Not every SaaS product needs AI, but for teams that use it well SaaS Products, large language models (LLMs) are unlocking product experience that simply weren’t feasible before.
We’re seeing AI in SaaS go beyond chat interface and autocomplete fields. In the best implementations, AI agents operate inside the product — reasoning over user inputs, retrieving context from past interactions, and generating highly personalize response. This isn’t just automation. It’s software that thinks alongside the user.
Structure output generation inside real UIs
Some of the most impactful AI feature don’t generate content — they generate structure you can build on.
Excalidraw AI is a perfect example. You describe the flow you want — “a user signs up, verifie email, and hits the dashboard” — and the AI write the Mermaid.js code SaaS Products to match. The diagram appears instantly, fully editable inside the app. You’re not starting from scratch — you’re getting a smart, structure base that fits the use case.
Excalidraw using LLMs to generate Mermaid Diagram
This isn’t a static graphic. It’s code that thinks, turne into a visual workflow you can manipulate.
Other tools are exploring this too — like Uizard, which inclusive marketing and digital accessibility turns prompts into UI layouts, and Retool, where AI configure frontends and backend querie base on user goals.
In all these case, the LLM isn’t just helping the user move faster — it’s producing outputs in the native language of the product.
Decision-support agents built into the workflow
Most SaaS tools assume the user knows what to do next. AI is changing that.
Now, we’re seeing embedd agents that can read the current state of a project, issue, or document — and propose the next action.
In Linear, AI summarize bugs and issue, then suggests whatsapp number prioritization base on severity, frequency, or blocker status. It’s not just summarizing tickets — it’s interpreting urgency and nudging the team
toward action taking upon a role of a SaaS Products vertical AI agent that is essentially acting as a bridge between departments.
Asana AI is doing something similar with project data. It spots stuck tasks, misaligne owners, or schedule drift —
and quietly propose update to rebalance the work.
This type of agent doesn’t generate content. It reads signals inside the system—
task progress, assignments, inputs—and make small, helpful move that shift the direction of the work.