Three Reasons Why Artificial Intelligence (AI) Pilot Projects Fail

Three Reasons Why Artificial Intelligence (AI) Pilot Projects Fail-01

As a result of the pandemic, most companies are expanding their AI efforts. Despite the fact that businesses have advanced their AI initiatives, they must be aware of the obstacles to AI adoption. It is not easy to build an AI system since every stage has its own set of challenges.

Artificial intelligence (AI) is a rapidly evolving field, with new solutions being developed and applied on a daily basis. According to Gartner, “Gartner Top 10 Trends in Data and Analytics for 2020” by 2024, 75% of enterprises will have operational AI.

There are many successful AI projects out there, but with all of these advancements, there is no single ideal strategy to construct and implement an AI product. However, there are a few reasons why firms may be missing the mark with their AI solution.

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Inadequate information

AI is constantly learning and improving its algorithms and findings in order to give better, more efficient, and more accurate solutions in the future. In order for AI projects to learn, they require a large amount of data. The more data AI can assimilate, the more accurate its output will be. However, a common problem is the scarcity of data sets for creating AI solutions.

In order to find patterns in a dataset, AI requires a large amount of data to be ingested. Predictions and output might be impacted by a lack of data. Providing AI with large training data sets can help combat this problem and reduce the risk of bias.

Where enormous data sets might be intimidating for humans, AI has the advantage of speed and can learn quickly. These solutions can achieve outstanding accuracy by providing the right quality and amount of data.

Using only one method of learning

Businesses require not only a large amount of data but also a substantial number of sources. AI must first learn in order to function. Limiting AI’s learning to a single source or knowledge base might have a negative impact on the final product’s functionality. An AI system that lacks a variety of data from various sources would have gaps in its deliverables, causing problems for both creators and end-users.

To create well-rounded solutions, successful AI technologies combine deep learning models. AI projects should integrate multiple methodologies, algorithms, and learning efforts in a way that is simple and easy for humans to interact in the event that they are required. These “ensemble” algorithms frequently outperform any single prediction approach.

When it comes to establishing an AI system, developers and business leaders should be laser-focused and aware of potential biases, such as cultural and environmental influences, as well as how they may intervene to prevent biases that may exist.

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Other employees’ lack of comprehension

Not everyone working on an AI project is a genius in the field. However, adopting an AI technology successfully necessitates a common understanding among all employees and end-users. Everyone in a company should be aware of the opportunities and constraints. A lack of deployment results from a lack of awareness among all parties involved.

For example, if the sales staff does not comprehend and know the use cases for AI solutions, they will be unable to offer them. HR departments, on the other hand, will be unable to incorporate AI into their daily workflows for onboarding new employees unless they understand how, when, and where to do so.

Everyone, from executives to staff, requires open feedback loops so that AI can be discussed and people can become familiar with the solution. Those who are more knowledgeable with AI can then explicitly express the amount of interaction required to ensure that everyone gets the necessary information for maximum efficiency.

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Umme Sutarwala is a Global News Correspondent with OnDot Media. She is a media graduate with 2+ years of experience in content creation and management. Previously, she has worked with MNCs in the E-commerce and Finance domain