Using Machine Learning, Robotic Process Automation to Create Intelligence Automation

The ultimate purpose of digital transformation is to “let humans do more with fewer resources”. Thanks to the digital era and our ability to collect and store data, which represents reality, we humans are capable to do and decide what to do to a level that we never could imagine before.

The ability to use data to estimate what is the most likely outcome next (based on historical patterns) and automate this process through a system that autonomously produces that outcome is called Machine Learning (ML). On the other hand, programming a combination of digital artifacts to create an action without human intervention is called Robotics Process Automation (RPA).

Intelligence Automation is the combination of ML and RPA. What it means that a system can use data to estimate the future measurable outcome of something and trigger a set of actions by itself, even combining multiple systems, software, platforms or frameworks. This combination leverages the strengths of both technologies: ML’s ability to learn from data and make predictions, and RPA’s capability to automate repetitive tasks based on predefined rules.

Use Cases

Imagine the following scenarios:

1. Customer Service: Chatbots powered by ML and RPA can handle customer inquiries, providing quick and accurate responses. No only that, imagine a chatbot that can trigger a shipping, record the PO or the invoice, evaluate inventory and activate a purchase or payment based on a conversation with a customer. All without human intervention.
2. Invoice Processing: Automating data extraction, validation, and entry into accounting systems using RPA, enhanced by ML for reading and understanding invoices. Also, the system can automatically compare the purchase or revenues against a budget and activate / de-activate discounts, promotional campaign, renewal of purchase supply.
3. HR Management: Automating onboarding, payroll processing, and benefits management with RPA, while ML can be used for employee sentiment analysis or prediction of employee turnover, activating actions in the system to initiate hiring process of more human force, and target specific websites sourcing potential talent and bringing this summary to the decision makers.
4. Supply Chain Management: RPA automates inventory management and order fulfillment, while ML optimizes demand forecasting and route planning. Based on this forecasting, the system can start searching for alternative suppliers, routes or even compare historical raw material cost against a budget.

Benefits to Organizations and Companies

One of the primary advantages of automating repetitive tasks based on predictions is increased efficiency by providing data-driven insights, while reducing operational costs. Scalability is another key benefit, as both RPA and ML can be expanded across various departments and processes, allowing organizations to handle larger volumes of work without a proportional increase in resources.

By leveraging the strengths of both ML and RPA, organizations can create intelligent, automated workflows that adapt and respond to real-time data, ultimately driving better business outcomes and fostering innovation.

Implementing ML and RPA with Azure and Power Automate

Yes, it is possible to integrate ML and RPA using Azure and Power Automate. Here’s a high-level overview of how this can be done:

1. Develop ML Models: Use Azure Machine Learning to build and train your ML models. These models can predict outcomes, classify data, or detect anomalies.
2. Deploy ML Models: Deploy the trained models as web services on Azure. This makes them accessible via APIs.
3. Create RPA Flows: Use Power Automate to create RPA flows that automate tasks. Power Automate can call the ML model’s API to get predictions or classifications.
4. Trigger RPA Flows: Set up triggers in Power Automate based on the results from the ML model. For example, if the ML model detects a potential fraud, it can trigger an RPA flow to handle the case.
5. Monitor and Optimize: Continuously monitor the performance of both the ML models and RPA flows. Use the insights to optimize and improve the processes.

This integration allows organizations to leverage the predictive power of ML and the efficiency of RPA to create intelligent, automated workflows that can adapt and respond to real-time data. The best part? This is all very cost effective.