Intelligent knowledge management-driven decision making model
A method includes receiving user inputs; receiving codified knowledge management information; receiving engine data; and processing the user inputs, the codified knowledge management information and engine data using a trained machine learning model to generate a living document. A computing system includes one or more processors; and a memory comprising instructions that, when executed, cause the computing system to: receive user inputs; receive codified knowledge management information; receive engine data; and process the user inputs, the codified knowledge management information and engine data using a trained machine learning model to generate a living document. A non-transitory computer-readable storage medium includes executable instructions that, when executed by a processor, cause a computer to: receive user inputs; receive codified knowledge management information; receive engine data; and process the user inputs, the codified knowledge management information and engine data using a trained machine learning model to generate a living document.
1. A computer-implemented method for improving efficiency and consistency of knowledge management living documents, the method comprising:
receiving, via one or more processors, one or more user inputs including one or more problems and associated respective parameters and/or variables;
receiving, via one or more processors, codified knowledge management information from a knowledge management environment;
receiving, via one or more processors, from one or more engines, respective engine data; and
processing, via one or more processors, the one or more user inputs, the codified knowledge management information and one or more of the respective engine data using one or more trained machine learning models in a knowledge artificial intelligence model to generate an output including one or more living documents.
2. The computer-implemented method of claim 1 , further comprising:
providing a customer recommendation based on the generated one or more living documents.
3. The computer-implemented method of claim 1 , further comprising:
processing the user inputs including the one or more problems and associated respective parameters and/or variables with a natural language processing model to identify a detailed problem statement, at least one associated parameter and at least one associated variable.
4. The computer-implemented method of claim 1 , wherein processing the one or more user inputs, the codified knowledge management information and the one or more of the respective engine data using the one or more trained machine learning models in the knowledge artificial intelligence model to generate the output including the one or more living documents includes processing the user inputs using a trained information collection machine learning model to generate the codified knowledge management information.
5. The computer-implemented method of claim 1 , wherein the respective engine data includes at least one of (i) intelligent cloud data and technology solutions engine data; (ii) smart domain expertise solutions engine data; (iii) AI-driven experimentation engine data; or (iv) security solutions engine data.
6. The computer-implemented method of claim 1 , further comprising:
updating the one or more living documents with new knowledge based on at least one of (i) a user approval; (ii) output of a machine learning information collection model; or (iii) output of a machine learning extraction, classification and strategy model.
7. The computer-implemented method of claim 6 , wherein updating the one or more living documents with new knowledge is performed continuously.
8. A computing system for improving the efficiency and consistency of knowledge management living documents, comprising:
one or more processors; and
a memory comprising instructions that, when executed, cause the computing system to:
receive, via one or more processors, one or more user inputs including one or more problems and associated respective parameters and/or variables;
receive, via one or more processors, codified knowledge management information from a knowledge management environment;
receive, via one or more processors, from one or more engines, respective engine data; and
process, via one or more processors, the one or more user inputs, the codified knowledge management information and one or more of the respective engine data using one or more trained machine learning models in a knowledge artificial intelligence model to generate an output including one or more living documents.
9. The computing system of claim 8 , the memory comprising further instructions that, when executed, cause the computing system to:
provide a customer recommendation based on the generated one or more living documents.
10. The computing system of claim 8 , the memory comprising further instructions that, when executed, cause the computing system to:
process the user inputs including the one or more problems and associated respective parameters and/or variables with a natural language processing model to identify a detailed problem statement, at least one associated parameter and at least one associated variable.
11. The computing system of claim 8 , the memory comprising further instructions that, when executed, cause the computing system to:
process the user inputs using a trained information collection machine learning model to generate the codified knowledge management information.
12. The computing system of claim 8 , the memory comprising further instructions that, when executed, cause the computing system to:
receive at least one of (i) intelligent cloud data and technology solutions engine data; (ii) smart domain expertise solutions engine data; (iii) AI-driven experimentation engine data; or (iv) security solutions engine data.
13. The computing system of claim 8 , the memory comprising further instructions that, when executed, cause the computing system to:
update the one or more living documents with new knowledge based on at least one of (i) a user approval; (ii) output of a machine learning information collection model; or (iii) output of a machine learning extraction, classification and strategy model.
14. The computing system of claim 8 , the memory comprising further instructions that, when executed, cause the computing system to:
analyze the one or more living documents to identify one or more domain experts.
15. A non-transitory computer-readable storage medium comprising executable instructions that, when executed by a processor, cause a computer to:
receive, via one or more processors, one or more user inputs including one or more problems and associated respective parameters and/or variables;
receive, via one or more processors, codified knowledge management information from a knowledge management environment;
receive, via one or more processors, from one or more engines, respective engine data; and
process, via one or more processors, the one or more user inputs, the codified knowledge management information and one or more of the respective engine data using one or more trained machine learning models in a knowledge artificial intelligence model to generate an output including one or more living documents.
16. The non-transitory computer-readable storage medium of claim 15 , storing further executable instructions that, when executed, cause a computer to:
provide a customer recommendation based on the generated one or more living documents.
17. The non-transitory computer-readable storage medium of claim 15 , comprising further executable instructions that, when executed, cause a computer to:
process the user inputs including the one or more problems and associated respective parameters and/or variables with a natural language processing model to identify a detailed problem statement, at least one associated parameter and at least one associated variable.
18. The non-transitory computer-readable storage medium of claim 15 , comprising further executable instructions that, when executed, cause a computer to:
process the user inputs using a trained information collection machine learning model to generate the codified knowledge management information.
19. The non-transitory computer-readable storage medium of claim 15 , comprising further executable instructions that, when executed, cause a computer to:
receive at least one of (i) intelligent cloud data and technology solutions engine data; (ii) smart domain expertise solutions engine data; (iii) AI-driven experimentation engine data; or (iv) security solutions engine data.
20. The non-transitory computer-readable storage medium of claim 15 , comprising further executable instructions that, when executed, cause a computer to:
update the one or more living documents with new knowledge based on at least one of (i) a user approval; (ii) output of a machine learning information collection model; or (iii) output of a machine learning extraction, classification and strategy model.