The current state-of-the-art in machine learning fails to perform in real-world environments. It lacks the intelligence businesses need. Causal AI is the only technology that can reason autonomously, by gaining an understanding of cause-and-effect relationships, to directly optimize KPIs. Causal AI can provide causal insights to human experts, forming human-machine partnerships that together directly solve businesses’ highest value problems.
Causal AI plays an ever more important role in our investment analysis. It empowers our strategists and portfolio managers to generate alpha by identifying new causal relationships in economic, financial and alternative data, with sophisticated, adaptive and explainable models that don’t suffer from overfitting.”
causaLens enables us to autonomously discover valuable signals in huge datasets and understand relationships between our data and other datasets.”
causaLens provides us with key causal insights that continuously unlock untapped value in our data.”
Transparency and explainability of AI models requires an understanding of causality—an inherent advantage of the causaLens platform”
AI adoption is a critical determinant of success across nearly all industries. A chasm is opening up between early and late adopters. As the pace of technological progress increases, this divide will only widen.
The path to AI leadership is becoming clear. Leaders are integrating AI capabilities throughout all business functions, democratizing access to AI across the enterprise and fostering opportunities for human-machine collaboration. Here we set out the levels of AI integration and maturity that separate laggards from leaders, and look to the future of AI in the enterprise.
The business has no Artificial Intelligence (AI) capabilities, though there may be a well-established digital infrastructure that can manage data across business functions. Solutions to the businesses’ highest value problems depend entirely on the intelligence of the human workforce.
One in two enterprises are currently still at L0. They fail to realise the value of their data. There is firmwide hunger for ML resources, but no way to supply the resource. Business functions burn fuel improvising solutions instead. Organizations at this level are at a disadvantage as compared to competitors who have begun their journey to AI adoption.