August 11, 2021
Every day, customers give businesses free information ranging from what they purchased to where and when they purchased it and how they paid for it. This wealth of information has transformed the retail industry, creating new leaders among those who embraced this progress and, conversely, new losers among those who didn’t.
Every day, hundreds of millions of employees also provide a vast supply of information, but few businesses have bothered to harness and use these data to their advantage. This has created a rich opportunity for businesses who recognize how valuable this “lost” information would be if it were properly captured.
By recording and analyzing millions of data points that no human could hope to manage by themself, artificial intelligence (AI) could well make the difference between successful and unsuccessful companies over the next few decades.
However, machine learning software is not a “one-size-fits-all” solution, and not all platforms perform equally. Even if a company has the most cutting-edge algorithms to make accurate predictions, those algorithms are only as good as the data sets from which they learn.
Simply put, if an AI reads data and creates a predictive model based on those data, the prediction may not reflect real-world scenarios accurately if the information forming the basis of the prediction isn’t as precise as possible.
Take this SQUARE payroll services AI patent as an example. The patent uses past employee behavior and trends to assess which employees are most reliable and offers those employees access to their accrued wages before payday.
The weak link in the chain here is likely not the algorithm itself, but the fact that it uses historical data to predict future human behavior. While past data are certainly useful, current data fluctuations could add a crucial layer of understanding to the machine learning model.
One of Gratuity Solutions’ founders, Aleks Stepanovic, explains, “Our solution accurately maps system endpoints creating a path to precise machine learning (AI) based on real-time data, as opposed to predictable machine learning (AI) based on the use of historical data.”
Consider an employee who has recently returned from maternity leave. Past data show a trend of absences and perhaps a part-time schedule as needed, which would paint this employee as “unreliable” in the system and therefore ineligible to receive early access to earned wages. However, who would be most in need of expedited pay if not this mother supporting her new baby? How long would it take for her current “reliable” performance to outweigh her “unreliable” past in the AI’s opinion?
This is just one of many scenarios where access to current and constantly updated, real-time data could make a huge difference.
Machine learning developers and business owners alike should keep in mind how accurate data form the backbone of precise AI. Contact Gratuity Solutions to access this real-time information for your business and stay ahead of the AI curve.
Sources : cbinsights.com