The AI software market is moving beyond assisted features toward agentic platforms. According to Ema.ai, AI SaaS is projected to grow from $71.5 billion in 2023 to $775 billion by 2031. That scale means almost every enterprise software purchase will soon carry an AI component. The question for buyers is no longer whether to adopt AI SaaS, but how to evaluate it.
Assisted versus agentic
AI-assisted software embeds intelligence into existing workflows: auto-completion, summarisation, recommendations. Agentic platforms go further. They pursue goals across multiple steps, interact with other systems and take action with limited human intervention.
The distinction matters because the risks and integration requirements are different. An assisted feature that gets a recommendation wrong is usually harmless. An agent that executes a procurement decision or customer refund incorrectly can create real damage.
A framework for evaluation
When evaluating AI SaaS, we suggest focusing on five areas.
First, integration depth. Does the product connect to the systems where work actually happens, or does it require users to change behaviour? Second, observability. Can you see what the AI did, why it did it and what data it used? Third, control. Can you define boundaries, require human approval for consequential actions and roll back mistakes? Fourth, data handling. Where does your data go, how is it used and what are the exit terms? Fifth, outcome evidence. Does the vendor have credible case studies with measurable results in environments like yours?
Build versus buy
For most organisations, buying AI SaaS will be the right choice for standard capabilities. Building makes sense only when the use case is genuinely proprietary, when data sensitivity prevents third-party processing, or when the capability is central to competitive advantage.
Even when buying, some internal capability is needed. You still need people who understand the technology, can negotiate contracts, manage implementation and govern use. AI SaaS does not remove the need for technology leadership; it changes where that leadership is applied.
A fractional CTO or AI operating lead can provide this oversight without requiring a permanent hire. They help set evaluation criteria, challenge vendor claims and ensure that bought-in AI fits the wider technology architecture.
The bottom line
The growth of AI SaaS is an opportunity to move faster, but only for buyers who evaluate products with the same rigour they apply to any other enterprise system.