These are the five steps to succeed in your artificial intelligence business

Too often, companies focus on defining a global artificial intelligence (AI) strategy that can be applied to all their operations in “Swiss Army Knife” mode. In practice, this approach rarely leads to concrete results.

According to IT consultant Gartner, when deciding to integrate artificial intelligence into its operations, a company should prefer an iterative approach based on 5 main phases.

The first is to identify specific business projects whose scope is clearly defined and whose potential impact is significant to the business. These projects should make it possible to generate measurable and impactful results, in particular through specific indicators whose evolution can be monitored. A classic example could be to optimize inventory management.

A dedicated team

The second step is to set up a dedicated team that brings together the talents needed to carry out the projects. This team will have to combine profiles that master AI technologies (machine learning, natural language processing systems, etc.), the company’s IT infrastructure and the business requirements associated with the projects. Depending on the size of the company, some of these skills will need to be outsourced, especially at the AI ​​level.

The third step is to identify, record and manage the data needed for the selected projects. The quality and relevance of the data must prevail over its quantity. Indeed, AI does not necessarily rhyme with big databut always with smart data. This data must, of course, meet quality standards to ensure that it fully and correctly “represents” the context of the targeted projects. However, depending on the AI ​​technologies used, the minimum amount of data required may vary.

This leads to the fourth step, which should make it possible to identify the AI ​​technologies adapted to the specific objectives of the projects selected by the company. For example, probabilistic reasoning techniques will be particularly suitable for uncovering “hidden” patterns in a large amount of data, such as fraud patterns. On the other hand, perfecting routes within a supply chain problem requires the use of optimization techniques.

Finally, the fifth step should allow the company to structure and perpetuate the expertise gained during the implementation of these first projects, in order to deploy it more quickly for other objectives. This step should also make it possible to identify problems or gaps in terms of skills, data and technologies, but also in terms of the general culture of the company in this particular discipline which is AI. program

This 5-step strategy is at the heart of the calls for projects proposed by the program, which has the ambition to accelerate the adoption of artificial intelligence by companies and organizations and to develop a reference ecosystem in Wallonia. These project calls (Start IA, Tremplin IA, Cap IA) aim to provide concrete support to companies that want to integrate artificial intelligence in their company up to the development of operational prototypes. They offer companies the opportunity to collaborate with technology partners and research centers to take advantage of advanced AI skills.

The HEC Digital Lab is also part of this dynamic, in particular through its initiative data science whose aim is in particular to bring together initiatives in the field of data science and to promote notable projects in this field.

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