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Artificial Intelligence – 7 Keys on how to go from experimentation to success

In 2025, one of the fastest growing areas in the field of digitization is expected to be Artificial Intelligence. Leading the technology investments of companies around the world.

Artificial Intelligence is a tool with great potential for companies and companies in the Pharmaceutical and Health sector. Since, thanks to the logarithmic techniques applied in these methodologies, great value can be generated for the business.

However, many organizations still don’t know how to use it to their full advantage. For this reason, the vast majority of AI projects are not reaching the production phases.

In this context, Keepler Data Tech points out 7 steps that any company should take to scale the use of Artificial Intelligence throughout the organization:

 

Artificial intelligence
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Data infrastructure

Every organization should consider building their infrastructures on top of public cloud environments. Some of the top public clouds out there spend more than $90 billion on R&D, ensuring they have the capabilities to meet future needs.

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Dedicated Resources

On the other hand, it is also important to dedicate specialized people to this type of Data Platforms. People who have the ability to lead initiatives.

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Organizational silos

Avoiding organizational silos is also of the utmost importance, as this prevents, for example, the reuse of investments in projects. One way to avoid this problem is by generating an organizational culture around data.

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Data silos

Just as important is to avoid data silos so that there is no operational disconnection.

Since over time, the data loses value. Therefore, the most appropriate approach would be to have them close to where they are produced and create a more federated model for data access.

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Value proposal

The companies that succeed are the ones that make mistakes quickly and quickly move on to another goal. For this reason, we always recommend incremental interactive approaches, where the data is explored, a pool of limited data is identified, a proof of concept is carried out, knowledge is extracted and, above all, decisions are made based on the results of this project.

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AI governance model

There are companies in which decision-making is highly centralized and others that can delegate decision-making to other business areas. It is the great dilemma between speed and control over data.

However, there are a series of minimums at the level of design security, design privacy, service control. All of this must be automated and centralized, but capable of supplying the different areas.

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Effective implantation

It is important to align AI efforts with process automation efforts. Since, it usually happens that small experimental projects that are proven to work, stay in the drawer, and are useless if an adequate implementation or start-up is not achieved, which is the added value of this type of project.

 

BAIA: The new Artificial Intelligence engine developed by BISIONA

BUSINESS ARTIFICIAL INTELLIGENCE ANGEL, is the new set of models and methodologies proposed by BISIONA that combines different Artificial Intelligence and Data Science techniques. And that helps companies create more precise business strategies and improve sales performance thanks to the recommendations generated by the system.

How do we do it? Through the application of different statistical and Machine Learning algorithms that enable learning and the search for patterns of success. Thus, generating a differential, unique and real value proposition for each of our clients.

In addition, at BISIONA we are aware of how important it is to ensure the proper integration of this type of technology in each business. Adapting it to the specific needs and characteristics of each laboratory and company.

That is why the AI ​​solutions we offer through baia are particular and specific, adapted to each business, product, pathology and objective.

 

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