Connected framework for identifying automation candidates

1. A method comprising, by a computer system:

receiving a process dataset comprising data related to a user-executed process;

automatically mapping the process dataset to multisource system data deemed relevant to automation of the user-executed process, the automatically mapping comprising performing a plurality of machine learning (ML) analyses, wherein the plurality of ML analyses are each associated with a preexisting dataset that is not particular to the user-executed process, and wherein the multisource system data comprises at least a portion of the preexisting dataset of each of the plurality of ML analyses, the performing a plurality of ML analyses comprises, for each ML analysis of the plurality of ML analyses:

receiving the process dataset;

determining relevant data within the preexisting dataset; and

performing a specific type of analysis using at least a portion of the relevant data and at least a portion of the process dataset, the specific type of analysis resulting in analytical data;

detecting a plurality of scenarios in the user-executed process via the automatically mapped process dataset;

performing scenario-based filtering of the plurality of scenarios; and

automatically generating an automation recommendation for the user-executed process.

2. The method of claim 1 , comprising generating at least a portion of the process dataset via interaction with a user.

3. The method of claim 1 , comprising automatically generating at least a portion of the process dataset from recorded user interface (UI) activity data that has been automatically correlated to a plurality of steps of the user-executed process.

4. The method of claim 1 , wherein the automatically mapping comprises performing a ML-based process analysis, the ML-based process analysis comprising at least one of the following: business process assessment, domain catalog mapping, sub-process analysis, automation existence analysis, process stability analysis, and decision-making analysis.

5. The method of claim 1 , wherein the automatically mapping comprises performing an ML-based productivity analysis, the ML-based productivity analysis comprising at least one of the following: process schedule assessment, repetitive task analysis, volume analysis, average handle time (AHT) analysis, peak time analysis, peak volume analysis, service-level agreement (SLA) analysis, process change analysis, and process history analysis.

6. The method of claim 1 , wherein the automatically mapping comprises performing an ML-based workforce analysis, the ML-based workforce analysis comprising at least one of the following: role mapping analysis, regional workforce analysis, stakeholder mapping, absenteeism analysis and attrition analysis.

7. The method of claim 1 , wherein the automatically mapping comprises performing an ML-based underlying application analysis, the ML-based underlying application analysis comprising at least one of the following: application touchpoint analysis, application downtime analysis, application environment analysis, and application stability analysis.

8. The method of claim 1 , wherein the automatically mapping comprises performing an ML-based insource/outsource analysis, the ML-based insource/outsource analysis comprising at least one of the following: data input and output analysis, information analysis, and storage analysis.

9. The method of claim 1 , wherein the automatically mapping comprises performing an ML-based standard operating procedure (SOP) analysis, the ML-based SOP analysis comprising at least one of the following: process SOP assessment and Suppliers, Inputs, Process, Outputs and Customer (SIPOC) analysis.

10. The method of claim 1 , wherein the automatically mapping comprises performing an ML-based environment analysis, the ML-based environment analysis comprising at least one of the following: server environment analysis, latency analysis, and downtime analysis.

11. The method of claim 1 , wherein the automatically mapping comprises performing an ML-based risk analysis, the ML-based risk analysis comprising at least one of the following: negative scenario analysis, issue analysis, data security analysis, password policy analysis, and preexisting issue analysis.

12. The method of claim 1 , wherein the detecting the plurality of scenarios in the user-executed process comprises matching at least a portion of the process dataset to a library of use cases.

13. The method of claim 1 , wherein the performing scenario-based filtering of the plurality of scenarios comprises analyzing data related to an application that is utilized in the user-executed process.

14. The method of claim 1 , wherein the scenario-based filtering comprises at least one of collaborative filtering and relational filtering.

15. The method of claim 1 , wherein the automation recommendation comprises a recommendation to automate at least a portion of the user-executed process and identifies at least one of a customer experience (CX) automation platform and a robotic process automation platform.

16. The method of claim 1 , comprising:

publishing the automation recommendation to a user; and

responsive to user acceptance of the automation recommendation, training a machine learning (ML) model for recommendations using the automation recommendation.

17. A computer system comprising a processor and memory, wherein the processor and the memory in combination are operable to implement a method comprising:

receiving a process dataset comprising data related to a user-executed process;

automatically mapping the process dataset to multisource system data deemed relevant to automation of the user-executed process, the automatically mapping comprising performing a plurality of machine learning (ML) analyses, wherein the plurality of ML analyses are each associated with a preexisting dataset that is not particular to the user-executed process, and wherein the multisource system data comprises at least a portion of the preexisting dataset of each of the plurality of ML analyses, the performing a plurality of ML analyses comprises, for each ML analysis of the plurality of ML analyses:

receiving the process dataset;

determining relevant data within the preexisting dataset; and

performing a specific type of analysis using at least a portion of the relevant data and at least a portion of the process dataset, the specific type of analysis resulting in analytical data;

detecting a plurality of scenarios in the user-executed process via the automatically mapped process dataset;

performing scenario-based filtering of the plurality of scenarios; and

automatically generating an automation recommendation for the user-executed process.

18. A computer-program product comprising a non-transitory computer-usable medium having computer-readable program code embodied therein, the computer-readable program code adapted to be executed to implement a method comprising:

receiving a process dataset comprising data related to a user-executed process;

automatically mapping the process dataset to multisource system data deemed relevant to automation of the user-executed process, the automatically mapping comprising performing a plurality of machine learning (ML) analyses, wherein the plurality of ML analyses are each associated with a preexisting dataset that is not particular to the user-executed process, and wherein the multisource system data comprises at least a portion of the preexisting dataset of each of the plurality of ML analyses, the performing a plurality of ML analyses comprises, for each ML analysis of the plurality of ML analyses:

receiving the process dataset;

determining relevant data within the preexisting dataset; and

performing a specific type of analysis using at least a portion of the relevant data and at least a portion of the process dataset, the specific type of analysis resulting in analytical data;

detecting a plurality of scenarios in the user-executed process via the automatically mapped process dataset;

performing scenario-based filtering of the plurality of scenarios; and

automatically generating an automation recommendation for the user-executed process.