Autonomous Analytics Alignment


The best way to predict the future is to invent it.

- Alan Kay

Cornell-r4 AI Collaboration


Cornell and r4 have announced the development of multidisciplinary courses for artificial intelligence (AI).
The Cornell-r4 Applied AI initiative will make AI courses available to students.
The initiative will provide opportunities for academic discovery in disciplines such as machine learning and statistics.

An initial focus of the initiative shall be to optimize the food chain, aligning demand.

The core goals are to impact social challenges and drive better business.

AI initiative for innovation, social impact

Data science and artificial intelligence (AI) alignment naturally narrows the gap the between your current and desired future state of Enterprise AI. But equally as important, connecting the dots on an ontology properly lets the machine do its magic with breathtaking results.

The symbioses between data science and artificial intelligence (AI) demands knowledge workers aligned with the way enterprises need to understand their customers - and with AI solutions that impact the enterprise bottom line.

The quantifiable success of data science relates directly to aligning it with Enterprise AI – an intersection between people and machines, the ontology and the yield outcomes.

Goal-driven yield management is guided by human creative, the brain-power judgement of people.

The prize, according to the World Economic Forum, is the imminent digital transformation will create over $100 trillion in economic value by unleashing the trapped value in transitional systems, processes and organizational cultures.

Enterprise AI is changing how business value is unleashed at an unprecedented pace. It is transforming core processes and business models in every industry. 

AI for the enterprise  is changing the rules by discovering perishable opportunities hidden in diverse data. 

Adaptive optimization provides enterprise yield, an AI-enabled discipline, enabling stakeholders to rapidly identify perishable opportunities across a myriad of business use cases.

These use cases are wide and varied across industries but share a common trait of fused internal and external data, providing cross-enterprise yield optimization with a rapid, automated action.

Thank You for Engaging Us

“You [have] the techno-functional knowledge of a manager  ...  which saves me an enormous amount of time and frustration.”

- Sr. Director, Fortune 500 Company

AI Micro-Concept Innovation

Internal and external data is mapped to rich attributes. Enterprise AI provides big data automation, machine learning clustering, collaborative filtering, and cluster engineering.  Clustering may then discover patterns, rate offerings/content and predict actions.
Clusters may be grouped along the most relevant consumer drivers to create micro-concept archetypes.
Machine learning actions then guide people with micro-forecasted yield optimizing recommendations, such as for personalized loyalty, omnichannel orchestration and demand forecasting.