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Top challenges faced during AI implementation in the business

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Artificial Intelligence is the most widely discussed word in the technology trend. Many strong views about the applications and the benefits of AI arises. Few implementers also feel that the AI implementation will strongly bring in an apocalyptic day to the machines that will control human beings. 

On the contrary, AI lovers are strongly believing that it might bring in extreme benefits to humans. In the not so far future, AI and ML will involve in solving important business and social problems. Many cases involve ML as a supplement to humans. Thus, a scenario of “augmenting” roles down the human intelligence factor. 

Compared to the other terms, the commonly believed factor “mathematically complex” algorithms will become the greatest challenges. The power of leveraging machine intelligence will become a detailed discussion in every corporate blog you look upon. Integrating data across multiple sources also lands as an important challenge in ML. It helps in deriving simple rules and insights from the data. 

In the recent study, Evans Data Corporation’s study revealed that AI and machine learning will provide insights toward the challenges which might increase the enterprise level competitiveness.  The study also says that future interviews will be based upon AI and ML. 

Globally, ML engineers count ought to increase and thus the skills required for the Ml go on expanding. Focusing on the attitudes, adoption patterns, intentions, and other machine learning concepts, there are many comprehensive details inculcated in it. The noteworthy AI research goes deeply which enhances developers’ challenges today. Here are the top AI challenges faced by today’s businesses. 

Computing is not that advanced as expected!!

Computing is not that advanced as expected

Compared to the other normal techniques, machine learning and deep learning seems more beneficial these days. Both machine learning and deep learning require a series of calculations that have to be executed very quickly. AI techniques always utilize a lot of processing power that might clearly indicate the upward flow of the technological curve. 

Many experts include AI in their discussion. It has become a hot topic for experts. Always, it also comes out that AI has to be implemented with enough power. Cloud computing and massively parallel processing systems have created implementation techniques through short terms. If the particular process goes automated, then the creation of complex algorithms will be on the rise. At the same time, cloud computing has to become more advanced to tackle all those things. Hence, as of now, this becomes a great challenge while implementing AI. 

Few people go ahead supporting the technologies

Top challenges faced during AI implementation

AI implementation has been increasingly used in the present market. But, compared to its growth in the market, the AI use cases are not increasing. Without the increasing use cases, no organization will be interested in investing money in the AI-based projects. There is a clear visualization that helps in comparison to a few organizations that have been interested in putting money. Those investments are into the AI-based projects. There are other people who do not have enough business vision to understand machine progress in the world. Even the study says that there are not enough people who have knowledge regarding machine operation and self-learning. 

As a solvent of this problem, a mild cure is always necessary. But, a mild cure does not become a permanent solution anyway. A shift toward offering multiple platforms and tools that permits AI-driven work will become an excellent alternative in this case. Rather than starting from scratch, organizations will be able to make solutions and plug into their own data elsewhere. 

Trust creation

With AI, there is a great black box for people. Here, people will feel uncomfortable when they are not aware of the concept. This also leads to misleading while taking decisions in the business process. For instance, banks can use simple algorithms which might be based on the linear maths. In other words, it can also explain the algorithm and thus have reached from input to the output. 

However, there are parts where AI cannot build trust among the people. But, the solution is to let the people know the technology that really works out well. The reality is somewhat different from the illusioned one. There are multiple opportunities to make things better than before. It helps in making predictions that are more accurate. If there is a part of the regulation, then citizens might ask in explanation and thus the decisions can be made with the help of Artificial Intelligence.

Enhanced one-track minds

While implementing AI technology, a big problem is to keep in the minds to keep in a single track. It is necessary to build the operations of a particular task. It helps in learning new things in order to become better in the process. It looks upon more inputs that produce results. There are many processes that bring in generalized AI toward the operation. Generalized AI will bring in an associated process that undertakes jobs similar to the human being. However, there are many usual things that bring fortune to the future of the corporate. 

AI needs to be trained to make the solutions clear and thus it will not make any other issues. Specifically, all the areas will be undertaken when they are designed to consider as such. 


Organizations working under AI-based products may not demonstrate their vision towards AI growth. But, there are many AI techniques involved which might make people easier to handle multiple works. Even though people are benefited with the AI works, they may not have a clear understanding of the technology and the decisions related to it. 

In addition, many confusions arise which might surround the minds of the people. Ultimately, the probability of the mathematical uncertainty level behind AI may remain the same till now. Unclear with few regions, there are multiple ways to deal with things. 

AI’s decision-making process is fine and thus one lie in making AI explainable, provable, and other transparent works. Hence, organizations must implement AI to get good returns. 

Data Privacy and Security

Most of the AI applications are based on massive data volume and thus learn to make intelligent decisions. Machine learning system might bring sensitive and personal build up in nature. The AI systems might learn from the data and improve themselves. Due to systematic learning, many ML systems have become more prone to data breach and thus the identity theft has also taken under the hand. 

The step taken over due to the increasing awareness of customers has been increasing daily. The machine-made decisions might help in making a unique method toward the learning methods. With the unique learning method, the AI paradigm might help in dealing with it. Federal learning might encourage the data scientists to create an AI without any data effect toward the confidentiality things. 

Algorithm bias

AI systems have a big problem with their level of goodness. But, the data that has to be trained on is often associated with ethnic, communal, gender, or racial biases. It is necessary to use proprietary algorithms in order to find out things including granted bail, sanctioned loan details, etc. If the bias is hidden in the algorithm, then it may go unrecognized and thus it leads to unethical and unfair results.

In the future, the biases will be highlighted more and thus AI systems will continue to be practiced in the utilization of big data. Hence, the urgent need to fulfill the AI need has been shifted toward the process of training systems with unbiased data. It is also necessary to create algorithms that might easily be explained after a clear analyzation of it. 

Data Scarcity

In the present time, organizations have a way toward gaining access to more data. The data gained might bring in many benefits to it. Many datasets are applicable to AI applications and thus many employees are indulged in learning new sustainable facts. The most powerful AI machines will remain prominent in that it might indulge in supervised learning. 

The AI sort of training might require labeled data. The labeled data makes it understandable for machines to learn. Every labeled data has a limit for it. In the future automated creation, there are increasingly difficult algorithms. It will worsen the problem. But, there is a ray of hope. As time goes on, the organizations will invest in design methodologies and focus on creating more AI models. It will help in learning despite the scarcity of the labeled data. 

Bottom Line

There are risks and challenges that have been associated with AI implementation in business. Like two different faces in a coin, AI also has several opportunities for businesses. Due to opportunities associated with AI, many businesses conduct corporate training sessions.