Automation & AI
Automation through software is of utmost importance for companies to stay competitive and achieve operational excellence by meeting (SLAs). Harnessing the power of so-called software robots, software companies like Qrex; have opened up new avenues for process engineering in the IT industry. Information processing mechanisms are now created considering RPA as a viable and more reachable option than ever before.
Incorporating AI in Process Automation
For the success of the project, it is imperative to clearly identify and understand the mechanics behind a successful realization of RPA in presence of an AI driven strategy. RPA systems where AI is considered as the centerstage requires fundamental understanding of how both technologies co-exits in collaborative manner. Design constructs from both angles need to be clearly defined before going into implementation phase.
Taking holistic perspective, historical data for model training is inevitable. Besides, other aspects such as actors, trigger points, sub-system boundaries, domain knowledge, interfacing API/hooks, regulations, as well as corner cases where human intervention may be needed, exception handling, etc. are all going to be crucial.
Good news is that both AI and RPA can co-exist and can function well without issues.
- Some of the relevant questions pertaining to “RPA/AI amalgamation”, can be listed as under —
- What degree of manual intervention is involved in the process?
- Which process steps are repetitive?
- Do you have enough training data for conversational and other AI related pieces?
- How can conversational AI augment user experience in your case?
- What are the pitfalls that AI/ML piece may pose?
- Is AI/ML adding value without incurring too much cost and complexity?
- What is your success criteria? KPI, Satisfaction index, etc.
- What are the parameters/factors that influence process improvement?
- Are there off-the-shelf tools available to handle some or all of your needs?
- What are cost and time constraints?