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Beware of the 4 Types of Challenges that AI Projects Encounter

Companies are working on AI projects to leverage artificial intelligence in the digital era. Unfortunately, the majority of them are doomed to fail right from the beginning. How so? In this article at Towards Data Science, Ankit Rathi explores the 4 types of challenges that AI projects encounter.

Common AI Challenges

While more businesses want to invest their time and money on AI projects, it is time they knew the challenges. Following are the risks AI initiatives encounter regularly:

Culture: AI projects need various types of skills on the team. Multiple departments and leaders mean rising internal politics. Each one of them wants to get a stronghold and take the entire credit to themselves. Also, not many are equipped with the required knowledge of data management. So, this gives rise to expectations that teams cannot achieve. AI projects are still new and require more IT spending so non-technical stakeholders withdraw support sooner than expected.

Operations: Artificial intelligence is still new and experts in this field are rare. Lack of talents and relevant data are drawbacks for AI projects. Also, these initiatives depend on collaboration between various departments. It is important to figure out which AI projects will give you the best returns. A complete assessment is necessary before taking one up. Data security is another operational challenge the companies face. With attacks becoming more sophisticated, it is important to safeguard the initiatives with strong security protocols.

Data: AI projects cannot progress much with proper data. Maintaining the quality of data in these projects has been a challenge so far. Teams are unable to make sense of leveraging data which are in unstructured form. Data privacy laws like GDPR are also major roadblocks for AI projects.

Technology: Lack of well-established IT governance can cause unforeseen technology challenges. Since AI projects require integration with various IT projects on various levels, it is important to organize tech stack. Inadequate algorithms and model explainability too can stop the AI initiatives midway.

To view the original article in full, visit the following link: https://towardsdatascience.com/why-artificial-intelligence-projects-fail-9a3fbcbf7424

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Indrani Roy

Indrani Roy is currently working as a Content Specialist for CAI Info India. She has knowledge in writing blogs, product descriptions, brand information, and coming up with new marketing concepts. Indrani has also transcribed, subtitled, edited, and proofread various Hollywood movies, TV series, documentaries, etc., and performed audio fidelity checks. She started her career by articulating a knowledge base for an IT client, and, eventually, went on to create user manuals and generate content for a software dashboard. Writing being one of her passions, reading books is naturally her favorite pastime. When not lost in the world of letters, she is a foodie, movie buff, and a theater critic.

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