Over a million developers have joined DZone.
{{announcement.body}}
{{announcement.title}}

Impact of Robotic Process Automation

DZone's Guide to

Impact of Robotic Process Automation

Learn how robotic process automation will impact head-down works and knowledge works, and how cognitive and intelligent AI will change their workflow.

· AI Zone ·
Free Resource

Start coding something amazing with the IBM library of open source AI code patterns.  Content provided by IBM.

Every organization has two types of workers: head-down workers and knowledge workers.

  • Head-down workers perform their duty based on standard operating procedures (SOPs). This becomes a monotonous and routine job over time.

    • Example: A customer onboarding process will be performed by opening the same form over and again, filling in the details, validating and verifying IDs, and submitting it for approval. The reviewer, on the other hand, will go through the form details and approve or reject it. There is no extra intelligence required for dealing with such a scenario. It is a defined and streamlined process: A-B-C or A-B-D.

  • Knowledge workers, on the other hand, apply intelligence for their judgment and actions. It may or may not be a standard procedure.

    • Example: Let's consider an underwriting or healthcare claims fraud kind of a scenario. Even though there are standard procedures defined, a still person has to validate it based on the customer’s past history, transactions, relationship, health conditions, etc. before approving or rejecting a request.

If we map this to automation techniques/terminologies for enabling workforce productivity, it would look something like this:

[Head-Down Workers] : [Robotic Process Automation] : : [Knowledge Workers] : [Cognitive Intelligence or AI]

The journey to AI (the “Nirvana State” that every enterprise strives to achieve) can be broadly classified as:

  • Basic computing (scripts + repetitive steps in a single application)

  • Enhanced computing (RPA + monotonous repetitive jobs across applications)

  • Cognitive computing (machine learning + analytics)

Let's go into a little more detail with some definitions...

  • Basic automation: Human with tools and structured datasets
    • Goal: Labor efficiency
  • Robotic process automation (RPA): Humans augmented with robots; unstructured and patterned datasets; no decision-making (targeted toward head-down workers)
    • Goal: Labor efficiency
  • Autonomics: Robots augmented with humans; unstructured and patterned datasets
    • Goal: Labor elimination
  • Cognitive computing: End-to-end robots with human oversight; unstructured data and no patterned datasets (targeted toward knowledge workers)
    • Goal: Labor elimination
  • Artificial intelligence (AI): Fully automated with NO human involvement; unstructured data and no patterned datasets (targeted toward knowledge workers)
    • Goal: Labor elimination

Robotic process automation has had a great impact on business in scenarios where manual, mundane, or routine activities are being performed. The task force allocated for mundane activities can be utilized for more intelligent activities.

Some of the typical use cases are customer onboarding (i.e. AML, credit check, KYC), loan processing, payment processing, financial reporting (periodic), checking reconciliation, and extracting and reformatting multiple sources of data.

Simply put, robotic process automation is primarily targeted toward keyboard warriors — showing vengeance with every keystroke for form data entry.

In addition, most RPA tools have the concept of a “control room,” a dashboard similar to a helicopter cockpit with all the levers for RPA defined for all business scenarios. This helps the business have control and have a hawk eye on the system, rather than presume that control and automation have completely slipped from their hands. This “control room” also helps businesses have an incremental approach to enable RPA processes in a staged manner by capturing the ground info rather than going with a big bang approach (which may be risky, as in some cases, emotional/sentimental things are involved).

Note: Robotizing a broken process just makes a bad process run faster.

Conclusion

Bot adoption in an enterprise is not a mandate; it’s a trade-off between customer experience (if the business really needs it) and maintainability (with investment and supporting yet another set of a robotic process). It should be adopted judiciously.

To summarize, bots can be considered a Segway for business processes. If the nuts and bolts of the Segway are set perfectly right and complement the business process riding it, it can help you cover a great distance swiftly, enriching the customer experience. Otherwise, you never know where you will finally end up — it could be the bumpy roads of the enterprise or a dismantling customer sentiment.

Start coding something amazing with the IBM library of open source AI code patterns.  Content provided by IBM.

Topics:
ai ,robotic process automation ,rpa ,bot development ,robotics ,machine learning ,cognitive computing

Published at DZone with permission of

Opinions expressed by DZone contributors are their own.

{{ parent.title || parent.header.title}}

{{ parent.tldr }}

{{ parent.urlSource.name }}