SaaS Economics in the AI Era. The rise of SaaS was one of the most… | by Adarshdeep Singh | Jul, 2026 | MediumSitemapOpen in appSign up<br>Sign in

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SaaS Economics in the AI Era

Adarshdeep Singh

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Photo by Steve A Johnson on UnsplashThe rise of SaaS was one of the most important shifts in the technology industry.<br>Before SaaS, enterprise software was expensive, slow to implement, and painful to adopt. Businesses had to buy large upfront licenses, manage on-premise infrastructure, depend heavily on IT teams, and wait through slow upgrade cycles.<br>SaaS flipped that model. Software was no longer a product purchased once and installed. It became a continuously delivered service, hosted in the cloud, updated regularly, and billed through recurring seat-based pricing.<br>It was a win-win for everyone involved.<br>For businesses, SaaS lowered the risk of adoption. Companies could try software without committing to large upfront costs and expand usage over time. The operational burden of hosting, maintenance, and upgrades shifted to software vendors.<br>For software companies, cloud infrastructure made it easier to scale across customers. The SaaS model also gave them more control over the product experience and better visibility into usage analytics, enabling product-led adoption and unlocking large streams of recurring revenue.<br>For end users, SaaS generally meant better software. Products improved continuously through faster feedback loops, new capabilities shipped more frequently, and workflows became more efficient over time.<br>Over time, SaaS became the default way to buy enterprise software, generating hundreds of billions of dollars in annual revenue. One of the key enablers of this rapid growth was the parallel rise of cloud computing.<br>Cloud made it easier to deploy, scale and operate software without each company needing to acquire and manage its own infrastructure. AI is taking it a step further.<br>If cloud made it easier to deploy software, AI — especially coding agents such as Claude and Codex — is making it easier to build software.

At first glance, this sounds like another massive tailwind for SaaS. But the conclusion is not that straightforward.<br>AI is making software more powerful, but it is also changing the economics of running a SaaS business. Most software companies will not own the underlying model intelligence themselves. They will rent it from AI model providers that charge based on usage, often through token-based pricing.<br>That means every prompt, response, retrieval or reasoning step, and agentic action can introduce a variable cost. AI is therefore both a product unlock and a margin risk.<br>Moreover, with agentic AI, software can plan, decide, and act across tools or workflows with limited human intervention. When AI agents can operate across multiple SaaS tools, automate internal processes, build dashboards, optimize workflows, and customize experiences on demand, businesses will naturally start asking a harder question: Why are we paying for so many separate SaaS products if an agent can increasingly operate across them?<br>What Makes SaaS Durable in the Age of AI<br>Agentic AI exposes weak SaaS products.<br>Point solutions, basic workflow tools, and simple UI wrappers over databases are highly vulnerable. If a product only captures information, displays it, and enables a few basic actions, AI agents can increasingly replicate large parts of that value.<br>Even traditional switching-cost arguments may weaken. Deep integration into the company stack used to create meaningful defensibility. But if AI agents can operate across APIs, databases, documents, and internal systems, integration depth may no longer be sufficient on its own.<br>So the big question is: what still makes SaaS durable?

In my view, three factors will matter most.<br>1. End-to-End Workflow Ownership<br>Durable SaaS companies will not just solve isolated pain points. They will own entire workflows. That means understanding how work actually gets done across teams and systems, end to end. The opportunity is not merely to make one step faster. It is to rethink the whole workflow and optimize for the larger business outcome the customer actually cares about.<br>A product that only solves a narrow pain point may be easier for an AI agent to replace. But a product that owns the full workflow has deeper context, more surface area for automation, and clarity on driving business outcomes.<br>2. System of Record and Business Context<br>Products that sit deeply inside enterprise workflows accumulate structured business memory over time: institutional knowledge, past decisions, policies, exceptions, performance data and workflow patterns. This context compounds over time and becomes extremely valuable for AI-native software. A SaaS product that owns trusted, structured business context can produce better recommendations, better automation, and...