Artificial intelligence-powered incubation management system

1 . A computer-implemented method of innovation ideation and project management using an artificial intelligence (AI) model communicatively coupled to a server, a database, and one or more client devices by a network, the method comprising performing by the AI model:

using a query agent model to perform web searches to obtain time sensitive prompts and contextual data;

pre-process, using a machine learning model, the time sensitive prompts and contextual data to remove noise, formatting issues, and irrelevant data;

training the AI model using the pre-processed time sensitive prompts and contextual data to generate project data and key performance indicators (KPIs);

an ideation phase including the steps of:

receiving, from at least one of the one or more client devices, an ideation input associated with an innovation challenge or a proposed project;

tokenizing and encoding the ideation input to generate an ideation prompt;

generating, by the AI model, project details for at least one proposed project;

generating, by the AI model, a list of proposed personnel to staff the proposed project based on historical staffing of similar projects stored in the database; and

storing, in the database, the generated project details and the list of proposed personnel as a project record;

a project monitor phase including the steps of:

defining, by the AI model, at least one KPI for the proposed project and setting a trigger threshold for the KPI based on historical performance data of projects as stored in the database;

receiving, by the AI model, an alert related to a threshold violation of the KPI and generating at least one suggested strategy for mitigating the threshold violation, wherein generating the least one suggested strategy comprises:

generating, by the AI model, a mitigation prompt that includes details of the proposed project, the KPI, the trigger threshold, and a degree to which the trigger threshold is violated; and

performing a probabilistic analysis using the mitigation prompt and stored historical project records to output the at least one suggested strategy; and

storing, in the database, the alert and the at least one suggested strategy in association with the project record;

an external factor monitoring phase including the steps of:

monitoring, by the AI model, external factor data during performance of the proposed project;

generating, by the AI model, at least one what-if scenario for the proposed project based on details of the proposed project and market data stored in the database;

generating, by the AI model, at least one suggested strategy for mitigating the what-if scenario based on past problem-solution data stored in the database; and

storing, in the database, the at least one what-if scenario and the at least one suggested strategy for mitigating the what-if scenario in association with the project record;

a market monitoring phase including the steps of:

monitoring, by the AI model, market signal data including financial news and data stored in the database; and

generating, by the AI model, a market demand analysis based on the KPI and the market signal data, wherein generating the market demand analysis comprises generating, by the AI model, a market prompt based on desired market signals, the KPI, and the project record, the market prompt being generated using tokenization, encoding, and probability analysis;

and a competitor analysis phase including the steps of:

identifying, by the AI model, at least one competitor in an industry related to the proposed project;

generating, by the AI model, a competitor profile for the identified competitor;

monitoring, by the AI model, competitor performance and activity; and

generating, by the AI model, a competitor analysis in view of the proposed project details

and the KPI; and

analyzing, the AI model generated responses using a second AI model, to generate feedback regarding the accuracy of the responses with respect to target outputs;

in response to analyzing, retraining the AI model to update model parameters with the feedback to provide ongoing learning for subsequent project records.

2 . The method of claim 1 , wherein the ideation phase further includes the steps of, by the AI model:

receiving as input a name of an innovation challenge;

tokenizing and encoding the name of the innovation challenge;

generating a challenge problem statement and challenge description based on the input name;

generating the proposed project for solving the challenge problem statement; and

selecting personnel from the list of proposed personnel to staff the proposed project.

3 . The method of claim 2 , wherein the ideation phase further includes the step of, by the AI model, seeding user ideas via a chatbot presented via at least one of the one or more client devices based on project or company information stored in the database, the information including on one or more of previous ideas, internal company initiatives, competitor information, market information, and recent intellectual property filings.

4 . The method of claim 1 , wherein the project monitor phase further includes the steps of:

monitoring, by the AI model, adherence to the KPI during project performance;

detecting when the KPI violates the threshold; and

generating the alert related to the threshold violation for transmission over the network to at least one of the one or more client devices and for storage in the database in association with the project record.

5 . The method of claim 4 , wherein a risks, assumptions, issues, and dependencies (RAID) module generates the alert and transmits the alert to the client device via the network.

6 . The method of claim 4 , wherein a risks, assumptions, issues, and dependencies (RAID) module detects when the KPI violates the threshold by periodically comparing the threshold with the KPI.

7 . The method of claim 1 , wherein the external factor monitoring phase further includes the steps of, by the AI model:

collecting performance data of the proposed project;

comparing the performance data to the external factor data and internal policies;

generating the what-if scenario for the proposed project based on the comparison; and

detecting if the what-if scenario results in a negative result to the KPI, and, in response, generating and storing in the database an updated mitigation prompt for the at least one suggested strategy for mitigating the what-if scenario.

8 . The method of claim 1 , wherein the competitor analysis phase further includes the steps of, by the AI model:

identifying the industry related to the proposed project using industry classification information stored in the database;

identifying at least one similar industry;

identifying at least one competitor in the similar industry;

generating a competitor profile for each identified competitor;

monitoring performance and activity for each identified competitor; and

generating a competitor analysis for each identified competitor in view of the proposed project details and the KPI.

9 . The method of claim 8 , wherein identifying the at least one similar industry comprises cross-referencing an industry classification associated with the proposed project to one or more related industry classifications stored in the database and identifying competitors associated with the one or more related industry classifications.

10 . The method of claim 1 , further comprising using the stored alert and the stored suggested strategy for mitigating the threshold violation as training data to generate, during subsequent ideation phase for a subsequent proposed project, project details that reduce a likelihood of violating the trigger threshold for the KPI.

11 . An innovation ideation and project management system comprising:

an artificial intelligence (AI) model communicatively coupled to a server, a database, and one or more client devices by a network, wherein the AI model is configured to:

using a query agent model to perform web searches to obtain time sensitive prompts and contextual data;

pre-process, using a machine learning model, the time sensitive prompts and contextual data to remove noise, formatting issues, and irrelevant data;

training the AI model using the pre-processed time sensitive prompts and contextual data to generate project data and key performance indicators (KPIs);

perform an ideation phase including the steps of:

receiving, from at least one of the one or more client devices, an ideation input associated with an innovation challenge or a proposed project;

generating an ideation prompt based on the ideation input by tokenizing and encoding the ideation input and performing probabilistic analysis;

generating project details for at least one proposed project;

generating a list of proposed personnel to staff the proposed project based on historical staffing of similar projects stored in the database; and

storing, in the database, the generated project details and the list of proposed personnel as a project record;

perform a project monitor phase including the steps of:

defining at least one KPI for the proposed project and setting a trigger threshold for the KPI based on historical performance data of projects as stored in the database;

receiving an alert related to a threshold violation of the KPI and generating at least one suggested strategy for mitigating the threshold violation, wherein generating the at least one suggested strategy comprises:

generating a mitigation prompt that includes the KPI, the trigger threshold, and a degree of threshold violation; and

performing probabilistic analysis using the mitigation prompt and historical project records stored in the database; and

storing, in the database, the alert and the at least one suggested strategy in association with the project record;

perform an external factor monitoring phase including the steps of:

monitoring external factor data during performance of the proposed project;

generating at least one what-if scenario for the proposed project based on details of the proposed project and market data stored in the database;

generating at least one suggested strategy for mitigating the what-if scenario based on past problem-solution data stored in the database; and

storing, in the database, the at least one what-if scenario and the at least one suggested strategy for mitigating the what-if scenario in association with the project record;

perform a market monitoring phase including the steps of:

monitoring market signal data including financial news and data stored in the database; and

generating a market demand analysis based on the KPI and the market signal data, wherein generating the market demand analysis comprises generating a market prompt using tokenization, encoding, and probabilistic analysis based on desired market signals and the project record;

perform a competitor analysis phase including the steps of:

identifying at least one competitor in an industry related to the proposed project;

generating a competitor profile for the identified competitor;

monitoring competitor performance and activity; and

generating a competitor analysis in view of the proposed project details and the KPI;

analyzing, the AI model generated responses using a second AI model, to generate feedback regarding the accuracy of the responses with respect to target outputs;

in response to analyzing, retraining the AI model to update model parameters with the feedback to provide ongoing learning for subsequent project records.

12 . The system of claim 11 , further comprising an ideation module, wherein:

the ideation is configured to:

receive as input a name of an innovation challenge; and

interact with the AI model to generate a challenge problem statement and challenge description based on the input name by generating a prompt using tokenization, encoding, and probabilistic analysis; and

the AI model is further configured to generate the proposed project for solving the challenge problem statement.

13 . The system of claim 12 , wherein the ideation module is further configured to:

interact with the AI model to seed user ideas via a chatbot presented by at least one of the one or more client devices based on project or company information stored in the database, the information including on one or more of previous ideas, internal company initiatives, competitor information, market information, and recent intellectual property filings; and

store user responses received via the chatbot in the database for use as reference data in subsequent ideation prompts.

14 . The system of claim 11 , further comprising a risks, assumptions, issues, and dependencies (RAID) module, wherein:

the RAID module is configured to:

monitor adherence to the KPI during project performance; and

detect when the KPI violates the threshold; and

the AI model is further configured to generate the alert related to the threshold violation and to store the alert in the database in association with the project record.

15 . The system of claim 14 , wherein the RAID module is further configured to detect when the KPI violates the threshold by periodically comparing the threshold with the KPI based on KPI values retrieved from the database at a predetermined interval.

16 . The system of claim 14 , wherein the RAID module is further configured to transmit the alert to the client device via the network for display via a user interface.

17 . The system of claim 11 , further comprising an external factors module,

wherein:

the external factors module is configured to:

collect performance data of the proposed project; and

compare the performance data to the external factor data and internal policies; and

the AI model is further configured to:

generate the what-if scenario for the proposed project based on the comparison by generating a what-if prompt using tokenization, encoding, and probabilistic analysis based on the comparison; and

store the what-if prompt and the what-if scenario in the database in association with the project record.

18 . The system of claim 11 , further comprising a market module, wherein:

the market module is configured to monitor market data; and

the AI model is further configured to:

generate a market demand analysis for the proposed project based on the market data by generating a market prompt using tokenization, encoding, and probabilistic analysis; and

store the market demand analysis in the database in association with the project record.

19 . The system of claim 11 , further comprising a competitor module, wherein:

the competitor module is configured to:

identify the industry related to the proposed project;

identify at least one similar industry;

identify at least one competitor in the similar industry;

monitor performance and activity for each identified competitor; and

the AI module model is further configured to:

generate a competitor profile for each identified competitor;

generate a competitor analysis for each identified competitor in view of the proposed project details and the KPI; and

store the competitor profile and the competitor analysis in the database in association with the project record for use as reference data in subsequent ideation prompts.