All too often, companies focus on defining a global artificial intelligence (AI) strategy that can be applied to all of their activities in “Swiss Army Knife” mode. In practice, this approach rarely leads to concrete results.
According to IT consultant Gartner, when a company decides to integrate artificial intelligence into its activities, it should favor an iterative approach based on 5 main stages.
The first is to identify specific business projects whose scope is clearly defined and whose potential impact is significant to the business. These projects must make it possible to create measurable and effective results, in particular through specific indicators whose progress can be monitored. A classic example might be optimizing inventory management.
A dedicated team
The second step is to create a dedicated team that brings together the talents needed to complete the projects. This team will need 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 need to be outsourced, especially at the AI level.
The third step is to identify, capture and manage the data needed for the selected projects. The quality and relevance of the data must take precedence over the quantity. In fact, AI does not necessarily rhyme big databut always with smart data. These data must, of course, meet quality standards to ensure that they “represent” completely and correctly the context of the projects envisaged. However, depending on the AI technologies implemented, the minimum amount of data may vary.
This leads to the fourth step, which should make it possible to identify the AI technologies tailored to the specific objectives of the projects chosen by the company. For example, probabilistic reasoning techniques will be particularly well suited to bring out “hidden” patterns in a large amount of data, such as fraud patterns. On the other hand, perfection of routes within a supply chain problem will instead require the use of optimization techniques.
Finally, the fifth step should allow the company to structure and continue the expertise acquired during the implementation of these initial projects, in order to more quickly implement it to other goals. This step should also make it possible to identify problems or gaps in terms of skills, data and technologies, but also in relation to the general culture of the company in this specific discipline, which is AI.
This 5-step strategy is at the heart of the calls for projects proposed by the DigitalWallonia4.ai program, whose ambition is to accelerate the adoption of artificial intelligence by companies and organizations and to develop a reference ecosystem in Wallonia. These calls for projects (Start IA, Tremplin IA, Cap IA) aim to provide concrete support to companies wishing to integrate artificial intelligence into their business up to the development of operational prototypes. They enable companies to work with technology partners and research centers to take advantage of cutting-edge AI skills.
HEC Digital Lab is also part of this dynamic, especially through its initiative computer science whose aim is in particular to bring together initiatives in the field of computer science and to promote remarkable projects in this field.