The software-as-a-service (SaaS) model that dominated the last two decades is not dying—it is evolving. Contrary to predictions of a 'SaaS apocalypse,' venture capitalist Orlando Bravo recently declared that AI is an enormous tailwind for software companies. Rather than replacing software, AI enables a new level of business automation, including automating parts of human judgment. This shift is giving rise to a hybrid model called 'services-as-software' (SaS), where software firms and service providers converge.

Key facts about the new SaS model

• The SaaS apocalypse is officially over, according to leading VC Orlando Bravo.
• Salesforce reported $11.1 billion in quarterly revenue in May 2026, up 13% year over year, showing no signs of decline.
• IBM's software segment grew 5% even as overall revenue struggled.
• Enterprises are not ready to deploy AI at scale due to technology, data, process, and talent debt.
• A hybrid model of services-as-software is emerging, where service firms become software businesses and vice versa.

The myth of the SaaS apocalypse

For months, tech investors and analysts warned that artificial intelligence would render traditional SaaS obsolete. The narrative suggested that AI-native tools would directly deliver outcomes, bypassing the need for subscription software. But Bravo, founder of private equity firm Thoma Bravo, pushed back. In a CNBC interview, he argued that AI is the biggest opportunity for software companies, enabling them to automate not just repetitive tasks but also decision-making processes. "Software companies can move to a completely new level of business automation by automating some parts of human judgment," he said.

Salesforce, the poster child of SaaS, reported robust earnings earlier this year. Its acquisition of customer service software company Fin (formerly Intercom) for $3.6 billion underscores its commitment to the software space. Meanwhile, IBM’s software segment grew 5% in its most recent quarter, even as its consulting arm faced headwinds. These numbers suggest that SaaS companies are not crumbling; they are adapting.

Why the apocalypse didn't happen

The idea that AI would replace SaaS assumes that enterprises can deploy AI at scale overnight. Analysts Saurabh Gupta and Phil Fersht of consulting firm HFS argue that this assumption is flawed. "Until organizations resolve their technology, data, process, and talent debt, AI will remain trapped in pilots and proofs of concept rather than fundamentally changing how businesses operate," they write. Most companies are still figuring out where AI fits into their operations. They will continue to rely on trusted SaaS solutions for day-to-day tasks while experimenting with AI in controlled environments.

Wall Street may have declared SaaS dead, but Main Street businesses are not cooperating. The transition to AI-powered services will take years, not months. In the meantime, the SaaS model remains highly profitable. Public SaaS companies generate tens of billions of dollars in annual revenue, serve the world's largest enterprises under multi-year contracts, grow roughly 5% a year, and deliver operating margins of 15% to 20%. They are cash-rich and deeply embedded in the global economy.

The rise of services-as-software

What is emerging instead is a hybrid approach: services-as-software (SaS). This model blurs the line between software firms and IT service providers. Gupta and Fersht describe it as a convergence: "Services firms are increasingly becoming software businesses, software companies are moving deeper into implementation and business transformation, and both are converging on the same outcome-based economic model, even if investors have yet to recognize it."

IBM is a prime example. The company has transformed into a software and AI business that also owns a consulting arm—not a consulting firm trying to sell AI. This integration allows IBM to offer both software platforms and deep transformation expertise, creating a unified value proposition. Other players like Accenture and Deloitte are also building software capabilities, while Salesforce and Workday expand their consulting and implementation services.

The key for enterprises is to look for partners that combine AI with deep client relationships, transformation expertise, and privileged access to enterprise systems. Pure software vendors that lack implementation skills may struggle, as may pure service firms without software platforms.

5 ways to adapt to services-as-software

Gupta and Fersht offer five concrete tips for organizations navigating this shift. These recommendations focus on treating enterprise debt seriously, prioritizing business outcomes, and aligning AI and services partners.

1. Treat enterprise debt as an up-front business issue

Technology, data, process, and talent debt can cripple AI initiatives before they start. Companies must measure, prioritize, and fund these debts with the same discipline they apply to capital investments. For example, if an organization's data is scattered across siloed legacy systems, AI models will produce unreliable results. Addressing this debt is not a cost—it is an investment in future AI readiness. Leaders should create a formal debt register, assign ownership, and allocate budget to reduce the most critical items.

2. Focus on business outcomes, not pilots

Many AI projects linger in proof-of-concept limbo because they lack measurable commercial impact. The recommendation is simple: if an AI initiative cannot demonstrate meaningful business value within 90 days, question whether it deserves continued investment. Every failed pilot delays the real transformation. Instead of chasing technology for its own sake, define clear KPIs tied to revenue, cost savings, or customer satisfaction. This outcome-focused mindset ensures that AI deployments move from experimentation to production.

3. Buy outcomes instead of effort

Traditional procurement models buy licenses, tokens, or full-time equivalents. The new model demands buying business value. If a supplier cannot explain how they improve your profit and loss statement, they are selling technology rather than transformation. Contracts should be structured around outcomes—for example, cost per transaction, revenue lift, or efficiency gains. This shifts risk from the buyer to the provider and aligns incentives toward real results.

4. Align your AI and services partners

Many enterprises engage separate partners for AI technology and business transformation services. If these partners work independently, the disconnect will cost time and money. Gupta and Fersht stress the importance of ensuring that AI vendors and system integrators collaborate closely. Ideally, a single partner can deliver both software and services in a unified offering. At minimum, the enterprise must facilitate regular joint planning sessions, shared roadmaps, and integrated governance.

5. Earn trust before expecting scale

For AI-native firms—startups built around AI rather than adding it as a feature—trust is the new currency. Enterprise clients are risk-averse and will not hand over critical processes without proof. Every successful deployment that delivers measurable value strengthens long-term valuation far more than another funding round. AI-native companies should focus on a few referenceable clients, deliver outsized results, and then scale with credibility. Rushing to market without trust leads to pilot fatigue and wasted resources.

The services-as-software model is not a temporary trend; it is the logical next step in the evolution of enterprise technology. Companies that adapt quickly—by addressing debt, focusing on outcomes, and aligning partners—will thrive. Those that cling to old classifications of software versus services may find themselves left behind. As Bravo noted, AI is an enormous tailwind. The question is not whether to use it, but how to integrate it effectively.

Source: ZDNET News