Managed AI and the end of SaaS – Why companies are now building their own software again

The return of in-house development: Why the SaaS market is currently under massive pressure

For years, an unshakeable rule prevailed in the IT world: software is rented, not built. Software as a Service (SaaS) promised flexibility, low entry costs, and constant innovation – and thus became the standard answer to almost every digital problem. But this golden era is drawing to a close. Exploding subscription costs, bloated software portfolios, and often stagnant added value are increasingly driving companies into an expensive dependency. At the same time, artificial intelligence is fundamentally changing the rules of software development: where legions of programmers once needed months, AI assistants now generate functional, customized prototypes in just a few days. This is leading to an economic paradigm shift. Instead of buying expensive, off-the-shelf standard solutions, companies are increasingly turning to "managed AI" and taking control of their IT architecture again. Read on to find out why the old "build-versus-buy" paradigm is obsolete, how IT decision-makers are reacting now, and why the future of enterprise software lies in intelligent orchestration.

When the tenant becomes the owner: The $300 billion question no one is asking out loud

The certainty that permeated the software industry like a mantra throughout the past decade has faded into silence. Software as a Service, or SaaS, was the answer to virtually every enterprise IT question. Need a CRM? SaaS. Project management? SaaS. Accounting, analytics, communications? SaaS, SaaS, SaaS. Companies worldwide have grown accustomed to a model where software is no longer owned, but rented. But by 2026, this certainty is crumbling, and the cracks are becoming increasingly apparent. What was once celebrated as a cost-effective and flexible solution has transformed for many organizations into an expensive dependency that stifles innovation and consumes resources.

The numbers paint a sobering picture. According to an analysis of more than 115 publicly traded SaaS companies, the industry's average annual revenue growth fell from 21 percent to 12 percent in 2024. Even more alarming, in the first quarter of 2025, sector-wide SaaS revenue growth was minus two percent. This isn't a cyclical hiccup. It's a structural shift that challenges the very foundations of the business model. At the same time, SaaS spending per employee has risen to an average of $4,830 in 2025—a 21.9 percent increase year over year—with companies managing an average of 275 different SaaS applications. Costs are rising, complexity is increasing, and value is being questioned more and more.

The economic turning point in the software market

The transformation currently underway can be summarized in a simple formula: When the creation and further development of software becomes cheaper than the distribution of standardized products, the SaaS model destabilizes its own foundation. This is precisely what is happening due to the rapid development of AI-powered development tools. The cost structure of software development has fundamentally shifted. Where once a team of specialized developers worked on a solution for months, today even small teams can create functional prototypes in days with AI support.

Studies show that AI assistants can increase development speed by 30 to 70 percent. Between 80 and 85 percent of all developers now regularly use AI-powered programming assistants, with daily users saving an average of five to eight hours per week. The proportion of AI-generated code actually deployed to production systems has risen to 26.9 percent, with intensive users already generating a third of their combined code from AI. These figures clearly demonstrate that the technological foundation for a fundamental reshaping of the build-versus-buy ratio has been laid.

AlixPartners describes this shift as the transition from the SaaS era to the AI ​​era, comparing it to the earlier paradigm shift from perpetual licenses to SaaS, which at the time enabled a four- to six-fold increase in revenue multiples. The consultancy argues that generative AI and AI agents are fundamentally changing the traditional SaaS architecture by replacing the logic and presentation layers that SaaS providers rely on.

The data of the revolution: What companies are already doing

The Retool Study from 2026, based on a survey of 817 developers and companies, provides the most detailed snapshot of this transformation to date. 35 percent of the surveyed teams have already replaced at least one SaaS tool with a custom-developed, in-house solution. 78 percent plan to build even more of their own tools in 2026. The SaaS categories under pressure to be replaced are diverse: Workflow automation (35 percent) and internal administration tools (33 percent) lead the list, followed by business intelligence tools (29 percent), CRM systems and form builders (25 percent), project management (23 percent), and customer support (21 percent).

Particularly revealing is the fact that 60 percent of developers built something outside of IT oversight last year. Twenty-five percent even do so regularly. This isn't just a phenomenon of inexperienced lone wolves: 64 percent of the surveyed shadow IT players are senior managers or higher-ranking executives. They consciously choose speed over the official procurement channels. The most common reason is speed (31 percent), followed by unmet needs (25 percent) and excessively slow IT processes (18 percent).

Retool CEO David Hsu sums up the dynamic: The cost of developing custom software has fallen dramatically. Processes that once required extensive technical resources and substantial budgets can now sometimes be prototyped within days. With such a fundamental shift in costs, behavior changes. The prevailing question shifts from considering what to buy to whether to build it oneself.

A closer look at the SaaS cost structure

To understand the scope of this transformation, it's worth taking a closer look at the SaaS cost structure. The average company now spends $49 million annually on SaaS subscriptions. In sectors like healthcare and IT, spending rises to over $10,000 per employee, and in the financial sector, it reaches $8,750. This increase in spending isn't due to companies adding more applications. The portfolio grew by only 2.2 percent, while spending increased by 9.3 percent. The reason lies in rising vendor prices. SaaS companies, themselves struggling with slowing growth, are seeking new revenue streams through premium add-ons, AI features, and new pricing models—particularly usage-based billing.

At the same time, companies' purchasing behavior is fundamentally changing. Instead of adding more tools, they are consolidating their software portfolios, reducing unused licenses, demanding flexible, usage-based pricing models, and renegotiating contracts upon renewal. Where SaaS companies once benefited from the land-and-expand principle, today a prove-and-justify paradigm prevails, where every expenditure must be justified.

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Managed AI: The third option between buying and building your own

In this complex situation, a third option emerges that requires neither a complete in-house development nor the uncritical acceptance of SaaS subscriptions. The concept of managed AI platforms combines the speed and accessibility of cloud services with the control and adaptability of in-house systems. These platforms enable companies to build customized solutions on a managed infrastructure, with security, governance, and scalability integrated from the outset.

Gartner confirms this trend: 65 percent of companies are already using hybrid AI architectures that combine commercial APIs with internal models and tools. The smartest teams are designing systems that can evolve over time, rather than committing to a single path from the outset. 41 percent of companies cited a lack of flexibility or customization options as the primary reason for switching from purchased AI to in-house development.

Deloitte's perspective on managed services reveals how AI is transforming traditional service delivery: from automating everyday tasks like generating real-time reports and automating processes to delivering complex analytics and strategic recommendations. The key difference lies in companies first doing things differently and then gradually moving on to entirely different things. McKinsey's State of AI research shows that organizations that embed AI directly into decision-making processes—rather than treating it as a mere analytics add-on—are almost three times more likely to redesign their workflows around AI, thereby creating measurable value.

The maturation process: From experiment to strategic in-house development

Forrester has identified a maturation process they call "progressive internalization." Organizations that follow this phased approach first purchase AI to validate value, then move to a hybrid model, and finally build their own to differentiate themselves. According to the research, this approach leads to a sustainable AI ROI 60 percent faster than jumping straight into in-house development.

The Zartis AI Maturity Framework describes three distinct phases. In the experimentation phase, teams rely on pre-built APIs and SaaS platforms to validate ROI and achieve initial successes. In the extension phase, vendor APIs are combined with orchestration layers and light retraining to customize workflows and access internal data. Finally, in the build phase, companies launch their own finely tuned models on their own servers, reducing costs by up to 40 percent and transforming the system into a strategic differentiator.

Practice confirms this pattern. ClickUp, a productivity platform with 14 million users, evaluated a wave of AI providers for its go-to-market operations and found none offered the right integrations or the necessary consistency. Instead of continuing its search, the company built six of its own AI tools connected to Salesforce, Zendesk, and Snowflake. The result: hundreds of automated work hours per week, significant savings in labor costs, and $200,000 less in annual automation software spending.

The downsides of self-build and why governance is crucial

As tempting as a return to in-house development sounds, it's not without risks. Sam Altman has already warned of a fast-fashion era of SaaS replacement, in which there will be an explosion of inexpensive, single-purpose tools that prioritize speed over quality. The future of software lies somewhere between rigid, inflexible SaaS and sloppy, uncontrolled proliferation—where developers can solve their problems in secure, managed environments.

Only eight percent of developers use AI-generated code without modifications. Forty-four percent test thoroughly before deployment, and 32 percent at least briefly review the code. AI-generated code without proper review has 1.7 times more errors. The technical barriers to production deployment are numerous: insufficient technical resources and developer capacity (42 percent), security and compliance concerns (41 percent), and integration issues between systems (39 percent).

On the organizational side, unclear ROI ranks first at 33 percent, followed by budget constraints (30 percent) and maintenance costs (26 percent). Particularly problematic: 35 percent of organizations have not yet established AI productivity metrics. You simply can't prove the ROI of what you don't measure. 75 percent of developers now work under their organizations' AI directives, but performance measurement hasn't kept pace.

The strategic dimension: When to build, when to buy

The decision between building and buying is no longer binary. The HatchWorks Framework of 2026 identifies five dimensions that drive this decision: competitive differentiation, data advantage, risk tolerance, integration complexity, and the specificity of required workflows. Companies should buy if they are paying for decades of edge cases, testing, and availability expectations. They should build if capability is their competitive advantage—for example, with AI co-pilots, agent-based workflows, or decision support. And they should take a hybrid approach if the core functionality is standard, but their workflows and integrations are unique.

The Silicon Valley Product Group argues that AI is rapidly eroding the costs, time, and expertise barriers that historically favored buying over building. OpenAI's CFO has indicated that the company is developing an AI agent capable of performing all the work of software engineers, rather than simply augmenting their skills. While fully autonomous AI engineers may still be on the horizon, the direction is clear.

The future of enterprise software: Orchestration instead of ownership

The companies that succeed with AI in 2026 will not be defined by what they own, but by how well they orchestrate it. AI ecosystems will be modular, distributed, and collaborative. The advantage will belong to those organizations that can seamlessly connect third-party tools, open-source intelligence, and internal systems. This requires a fundamental shift in partner roles: from configurator to co-creator, from system integrator to intelligence architect.

For traditional SaaS companies, the message is clear. Salesforce has already signed 5,000 contracts for its Agentforce AI platform by October 2024, including more than 3,000 paying customers. ServiceNow is expanding its AI agent capabilities through the acquisition of Moveworks. HubSpot has launched Breeze, a suite of AI-powered agent tools. The SaaS giants are transforming themselves—not because they want to, but because they have to.

The future of enterprise software will be neither purely SaaS nor purely in-house developed. It will be an ecosystem of managed AI, modular platforms, and strategic in-house development, where each company exercises control where it matters and accepts abstraction where it makes sense. For CIOs and CTOs, this means the question is no longer whether to build or buy. The question is where control is needed and where abstraction can be accepted.

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