The Rise of AI-Native Companies

Employees do not have as much access to AI as they would like

Two and a half years after ChatGPT entered our lives, the emergence of LLMs in our software and everyday tools still seems limited. As shown by the articles and content that continue to be published on "how to integrate AI into your company".

It is not, however, a lack of will. A Boston Consulting Group study conducted on 13,000 employees worldwide stated in 2024 that 42% of employees said they were confident about the impact of AI on their work. Another study by Slack reported that, among people who had used AI at work, 71% said these technologies improved their productivity.

But it must be noted that adoption in the office is not progressing as quickly as expected. The cause: a difficulty for many publishers of our daily B2B software to integrate artificial intelligence into their already complex technology stack.

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Software publishers struggle to integrate AI into their tools

Indeed, integrating an LLM requires rethinking the entire feature set and user interface of the software. Which existing features can be better delivered by AI? What new features can it bring? How can we integrate the ability of LLMs to provide a more personalised and adaptive user experience? So many questions to ask before starting, and iteration choices to make once the vision is clear.

It is always difficult to destroy something that works. Going backwards in order to bounce back better. And today, this context is shaking up the SaaS markets with the arrival of new highly agile players that do not have to make these choices: AI-native companies.

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AI-native companies are reshuffling the SaaS deck

Less concerned with finding new markets (as was the case with FinTech) than with doing better than legacy players in well-established markets, AI-native companies have undeniable advantages:

First, a shift in technological vision: the entire technology stack is based on LLMs. Every key information-processing step is handled by proprietary models, capable of vectorising the semantic universe of data. This allows for a greater understanding of data, and therefore better cross-referencing between them.

Second, advanced task automation: LLMs are particularly effective at understanding and creating information flows, thus reducing the amount of manual tasks the user has to perform. This means that the entire information workflow is automated by AI. The human is now only at the two ends of this workflow. At the beginning to define the working axes of the tool, and at the end to analyse the results returned by the tool.

Also, continuous improvement of the tool: something impossible for traditional software, which is fixed by nature; AI-native companies develop their own RAG systems (retrieval-augmented generation) that allow the tool to improve over the course of interactions with users and that refine its recommendations.

Finally, a change in usage: the user can ask specific questions to the tool to discover insights, instead of digging into the data and manually extrapolating information of interest. We no longer just read and click. We talk. We interact.

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Being AI-native: a time saver and a guarantee of continuous improvement

Building an AI-native solution makes it possible to reduce the time spent searching for information in favour of analysis and decision-making. Errors are also limited: no more line breaks on a data table, no more selecting wrong data filters, no more errors in task configuration.

Above all, it provides the ability to fully benefit from the continuous improvements offered by the major LLM publishers, because their improvements become ours as soon as they are accessible: instead of having to reconsider the choice of LLM when the small building block of tasks used has become more complex and now undermines its initial objective.

It is a guarantee of quality and innovation, for the company and for the users of the solution.