McKinsey recently published its third global survey on AI, which indicates that AI adoption continues to grow and that the benefits remain significant. As AI increasingly takes on larger business applications, the tools and best practices designed around making the most out of AI have also become more sophisticated.
McKinsey sought to understand more about the factors and practices that differentiate the best AI programs from all others: specifically, at the organizations at which respondents attribute at least 20 percent of EBIT to their use of AI - the "AI high performers".
In this blog post, I demonstrate how Cognaize is already helping its clients to be an AI high performer using the key differentiators discovered by McKinsey.
Cognaize elevates its clients in seven out of eight best practices defined by McKinsey.
I have published a separate blog post about the importance of choosing the right metrics. With the Financial Insights Engine™, the testing of AI models, including audit-proof reporting, is fully automated, which is a key success factor. Further, with the Cognaize business-centric approach, AI models are seamlessly integrated into core business processes where they are exposed to actual relevant data. Consequently, the Financial Insights Engine™ can further track production metrics automatically. Besides statistical metrics, the Financial Insights Engine™ also tracks the time savings over time, delivering the most relevant of all data points. This is only possible through Cognaize's unique business-centric approach.
Data governance, another important factor, is the set of measures to ensure data is secure, private, accurate, available, and usable. It includes the actions people must take, the processes they must follow, and the technology that supports them throughout the data life cycle.
Principles of Data Governance
The Cognaize Financial Insights Engine™ is the fastest way to create actionable intelligence from unstructured data. Its speed and accuracy in extracting key insights are uniquely enabled by unequaled, ultra-deep domain knowledge and advanced, pre-trained AI. It supports any custom workflow - guiding, enhancing, and tracking each process. This is also true for AI governance, for which Cognaize has developed a methodology based on the latest research and best practice of AI champions.
The biggest key differentiator of AI high performers is having a scalable internal process for labeling AI training data, which, incidentally, is also Cognaize's core competency. The Cognaize business-centric approach enables training data to be generated as a by-product of existing extraction processes through the core business team. This method delivers a constant stream of relevant data of the highest quality and constitutes one of the most significant factors for AI success.
Importantly, one of the Financial Insights Engine™ 's most essential features is the flexible definition of data dictionaries, referred to as Insights, delivering yet another success factor.
The Cognaize Financial Insights Engine supports flexible AI workflows uniquely allowing the division of a single problem into several reusable AI models. AI models can efficiently be retrained using new training data or novel model architectures. After successfully passing metrics tests, new models are effortlessly deployed into production with just one click.
With these and many more features, Cognaize empowers its users in all of the critical differentiators highlighted by McKinsey.