report that rising SaaS subscription fees are motivating them to rethink their technology stack. Building custom tools — aided by AI — can reduce recurring expenses while providing software that aligns more closely with specific operational needs.

For example, rather than subscribing to a suite of tools for project management, CRM, data analytics and workflow automation, some organizations are opting to build unified platforms internally. These platforms can integrate across functions without the complexity of multiple disparate SaaS applications, reducing licensing burdens and simplifying IT governance.

Yet building custom tools isn’t without its challenges. Nearly half of surveyed companies have yet to produce fully workable internal software via AI — reflecting current limitations in tooling, expertise and data infrastructure. There’s also the issue of governance: many tools are created outside the oversight of centralized IT teams, raising questions about security, maintainability and compliance.

The Future of Software in an AI-Driven World

Despite these hurdles, the general direction is clear: AI is not just augmenting how companies use software — it’s increasingly reshaping how they build it. The growth of AI-assisted development suggests a future in which enterprise software may be more modular, more customizable and less dependent on large subscription models.

That doesn’t mean traditional SaaS vendors are doomed. Many are integrating advanced AI into their platforms, offering automated features and machine-assisted workflows that retain value for companies without internal development capacity. The most successful SaaS providers may be those that embrace these changes — blending AI’s advanced capabilities with robust, managed enterprise platforms.

For organizations evaluating their technology strategies in 2026 and beyond, the central question is no longer just “build or buy” — it’s “how intelligently can we leverage AI to create software that fits our needs most efficiently?” As AI continues to progress, that question will shape the future of enterprise software spending and architectural design for years to come.