| |JUNE 202419CIOReviewCXO INSIGHTSNAVIGATING THE IMPLEMENTATION OF AI AND MLBy Agustín Izquierdo, Business Intelligence and Data Management Director, Campofrio Food GroupArtificial intelligence (AI) and machine learning (ML) have become widely available to businesses recently. The availability of these technologies in cloud environments, along with their "API-ification" and integration into ML as a service framework, has allowed businesses of all sizes to benefit from their capabilities.In addition to being technologically ready regarding availability and accessibility, the point at which technology and business converge is key to these implementations. Without a real business need and improvement, technology has no proper use. That is why the main ingredient in these implementations is to have a team with technical, business, process, and hybrid (integration) profiles that make everything flow.In the case of Sigma Europe, we have undertaken several initiatives in the field of ML, and it has been a challenging journey. When we started four years ago, ML was seen as a crystal ball, a cocktail to which data was thrown, and it returned what you wanted, typically an improved KPI.Over time, we have gone through several initiatives in which we have learned to integrate this new technology into our processes and to understand the importance of the path of learning and failure that exists before production deployments, as well as the complexity of finding a good use case that brings real value to the business. Our experience has led us to undertake these projects with a defined roadmap based on three pillars, applicable regardless of the business area associated with the use case.MethodologyWe always start with a pilot project, which aims to know if the production project would benefit the company. It is done in the simplest way in terms of integration but more complex in terms of questioning and discovery.Given that the phase of problem definition, the variables to be handled, and their correlation, as well as all the discoveries made in exploratory analyses, are significantly changing, pilot projects are developed with agile methodology.Business questions, as well as requirements, change as we learn from the data. Only when this pilot produces relevant results for the business is a production project proposed. Agustín Izquierdo
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