Copyright Newsweek
Artificial intelligence (AI) is often described as a transformative force for software providers, promising dramatic gains in productivity and efficiency. But despite the hype, the most profound changes AI brings to the SaaS industry may not be where most expect. While AI has the potential to reshape how software is built and delivered, its impact on the demand side— how customers buy, use and value SaaS products— is proving to be more complex and, in many cases, slower to materialize than anticipated. The prevailing narrative suggests that AI will revolutionize SaaS by enabling mass customization, smarter automation and entirely new product categories. But the reality is more nuanced. While some of these changes are underway, the most immediate and measurable impact of AI in the SaaS ecosystem is not in creating new revenue streams but in improving the productivity of software engineering and modernizing legacy systems. The promise of AI-enabled SaaS is real, but the path to realizing that promise is uneven, especially for incumbents. To understand the current state of AI's impact on SaaS demand, it helps to examine four principles that separate myth from reality: 1. AI Is Transforming Software Engineering, Not SaaS Revenue—Yet The most widespread and successful application of AI in the SaaS world today is not in customer-facing features but in the software development process itself. AI-powered tools are helping engineering teams write, test and maintain code more efficiently. These gains are particularly pronounced among senior engineers, whose domain expertise allows them to use AI tools more effectively. This shift is reshaping team structures, reducing reliance on junior developers and accelerating modernization efforts. This is not just a supply-side story. For SaaS buyers, the downstream effect is that vendors can deliver updates faster, reduce technical debt and improve product quality. But these improvements are largely invisible to end users and do not yet translate into new revenue streams or pricing power for SaaS providers. 2. AI Features Are Proliferating, but Buyers Aren’t Paying More for Them Many SaaS providers are racing to infuse AI into their products, adding features like predictive analytics, natural language interfaces and intelligent automation. These capabilities are often marketed as transformative, but in practice, they are rarely the basis for purchase decisions. Buyers increasingly expect AI features as part of the standard offering, not as premium add-ons. This creates a paradox: while AI is becoming a must-have for competitive parity, it is not yet a reliable driver of pricing premiums or customer acquisition. For incumbents, this means that investments in AI features may be necessary to stay relevant, but they are unlikely to deliver immediate returns. The result is a cautious approach to AI investment, with many initiatives remaining in the proof-of-concept stage. 3. The Biggest Near-Term Opportunity Is Legacy Modernization One area where AI is delivering clear ROI is in the modernization of legacy software. AI-powered code converters and refactoring tools are reducing the risk and cost of updating outdated systems. This is especially valuable in industries like finance, health care and manufacturing, where mission-critical applications often run on decades-old codebases. For SaaS providers, this presents a significant growth opportunity. By offering AI-enabled modernization services, vendors can help customers unlock value from existing systems while positioning themselves as strategic partners in digital transformation. Unlike speculative AI features, these modernization efforts have tangible, measurable benefits and buyers are willing to pay for them. 4. Automation Is a No-Regret Move, but Consumption Costs Are Rising AI-enabled automation is one of the most common and least controversial use cases in SaaS. From legal operations to cybersecurity to customer service, AI is helping organizations streamline workflows and reduce costs. These are the kinds of use cases where the benefits are clear, the implementation risks are manageable and the ROI is relatively easy to demonstrate. However, there is a hidden cost. As AI drives greater automation and usage, it also increases consumption of cloud resources and third-party APIs. This can lead to higher operating costs for both providers and customers, especially in usage-based pricing models. Few organizations have fully accounted for these downstream costs, which could become a source of friction as AI adoption scales. The Bottom Line: A More Grounded View of AI in SaaS The SaaS industry is at an inflection point. AI is undoubtedly a powerful tool, but its impact on demand is unfolding unevenly. The most immediate value is being realized in the back office, not the front end. While the vision of AI-powered, mass-customized SaaS is compelling, it remains largely aspirational for now. For SaaS providers, the challenge is to balance innovation with pragmatism. Investing in AI for software engineering and legacy modernization offers clear, near-term returns. Enhancing products with AI features may be necessary to stay competitive, but it is unlikely to drive significant new revenue without a corresponding shift in buyer behavior. Ultimately, the winners in the AI era will be those who understand not just what AI can do, but what customers are willing to pay for. That means focusing on measurable outcomes, managing consumption costs, and aligning AI investments with real-world demand. The hype around AI in SaaS is not unfounded, but the reality is more complex and more interesting than the headlines suggest. Ranjit Tinaikar is the chief executive officer and board member at Ness Digital Engineering.
