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How AI Affects Business Today

Artificial intelligence (or AI) in 2021 has taken several significant steps to become more commonly used and relied upon as a business tool in ways that just ten years ago were only possible in science fiction. AI offers potential for rapid growth and transformation in communication, customer relations, and financial management, just like when personal computers were first integrated into the workplace.

With the emergence of AI to date, we have seen many uses for artificial intelligence software. Chatbots now automate preliminary information collected from customers, prioritizing the need for human interactions to maximize staff effectiveness. Software as a service, or SAAS, options for just about any industry are as numerous as the companies they serve. While software such as this has already made many management-level employees’ organizational work much more streamlined, they are pushing to increase the ease and efficiency of using data and information to run businesses more effectively.

AI plays its most considerable role in business here. The joint report from Dell and Intel’s 2019 Making AI Real revealed that 25% of companies are now working with AI to streamline and better manage all the disjointed data they receive from their software use. Many companies now have access to large amounts of data regarding customer demographics, sales trends, financial decisions, and more to consider. However, much of it is spread across many platforms, and businesses struggle to effectively use the information.

Using AI and machine learning models, this data can be aggregated based on specific criteria. Simultaneously, the AI algorithm becomes more efficient at learning what data sets to use and which to discard based on its intended purpose.

Today we are at an exciting intersection of technological advancement in theoretical practice and the attempts to apply AI in a practical business way. AI capabilities are designed to emulate and automate human input. Namely, deep language learning in the form of “seeing,” “understanding,” and “creating”; however, this has presented its challenges. Business leaders trying to use this technology should be aware of a few things.

AI is a form of software designed to make a machine smarter, whereas machine learning is the field responsible for much of AI growth. Machine learning happens when data is presented to the software in the form of inputs and outputs. After a certain amount of data has been analyzed by the system, the machine learning model finds underlying relationships between the data, leading to insights that might have otherwise never come to light.

While this kind of information has tremendous competitive advantages, it has also created new challenges. For example, an agricultural company may be using a machine learning model to find specific correlations between soil nutrient levels and the year’s season. While AI tech might gather and aggregate this data and present it in the form of a prediction used for future nutrient levels and crop recommendations, there is no way to know if that prediction was accurate, especially if your AI is still in the early “learning” stage of data inputs.

If there are anomalies in the data, such as a natural disaster or economic forces, data can become skewed, and the model then loses its effectiveness. So although AI helps tremendously with data management, it doesn’t mean that all the data is “hands-off” just because AI implementations have been integrated into the business structure.

This technology is still developing, and experts on a global level still debate ethics, workflow, governance, and business concerns. Just as with any new technology, progression with a healthy dose of prudence and foresight can be the difference between spending money on a system that doesn’t help your business at all or having the competitive edge that can make your company an industry leader.


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