Automating repetitive tasks can save time and reduce errors, as seen in this creator's experience with contract summarization. By following a repeatable framework, viewers can apply the same process to their own automation projects. The framework involves defining the win, mapping the current process, designing the future, prototyping and validating, and building, testing, and deploying. This video showcases a real-world scenario where an AI automation was built using Python and hosted on AWS, demonstrating how to create effective automations. By the end of this video, viewers will have a clear understanding of how to build their own automation projects.
Introduction
The video introduces the creator’s problem with manually reviewing contracts and their decision to automate the process using AI. The creator promises to share their exact process for building effective automations.
Key Facts
- Automating contract review can save time and reduce mistakes.
- To build an automation, you need a clear goal (the “win”) and define it using the 3 W’s: What is the desired output? When should it run? Why automate?
- Mapping the current process involves walking through the pain points and creating a diagram of every step in the process.
- Designing the future involves selecting tools to replace manual steps, considering pricing and functionalities.
- Prototyping and validating involve building a quick demo version or testing individual tools.
- Building, testing, and deploying an automation requires choosing the right tool (e.g., low-code, no-code, pro-code) and hosting it on a platform like AWS.
- The creator’s contract automation uses Python, Postman, and AWS services to process contracts and extract relevant information.
Conclusion
The video concludes by emphasizing the importance of testing an automation with multiple triggers before deploying it to production. The creator shares their code for the contract automation and encourages viewers to download and use it as a starting point for their own automations.
