Digitalisation is one of the priority axes of business strategies both now and in the coming years. It is also a priority for the Spanish government, which has set digital skills targets in the 'Digital Spain Agenda 2025', focusing on both society and SMEs.
One of the tools available to companies is Artificial Intelligence. According to data from the consultancy firm Gartner, AI will lead technological investment in 2025. However, these Artificial Intelligence projects are not yet successful, since, as indicated by psychologist and Harvard University professor Howard Gardner, 85% of them fail.
In view of this circumstance, Francisco Díaz, business analyst at Compensates Human Capitalof the Howden Group, provides seven recommendations for the effective implementation of Artificial Intelligence in companies:
- Find an internal promoter for the project
One of the main causes of failure in IA projects is the lack of support and leadership. Initiatives in this field are very attractive, but the chances of failure are high. It is therefore desirable to create a prototype that illustrates the concept, without the need to use all resources, and helps to get a glimpse of 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 needed for the project. So there must be someone in a position to ask them for this information. Lack of collaboration is another of the most frequent causes of failure and will also manifest itself in a reluctance to allocate resources to the project for a wide variety of tasks to be executed outside of the development itself.
- 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 problem to be solved rather than on the technological characteristics. It should also include an approximate ROI (return on investment), the time to market the idea, the estimated effort and the pitfalls that will have to be overcome. Not forgetting a technical feasibility analysis.
- Drawing up a project charter (Project charter)
The definition of the project and its requirements is transcendental to be able to start developing it. This project charter must know the scope of the project, what we want to build and the business objectives.
- Team composition
To avoid a lack of expertise and a disconnect between software development and data science, we need to define the necessary profiles. We will need a data science specialist, but also a data engineer with IT and more traditional programming skills. It is essential that business experts are involved in the team so that they can keep track of the results.
They do not necessarily have to be brought in externally; often there are already in-house resources or more appropriate training possibilities.
- Engaging stakeholders
Over the life of the project, there will be interactions with a wide variety of professionals and suppliers that need to be managed appropriately. We must also be aware of the reluctance that IA may cause as a substitute for tasks that are currently performed.
- Constant monitoring
Problems can arise not 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 set up 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.
Finally, in addition to the above recommendations, Francisco Díaz explains that the range of technologies and algorithms that we can choose to implement our solutions is very broad. "It is important to choose simple and transparent solutions, and, above all, that it is easy to explain their inner workings," he concludes.