AI in Business: Why Industrialization Remains a Mirage

The Illusion of AI as a Magic Solution

The industrialization of artificial intelligence in businesses faces major obstacles. Although technologies are available and tests are multiplying, 90% of projects do not go beyond the pilot stage. This situation stems from a misconception of AI, often seen as a magic solution rather than a project requiring profound organizational transformation.

In discussions about artificial intelligence, the talent shortage is often highlighted. Yet, nearly 90% of projects do not exceed the pilot stage. The technological building blocks exist, there are numerous tests, and offers to conduct pilot projects within companies are multiplying… But scaling up fails massively. This blockage reveals a persistent confusion: AI is still perceived as a “magic” solution, whereas it actually pertains to a structuring and cross-cutting subject.

The Challenge of Industrialization

Companies approach AI primarily as an exploratory subject, multiplying pilot projects without a global vision. When it comes to scaling up, they face inherited ecosystems and complex business processes. Thus, what works in demonstration becomes difficult to integrate into operational reality, often leading to disillusionment.

On the ground, we observe that AI is still too often treated as an exploratory subject. Testing is done, and use cases are identified. But at the moment of scaling up, difficulties arise, especially since companies have multiplied pilot projects in recent years without a global vision. Within organizations, it quickly confronts inherited ecosystems, business processes, and heterogeneous tools. What works in demonstration is much more complex to integrate into operational reality. Solutions then remain “on the shelf,” due to a failure to anticipate the extent of the necessary transformations. It is often at this point that companies experience a form of disillusionment, a “hangover” from AI.

A Necessary Organizational Transformation

The real challenge lies in organizational transformation. AI redefines jobs and automates certain tasks, but rapid technological cycles complicate the reskilling of teams. Rather than focusing on an alleged talent shortage, companies must structure their capacity to integrate AI, mobilizing both external expertise and internal skills.

Today, jobs are evolving faster than organizations. AI is gradually integrating into the daily lives of teams, automating certain tasks and redefining the value of work. Technological cycles are accelerating, making continuous reskilling of teams extremely difficult.

However, talking about a talent shortage is misleading. The challenge is primarily organizational. Companies must find the right skills, of course, but above all, they must structure their capacity to integrate AI. This requires mobilizing both external expertise and internal skills capable of driving transformation over the long term. AI is a systemic revolution. It can no longer be treated as a purely technical subject. It becomes a corporate project that must be supported by top management and involve business units from the outset. It requires rethinking work modes, processes, and corporate culture at all levels of the organization.

The Importance of HR and IT Alignment

A strong alignment between HR and IT departments is essential for successfully achieving this transformation. HR must rethink skill frameworks, while IT must integrate AI into existing infrastructures. Companies must decide how to utilize the time saved through AI, whether to increase production or improve service quality.

This evolution necessitates a much stronger alignment between HR and IT. HR must deeply rethink skill frameworks and job descriptions. In certain functions, such as sales, AI already allows for a significant reduction in time spent on administrative tasks. But a key question then arises: what do we do with the time saved? Do we seek to produce more, or to improve the quality of customer relationships? These trade-offs are still largely exploratory within companies.

External Expertise as a Lever, Not a Solution

While external expertise is essential for quickly injecting skills, it should not be seen as a miracle solution. The success of AI relies on a balance between internal skills, which understand the business and processes, and external expertise, which brings specialized skills and a fresh perspective. Companies must move beyond the logic of POCs and structure their scaling efforts to succeed in their transformation.

In this context, resorting to external skills is indispensable. For an IT department, it is often the quickest way to inject expertise that it does not possess internally, especially regarding rapidly evolving technologies. Engaging experts who have already led such projects helps accelerate processes, limit errors, and stay up to date. Believing that the solution will come solely from external sources is an illusion. AI cannot be delegated; it must be embraced. Success relies on the balance between internal skills, which carry knowledge of the business and processes, and external expertise, which provides a fresh perspective and specialized skills.

It is time to change our perspective. The issue is not about filling a talent shortage, but about successfully achieving organizational transformation in a context of technological disruption. The companies that will succeed are those that can move beyond the logic of POCs, structure their scaling efforts, evolve their skills, and intelligently articulate internal and external resources.