Understand The Advantages And Difficulties Of Creating AI

As a result of the previous phase, you will certainly already have a clearer position on developing and creating AI or buying a market solution. 

But it is necessary to analyze the advantages and difficulties of creating an AI further. Let’s go to them:

Advantages Of Creating An Artificial Intelligence  

Build AI Skills From The Ground Up  

Most algorithms are freely available, but by themselves, they offer no advantages. The same thing with market artificial intelligence tools:  it’s not plug-and-play, unwrap and use.  

Most of them need to be adapted, configured, and trained. As this implies working closely with their customers for vendors and the ability to help them link tools to data, many organizations see this as an opportunity to build AI skills from the ground up.  


If AI solutions need to be designed for specific organizational conditions and calibrated to your data, another advantage of artificial intelligence is customization. 

This benefit will be given from the beginning. The organization builds what it needs rather than relying on packaged solutions with unnecessary functionality that it needs to adjust to. 


It allows the organization to develop features that align with specific or future needs. 


The so-called black box is the organization that owns the quality standards, whether in the prevention of bias or problems generated by the difficulty in understanding the algorithm’s path to producing certain results. 


By creating the AI ​​solution, the organization is confident that its data will be protected from potential breaches. In certain cases, suppliers will have better access to data than the company itself. The advantage of developing artificial intelligence lies in losing dependency on the supplier. Intellectual property. 

Difficulties Of Creating An Artificial Intelligence 

Lack Of Experience 

Many companies that intend to use AI aren’t exactly technology companies – and they don’t need to be. But internally, this implies inexperience with data and, consequently, difficulties in building a solid strategy in AI.  

Talent Acquisition 

Until recently, artificial intelligence was an academic niche with few business professionals. With the imbalance between supply and demand generated by the search for professionals by companies, difficulties arise in building data science teams. 

The suppliers of artificial intelligence products, more robust and linked to the technology area, concentrate most of the most qualified professionals, which makes acquiring talent challenging and expensive.

Sufficiently Large And Organized Database  

We said above that data needs to be trained, and applications need to learn about it in a supervised way.  Therefore, having a vast and organized database is a prerequisite for the development and accuracy of artificial intelligence.  

At this point,  suppliers may have advantages over companies because they can gather their own and third-party data in a breadth and depth that is difficult to find in companies. 

Sharing Knowledge 

The organization may find it difficult to leverage the knowledge of others, such as upgrades and performance improvements. 


Research and development are all in charge of the organization, not diluted among several clients. 

Implementation Period 

The production time of an artificial intelligence implies a delay that generates considerable costs for the organization.  

Build AI Or Buy: Don’t Let The Dilemma Hold Your Progress 

With the above analysis, we hope your decision to develop or buy a ready-made AI tool will be more effective and tailored to your case. The general rule of thumb is: that the closer the project is to your core business, the more likely you will need some customization. 

Even if such a decision remains anything but trivial, any remaining uncertainties should not impede your progress. The decision to create your artificial intelligence will never be against the search for a supplier of a finished product and vice versa.

Also Read: Six Most Common Challenges In AI Projects

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