February 1, 2024

AI: Situations where building makes more sense than buying – and vice versa

In 2024, businesses are faced with a crucial decision; whether to create their own AI solutions or purchase pre made products. This choice is similar to deciding between getting a tailor-made suit or buying one off the rack. Both options have their own advantages and challenges.

Building Custom AI

Let’s explore this complex situation by first examining cases where developing your own AI provides more benefits. The appeal of building customized AI systems lies mainly in their ability to be tailored specifically for certain industries. For example, in fields like advanced healthcare diagnostics or intricate financial forecasting, generic AI solutions often don’t fit perfectly. In these areas, having a custom-built AI system that is precise and specific can make a significant difference. Additionally, companies with exclusive datasets can utilize this asset to train unique AI models, uncovering insights that off the shelf solutions with generic datasets cannot comprehend.

However, embarking on this path is not without its difficulties. Creating a custom AI solution requires a substantial investment of time, resources, and expertise. The journey from conceptualization to implementation can be long and challenging, involving technical hurdles and necessitating the collaboration of skilled data scientists and engineers.

Furthermore, it is the organization’s sole responsibility to maintain and update the AI system, which is a commitment that should not be taken lightly.

Off-the-Shelf Efficiency

On the other hand, opting for premade AI solutions is a completely different approach. This option is perfect for businesses that want to implement AI quickly without the extensive effort of building it from scratch. Off the shelf AI products offer convenient plug and play functionality, allowing companies to swiftly deploy AI capabilities and quickly reap the benefits. This is especially attractive for small to medium sized enterprises (SMEs) or startups that lack the resources or expertise to develop their own AI systems. Moreover, these solutions often come with continuous support and updates provided by the vendor, making maintenance less burdensome.

However, this path also has its drawbacks. The off the shelf nature of these AI solutions means they may not perfectly align with an organization’s specific requirements or seamlessly integrate with existing systems. Additionally, relying on external vendors raises concerns about data security, privacy and control over the AI models and their outputs.


Ultimately, deciding whether to construct or purchase AI solutions isn’t a simple yes or no choice. It’s more like a spectrum, where the best decision relies on various factors such as the size of the organization, industry type, available resources, and specific requirements. Similar to choosing between a custom-made suit and an off the rack garment, each option offers a distinct fit that suits different situations. In the ever evolving and wide-ranging realm of AI, grasping these subtleties is crucial for harnessing its transformative potential.


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