For any company wishing to implement this technology, it is important to determine the cost of using existing solutions or developing its own model.
In the field of technological implementation, there is no universal solution. However, it is possible to identify potentially unnecessary investments for an organization, which could be considered costly.
When an entity, regardless of its size, considers the digitization of a process, starting with the development of a model, without prior evaluation Given the existing advances in models related to that area of knowledge, it is highly likely that such an effort will be costly.
It is essential that organizations understand:
• What aspects of automation and digitization can be addressed through a model?
• What are the existing gaps between solving that specific problem and the capabilities of current models?
With this information, it is possible to determine the cost of using existing solutions or to undertake the challenge of developing your own model.
If developing a model is not essential for the organization, but simply using one, the cost is primarily determined by the adoption time and by the volume of use, in a manner analogous to how current systems are evaluated.
AI specialist firm vs in-house development
The choice between in-house development and outsourcing of AI solutions should consider the strategic nature of the process to be addressed.
For processes that are part of the core of the businessInternal talent development is recommended, taking advantage of the benefits of greater control, flexibility, and knowledge retention within the organization.
On the other hand, for support systems not central to the businessOutsourcing emerges as the most efficient option, allowing the organization to accelerate implementation and concentrate its resources on its core business.
However, the optimal strategy for many organizations might be a hybrid approachCombining outsourcing with internal project control, this model allows you to capitalize on the best practices from both sides.
And the data?
The quality and quantity of data significantly impact the cost to develop AI solutions, especially in the Latin American and Chilean context.
Data quality directly affects the effectiveness and efficiency of development:
• High-quality data reduces preprocessing and cleaning costs, accelerating development and improving model accuracy.
• Low-quality data increases costs by requiring more time and resources for cleaning, and can even compromise the viability of the project.
In terms of quantity, a larger volume of data generally improves model performance, but It increases storage, processing, and management costs. In emerging markets like Chile, where data infrastructure can be limited, this represents a significant challenge.
The balance between quality and quantity is crucial. An effective strategy can be prioritize quality over quantity in the initial stagesgradually expanding the data volume as the model is refined. This allows for the optimization of resources and the mitigation of risks, which is especially relevant in contexts of budget constraints.
Additionally, the availability and accessibility of data in specific sectors can vary significantly in the region, impacting the costs and feasibility of certain AI projects.
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