By now, most of us have heard of ChatGPT. (For those that haven't, it is essentially a really smart chat bot, like the customer service ones you get frustrated with, but infinitely better.) And even though ChatGPT is an incredibly powerful tool, the potential of AI (artificial intelligence) extends far beyond large language models. From data to drug discovery to clinical trials, the Life Science industry can utilize AI in a multitude of business areas - especially where IT is involved.
If Life Science companies have a lot of one thing, it is data. From molecules in drug development to SOPs to clinical trial data, these companies have to keep track of vast amounts of data. Now, enter artificial intelligence. In the Life Sciences and beyond, AI can generally be applied anywhere that you want to automate an activity. This includes potentially automating the classification, categorization, and visualization of both structured and unstructured data.
And the best part? Once the automation is set up, it requires little to no supervision from the customer, vendor, or partner, opening your time up to focus on other tasks. One concrete case for the use of AI for automation is in migrations.
When it comes to data migrations, many of Epista's customers have copious amounts of data, both digital and on paper, from their early days before they had implemented control systems. Preparing this type of data is a huge undertaking because if you have produced pharmaceuticals for, say, the past 10 years, you have an unimaginable amount of data that needs to be organized, analyzed, and migrated.
If the data is old, the responsible person has often left the company. This can result in newer employees being unaware of the data, not understanding the organizational structure, or even a new IT system, making the data both hard to access and especially difficult to organize.
To put this into perspective, imagine you have 50.000 documents to categorize. If you're fast, you can spend about 1-2 minutes per document opening, going through the content, determining the classification, and changing the meta data. Seems fast, right? Even at the speed of 2 minutes per document, you would still have to work more than 200 8-hour days or 10,4 months (about 41,6 weeks) working full time to categorize all the data! And, to top it off, categorization is just one step in the process. Imagine how much longer it would take including input and migration.
*Full time work week calculated at 40 hours per week
With trained AI, a task like this could be automated freeing up months and months of time for an employee or team. An automation such as this could have been done a few years ago. However, an automation at that time would still have required supervision from a trained professional with system knowledge. The main difference today is that this type of task can require little to no supervision if done correctly and under the right circumstances. Once the AI is trained, the system can run on just the data and has the potential to require almost no external knowledge.
This may sound too good to be true, but with the advances in artificial intelligence and machine learning, it is a very real possibility that many companies have already started implementing. So, to reiterate, AI is a powerful tool with the potential to handle tens of thousands of documents, if not more, quickly, efficiently, and with little expert supervision.
At Epista, we have years of experience working with automated systems and data. Our knowledge, combined with advances in AI and ML technology opens many doors for our clients to harness the power of AI. If you're considering adding automation capabilities to your business, get in touch. Our experts can help you decide the best course of action to use AI to free up time and drive your Life Science business forward.
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