Evolution of RPA DO/Learn/Think
Do (Structure data and rule base flow)
- RPA Tool (Automation anywhere, UiPath,etc..)- VBA (Macro)
- VBScript/ BAT files/ Powershell
- Javascript/Jquery
- Excel formula
Learn (Unstructured data and complex process)
Need to train the BOT frequently to match the decision-making frequency depending on the diversity of the input data.
- Python
- Machine Learning
- Deep Learning
- Cognitive Learning
Think (BOT can take a decision)
- AI
Advantage of RPA
- Frees up employees to do higher-value work- Save FTE
- Speed up the processing time (Reduce SLA)
- Work for 24*7
- Communicate with multiple technologies (can eliminate development efforts)
- Checker BOT
- Maker BOT
- Generate Report (data massage)
- Process improvement
- Data migration
- Higher outcome quality
- Scalability and agility
etc...
Timeline of a project in RPA
- BRD not having all the details
- Not getting application access on time
- POC
- It is depending on many factors like Project Complexity, RPA tool used, Citrix automation or not, available reusable assets (bots, libraries and etc) and the project team size.
- Change in requirement post development
RPA can fail due to below reason
Organizational pitfalls:- Lack of time commitment from the local team
- Lack of leadership buy-in
- Lack of IT ownership
- Unclear responsibilities
- Unrealistic expectation
- Communication between operation and application team
Process pitfalls:
- Choosing a process with insignificant business impact
- Choosing a too complex process
- Choosing a process where better custom solutions exist
- Lack of focus in process selection
- Striving for end-to-end automation when it is not cost-effective
- Business exception (data is not proper)
Technical pitfalls:
- Should follow SDLC properly
- Architecture and Authentication
- Choosing a solution that requires intensive programming
- System exception (an application bugs)
Post-implementation pitfalls:
- Operation team governance
- Scalability
- Maintenance
Test Cases for Automation
- Test case executed with a different set of data- Test case executed with complex business logic
- Test case executed with a different set of users
- Test case Involves a large amount of data
AI Product
- Chatbot
- Voicebot
- Siri/Cortana
etc...