AI in Business: Governance Challenges and Cultural Adaptation

The Illusion of Innovation Through License Purchases

In the business world, it is common to see companies confuse the purchase of licenses for artificial intelligence tools with a genuine innovation strategy. This approach, while widespread, is often misguided. At Inside, we have understood that success in AI does not lie in the mere acquisition of technologies, but in preparing and adapting teams for their use. True innovation requires a transformation of mindsets and processes, far beyond just buying software.

History teaches us that during the digital revolution, companies that thrived did not simply buy computers and software. They rethought their business and organizational models. Today, artificial intelligence demands a similar reevaluation. It is with this perspective that we created the iAxLab, a dedicated unit within our company. This structure serves a dual role: it is both a driver of innovation and a guarantor of security. The goal is to provide structured and secure access to AI tools while protecting the company's sensitive data.

The Urgency of Cultural Adaptation Against the Tool Trap

Many companies approach AI solely from a technological standpoint, focusing on the tools. This exclusive focus is a major strategic error. Without solid governance, a rigorous security framework, and, above all, an adapted corporate culture, the industrialization of AI cannot be sustainable. The cultural adaptation of teams is essential for successfully navigating this transformation. At Inside, we have implemented concrete workshops to raise our employees' awareness of the impact of AI on their professional lives.

However, adopting AI remains a significant challenge. While enthusiasm is generally present, moving from intention to practice is complex. Effectively using AI requires stepping out of one's comfort zone. This involves rethinking work habits, abandoning traditional reflexes like the immediate use of Google or Word. AI must become the starting point for reflection, not a fallback solution.

Concrete Applications Beyond Rhetoric

Once the ethical and security framework is established, it is crucial to move from theory to practice. AI should not be used for its own sake, but to target repetitive tasks where human intervention adds little value. At Inside, we have moved beyond the gadget stage to integrate AI into our daily activities. For example, our sales teams use a custom CV generator, CV Gen, which automates the adaptation of profiles to the specific needs of each client, thus optimizing work time.

In the field of human resources, we have developed a tool that cross-references market data, experience levels, and our equity policy to propose fair compensation. Additionally, our pre-sales process has been enhanced through solutions like NotebookLM, which allow us to quickly analyze large specifications and formulate strong commercial proposals based on our previous successes.

In the software development sector, we are already observing productivity gains of around 15 to 20%. With the emergence of AI agents, the role of developers is evolving. They are transitioning from code producers to controllers and validators, requiring managerial and psychological support to facilitate this transition.

The Challenge of Sovereignty and Return on Investment

It is crucial not to focus solely on an immediate and quantifiable return on investment (ROI) in financial terms. The question of AI ROI must be considered from a temporal perspective: when can we expect a return on investment? While these tools may allow for short-term time savings, their long-term profitability is questionable. Quickly producing unmaintainable applications or poor-quality code can lead to costly technical debt in the long run. Initially, the ROI of AI is measured more in terms of employee experience, work comfort, and the quality of deliverables.

Moreover, it is important to keep in mind that AI is not free. Each request and each content generation has a cost, often referred to as "compute." Without a thoughtful strategy, a company can quickly see its costs soar, jeopardizing the desired ROI. This is why a FinOps approach applied to AI is now essential to monitor, optimize, and control the use of these tools on a daily basis.

Finally, as Cloud architectures reach maturity, data sovereignty becomes imperative. By 2026, it will be crucial for companies to have complete control over their data. Large accounts, in particular, demand this control. Using European solutions, such as Mistral AI models, deployable in closed environments or on internal infrastructures, constitutes a significant competitive advantage.

AI as an Accelerator: Choosing the Right Direction

An essential piece of advice for leaders looking to integrate AI into their companies is to never underestimate the importance of the human factor. AI is an accelerator that amplifies existing reality. If processes are flawed and insecure, AI will only accelerate the production of flaws and poor results on a large scale. It is crucial to secure the foundations of the company and involve the CIO as a strategic partner to avoid Shadow IT, which refers to the leakage of sensitive data to uncontrolled public tools.

Training teams is also paramount. Traditional jobs will not disappear, but they will evolve into supervisory roles. The future belongs to "augmented employees," such as the Product Owner capable of creating concrete and immediately usable applications. This approach allows for real testing, precisely identifying business value, and determining relevant financial investments. Similarly, the augmented recruiter will focus on human and relational skills. Those who refuse to adapt or believe that a simple software license is enough to transform their company risk falling behind.