How AI Can Modernize Your Business Processes


How AI Can Modernize Your Business Processes

Artificial intelligence is all over the news – you’ve probably heard your fair share of theories and predictions about it. But did you know there are already real-world AI applications that save time and increase productivity? Yep, that’s right. In fact, helping our workflows is one of the key strengths of AI—especially when it comes to contracts. We say it all the time, but AI is making our jobs more human – meaning we can focus on the things people naturally outperform machines on, like creativity and collaboration.

In case you haven’t noticed by now, we’re real AI geeks. That’s why we recently partnered with AgileThought to present a webinar on the topic: How AI Can Modernize Your Business Processes. It features AgileThought Chief Data Scientist, James Parks, as well as CoLabs’ co-founder and EVP, John Wagner. You can listen to the whole thing here—and get a sneak peek below.

Artificial Intelligence can streamline manual processes while enhancing the user experience

Contracts don’t have to take hours of your team’s time. One of the key benefits of AI is that it can quickly read and understand your contracts, summarize them for you, and leave those hours of arduous reading and digging for the really important work – not the busy work.

Machine learning use cases

You can benefit from machine learning when the problem you’re solving has a long list of rules, is too complex for traditional software, or requires analysis and adaptation every time it needs new rules. For example, contracts can be written in an infinite number of ways thanks to the humans writing them. But machine learning can handle the complexity and adapt quickly to changes and new rules.

Methods for building and evaluating Artificial Intelligence and machine learning

You’ll always be learning and iterating throughout the process, so a CRISP-DM (cross-industry process for data mining) approach is crucial. It’s similar to agile but also favors experimentation—necessary when dealing with data science and machine learning. This approach means we can work in short cycles to learn and iterate quickly. Plus, we can build in customer feedback as we go, so our product is creating real solutions for our users.

Listen to the entire webinar here, and stay tuned until the end for information on how to determine your own machine learning readiness.

twitter linkedin facebook