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Top 10 Rules For RPA Success

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By Leslie Joseph, Principal Analyst, Forrester

Robotic process automation (RPA) in business has been significantly transforming the way that organizations operate. It allows people to provide a set of instructions for a robot, or “bot,” to perform, enabling them to undertake tasks with minimal errors at a high volume and speed, leading to improved efficiency and accuracy as well as sparing workers from mundane and repetitive work. How can businesses adopt RPA without compromising the human element? What are the key rules to follow for RPA success?

1. Incorporate RPA Into Your Company’s Automation Fabric
Firstly, it is key to incorporate RPA into your company’s automation fabric. The concept of “automation fabric” is built on the notion of automation becoming a central tenet of digital transformation. Forrester defines the automation fabric as:

A system for whole-of-business automation that integrates multiple adjacent and complementary automation technologies, process architectures, organizational behaviors, and partner co-innovation models to support the goals of human-centered automation and an autonomous enterprise.

Companies that seek continued competitive advantage in the future must focus on imbuing automation throughout their business operations and cultural DNA, rather than adopting it as a collection of tactical, piecemeal initiatives focused on cost reduction or small-scale process efficiency.

The leading RPA products of today recognize this evolution and have been moving toward a better-integrated portfolio of building blocks for the automation fabric, which includes capabilities such as process mining, task mining, conversational intelligence, data automation features, machine-learning workbenches, and so on.

2. Focus On Building A Sustainable Value Model For RPA

Secondly, enumerating the benefits of automating simple processes should be easy enough. The value that RPA creates drives consumption, which in turn fosters more ideation that leads to greater automation. But as process complexity increases, it also becomes more difficult to calculate ROI because of the sheer number of factors and dependencies involved. A pragmatic business case neither overstates the potential value or savings delivered nor underestimates the costs involved. These should be considered when prioritizing a sustainable value model for RPA.

3. Treat RPA As An Enterprise Platform

Organizations must hold RPA to the same standards and guidelines as other enterprise technology platforms and software, including putting the focus on user experience, design and build standards, architecture that emphasizes reusability, and advocating data privacy, security, and resilience.

4. Secure Your Bots With Zero Trust Principles
Businesses should treat bots as digital workers. In essence, they should treat them the same way as human employees are treated. All bots should have a lifecycle, identity, and limitations to access company data and functions. Over the past decade, Forrester originated the Zero Trust Model, which is a security framework built around the concepts of “Never trust; always verify” and the “assume breach” principle. This has proven to be a very robust philosophy over the years, and it should be applied to everyone in your organization — for both humans and nonhuman (RPA) bots.

5. Prioritize Your Processes
When embarking on an RPA program, even at the pilot stage, technology teams should take a pipeline view of processes. Many successful RPA programs have, early on, built pipelines of candidate processes with intake, assessment, and prioritization for up to 12 to 18 months ahead, led by a center of excellence or strike team. They have structured ways to drive intake, business value assessment, and prioritization (often supported by tools such as process mining and digital worker analytics) to deepen their understanding of these processes and build effective automation roadmaps. And they also often enough have investment roadmaps for technology areas outside of vanilla RPA (for example, in AI) that support the development of complex automations that would not be possible with just plain RPA alone.

6. Lay Early Foundations For Effective Automation Management And Governance
It is surprising to see how many companies ignore the need to focus on building effective structures and capacity for governance and change management. In particular, with those that have chosen to start automating through the citizen development route, there is a tendency to procrastinate on governance. In fact, companies sometimes “sell” the automation program internally, which helps with encouraging adoption but ends up creating a huge problem in the long term. On the flip side, a well-functioning governance mechanism can contribute up to 10% of the value of the automation program.

7. Plan For AI, But Do Not Rush It

Companies should have a roadmap to use AI and intelligent features in their automation processes but must do so with a clear roadmap and pragmatism around the outcomes that they can expect. They should not hasten the process.

8. Take An Innovation View Of Intelligent Automation
Customer experience, internal capacity building, and achieving automation outcomes should be prioritized in early-stage RPA initiatives. As the programs scale and confidence in RPA grows, however, RPA initiatives can play a critical goal in innovation, such as adopting a business services view of innovation, supporting citizen development, and fostering in-house automation skills.

9. Design For Humans
People are central to the success of automation. It is important to maintain and prioritize the employee experience, the human-in-the-loop elements, and the change management that is crucial for long-term automation viability. Machine learning requires humans to be part of the process. Scenarios in which bots and humans interact need to be carefully designed so that they can be human-centric.

10. Cultivate An Automation-First Culture
Lastly, an automation-first culture should be fostered by focusing on the positive changes that automation brings to the workforce while actively working to mitigate the negative impacts. Today, nobody is really talking about automation to reduce their workforce but is focusing on improving efficiency and innovation. Yet fear still exists in the workforce of the displacement effects of automation, and most companies have not given much thought to the need to reskill their workforce, revisit their overall workforce strategy, and help employees cope with these changes

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