In short, we would say SaaS isn’t dead—it’s being rebuilt around intelligent systems that reason, act, and deliver outcomes, making this the most profound replat forming since the move to the cloud. For teams approaching SaaS product engineering, the strategic question is how to design AI-native experiences that automate work while preserving trust, governance, and measurable ROI. Agentic AI, falling model costs, and new orchestration layers are transforming how software is built, used, priced, and adopted across the enterprise.
SaaS is evolving, not ending
There is no cloud without AI anymore, as virtually every cloud and legacy application integrates AI to elevate core workflows rather than replace them outright. The 2025 Cloud 100 underscores this shift, with the list dominated by companies embedding AI at the heart of their offerings.
Enterprise adoption is accelerating in parallel, with research showing rapid mainstream use and multi-trillion-dollar value potential from generative AI across functions like software engineering, customer operations, and marketing.
From apps to autonomous agents
Agentic AI is already drafting code, handling support tickets, preparing journal entries, and writing marketing copy inside leading platforms—evidence that routine digital tasks are migrating from click-driven UI to goal-driven automation.
Bain projects a decisive shift from “human plus app” to “AI agent plus API” within a few years as models get cheaper and more accurate, reshaping both product design and user experience.
Complementing this trend, Gartner expects at least 15% of day-to-day work decisions to be made autonomously by 2028, signaling a practical, staged pathway to agentic operations.
Pricing shifts to outcomes
As agents begin doing the work, seat-based pricing loses fidelity, giving way to usage- and outcome-based models that charge for tasks completed, cases resolved, or results delivered.
Zylo’s 2025 SaaS Management Index reports the first increase in average SaaS spend in three years alongside a surge in AI-native app adoption, pushing finance and procurement toward FinOps discipline and contract models aligned to realized value.
In parallel, private cloud benchmarks show compressed multiples and a premium on efficient growth, strengthening the case for ROI-tied commercialization over access-based pricing.
Data, memory, and moats
Unique data is the defensible moat in AI-era software, and leaders are doubling down on proprietary schemas, usage patterns, and domain logic while constraining how external platforms can learn from their systems. Bessemer flags memory and context as the new strategic differentiators, where AI-native apps that remember, adapt, and personalize create switching costs that feel almost emotional for users. Robust evaluation tooling and data lineage become mission-critical, with enterprises demanding trusted, reproducible performance metrics beyond public benchmarks before scaling deployments.
Vertical AI takes center stage
Vertical AI is poised to outgrow traditional vertical SaaS by solving language-heavy, regulation-shaped, and service-intensive workflows with products that feel less like software and more like real leverage.
Early wins show AI-native “systems of action” displacing manual steps and superficial automations with end-to-end agents embedded directly in operational lifecycles. Systems of record are under mounting pressure as code generation, data auto-mapping, and natural language interfaces reduce migration friction and accelerate time-to-value.
Read More: Building Scalable SaaS Platforms for Retail Startups: A CTO’s Playbook
Implications for builders and buyers
Bain outlines four scenarios for each workflow—enhance, compress spending, outshine, or cannibalize—and the winners will tailor investments accordingly rather than apply AI indiscriminately.
Strategy now spans agent orchestration, secure API exposure, and semantic standards like MCP to close the “intent-to-action” gap across stacks and vendors. Organizations that institutionalize governance, continuous evals, and outcome-oriented go-to-market motions will capture a durable advantage as AI moves from pilots to production.
An AI-native SaaS playbook in practice
Modern SaaS engineering demands architecture for agents, Model Context Protocol integrations, privacy-centric data pipelines, and private evaluation suites—capabilities that expert partners in hubs such as Ahmedabad are bringing together with pragmatic velocity. Teams like ViitorCloud align discovery to measurable outcomes, build systems of action over systems of record, and apply FinOps discipline to model and infrastructure costs to protect margins at scale. The result is cloud software that operates like a business copilot—faster to implement, easier to adopt, and designed for compounding ROI in the age of AI.
Top comments (0)