Book Image

Democratizing Artificial Intelligence with UiPath

By : Fanny Ip, Jeremiah Crowley
Book Image

Democratizing Artificial Intelligence with UiPath

By: Fanny Ip, Jeremiah Crowley

Overview of this book

Artificial intelligence (AI) enables enterprises to optimize business processes that are probabilistic, highly variable, and require cognitive abilities with unstructured data. Many believe there is a steep learning curve with AI, however, the goal of our book is to lower the barrier to using AI. This practical guide to AI with UiPath will help RPA developers and tech-savvy business users learn how to incorporate cognitive abilities into business process optimization. With the hands-on approach of this book, you'll quickly be on your way to implementing cognitive automation to solve everyday business problems. Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this book will help you understand the power of AI and give you an overview of the relevant out-of-the-box models. You’ll learn about cognitive AI in the context of RPA, the basics of machine learning, and how to apply cognitive automation within the development lifecycle. You’ll then put your skills to test by building three use cases with UiPath Document Understanding, UiPath AI Center, and Druid. By the end of this AI book, you'll be able to build UiPath automations with the cognitive capabilities of intelligent document processing, machine learning, and chatbots, while understanding the development lifecycle.
Table of Contents (16 chapters)
1
Section 1: The Basics
5
Section 2: The Development Life Cycle with AI Center and Document Understanding
10
Section 3: Building with UiPath Document Understanding, AI Center, and Druid

Understanding the opportunity

Building and maintaining an automation pipeline is the catalyst to a successful automation program. A successful automation pipeline should always be flowing with ideas of differing levels of complexity and value. In this section, we will investigate how to keep the automation pipeline staffed by evaluating, investigating, and prioritizing opportunities.

Evaluating opportunities

Now that our master opportunity log is full of automation opportunities and is prioritized based on estimated data (including value, complexity, and suitability), we can venture into each opportunity and assess which candidates are best suited for automation. During these assessments, additional meetings with business stakeholder(s) might be conducted to vet the feasibility and calibrate the qualitative ratings before starting implementation. For vetting and further evaluating the opportunities within our master opportunity log, we perform the following exercises:

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