3 Key Elements for Building an Intelligent Automation Strategy

Intelligent automation (IA) is here, but it needs to be done right. To truly accelerate business processes and achieve maximum ROI, intelligent automation needs to advance beyond just a collection of tools that solve specific problems and on a wider spectrum of business functions and tie the outcomes to business KPIs.

According to a recent Zinnov survey of 250 enterprise CXOs, Intelligent Automation emerged as the top technology area that CXOs prioritize within digital transformation charters, making intelligent automation core to digital transformation 2.0.

This view is also echoed by Forrester Research, which outlines the impact automation has on customer experience and the organization’s bottom line. Organizations looking to capitalize on IA must recognize that automation in itself can’t make business processes more efficient.

Before building an intelligent automation strategy, enterprises must identify and measure ways IA improves their crucial business KPIs.

In this blog, we will explore the 3 key elements of building an intelligent automation strategy.

Learn More: What Is Business Process Automation?

What is Intelligent Automation?

Intelligent automation is defined as the use of artificial intelligence (AI) and robotic process automation (RPA) to automate mundane, repetitive tasks. Intelligent automation helps streamline processes, cuts costs, and improves the efficiency of business operations.

Some key elements of IA include AI-powered process discovery, Robotic process automation (RPA), task mining, natural language processing (NLP), and conversational AI.

Learn More: Understand Task Mining and Process Mining, Software & Tools

Intelligent Automation Strategy: 3 Key Components  

1. Get granular visibility into processes

Enterprises run many processes but need more visibility and insights into how these processes are performing. Siloed processes and a lack of as-is process visibility can impact speed-to-delivery.

For instance, employees resort to email and spreadsheets to complete work and collaboration when there are too many unintegrated solutions or siloed business functions.

This can cause delays and increase risk to organizations. The first step to process visibility is process discovery which can help get an as-is view of the state of the process.

By getting insights into their business processes, CXOs can get a more expansive view of enterprise operations at scale. They can use the data to build credible automation pipelines, identify execution gaps and inefficiencies, address them through user training and improve compliance.

Learn More: How to Achieve Successful Automation Outcomes?

2. Build for wide-scale implementation

When CXOs have granular visibility into the last-mile of work, they can enhance operational efficiency by standardizing processes at scale and identifying opportunities within those processes for automation.

They can broaden the scope of automation by assessing multiple automation capabilities that could be configured to meet business requirements. Additionally, managers can improve work across teams within the organization through user training and PDDs.

This can help standardize and speed up deployment by removing the reliance on email and spreadsheets. These measures also support broader transformation initiatives across the enterprise.

Learn More: 3 key learnings from our webinar with Bayer and what it means for your automation program

3. Go beyond RPA-centric automation

While much of the discussion on automation centers around RPA, intelligent automation provides enterprise leaders with comprehensive capabilities to realize business outcomes using tools such as Intelligent Document Processing (IDP), email automation, RPA, and workflow automation, among others.

Critical to this is unstructured interaction data that captures information outside systems of records like ERPs and CRMs, providing a holistic view of how work happens in the enterprise and empowering leaders with powerful insights to build and scale automation pipelines.

Learn More:  Intelligent Process Automation vs. RPA: 4 Key Differences

How Scout^® AI Model Helps Build Credible Automation Pipelines 

The Scout^® AI model generates a work graph — a map of hidden pains teams experience at work and their impact on business outcomes. Scout^®, a multi-modal transformer model, learns from your organization, builds the work graph, and reveals what you do not see – the hidden pains your teams experience at work and how they impact business outcomes.

Scout^®contextualizes these hidden pains to your unique business environment, interprets them for all stakeholders, and democratizes access to this data. Scout^® also autonomously generates solutions to resolve your teams’ pains.

By transforming interaction data into business value, Scout^® helps enterprises uncover gaps and inefficiencies and identify the right processes for automation.

To get started with Scout^®, click here.