Protected Frameworks/Models
1. ExecutiveAI CompassTM
A discernment model invented by Sol Rashidi in 2014 that allows companies to discern when AI should be used (or not be used). In an objective and quantitative way, it indicates when a solution to a business problem can be 100% AI-led, or, should be 100% Human-led, AI-led & Human supported, or Human-led & AI supported.
2. ExecutiveAI MatrixTM
A framework invented by Sol Rashidi in 2016 that enables organizations to quickly identify which use case are eligible for AI and have the best chance of succeeding within an organization that takes into consideration data, infrastructure, talent, regulations, and cultural maturity. The framework debunks the myth that business value is the primary marker for use cases and instead measures the Criticality of the use case and the Complexity to deploy and places them in a quadrant.
3. ExecutiveAI TrinityTM
The ‘A’ in Artificial Intelligence sets unrealistic expecations on what can be accomplished with AI and the level of effort it takes. So after having over 200+ deployments, Sol invented the ‘Trinity’ model to clearly articulate how AI is being deployed across enterprises and how most use cases succeeding are ‘Automated Intelligence’, ‘Augmented Intelligence’, or ‘Anticipatory Intelligence’.
4. Intellectual AtrophyTM
refers to the decline of our cognitive abilities and critical thinking as our over-dependence on technology increases with time. Sol 1st coined this term back in 2017 when she first observed diminished independent thought and problem-solving skills as individuals increasingly depend on automated systems like AI for decision-making and information processing. Her focus is on Intellectual AtrophyTM education and consulting across companies, conferences, and campuses. She teaches intentional cognitive engagement to maintain mental agility and prevent intellectual stagnation in an era of rapid technological change. Sol teaches this as part of her at Carnegie Melon University as an adjunct professor and recently did a TEDx talk titled ‘Brain Rust’ which reached nearly 1 million views.
5. Human Amplification IndexTM (HAI)
The Human Amplification IndexTM is the 1st of its kind in that it’s the only quantitative way of measuring your ROI with AI, and the ROI we measure is how well you’re strengthening your workforce and your business with AI. AI was commercialized to amplify human capability, not displace it – and this global index is a way to measure how well you’re preserving your greatest asset, human capital, while being as efficient, effective, and productive as possible for your organization.
6. workFLOW, workFUNCTION, workFORCETM
The Human Amplification IndexTM ,is generated by defining how efficient, effective, and productive you are across your workFLOW, workFUNCTION, and workFORCETM. [make these clickable that takes them to the HAI page] Each area gets measured in a very distinct way that gives us a point in time view of how well you’re operating your business and how well you’re leveraging your talent. For more info click here.
Patents
Abstract: A computer-implemented method of cleansing data is provided that comprises determining a criticality score and a complexity score for identified attributes of an enterprise, wherein the criticality score represents a relevance of an attribute to one or more enterprise dimensions and the complexity score represents complexity of cleansing data for an attribute. The identified attributes for data cleansing based on the criticality and complexity scores are prioritized, and data of the identified attributes is cleansed in accordance with priority of the identified attributes. Embodiments further include a system, apparatus and computer readable media to cleanse data in substantially the same manner as described above.
Abstract: A system transfers data between source systems and a target system. The system determines a domain score for data domains of source data from the source systems based on data quality metrics for the target system. The domain score indicates data quality with respect to the target system. Corresponding processes of the target system are identified for the data domains, and a process score is determined for the identified processes based on a corresponding domain score. The process score indicates data quality with respect to the identified processes. The system cleanses the source data based on the domain score and/or process score, and validates the cleansed source data against the target system for transference. Embodiments of the present invention further include a method and computer program product for transferring data between source systems and a target system in substantially the same manner described above.
Abstract: A computer-implemented method for processing information related to an extract-transform-load (ETL) data migration, including aggregating operational metadata and determining: a plurality of metrics, organized by business object, corresponding to the migration; a number of business object instances not successfully loaded; a first end-to-end execution time for at least one business object; relevant input metadata; load readiness status per business object; impact of a business object that is not load ready by analyzing business process hierarchies; business object load readiness by reference to incomplete development status or data defects; scope per test cycle based, at least in part, upon business object load readiness; and high-priority defects of business objects that stop testing based, at least in part, upon analysis of business process hierarchies.
Abstract: In one or more embodiments of the invention, functional data analysts may use a functional data authoring module to capture functional metadata in a consistent manner. Conflict reports for the business processes may be generated for a subset of the business processes or as an overall report across all business processes. One or more embodiments of the invention may provide early detection of data usage and type conflicts from functional data requirements, automated detection of conflicts from functional data requirements, reports listing detected conflicts, conflicts resolution tracking mechanism, ongoing notification regarding changes in functional data requirements or detected conflicts, and avoidance of conflicting functional requirement in the realization phase, thereby reducing costs and project risks and avoiding project delays.
Abstract: A computer implemented method, system, and/or computer program product generates technical business data requirements from functional process requirements. Such method, system, and/or computer program product include data processing infrastructure. The data processing infrastructure may further include at least one data persistence component and at least one business process hierarchy. The computer implemented method, system, and/or computer program product obtain a set of technical elements concerning functional process requirements for each business process within the business process hierarchy. Such method, system, and/or computer program product classify technical elements into functional objects, link each of the functional objects to at least one process within the business process hierarchy and generate a plurality of business data roadmap templates in the data processing infrastructure.
Abstract: For creating a data governance assessment, a response module receives responses to an automated questionnaire. A scoring module computes a data governance assessment comprising a maturity level describing organizational adoption of data governance, a data governance model describing a data centralization level, and a framework describing a tier of data governance by calculating the maturity level, the data governance model, and the framework from the responses.