New Forrester research digs deep into RPA and looks at its key philosophies as well as the similarities and differences of RPA and test automation.
Two recent Forrester reports focus on two key robotic process automation (RPA) issues. The first identifies important RPA considerations, as it examines RPA’s key philosophies, and the second outlines how RPA and test automation are more aligned than commonly assumed, with a specific difference: Test automation technology.
Lasting automation value
When a discussion evolves about enterprise automation, robotic process automation (RPA) is unquestionably a conversation starter, even though some organizations have struggled with ramping up to stable, scaled automation.
Chalk up RPA disappointments to the nature of RPA’s inherent ups and downs, and the responsibility of stabilizing is dealt with by application development and delivery professionals (AD&D). It’s easy to get caught up “in the weeds” of RPA’s fickleness, so Forrester’s latest research identifies essential RPA issues and offers up a top 10 list to help AD&D succeed.
Forrester’s report asserts that while it’s easy to get into RPA, it’s difficult to master, notably when scaling an organization-wide RPA program. AD&D should initially thoroughly focus on aspects such as business case, change management, and bot security.
AD&D should always consider that automation is designed to make work life easier on staff, even though a human workforce can present a major obstacle. Turn that human workforce into the enterprise’s greatest asset by building a strong automation culture in which you’ve included your people.
SEE: An IT pro’s guide to robotic process automation (free PDF) (TechRepublic)
“Robotic process automation started with a clear commercial proposition: Take costs out of repeatable, predictable tasks with software robots that execute the way a human would,” the report said. RPA is foundational to intelligent automation, but Forrester reminds AD&D professionals:
- Scale remains its Achilles’ heel. Programs stall when automation initiatives are fragments, vendors are random, and governance models are incomplete. Choosing complicated tasks to automate also halts the process. To grow an RPA program: overcome process, governance, and culture obstacles.
- Enterprise programs lack the momentum needed to meet ROI targets. ROI targets to meet include new center of excellence (CoE) or strike team staff, infrastructure, and software licenses in the enterprise’s investments in RPA. Research found a quarter of companies struggle to meet ROI targets, so AD&D must find and automate more tasks.
- Finding enough tasks to automate is the biggest scale issue. Justify the cost of building a bot with simple, repetitive, high-volume tasks, but finding those tasks is a challenge.
Forrester’s 10 “golden rules” for RPA success
1. Align RPA efforts with broader digital transformation goals
2. Build a pragmatic business case for RPA
3. Treat RPA as an enterprise platform
- Align RPA with the right use case.
- Formalize approaches to data privacy and resiliency.
- Uphold software development and testing best practices.
4. Secure your bots with zero-trust principles
- Avoid reusing human workers’ credentials for bots to save short-term costs.
- Treat each bot as an IT asset.
- Assign each bot to an automation owner.
- Apply zero-trust principles to secure your bots.
- Remember that RPA bots can be an internal or external attack point.
5. Build a pipeline of processes
- For simple processes, use DWA.
- For complex processes, go deeper.
6. Look for opportunities to improve, standardize, and automate
- Partial or poorly documented processes are dangerous.
- It pays to simplify and standardize processes before automating them.
- Process mining can lead to success in RPA task automation.
7. Plan for artificial intelligence (AI), but don’t rush in
- Ingest and extract data.
- Create signal-based triggers.
- Augment human decisioning.
8. Take an innovation view of intelligent automation
- Taking a business services view of innovation.
- Fostering in-house automation skills.
- Supporting the chosen governance model.
9. Design for humans in the loop
- Architect human failsafes.
- Put your employees’ well-being at the center.
- Automation requires a new approach to assess employee experience (EX).
10. Develop the right automation mindset
- Refocus company organizational process on the customer.
- Build leadership support for automation.
- Discuss emerging skills proactively and transparently.
The report concludes: “RPA will not fuel the automation revolution unless and until it changes. As RPA aligns more closely with adjacent technologies such as AI, the opportunity for AD&D pros to leverage it for broader transformation will multiply. Use RPA as a stepping stone to a broader automation strategy, but don’t drop the ball on the basics.”
The second report compares and contrasts RPA and test automation. Even though test automation has been evolving for more than 30 years, its peak growth has been in the past five years. The RPA market is less than 10 years old.
- Testing is in the app dev world
- RPA focuses on business efficiency
Forrest asserts: Despite differences, however, AD&D leaders should learn common lessons from both, and these two technologies can work together to accelerate innovation and scale automation.
The report offered both explanations and advice for AD&D:
RPA needs bot resiliency and lower maintenance costs
- More resilient automation will help.
- Software Development Life Cycle (SDLC) for bot development is not well structured.
- RPA platforms weakly address testing.
RPA and automation tools have unique and common capabilities
Forrester found areas of overlap, clear areas of depth, and the differences are numerous.
RPA brings production environments to the table
- Production-level governance to secure automation and comply with policies
- Management of a wide range of use-cases.
- Orchestration of complex processes.
- Digital analytics and process mining that identify automation opportunities.
Manual and automation application testing brings strength to pre-production, mostly
- Flexible and extensive bot design and development.
- Functional testing of business requirements.
- Nonfunctional testing.
- Simulation through service virtualization testing (SVT).
- Application quality analytics and reporting with AI and ML.
Common characteristics align them more closely
- Functional automation design and execution environments.
- Automation of human-machine interactions.
- AI and machine learning (ML) integration for reporting and self-healing.
- Automation assets versioning and audit trails.
- Automation orchestration (automating the automation).
The opportunity both platforms help scale automation
- Large-scale process testing.
- Testing to expand Agile and federated development teams.
- Earlier testing to support emerging governance and total cost of ownership needs.
It’s not one or the other, both are in the automation future
- Test with RPA technology if the RPA solution has low or no business risk.
- Do not replace testing tools with RPA tools for application testing.
- Use testing solutions for RPA deployment with business risk.
- Keep federating test responsibilities to product/application teams.
T&A and RPA will not converge or infringe on each other
- Keep federating test responsibilities to product/application teams.
- RPA and automation in general have a growing need for specialized testing tools.
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