Abstract: In some implementations, a device may receive training data identifying a set of workflows. The device may generate a set of workflow templates based on the training data identifying the set of workflows, wherein a workflow template, of the set of workflow templates, is associated with a set of steps for completing a task associated with the workflow template. The device may receive, from a client device associated with an entity, a new task for automation using a new workflow. The device may parse the new task to identify one or more steps associated with the new task. The device may identify one or more workflow templates. The device may a workflow recommendation relating to the one or more workflow templates. The device may output workflow data associated with the workflow recommendation.

Abstract: In some examples, artificial intelligence based project implementation may include implementing, for a project team, self-evaluation of viability for utilizing a project implementation framework. The project team may be guided on utilization of the project implementation framework. A discussion by the project team may be documented in a digital format during envisioning associated with the project implementation framework, and documented information may be maintained for reference during execution, by the project team, of a journey associated with the project implementation framework. During execution of the journey, implementation of a social contract by the project team may be evaluated. A determination may be made as to whether the social contract is not implemented for at least one specified occurrence associated with the project implementation framework, and at least one impediment associated with the project implementation framework may be identified.

Abstract: In some examples, artificial intelligence and machine learning based product development may include ascertaining an inquiry, by a user, related to a product that is to be developed or that is under development, and ascertaining an attribute associated with the user. The inquiry may be analyzed to determine at least one virtual assistant from a set of virtual assistants to respond to the inquiry. The determined at least one virtual assistant may be invoked based on an authorization by the user. Further, development of the product may be controlled based on the invocation of the determined at least one virtual assistant.

Abstract: In some examples, artificial intelligence and machine learning based project management assistance may include ascertaining an inquiry by a user. The inquiry may be related to a project. An attribute associated with the user and an attribute associated with the project may be ascertained. The inquiry may be analyzed based on the ascertained attributes associated with the user and the project. A predictor category may be identified, based on the analyzed inquiry, from a plurality of predictor categories that include a performance predictor category, a quality predictor category, a retrospect predictor category, and a planning predictor category. A predictor from a plurality of predictors may be identified based on the identified predictor category. A response to the inquiry may be generated based on execution of the identified predictor. Further, a display responsive to the inquiry may be generated based on the generated response.

Abstract: A project management platform may train a machine learning model with historical project data to generate a trained machine learning model that determines or analyzes a release schedule of a project. The project management platform may receive new project data identifying project information associated with a new project. The project management platform may perform natural language processing on the new project data to convert the new project data to processed new project data. The project management platform may receive resource data identifying resource availability for the new project. The project management platform may process, using the trained machine learning model, the processed new project data and the resource data to determine release information for the new project, wherein the release information includes a release schedule for the new project. The project management platform may perform, according to the release schedule, an action associated with the release information.

Abstract: In some examples, artificial intelligence based project implementation may include implementing, for a project team, self-evaluation of viability for utilizing a project implementation framework. The project team may be guided on utilization of the project implementation framework. A discussion by the project team may be documented in a digital format during envisioning associated with the project implementation framework, and documented information may be maintained for reference during execution, by the project team, of a journey associated with the project implementation framework. During execution of the journey, implementation of a social contract by the project team may be evaluated. A determination may be made as to whether the social contract is not implemented for at least one specified occurrence associated with the project implementation framework, and at least one impediment associated with the project implementation framework may be identified.

Abstract: In some examples, artificial intelligence and machine learning based project management assistance may include ascertaining an inquiry by a user. The inquiry may be related to a project. An attribute associated with the user and an attribute associated with the project may be ascertained. The inquiry may be analyzed based on the ascertained attributes associated with the user and the project. A predictor category may be identified, based on the analyzed inquiry, from a plurality of predictor categories that include a performance predictor category, a quality predictor category, a retrospect predictor category, and a planning predictor category. A predictor from a plurality of predictors may be identified based on the identified predictor category. A response to the inquiry may be generated based on execution of the identified predictor. Further, a display responsive to the inquiry may be generated based on the generated response.

Abstract: In some examples, artificial intelligence and machine learning based product development may include ascertaining an inquiry, by a user, related to a product that is to be developed or that is under development, and ascertaining an attribute associated with the user. The inquiry may be analyzed to determine at least one virtual assistant from a set of virtual assistants to respond to the inquiry. The determined at least one virtual assistant may be invoked based on an authorization by the user. Further, development of the product may be controlled based on the invocation of the determined at least one virtual assistant.