The first AI program can be traced back to the 1950s and has been evolving ever since, but with the recent launch ChatGPT AI has gone from the realm of the tech savvy to a mainstream, household topic. Of course, companies across industries have long been assessing how they can leverage AI to improve every aspect of their business, from their tech stack to their product to their customer support.
One less sexy but very powerful application of AI is in the implementation of big software solutions. Historically, adopting enterprise-level software, like Quality Management and Regulatory Compliance Software, has been an extremely complicated, time-consuming – and therefore expensive – process. After assessing, testing and choosing a system (in itself no easy task), companies find themselves in long implementation processes that rely on external consultants who do a lot of heavy lifting to implement the system and customize it to meet the company’s unique specifications.
In life sciences, where compliance systems are core to a company’s business, ensuring that systems meet the very specific requirements of the company is critically important. But the cost and time of implementing such systems is a massive overhang for small and medium companies that need to meet the same compliance standards as the big players, but don’t have the same resources to put towards long, labor-intensive implementation projects. (Of course, large companies may also have better things to do with their resources than lengthy and expensive implementation processes…).
AI is changing that. When a system is trained with big enough data sets, it can analyze the procedures and self configure the system. These large language model technology tools allow companies to implement their requirements into a system automatically instead of necessitating a team of experts and months of work.
With AI, we are stepping into a new era of streamlined, effective, and intelligent system integration that promises to redefine how we perceive enterprise-level software. For small and medium sized enterprises, this less “sexy” part of AI will result in massive savings as they forego long and costly implementation processes, while allowing larger companies to redirect their resources to other strategic initiatives.