Abstract: A method may include applying, to various factors contributing to a sentiment that an end user exhibits towards an enterprise software application, a first machine learning model trained to determine, based on the factors, a sentiment index indicating the sentiment that the end user exhibits towards the enterprise software application. In response to the sentiment index exceeding a threshold value, a second machine learning model may be applied to identify remedial actions for addressing one or more of the factors contributing to the sentiment of the end user. A user interface may be generated to display, at a client device, a recommendation including the remedial actions. The remedial actions may be prioritized based on how much each corresponding factor contribute to the sentiment of the end user. Related systems and articles of manufacture are also provided.

Abstract: A method for machine-learning based process flow recommendation is provided. The method may include training a machine-learning model by at least processing training data with the machine-learning model. The training data may include a matrix representing one or more existing process flows by at least indicating actions that are performed on a document object to generate a subsequent document object. An indication that a first document object is created as part of a process flow may be received. In response to the indication, the trained machine-learning model may be applied to generate a recommendation to perform, as part of the process flow, an action to generate a second document object. Related systems and articles of manufacture, including computer program products, are also provided.

Abstract: Disclosed herein are system, method, and computer program product embodiments for developing natural language skills automatically. In order to generate a skill, metadata is read from a connected service, wherein the metadata specifies entities provided by the service. A relevant entity of the entities provided by the service is determined, and a likely request on the entity is generated.

Abstract: A method may include applying, to various factors contributing to a sentiment that an end user exhibits towards an enterprise software application, a first machine learning model trained to determine, based on the factors, a sentiment index indicating the sentiment that the end user exhibits towards the enterprise software application. In response to the sentiment index exceeding a threshold value, a second machine learning model may be applied to identify remedial actions for addressing one or more of the factors contributing to the sentiment of the end user. A user interface may be generated to display, at a client device, a recommendation including the remedial actions. The remedial actions may be prioritized based on how much each corresponding factor contribute to the sentiment of the end user. Related systems and articles of manufacture are also provided.

Abstract: Methods and systems are used for improving benchmark key performance indicators (KPIs). As an example, a set of KPIs associated with a particular client is identified, each KPI of the identified set of KPIs associated with a plurality of KPI attributes. A set of particular KPI attributes associated with the identified set of KPIs associated with the particular client is identified. A recommendation assessment of the identified set of particular KPI attributes is performed using a trained recommendation reference model (RRM) to identify at least one operational recommendation for the particular client. A ranked set of the at least one operational recommendation is generated based on a ranking associated with each KPI of the at least one KPI of the identified set of KPIs. The ranked set of the at least one operational recommendation is provided to the particular client.

Abstract: Disclosed herein are system, method, and computer program product embodiments for developing natural language skills automatically. In order to generate a skill, metadata is read from a connected service, wherein the metadata specifies entities provided by the service. A relevant entity of the entities provided by the service is determined, and a likely request on the entity is generated.

Abstract: Briefly, embodiments of a system, method, and article for processing a set of indicators from an indicator repository are disclosed. An indicator anomaly may be detected within one of more of the individual indicators of the set of indicators based, at least in part, on a threshold increase in publication of the one or more of the individual indicators within a particular time period. A determination may be made as to whether one or more particular indicators of the set of indicators had a causal impact on a transactions anomaly within the particular time period. A notification may be generated to identify the one or more particular indicators at least particularly in response to the determining that the one or more particular indicators of the set of indicators had a causal impact on a transactions anomaly.

Abstract: A method for machine-learning based process flow recommendation is provided. The method may include training a machine-learning model by at least processing training data with the machine-learning model. The training data may include a matrix representing one or more existing process flows by at least indicating actions that are performed on a document object to generate a subsequent document object. An indication that a first document object is created as part of a process flow may be received. In response to the indication, the trained machine-learning model may be applied to generate a recommendation to perform, as part of the process flow, an action to generate a second document object. Related systems and articles of manufacture, including computer program products, are also provided.