Latest release of Red Hat Process Automation features new capabilities for applied AI

Red Hat, the world’s leading provider of open source solutions, announced the latest release of Red Hat Process Automation, unveiling new applied artificial intelligence (AI) capabilities for predictive decision modeling, and support for the development of process- and decision-based business applications using micro-frontend architectures.

Together with additional enhancements targeted at improving the overall user experience for Red Hat Process Automation customers, these capabilities further strengthen the business developer’s toolbox.

Red Hat Process Automation is a set of products for automating business decisions and processes by enabling closer collaboration between IT and business teams.

This helps IT organizations to better capture and enforce business policies and procedures, automate business operations and measure the results of business activities across heterogeneous environments including physical, virtual, mobile and cloud.

Applied AI

Red Hat Process Automation now supports an applied AI approach to automated decisioning. This enables users to incorporate predictive analytics into their decision management applications to create intelligent, automated systems that help them better interpret and respond to changing market dynamics.

With the latest release of Red Hat Process Automation, customers can import and execute predictive models expressed in Predictive Model Markup Language (PMML), an industry standard for integrating and exchanging information between machine learning (ML) platforms where the predictive models are created and trained, and decision management applications that use such models to automate rules for specific business outcomes.

By incorporating predictive capabilities within a Decision Model and Notation (DMN) decision model, users can not only analyze and act on data in an automated way, but also gain greater visibility into how an automated system reached a given conclusion.

This transparency and control contribute to a more explainable AI and can help organizations better address regulatory requirements such as the European Union’s General Data Protection Regulation (GDPR), which includes specific provisions that support a right to explanation for automated decisioning.

Micro-frontend development

Business analysts are playing an increasingly influential role alongside traditional developers in building and deploying applications to automate business processes and decisions.

With Red Hat Process Automation, each group can use tooling tailored to their specific needs and expertise, retain the governance and oversight required by IT, and take advantage of cloud-native architecture.

Monolithic frontend codebases can hinder an organization’s ability to take full advantage of more modular and lightweight approaches like microservices based on containers, which are germane to developing cloud-native applications.

Following a similar path toward modularization, customers can now decompose client-side interfaces for process- and decision-based business applications using a micro-frontend architectural approach through the updated Red Hat Process Automation app builder component.

These micro-frontends can be independently managed, and enable scalability, agility and control over the entire application.

The latest release of Red Hat Process Automation also delivers enhancements to improve the overall user experience, including:

• Automated operations via L2 OpenShift Operators – Level two Operators simplify the deployment and management of Red Hat Process Automation on Red Hat OpenShift Container Platform by providing automated lifecycle management capabilities for installation, minor version upgrades and patches. Using OpenShift Operators, customers can minimize operations workloads, keep installations up-to-date and reduce unplanned downtime.
• Improved process visibility – New capabilities, including heat maps, make it easier for users to visualize commonly used process pathways and bottlenecks.
• Continuous operation through node failure – The complex event processing engine can be configured to operate across multiple nodes to enable continuous operation in the event of a node failure. High availability support for the business central component on Red Hat OpenShift is available as a technology preview in this release, providing further protection against data loss in the event that the OpenShift node supporting the business central instance fails. Node failure will not interrupt a user’s session or cause a loss of BPMN, DMN or other artifact that the user is working on.
• Customizable templates for business resource optimization – New customizable templates are available in the business optimizer for common constraint satisfaction use cases, based on the upstream OptaPlanner community project.

Mike Piech, vice president and general manager, Middleware, Red Hat: “Trust is fundamental to how we conduct business today. With intelligent, automated systems increasingly performing customer-facing operations, the need for visibility into how and why these systems make decisions is more important than ever.

“Red Hat Process Automation enables customers to combine standards-based predictive and decision models to not only drive greater efficiency, agility and intelligence through their process-centric applications, but also achieve greater transparency for a more explainable AI.”

Neil Ward-Dutton, vice president, European AI and Intelligent Process Automation Practices, IDC: “Successful digital transformation ultimately demands transformation of internal operations and decision-making, just as it demands transformation of customer interactions.

“AI technologies can add value to automation projects in this context in multiple ways, helping transformation efforts reach more use cases and deliver more value.

“Predictive decisioning has historically been implemented in silos that are separated from the flow of everyday work, but increasingly organizations’ aspirations demand that they’re brought together to create intelligent process environments.”