Intelligent Automation (IA) – Part 2

Journey through the lifecycle of Technology Systems
May 10, 2021
Intelligent Automation (IA) – Part 3
August 12, 2021

Intelligent Automation (IA) – Part 2


Introduction

Simon Abela co-founder at STABS.
Inspired by the book - "INTELLIGENT AUTOMATION: Learn how to harness Artificial Intelligence to boost business & make our world more human" by Pascal Bornet, Ian Barkin, Jochen Wirtz, Simon shares his thoughts regarding a subject his passionate about.

What is Intelligent Automation or IA?

It is a new generation of software-based automation. It combines methods and technologies to execute business processes automatically on behalf of knowledge workers. This automation is achieved by mimicking the capabilities that knowledge workers use in performing their work activities (e.g., language, vision, execution, and thinking & learning). The goal of using IA is to achieve a business outcome, through a redesigned automated process, with no or minimal human intervention.

The model of the four Intelligent Automation Capabilities





As a result, IA increases process speed, reduces costs, enhances compliance and quality, increases process resilience, and optimizes decision outcomes. Ultimately, it improves customer and employee satisfaction and boosts revenues.

1st of the four Intelligent Automation Capabilities – VISION




In 2018, Amazon created the first automated supermarket … Amazon Go. In this store, cameras are placed in the ceiling above the aisles and on shelves. Supported by computer vision, these cameras can determine when an object is taken from a shelf and who has taken it; it gets then added to the customer’s virtual basket. If an item is returned to the shelf, the system is also able to remove that item from the basket. The network of cameras allows the platform to always track people in the store, counting them, and providing traffic analytics. Some detectors can also connect to people’s phones to propose customized promotions based on demographic or behavioural data. Cameras and detectors also ensure that the right items are billed to the right shopper when clients walkout. The technology used in Amazon Go is very advanced. Example of other off-the-shelf solutions

2nd of the four Intelligent Automation Capabilities – EXECUTION
The Hands and legs of a Digital Workforce


These may be classified as follows >Smart Workflow Platform which helps automate Standard routines (e.g. Client onboarding)
>Low Code Platforms – allow anyone in a company to create applications without specific coding skills. For example, they enable any user to create interfaces between systems and workflows, such as forms to collect data, reports, and approval processes. For an example of this visit here
>Robotic Process Automation (RPA) helps to produce two types of automation: assisted (also called attended) and unassisted (or unattended) automation. Configuring RPA programs is quite a user- friendly. It usually requires a few weeks of training for a business user to master most of the functionalities.

3rd of the four Intelligent Automation Capabilities – LANGUAGE
The ears and mouth of a Digital Workforce


An example of this is Intelligent Chat boxes - There are two key types of chatbots: one powered by a set of rules (basic chatbots), and the other powered by deep learning (called intelligent chatbots or cognitive agents). The use of chatbots covers a wide range of fields. These include front-office processes such as sales, services, and support, and back-office functions such as human resources and IT helpdesks. Chatbots can answer questions, converse, collect information, schedule appointments, and much more. The following link blew my mind off… take a look here

4th of the four Intelligent Automation Capabilities –THINKING AND LEARNING
The brain of a Digital Workforce



Three main technologies support this capability: big data management, machine learning, and data visualization. Big data management allows the extraction and preparation of data. Data is then fed to algorithms to generate insights using (machine learning) required for decisions or actions made by computers (again using machine learning) or by humans (using data visualization)
Here is an example of an open-source platform for machine learning

That’s a wrap for my second article on Intelligent automation I hope the above inspired you in the same way it inspired me when I read this amazing book – Goodbye till my next article.

Article by Simon Abela - STABS

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