A virtual assistant is quietly taking meeting notes. A document offers to reword a sentence for clarity. An off-site appointment reminder adds the current traffic and weather conditions.
Artificial intelligence is quickly integrating itself into our everyday work life. While bringing massive benefits of enhanced productivity and efficiency, there are some drawbacks to consider when giving AI the informational fuel it needs to make our lives easier.
AI works by collecting information, passing it through pre-programmed algorithms and information models to make predictions and provide results to the user. The information used in this process can come directly from the user but can also be gathered from public sources such as knowledge bases, websites and forums.
As businesses continue to adopt AI to streamline operations and increase productivity, it is very easy to be lulled into believing the technology doesn’t have any major downsides. While there are many benefits, they do not come without some caveats. Below are three significant pitfalls companies are facing when integrating AI into their workflows.
Automation. The automation of time-consuming, repetitive tasks is one of the largest uses for AI in today’s business environment. Tasks that would have normally taken a few hours can be handled in a few minutes. Today, for example, automated hiring tools are being used to filter out hundreds of unqualified candidates to get down to a small list of resumes to be reviewed by a human. The time saved is then used for higher-value tasks. Unfortunately, the allure of that automation brings consequences of possible errors going unnoticed and no allowance for human judgment or intervention. Maybe some of those discarded resumes had qualified candidates that didn’t fit the algorithm.
A reoccurring solution to many of the AI issues is to include people in the process where critical decisions need to be made.
Data leakage. AI runs on data. The more information that a platform has to draw from, the higher the value of the results. However, should that data include sensitive or proprietary information on an insecure AI platform, it is open to being inadvertently exposed in the event of a cyberattack or simple mismanagement. For example, a small accounting firm was using an AI-powered chatbot to schedule client appointments. One client made a request to the chatbot about filing deadlines. The response contained another client’s personal tax information, including their Social Security number and income details. This AI was not restricted from accessing sensitive data in its responses.
When moving forward with AI, it is imperative to understand where AI is drawing data from, securing the platform itself and limiting the information processed by and stored by the platform.
Lack of transparency. AI systems, especially those based on deep learning, can often be “black boxes” where their decision-making processes are not easily understood, even by their creators. This lack of transparency makes it difficult to explain decisions made by AI and increases the risk of errors going unnoticed. Users are often forced to trust without the ability to verify. Businesses should establish clear accountability structures for AI-related decisions, ensuring that human oversight remains integral to the process.
While AI offers substantial benefits for improving workplace efficiency and productivity, it’s not without risks. From data security concerns to possible errors in decision-making, businesses need to take proactive steps to protect against these pitfalls.
Peter Nelson has been the vice president of engineering for NetCenergy LLC, an outsourced information technology provider based in Warwick, since its founding in 2003.
