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Implementing centers of excellence to help scale intelligent automation

By 2020, more than 70 percent of enterprises will have adopted robotic process automation, according to a recent Deloitte survey. These organizations are embracing automation technology as a way to hopefully reduce strain on their workforce while also potentionally reaping benefits such as improved compliance, higher productivity and reduced costs.

Yet, despite the enthusiasm, scaling RPA is proving more difficult than anticipated. In fact, Deloitte found that only 3 percent of organizations say they’ve scaled their RPA digital workforce.

How can organizations make this leap and move seamlessly from a viable RPA proof-of-concept to enterprise-wide intelligent automation program? It’s useful to first think about scaling within a structured framework that consists of three pillars – process, technology and people.

Working through the details of each pillar will help organizations answer the big questions about scaling while also creating a workable strategy for replicating their initial success with RPA.

Pillar #1: A Well-Defined Process

The core of every project is a plan, and scaling automation is no exception. The following steps will help you define requirements and uncover obstacles or issues early.

Step 1: Conduct a maturity model assessment. Evaluate your organization’s readiness for automation from a technology and process perspective. It’s critical that your IT team is involved from the start. They’ll advise on how automation technologies fit within your enterprise IT stack as well as how to ensure adherence to security protocols. After implementation, the IT team will be responsible for managing the software – yet another reason to reach out to them early. A best practice is to evaluate the business areas where you’re seeking to apply automation and optimize the operational processes prior to automation.

Step 2: Create an implementation journey map. The blueprint defines the change process. In this step, you’ll identify the business areas that are ripe for automation. You want to start with ‘quick wins’ to gain executive and broader workforce buy-in. In parallel you’ll begin designing your Enterprise Automation program office and the operating model required to support your program at scale.

Step 3: Scale and innovate. Scaling your Automation program will require deliberate consideration and planning of a centralized, decentralized or federated model. Most programs start centralized to establish policy and governance and mature into a federated model once policy’s in place and the business units have gained a certain level of proficiency to design and build their automations. As you scale, keep an eye on innovation so you don’t miss the opportunity to adopt emerging technologies that may be ideal for your business needs.

Pillar #2: A scalable Intelligent Automation (IA) platform with strong technologies

To scale, organizations need a suite of technologies that applies automation to various use-cases. This is where the typical RPA-only solution will fall short. Most organizations have complex operations that span front and back office and often find they’ve unstructured data in documents or emails that must be transformed into structured data prior to being processed by RPA. This capability is typically called cognitive capture and involves technology that uses intelligent-optical character recognition and machine learning.

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What this means is that for businesses to achieve automation, they’ll need to combine multiple technologies, according to Everest Group. However, since integrating solutions from multiple vendors is cumbersome, an intelligent automation platform approach is recommended to minimize multiple procurement, vendor management and stitching the technologies together.

Thus, it’s important to evaluate vendors in terms of their full technology portfolio. An integrated, unified platform with a complete mix of technologies should be capable of intelligently processing documents while automating downstream activities. In addition, organizations should evaluate IA platforms for the following capabilities:

Application emulation: Manage tasks in web application, Excel files, PDFs and more without out a desktop machine.

Minimal desktop footprint: Deploy automation at runtime without a separate installation.

Flexible architecture: Support for cloud, on-premise and hybrid deployment, and simple integration with third-party applications.

Vendor agnostic platform analytics: Analyze and measure the results of automation to identify efficiencies, compliance and risk exposure.

Mobile support: Monitor and manage automation from any location.

Pillar #3: The right people to lead

At the core of the digital workforce are the employees. A six-competency model is a best practice designed to ensure organizations empower the right people to support an automation program. Resource levels will depend on the size of your IA program, but there should be dedicated thought given to managing each of the following areas:

Governance: This team oversees the overall automation program and ensures employees are aware of upcoming changes as scaling takes place.

Technology: Members of this team are responsible for managing the external relationship with the automation vendor, along with internal technology considerations, such as server and infrastructure requirements.

Tools and training: Resources should be dedicated to creating tools and documents for training and adoption.

Change management: Even positive change can be daunting for employees. Organizations need resources committed to change management. This group identifies how automation will impact the workforce and finds ways to keep employees empowered during the transition.

Reporting: Expansion is driven by a proven success record. Performance goals should be clearly defined upfront, so they can be tracked, measured and shared across the organization, particularly with executives who have decision-making power over automation initiatives.

Knowledge sharing: For every use case in which IA is implemented, there are lessons to learn. A dedicated group of individuals should capture, document and share this information, so it can be used to improve the next implementation or iteration.

Replicating proof-of-concept results can be challenging for any organization, but a methodical approach built on a well-defined process, strong technologies and empowered people in the right roles provides the right structure to foster change and achieve scale.

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