What Are The Challenges That Hinder The Implementation Of Artificial Intelligence In Business Settings?

It is undoubtedly true that Artificial Intelligence is no more restricted to innovation labs only. It is being admired for its incredible potential to revolutionalise businesses. However, companies need to tackle specific challenges before they can find out the real potential of this technology.

Artificial intelligence is the pride of every tech-powered business enterprise. Such enterprises are taking artificial intelligence to integrate several transformative attributes to leverage the value chain. However, business-friendly it may sound; the path to implementing AI has not been a smooth ride. According to the latest survey, only 6 percent of tech-powered business enterprises are having a smooth ride with artificial intelligence. This leaves a staggering 94 percent that faces challenges in executingbusinesses developing artificial intelligence to their enterprise. Following are the top challenges that enterprises face while implementing an AI solution to their business settings.

Lack of Technical Knowledge

To find the deployment of Artificial Intelligence applications, companies require experts who havea deep understanding of the existing AI technologies, its restrictionsand the current advancements. Artificial Intelligenceis a niche domain; the lack of AI knowledge in management is impeding its adoption in most of the cases.

Another obstacle in emerging technology implementation is hyper-optimism, which makes business executives work without an ROI tracking of Artificial Intelligence adoption, towards objectives that are far to achieve. The deficiency of skilled human resources that could implement AI and ML solutions to business is another reason.

Data Acquisition and Storage

Data acquisition and storage is a significant challenge in Artificial intelligence implementation. Industrial AI systems rely on sensor data as its input. The extensive amount of sensor data collected for Artificial intelligence validation might show noisy datasets that are hard to store and analyse, thus causing an obstruction.

The Cost Factor

Artificial Intelligence technologies are an expensive deal to a business enterprise. While big names like Facebook, Microsoft, Apple, Google, and Amazon have separate budget allocations for AI adoption, it is the small and mid-size business enterprises that struggle to implement artificial intelligence seedin business processes. Comparing the multi-billion dollars Artificial intelligence opportunity, analysed by consulting majors, it is clear that AI adoption is an expensive venture.

Ethical Challenges

The admiration of Artificial Intelligence has elevated a plethora of challenges that come out of ethics and principles that are yet to be resolved. AI bots are progressively mimicking human chats to perfection. The humanoids like Junko Chihira, Sophia,Nadine have perfected human caricature and emotions that is spookily true to accept.  It is becoming challenging to determine whether the customer service representative we are chatting with is a machine or a human. This causes an ethical and moral challenge, which makes the Artificial Intelligence solution a tough technology to implement.

Costly Human Resource

Artificial Intelligence needs a human resource that is explicitly trainedfor its adoption. Data engineers, data scientists and subject matter specialists in today’s market are rare and expensive. Companies which have tighter budgets are not in a position to hire talent as per their project requirements. Hence that causes an impediment.

Lack of Computation Speed

Artificial Intelligence and the promising machine learning (ML) and deep learning solutions claimed by the market, require a considerable amount of calculations to be computed instantly. This needs processors that contain much-advanced processing power as compared to what is in general technological adoption today. As animmediate solution, cloud computing, as well as massively-parallel processing systems, have provided to fulfil business requirements. The issuearises, as the volume of data continues to grow, and deep learning carries more complex algorithms into existence. The solution to this problem is hidden in the development of next-generation computing infrastructure solutions, such as quantum computing that is built on quantum superposition concept to execute operations on data far more quickly as compared to today’s computers.


Although artificial intelligence is a costly retreat requiring a human resource that is difficult to find and entails supercomputer processing speeds for successful adoption, one cannot let go the innovatory changes it is bringing to an enterprise. Rather stay back evaluating the negatives, enterprises should focus on how they can responsibly decrease the ill effects of this technology. The key lies in reducing the challenges and maximising the advantages through the development of an extensive technology adoption roadmap that comprehends the core capabilities of AI.

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