2025 will go down as the year every SaaS company added AI features to their product. Most did this for optics – to avoid looking like they were being left in the dust. By and large, these features were bolt-ons to existing functionality with limited scope or impact.
In 2026 we’re already seeing ground-up rebuilds of familiar products with AI at their core. These AI-native SaaS companies will shape the future of how software really works and what it does.
The world has not yet converged on the exact definition of what being AI-native means, but there is an emerging set of requirements coming from leading AI-native SaaS vendors.
What is AI-Native SaaS?
100% Conversational
To be AI-native, you need to think of chat itself as the product’s primary interface. This is not a chatbot sidebar that just regurgitates your existing FAQs. AI-native SaaS should allow users to experience a large number of a product’s features or value conversationally. Take Cursor, for example: I can plan, develop, test, and deploy a complete application from a chat window.
Our belief is that conversation UI’s will evolve to be more than text. You can already see this everywhere, from ChatGPT Apps, Claude Skills, or Cursor’s planning mode, which drops functional multi-choice widgets into the conversation. Conversational UI deconstructs SaaS applications into their component parts and brings UI into the chat when most effective. The beauty of conversational interfaces is that they can be multi-modal. Sometimes you’re chatting in text, sometimes you’re interacting with a UI. The right mode at the right time.
Contextually Aware
Contemporary AI prompts are already getting contextually smart. You @mention files to work on in Cursor, Atlassian’s Dia lets you reference browser tabs to operate on, and Winslow lets you @mention employees for context. AI-native products will have extensive contextual awareness of the data inside them and how you might want to use it.
Instructive AI
AIs are moving from generative to instructive AI. Instructive AI enables the user to tell the AI to do things related to the application itself. This could be as simple as changing a setting: “turn on dark mode”. It could also be a more sophisticated workflow: “Add a new dependent to my healthcare plan because I just had a baby”. In both cases, the AI executes the command or brings the relevant UI needed for the action into the conversation.
For domain-specific SaaS, this interplay of natural language and UI becomes incredibly valuable. Many times, there is a gap between the domain knowledge of an expert and that of a casual user. Take payroll as an example. A CEO may not know that to pay a contractor’s urgent invoice, you need to set up an off-cycle payroll run. They just want to pay them. Instructive AI allows the CEO to describe what they need to get done in their own words and then brings them to the right part of the software to get the job done.
Agentic
We’re clearly moving into a world where our jobs are orchestrating multiple asynchronous agents at once. In almost every role, you can think about a team of agents working on different activities (perhaps even coordinating) with you centrally in command. AI-native software should understand this is the world we are moving into.
System of Record becomes System of Intelligence
Traditional SaaS applications strived to become the definitive System of Record for an important piece of information in a company (customer, employee, piece of code, customer ticket). In today’s world, this information is heavily connected to other data that can be collected (e.g. every touch point a prospect had with your ads or marketing material). SoR’s are transforming to become systems of intelligence.
As an example, employee data (home address, title, etc) is now connected to the work they are doing (Slack messages, JIRA tickets, Google Docs, Zoom transcripts). AI-Native apps are helping to collect and organize this information so that AI models can intuitively query and reason across it. A SoR is still important, but fluid connectivity to related data and open reporting across it is key.
Integrated (Slack/GPT)
Work gets done in lots of places now. AI-native applications are aware of this. Whether it’s allowing approval workflows in Slack or MCP integrations into GPT, AI-native applications attempt to fit into existing workflows rather than dictate new ones.
Accessible and Extensible (Vibe Coding)
We are moving into a world where the default behavior is to build what you need. To enable this, AI-native applications need to be designed from the ground up to be accessible (data out) and extensible (data in).
Vibe coding tools reduce the barriers for anyone wanting to extend software to fit their needs. If your core HRIS is missing a process, simply vibe code an extension to it and connect it to your workflows. If you want a custom dashboard of data for your team pulled from three places, the software should support it. In an Ai-native world, we’ll see smaller core feature sets and much better building tools.
Where does the AI-native revolution happen?
It’s an age-old tale, but entrenched incumbents will struggle to deliver truly AI-native SaaS experiences. AI-native tool adoption will start with AI-first startups that have no existing stack and a desire to eventually build anything they want on top of their stacks. This will be the battleground for new tools. SoR’s that get built on top of the most will be the winners. Lock-in will come not from complexity (which is true for today’s bloated SaaS) but from extensibility.
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