Dynamic maturity model

1 . A computer implemented method comprising:

processing one or more text based maturity model document of a maturity model, wherein the maturity model specifies a plurality of domains, and for at least one domain of the plurality of domains, capabilities are associated to respective different maturity levels of the at least one domain, wherein the processing the one or more text based maturity model document includes applying natural language processing to extract from the one or more text based document a parameter value dataset;

processing one or more maturity model text document of a second maturity model, wherein the second maturity model specifies a second plurality of domains, and for at least one domain of the second plurality of domains, capabilities are associated to respective different maturity levels of the at least one domain, wherein the processing the one or more maturity model text document includes applying natural language processing to extract from the one or more maturity model text document a second parameter value dataset, wherein the second maturity model is a subsequent generation of the maturity model;

comparing the second maturity model to the maturity model, wherein the comparing is performed in dependence on at least one value of the parameter value dataset and at least one value of the second parameter value dataset;

wherein the comparing includes detecting latent generational trends that are not practically perceivable by humans, including statistically significant changes in domain or capability frequency;

generating prompting data in dependence on a result of the comparing;

wherein the prompting data comprises system-generated guidance to modify the maturity model, including at least one of splitting, merging, or restructuring domains or capabilities, based on the detected latent trends;

wherein the prompting data is generated without reliance on subjective human judgment and in accordance with a rule-based or statistical decision model; and

presenting the prompting data to a user;

wherein the prompting data is presented in real-time, enabling automated maturity model progression that is free from human bias or intuition.

2 . The computer implemented method of claim 1 , further comprising obtaining text based documents associated to candidate core group expert users, subjecting the text based documents to natural language processing to extract natural language processing parameter value datasets associated to respective candidate core group expert users, wherein the method includes selecting invitee core group expert users from the candidate core group expert users in dependence on an emerging topic identified in the second maturity model, wherein identifying the emerging topic has resulted from the comparing the second maturity model to the maturity model.

3 . The computer implemented method of claim 1 , further comprising obtaining text based documents associated to candidate core group expert users, subjecting the text based documents to natural language processing to extract natural language processing parameter value datasets associated to respective candidate core group expert users, wherein the method includes selecting invitee core group expert users from the candidate core group expert users in dependence on an emerging topic identified in the second maturity model, wherein identifying the emerging topic has resulted from the comparing the second maturity model to the maturity model, wherein the selecting includes evaluating a topic strength profile of the candidate core group expert users against a predicted topic strength profile of the maturity model in a generation subsequent to the second maturity model.

4 . The computer implemented method of claim 1 , further comprising obtaining text based documents associated to candidate core group expert users, subjecting the text based documents to natural language processing to extract natural language processing parameter value datasets associated to respective candidate core group expert users, wherein the method includes selecting invitee core group expert users from the candidate core group expert users in dependence on an emerging topic identified in the second maturity model, wherein identifying the emerging topic has resulted from the comparing the second maturity model to the maturity model, wherein the selecting includes evaluating a topic strength profile of the candidate core group expert users against a predicted topic strength profile of the maturity model in a generation subsequent to the second maturity model, wherein predicting of the topic strength profile of the maturity model in the generation subsequent to the second maturity model has included querying a predictive model that has been trained with training data defined by natural language processing parameter values derived from natural language processing of documents of the maturity model and the second maturity model.

5 . The computer implemented method of claim 1 , further comprising selecting invitee core group expert users from candidate core group expert users, wherein the selecting includes evaluating natural language processing parameter values of the candidate core expert users against predicted natural language processing parameter values of the maturity model in a generation subsequent to the second maturity model, wherein predicting the natural language processing parameter values in the generation subsequent to the second maturity model has included querying a predictive model that has been trained with training data defined by natural language processing parameter values derived from natural language processing of documents of the maturity model and the second maturity model.

6 . The computer implemented method of claim 1 , further comprising evaluating natural language processing parameter values extracted from one or more maturity model document against predicted natural language processing parameter values of the maturity model in a generation subsequent to the second maturity model, wherein predicting the natural language processing parameter values in the generation subsequent to the second maturity model has included querying a predictive model that has been trained with training data defined by natural language processing parameter values derived from natural language processing of documents of the maturity model and the second maturity model.

7 . The computer implemented method of claim 1 , further comprising evaluating natural language processing parameter values extracted from one or more maturity model document against predicted natural language processing parameter values of the maturity model in a generation subsequent to the second maturity model, wherein predicting the natural language processing parameter values in the generation subsequent to the second maturity model has included querying a predictive model that has been trained with training data defined by natural language processing parameter values derived from natural language processing of documents of the maturity model and the second maturity model, wherein the method includes identifying an emerging topic from the evaluating, and wherein the generating the prompting data is in dependence on the identifying, and wherein the presenting the prompting data includes presenting the prompting data to core group expert users so that the core group expert users specify a domain for the maturity model in the generation subsequent to the second maturity model in accordance with the identified emerging topic.

8 . The computer implemented method of claim 1 , further comprising evaluating natural language processing parameter values extracted from one or more maturity model survey result document against predicted natural language processing parameter values of the maturity model in a generation subsequent to the second maturity model, wherein predicting the natural language processing parameter values in the generation subsequent to the second maturity model has included querying a predictive model that has been trained with training data defined by natural language processing parameter values derived from natural language processing of documents of the maturity model and the second maturity model, wherein the method includes identifying an emerging topic from the evaluating, and wherein the generating the prompting data is in dependence on the identifying, and wherein the presenting the prompting data includes presenting the prompting data to core group expert users so that the core group expert users specify a domain for the maturity model in the generation subsequent to the second maturity model in accordance with the identified emerging topic.

9 . The computer implemented method of claim 1 , further comprising evaluating natural language processing parameter values extracted from provisional maturity model specification document produced based on input from core group expert users against predicted natural language processing parameter values of the maturity model in a generation subsequent to the second maturity model, wherein predicting the natural language processing parameter values in the generation subsequent to the second maturity model has included querying a predictive model that has been trained with training data defined by natural language processing parameter values derived from natural language processing of documents of the maturity model and the second maturity model.

10 . The computer implemented method of claim 1 , further comprising evaluating natural language processing parameter values extracted from provisional maturity model specification document produced based on input from core group expert users against predicted natural language processing parameter values of the maturity model in a generation subsequent to the second maturity model, wherein predicting the natural language processing parameter values in the generation subsequent to the second maturity model has included querying a predictive model that has been trained with training data defined by natural language processing parameter values derived from natural language processing of documents of the maturity model and the second maturity model, wherein the generating the prompting data is in dependence on the evaluating, and wherein the presenting the prompting data includes presenting the prompting data to the core group expert users, wherein the prompting data is configured to prompt the core group expert users to present inputs for the production of a revised provisional maturity model.

11 . The computer implemented method of claim 1 , further comprising evaluating natural language processing parameter values extracted from provisional maturity model specification document produced based on input from core group expert users against predicted natural language processing parameter values of the maturity model in a generation subsequent to the second maturity model, wherein predicting the natural language processing parameter values in the generation subsequent to the second maturity model has included querying a predictive model that has been trained with training data defined by natural language processing parameter values derived from natural language processing of documents of the maturity model and the second maturity model, wherein the generating the prompting data is in dependence on the evaluating, and wherein the presenting the prompting data includes presenting the prompting data to the core group expert users, wherein the prompting data is configured to prompt the core group expert users to present inputs for the production of a revised provisional maturity model, wherein the comparing includes establishing the predictive model trained by the training data defined by natural language processing parameter values derived from natural language processing of documents of the maturity model and the second maturity model.

12 . The computer implemented method of claim 1 , wherein the one or more text based document includes a production document that specifies the plurality of domains of the maturity model.

13 . The computer implemented method of claim 1 , wherein the one or more text based document includes a source document.

14 . The computer implemented method of claim 1 , wherein the prompting data prompts the user to revise the second maturity model.

15 . The computer implemented method of claim 1 , further comprising identifying an emerging capability associated to a domain of the second maturity model in dependence on the comparing, and wherein the method includes querying a maturity level placement predictive model for predicting a maturity level placement for the identified emerging capability, wherein the maturity level placement predictive model has been trained with historical maturity model data of a plurality of maturity models associated to a plurality of different industry missions, wherein the prompting data prompts the user to specify a maturity level assignment for the emerging capability in accordance with the predicted maturity level placement.

16 . The computer implemented method of claim 1 , wherein the prompting data prompts the user to join a core group of expert users of a generationally advancing maturity model, wherein the maturity model is a preceding version of the generationally advancing maturity model.

17 . The computer implemented method of claim 1 , wherein the prompting data prompts the user to specify a maturity level assignment for an identified emerging capability.

18 . A system comprising:

a memory;

at least one processor in communication with the memory; and

program instructions executable by one or more processor via the memory to perform a method comprising:

processing one or more text based maturity model document of a maturity model, wherein the maturity model specifies a plurality of domains, and for at least one domain of the plurality of domains, capabilities are associated to respective different maturity levels of the at least one domain, wherein the processing the one or more text based maturity model document includes applying natural language processing to extract from the one or more text based document a parameter value dataset;

processing one or more maturity model text document of a second maturity model, wherein the second maturity model specifies a second plurality of domains, and for at least one domain of the second plurality of domains, capabilities are associated to respective different maturity levels of the at least one domain, wherein the processing the one or more maturity model text document includes applying natural language processing to extract from the one or more maturity model text document a second parameter value dataset, wherein the second maturity model is a subsequent generation of the maturity model;

comparing the second maturity model to the maturity model, wherein the comparing is performed in dependence on at least one value of the parameter value dataset and at least one value of the second parameter value dataset;

wherein the comparing includes detecting latent generational trends that are not practically perceivable by humans, including statistically significant changes in domain or capability frequency;

generating prompting data in dependence on a result of the comparing;

wherein the prompting data comprises system-generated guidance to modify the maturity model, including at least one of splitting, merging, or restructuring domains or capabilities, based on the detected latent trends;

wherein the prompting data is generated without reliance on subjective human judgment and in accordance with a rule-based or statistical decision model; and

presenting the prompting data to a user;

wherein the prompting data is presented in real-time, enabling automated maturity model progression that is free from human bias or intuition.

19 . The computer implemented method of claim 1 , wherein presenting the prompting data to the user comprises presenting the prompting data via a user interface that includes selectable prompts corresponding to suggested model modifications, and wherein the user interface is configured to receive user input in response to the selectable prompts, the user input comprising at least one of: affirming the suggestion, modifying a capability or domain, or rejecting the suggestion.

20 . A computer program product comprising:

a computer readable storage medium readable by one or more processing circuit and storing instructions for execution by one or more processor for performing a method comprising:

processing one or more text based maturity model document of a maturity model, wherein the maturity model specifies a plurality of domains, and for at least one domain of the plurality of domains, capabilities are associated to respective different maturity levels of the at least one domain, wherein the processing the one or more text based maturity model document includes applying natural language processing to extract from the one or more text based document a parameter value dataset;

processing one or more maturity model text document of a second maturity model, wherein the second maturity model specifies a second plurality of domains, and for at least one domain of the second plurality of domains, capabilities are associated to respective different maturity levels of the at least one domain, wherein the processing the one or more maturity model text document includes applying natural language processing to extract from the one or more maturity model text document a second parameter value dataset, wherein the second maturity model is a subsequent generation of the maturity model;

comparing the second maturity model to the maturity model, wherein the comparing is performed in dependence on at least one value of the parameter value dataset and at least one value of the second parameter value dataset;

wherein the comparing includes detecting latent generational trends that are not practically perceivable by humans, including statistically significant changes in domain or capability frequency;

generating prompting data in dependence on a result of the comparing;

wherein the prompting data comprises system-generated guidance to modify the maturity model, including at least one of splitting, merging, or restructuring domains or capabilities, based on the detected latent trends;

wherein the prompting data is generated without reliance on subjective human judgment and in accordance with a rule-based or statistical decision model; and

presenting the prompting data to a user;

wherein the prompting data is presented in real-time, enabling automated maturity model progression that is free from human bias or intuition.