During system automation, there are often key challenges for manual updates. In every system deployment, human behaviour is the topmost problem for data integrity. Thus, one of the key digital transformation objective is to automate manual updates. I am definitely not referring to RPA as the means. Instead, one key method is to build the rules in the system.
A classic way to automate manual updates is using business rules for auto assignment and pre-population of data in a structured manner. This requires deep understanding and observation of existing SOP (Standard Operating Procedures). You can easily setup auto assignment rules within your system. However, you will need to identify and use the correct parameters. This often requires SME (Subject Matter Expert) because they are well versed in business domain and technical expertise.
ML (Machine Learning) is an emerging field that are likely to replace the business rules. You can now see the permeation of ML in many daily systems like email, photo storage, data storage. They have common rules to identify and categorise your data and usage. However, ML is complex to deploy and limited to normal scenarios. It will not work well for exception scenarios and does not resonates well with end users.
The trend of automation will continue to be simplify with ML. It will not be worthwhile to invest in transition tool like RPA. You will be better off to build your SME team and grow your ML expertise.