Artificial Intelligence and Machine Learning capabilities are still developing, and it is important to understand their limitations and requirements. The adoption time can be fast, but it depends on resources, quality of the data, how it will be used, the nature of each business, and its technology infrastructure.

Here, we might think that technology is one of the main problems – or let's call it a challenge – in the adoption of Advanced Analytics solutions. However, the main challenges we've detected from our experience in projects are implementing them into business processes and applications, the internal company culture, and its resistance to change and evolve its team's roles and training.

1. Difficulty in developing and implementing processes and applications: This is because the business area and the technology areas are not aligned. As a solution, the "Analytics Translator" profile arises to mediate and be a communication channel, to be the Citizen Data Scientist with knowledge of both the business and Data Science. This role serves to scale Advanced Analytics in the market since there still aren’t enough Data Scientists to cover the existing demand. 

2. Company culture: The company's management must detect and be clear about the need for Advanced Analytics projects and ensure they align with the strategic objectives.

3. Roles and training: Many companies don't have the appropriate roles in developing Advanced Analytics solutions because they believe it's only necessary to have Data Scientists. However, experts in all key areas are needed:  Data Engineering, ML Engineering, Data Science, and Business Insights. It's also key to involve the business users who will use the solution for it to be successful.


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