Sunday, December 4

AI, seven keys for its implementation in companies

Digitization has become a key point in terms of business strategy for the coming years. Within this, one of the tools they have is Artificial Intelligence. According to data from Gartner, in 2025 AI will lead their technological investment.

However, these Artificial Intelligence projects are not yet successful. Given this circumstance, Francisco Díaz, business analyst at Compensa Capital Humano, of the Howden group, has presented seven recommendations to ensure that AI is implemented effectively in companies.

Keys for the implementation of AI

These are the seven recommendations to ensure that Artificial Intelligence is implemented effectively in companies:

  • Find an internal promoter for the project, One of the main causes of failure in AI projects is the lack of support and leadership. Initiatives in this field are very attractive, but their chances of failure are high. For this reason, it is desirable to create a prototype that illustrates the concept, without the need to use all the resources, and helps to glimpse its results.
  • collaboration on data, Artificial Intelligence is based on data and, to a greater or lesser extent, the company will have people or groups that handle information necessary for the project. So there has to be someone willing to ask them for this information. The lack of collaboration is another of the most frequent causes of failure and will also manifest itself in the reluctance to assign resources to the project for a wide variety of tasks to be executed outside of the development itself.

These are the seven keys that will help you avoid the failure of Artificial Intelligence projects

  • Optimal selection of Machine Learning initiatives, A project of these characteristics requires an investment in resources, which will need to be well planned to justify its cost. In the proposal, it is preferable to focus on the business problems that they solve instead of on the technological characteristics. In addition, you should include an approximate ROI (return on investment), the time it takes to market the idea, the estimated effort and the pitfalls that will have to be overcome. Without forgetting a technical feasibility analysis.
  • Prepare a project charter document, The definition of the project and its requirements is transcendental in order to start its development. This project charter must know the scope of the project, what we want to build and the business objectives.
  • team composition, To avoid the lack of experience and the disconnection between software development and data science, the necessary profiles must be defined. We will need a specialist in data science, but also a data engineer with knowledge of IT and more traditional programming. It is essential that business experts are involved in the team so that they can monitor the results. They will not necessarily have to be incorporated externally, many times there are already resources in the company itself or more adequate training possibilities.
  • Involve stakeholders, In the useful life of the project, there will be interactions with a wide variety of professionals and suppliers that must be managed properly. We must also be aware of the reluctance that AI can cause as a substitute for tasks that they currently perform.
  • constant monitoring, Problems cannot arise only in the implementation of the project, but it is necessary to pay attention to how to execute what we have drawn. The possibilities of artificial intelligence are endless, so it is advisable to keep a conservative scope and establish development phases. Also, keep in mind that AI projects have a software development component, but it is also important to choose the right management method.
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