Artificial intelligence (AI) tools have begun to make waves in many industries around the world, entering workplaces from office buildings to factories. Certainly, these tools seem to be helping make certain tasks easier, improving productivity and streamlining many mundane, time-consuming tasks that were previously done by humans.
But AI tools are tools just like any other kind. Their productivity and effectiveness depend in large part on the people who implement and operate them, along with many other factors. Here is a look at some of the challenges and limitations you may run into when implementing artificial intelligence tools into your business.
The Importance of Data
Artificial intelligence tools are often used in business contexts to make sense of acquired data, identifying patterns and making intelligent predictions. For example, many companies are beginning to use AI tools to help in the hiring process, such as identifying the qualities that comprise an ideal employee for a particular position. This typically involves collecting data on existing employees who are doing well in a specific role. What this data fails to do is identify other potential candidates, perhaps those who would have been ideal employees but were missed in the original hiring process, going on to become successful employees at other companies. What you are working with is limited, nonrepresentative data.
This often creates problems for companies attempting to use AI tools to weed out or limit bias in the hiring process. The AI can only be as unbiased as the human beings operating it and feeding it data. For truly representative data, you must consider more than just the data points that were selected, you must consider the points that were not. In hiring, this will give you a better idea of the qualities that truly make an ideal candidate.
The same thought process applies to any decision in which you must provide an AI with representative data, from supplying to customer targeting. The more representative and unbiased your data is, the better your result will be.
Generalizing Learning
Another limitation of AI tools is in the way they learn—more specifically, how they use knowledge in different situations.
Unlike human beings, AI tools cannot apply knowledge gained in one situation to other situations. Instead, they must be continually retrained for new uses, even when the new situations are extremely similar to older ones. That severely limits the use of these AI tools, since one AI model remains useful only for the situation it was made for and is unable to be applied to any others, no matter how similar. There is currently some research underway into transfer learning, which could potentially help eliminate this issue.
Proving It
As artificial intelligence tools continue to increase in complexity, businesses using them are facing an increasingly difficult problem: proving it. In other words, they find it difficult, if not impossible, to explain to people exactly how the AI has come to the end result through its calculations. With greater applications of deep learning and increased complexity, AI tools have become increasingly opaquer in their processes. This is a problem for industries where transparency is important. Many of these industries are slow to adopt AI tools, as their conclusions and decisions are difficult to explain to stakeholders. One solution for this problem involves researching which parts of the given data an AI tool is focusing on to make its decisions, but the issue has yet to be solved completely.
Helping Employees Understand
More widespread use of AI tools means that employees will need to be trained on what these tools can and can’t do. To get the most use out of AI tools in the workplace, additional training and education for your employees must be implemented. Unfortunately, there are several myths about what AI can do exactly, causing slow adoption due to understanding or misunderstandings about how they work and what they can do for businesses.
Lack of Appropriate Infrastructure
It’s not as simple to add AI tools to your business as you might think. These types of solutions require more than just adding a plugin to your browser or installing a new app on your desktop. To use your AI tools successfully, you will need to implement an IT infrastructure capable of constant maintenance and upgrades for your AI to run without issues. For some smaller companies, this can be a big stumbling block in implementing AI technologies. A large IT infrastructure will cost a significant amount of money to add to your operation—it will require new employees and even new equipment.
AI won’t solve all your problems instantly. Like any new technology, there is an adjustment period in which the infrastructure must be put into place and employees must learn how it works. Knowing the limitations and challenges of AI tools will help you use them more effectively.