Keys to Achieve the Industrialization of Artificial Intelligence

Adopting Artificial Intelligence (AI) by companies in all sectors is becoming more widespread. Thus, according to the latest report, this technology is expected to generate 407 billion dollars in 2027, reaching a compound annual growth rate of 36.2%.

Although it is a mature technology, the vast majority of AI models remain in the initial testing phase, without being produced on a large scale, mainly due to the effort required to scale the model.

The lack of progress in the industrialization of these models supposes a great inefficiency of both resources for the company and the employees, who need access to technologies that could facilitate their daily work. 

In this context and due to the importance of Artificial Intelligence (AI) in the future of organizations, Sistemas highlights three key methods to achieve the industrialization of AI. 

Increase the quality, traceability and accessibility of data

The results of any AI, ML or DL ​​model, are directly related to their quality. Therefore, if an organization intends to industrialize its AI, the first step to achieve it would be to focus on its databases. 

These are usually scattered and, on many occasions, even in isolated silos, which makes it difficult to use them for large-scale AI models since if the team only has access to a subset of data, they will never be able to generate models that reflect the reality of the organization.

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Thus, the organization’s objective must be to generate data products with higher quality, availability and easier access for the operation to prevent each of the areas of the company from seeking to create its own copy to cover its specific needs and avoid duplicities. 

Once the information is centralized, it will be essential to guarantee the quality of the data, both from a structural point of view and a business perspective. In other words, in addition to being correctly completed, they are consistent and add value to employees and the company. 

Bet on Multidisciplinary Interaction

AI is directly associated with innovation. The key to innovating lies in combining different perspectives on the subject to enrich each other and provide a more complete vision. 

Therefore, the more groups that work together, the better the results. This is also essential to the industrialization of AI since the contribution and global collaboration of the different teams involved are necessary to achieve an optimal result.

With this cooperation, it is easier to build models that provide added value to the entire organization and could incur the problem of generating isolated ecosystems. 

It is also necessary that the collaboration of the different teams occurs in all cycles, from data ingestion to the implementation of analytics, working in a synchronized way so that the data is available in the shortest possible time, with good quality and ease of access.

Create operating environments

It is worth having good data and the tools to draw useful conclusions for the company. Still, it is also necessary to prepare the company for using that data by creating a global strategy.

The company must identify which of its operations must be modified to allow the adoption of new technologies and, above all, establish standards for creating, testing and deploying new AI models.

This makes it much easier to iterate and extend new models as they are created. One of the essential parts of the strategy is to establish where the selection of the algorithms and the development of the models will reside, in the technical or functional teams, and in providing them with resources to achieve greater autonomy without losing control over the developments.

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