5-Step Guide to Successful End-to-End Process Automation

Posted 30 April 2019 8:00 AM by Scott Mackey

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Too many times process automation projects, especially those involving RPA tools, fail because of poor planning, implementation challenges, change management issues and an overall inability to cope with volumes of unstructured data.

Read on to learn how your company can avoid these potential pitfalls and realize the full benefits of process automation such as:

This graphic details some of the benefits of process automation.

This graphic details some of the benefits of process automation.

Step 1. Initiation

The initiation phase involves identifying business processes that will have the biggest return on investment if they were streamlined and automated using RPA tools.

Most importantly, initiation involves identifying the scope of the process automation project. When the parameters of the initiative aren’t clearly defined, companies can find themselves getting into unexpected trouble. For instance, if an insurance company tries to automate every single workflow in its policy administration system (including new business intake, underwriting, billing, claims and payments) it will find itself overwhelmed. Costs escalate and resources are drained as teams grapple with a myriad of process automation breakdowns across a variety of different systems – including their RPA tools (more on this in step 2). What should have been a cost-effective way to streamline workflows and increase operational efficiency, instead becomes a drag on valuable IT and business resources.

We recommend targeting one important workflow that still requires manual input (like invoice ingestion or claims handling). This singular focus on the low-hanging fruit makes it easier to get process automation right the first time and generates quick wins. And any learnings from that first success can be carried forward to ensure subsequent process automations are optimized.

Step 2: Planning

The planning phase of the project —which describes how a specific process will be automated—is the time to consider all the possible risks, and determine how best to overcome them.

Poorly planned process automation projects often fail because they encounter barriers when data can’t be extracted from unstructured formats to fuel RPA tools.

This graphic outlines how unstructured data presents a challenge for RPA.

This graphic outlines how unstructured data presents a challenge for RPA.

For instance, the process of onboarding a new customer can be automated, but the data contained within documents being exchanged as part of that workflow may be inaccessible by RPA tools, creating a “break” in the automation, which requires manual intervention to fix.

Proper planning can help companies avoid this pitfall. Effective plans include provisions for a review of the original workflow and identify how to surface, clean and validate unstructured data—so that it can be used in process automation without causing hiccups.

Step 3. Execution

The biggest issue in the execution phase is dealing with the change management issues that arise when process automation impacts your most important resource—people.

Whenever large-scale process automation happens, there is the potential for an understandable amount of anxiety. Team members worry about their roles, potential down-sizing and how their day-to-day lives will be affected.

That’s why, during the execution phase, it’s critical to communicate what is going to happen, how things will change and, most importantly, focus on the benefits that process automation will bring to staff who will be affected. Highlight how the automation will reduce frustration and free employees up to spend time working at the highest level of their capability on higher-value activities.

Step 4: Monitoring and control

During the implementation of process automation, the idea is to test often, test early and accept that it’s better to fail fast and make a quick adjustment, than to drag out the pain by continuing with a less than optimal solution.

Process automation – including any projects using RPA tools – is just one tactic in a business’ larger digital transformation journey. If course corrections are not made as needed, the project can break down. In successful projects, monitoring begins as soon as execution starts. For instance, a bank might measure data processing accuracy against well-defined targets—assessing speed and efficiency, and implementing the feedback loops necessary to process that information. This enables them to catch systemic break-downs in the workflow at an early stage and take steps to correct the process.

Step 5: Post Implementation

Once a company’s process automation project has been completed, it’s important to conduct a post-mortem on what happened in the first four steps. Review what worked and what didn’t—from goal setting to planning to execution, communication and monitoring. A key part of capturing the lessons learned is to identify whether the access, quality and efficiency of the unstructured data that fueled the process and any RPA tools was sufficient, and factor improvements into the next project.

Wrap up

The sizable benefits promised by the application of process automation to a company’s manual workflows can only be achieved if a thorough planning process is followed, and if the organization integrates an automated data enrichment solution to prepare unstructured data for use in process automation. Successful automation of the processes that were previously a drag on the business means setting clear goals, developing a comprehensive plan, managing change, monitoring and implementing lessons learned.

Adlib Elevate™: Gaining Insight From Unstructured Data

Learn more about how Adlib ElevateTM can accelerate innovation, improve customer experiences and reduce compliance risks in your organization by downloading the Adlib ElevateTM datasheet.

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