AI is slowly progressing in business, hampered by rigid B2B tools. A-native actors, more agile, automatize and transform uses while providing a more interactive experience.
Employees do not have as much access to AI as they would like
2 and a half years after the arrival of Chatgpt in our lives, the emergence of LLM in our daily software and tools seem to be limited. Demonstrates the articles and content which continue to appear on “how to integrate AI into your business”.
However, it is not the desire that is missing. A study by the Boston Consulting Group carried out on 13,000 employees worldwide said in 2024 that 42% of employees declared themselves confident about the impact of AI on their work. Another Slack study expressed that among people who used work at work, 71 % declared that these technologies were improving their productivity.
But it is clear that adoption at the office does not advance as quickly as expected. In question, a difficulty of many publishers of our daily B2B software to integrate artificial intelligence into their already complex technological stack.
Publishers find it difficult to integrate AI into their tools
Indeed, integrating an LLM asks to rethink all the features and the user interface of its software. What existing features can be better carried out by AI? What new features can it bring? How to integrate the ability of LLMs to provide a more personalized and adaptive user experience? So many questions to ask before you start, and choice of iteration to make once the vision is clear.
It is always complicated to destroy something that works. Go back to bounce better. And today, this context turns the markets of the Saas with the arrival of new ultra-argile actors who do not have to make these choices; AI-NATIVE companies.
Ai-Native companies rebuild the cards of the world of SaaS
Less in a hurry to find new markets (as was the case with fintech) than to come and do better than historic players on very established markets, AI-Native companies have undeniable advantages:
- First, a change in technological vision: all of their technological stack is based on LLM. Each key information processing step is handled by proprietary models, capable of vectorizing the semantic universe of data. This allows a greater understanding of the data, and therefore a better crossing between them.
- Then, a advanced automation of tasks: LLMS are particularly efficient to understand and create information flows, thus reducing the amount of manual tasks to be performed by the user. This means that the entire informational workflow is automated by AI. Human is only at the two ends of this workflow. At the beginning to define the work areas of the tool and at the end to analyze the results raised by the tool.
- Also, a continuous improvement of the tool: something impossible for traditional software that is frozen by nature, AI-Native companies develop their own RAG systems (increased recovery generation) which allow the tool to improve through interactions with users and which refines its recommendations.
- Finally, a change of use: the user can ask specific questions to the tool to discover insights, instead of digging data and manually extrapolating information of interest. We only read and click. We are talking. We interact.
Be a-native, a time saving and a guarantee of continuous improvements
Developing an ai-native solution helps reduce information time for information for the benefit of analysis and decision-making. The error is also limited: more line jump on a data table, more selection of bad data filters, more errors in a task setting.
This above all gives the ability to fully benefit from the continuous improvements offered by the large LLM publishers, because their improvements become ours as soon as they are accessible: instead of having to review its LLM choice when the small brick of tasks used has become more complex and now serves its initial objective.
It is a guarantee of quality and innovation, for his company and for users of the solution.
