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Identifying the Three Biggest Myths About AI in Enterprise

Artificial Intelligence is becoming more and more of a reality every day, but there are still a large number of myths about what AI is and isn’t, especially within the business sector. How AIs can learn and automate enterprise systems isn’t completely clear, even to some corporate leaders who work in companies that utilize AI. Addressing these myths and bringing to light the truth about them can help business leaders see where AI has potential and how it can help their businesses grow.

Myth: AI Is a Fad

AI may seem like a fad that will eventually pass, but the truth is that machines capable of learning are becoming more and more useful in the workplace. These machines are beating humans in areas such as voice-to-text translations, image recognition, and even various games. The world’s fascination with AI isn’t something that can be ignored.

The growth of AI has been driven by several different factors. The first of these is the innovation of learning algorithms. These algorithms have made it possible for computers to learn more in a shorter period. They’re learning in ways that are similar to how humans learn, giving them the capability of doing more. Another factor is that many more businesses, including small businesses, are collecting big data. AI can use this data to fuel its thought processes and learning. It can also parse and analyze this information much faster than can human beings.

In addition to advances in programming and algorithms, hardware improvements have also fueled the growth of AI. With faster processors and GPUs, AI can now learn in hours what once took days or even months. Hardware continues to advance on a regular basis, and many experts anticipate that the next generation of GPU chips will allow machine learning to improve even more. Consequently, it can be expected that AI will continue to evolve and move into many industries, making it impossible for companies to ignore.

Myth: AI Is Already Widespread

AI has become more widespread and has been adopted by some different industries, but currently, it’s still being used in fairly limited ways. Most experts anticipate that this trend will continue over the next five years or so as companies cautiously begin integrating AI into their workflow. Most companies will likely use AI for single-task solutions— machines being used to master one job each rather than learning about how an entire process works and becoming able to handle any task within an overall process. AI will also most likely still need to be supervised, meaning industries won’t need fewer workers. In fact, they may need more employees as businesses begin hiring AI experts.

While there are certainly risk-takers out there, many business leaders prefer to use what’s been proven. They’re unlikely to look at advanced, unsupervised AI tools at present since those tools haven’t been fully tested and validated. For example, a company might make use of an AI to validate invoices and handle accounts payable, but AI may not be able to do such things as evaluate invoices and accounts for fraud because it isn’t yet capable of learning beyond a single task.

Shortly, these narrow solutions will likely be as far as AI goes, and this will allow for the more supervised use of AI and machine learning, rather than letting AI take over an entire process. That fact will likely give business leaders a sense of confidence about using AI and help dispel the myth that many employees have in their minds about machines taking over their jobs.

Myth: Implementing AI Is all about Technology

Finally, the third biggest myth about AI is that it’s a technology issue only. This isn’t the case. There are some other factors that have to be evaluated to implement AI. Of course, some uses of AI are easier to implement than others, but that’s also likely to change as technology and algorithms improve. When thinking about whether or not a company should implement an AI solution, there are four different factors to consider.

The first is the one-time cost. How much initial capital does the AI solution require? This includes the resources needed to develop the algorithm and train the AI. Second, what is the cost of switching from the current process to one handled by the AI? This includes the technical barriers that could be encountered.

The third factor is to look at how an AI solution will fit into a business as a whole. Will other technology or algorithms need to be implemented to get the most out of the AI? Finally, AI has to be viewed regarding the industry as a whole. Will other parties, such as vendors or partners, be negatively impacted by an AI solution? Will they need to adopt their own AI solutions to continue the partnership?

The value of using AI to automate processes is becoming more recognized, but it’s still not as widespread as it will likely become. In the coming years, more businesses are likely to begin experimenting with AI to see what it can do, especially as business leaders recognize the myths and truths about AI. Partnering with a company such as SafeNet is one-way business leaders can do this. SafeNet can provide leaders with more information about how AI can fit in their enterprises, as well as how it can be implemented in an affordable, effective manner.

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