AI is often not the magic solution that it’s made out to be. You need to take multiple considerations into account before you make the decision to automate your business processes.
How do you select a process that is a good candidate for cognitive automation?
The standard spot for a rule-based RPA use case is in a region with high volume and low complexity. The process there should have the volume factors, stability, and frequency for a suitable business case. The automation effort should also be manageable. The systems and applications used in a process should be conducive to RPA. The process input and output should be accessible to a bot. The process should be optimized and standardized.
On the other hand, cognitive processes lie in the region of high volume and high complexity.
Here are some other factors that you need to look at when considering processes for automation.
- Variability of input sources
The potential for cognitive automation is a function of the process’s input sources. If the variation is too large, it might not be a good candidate for cognitive automation. In an email management process, for instance, you cannot automate the resolution of every type of email that you might receive. The mail types that you automate should be restricted.
- Machine readability of input sources
The input sources should be machine readable. The input of the process needs to be converted to a machine-readable format.
- Technology availability
The technology to automate the process needs to be available. It should be mature and the cost should be viable. Also, he firm’s security policies should allow its use, whether it’s free or commercial software.
- Data availability
When applying machine learning, there should enough data to form a suitable model. This data should be available to the AI engine.
- Opportunities for standardization and re-engineering
Sometimes cognitive automation is best avoided when you can standardize the format your customer sends messages in instead of applying a ML model to it. You can adopt a common format instead of trying to apply algorithms to unstructured data. Reengineering a process can often give you efficiencies that applying AI never will. There may also be efforts in your industry to standardize a messaging format or use a common platform.
There are processes like underwriting where cognitive automation can help, but they should not be used to make final decisions for high-value cases. The risk involved in using a machine to solve a human judgement problem is high and the benefit should be evaluated carefully.
- Other points
- Cognitive automation is not obvious.
- Most people think of processes with scanned documents or voice inputs as candidates for cognitive RPA, but processes like reconciliation of data are also suitable candidates.
- Not everything is cognitive.
- Multi-step processes can be misconstrued as cognitive when they are actually complex decision trees.