Too often, companies focus on defining a global artificial intelligence (AI) strategy that could be applied to all of their businesses in a “Swiss Army Knife” fashion. In practice, this approach rarely leads to concrete results.
According to IT consultant Gartner, when a company decides to integrate artificial intelligence into its operations, it must favor an iterative approach based on 5 main phases.
The first consists in identifying specific business projects, whose perimeter is clearly defined and whose potential impact is significant for the company. These projects must make it possible to generate measurable and impactful results, in particular through specific indicators whose evolution can be monitored. A classic example could be the optimization of inventory management.
A dedicated team
The second step is to create a dedicated team, which brings together the talents needed to carry out the projects. This team will have to combine profiles that master artificial intelligence technologies (automatic learning, natural language processing systems, etc.), the company’s IT infrastructure and business requirements related to projects. Depending on the size of the company, some of these skills will need to be outsourced, particularly at the AI level.
The third step is to identify, acquire and manage the data needed for the selected projects. The quality and relevance of the data must take precedence over its quantity. In fact, AI doesn’t necessarily rhymes with big databut still with intelligent data. These data must obviously meet quality standards to ensure that they fully and correctly “represent” the context of the planned projects. However, depending on the AI technologies implemented, the minimum amount of data required may vary.
This leads to the fourth step, which should allow for the identification of 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, refining paths within a supply chain problem will instead require the use of optimization techniques.
Finally, the fifth step should allow the company to structure and perpetuate the skills acquired during the implementation of these first projects, in order to deploy them 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 specific discipline which is AI.
This 5-step strategy is at the heart of the calls for projects offered by the program DigitalWallonia4.ai whose ambition is to accelerate the adoption of artificial intelligence by companies and organizations and to develop a reference ecosystem in Wallonia. These calls (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 offer companies the opportunity to work with technology partners and research centers to benefit from cutting-edge artificial intelligence skills.
The HEC Digital Lab is also part of this dynamic, in particular through its initiative data science whose objective is in particular to federate initiatives in the field of data sciences and to promote important projects in this field.