Causal AI goes beyond standard predictive analytics, directly augmenting human decision-making. Its recommendations are intrinsically explainable, reliable in real-world scenarios, and sensitive to business and governance constraints.
AI maturity is a critical determinant of success in almost every industry. A chasm is opening between early and late adopters. Leading organizations use traditional AI to find solutions to business-critical problems, but their challenges cannot be solved with standard correlation-based machine learning. Forward-thinking organizations are turning to Causal AI.
The organization doesn’t use artificial intelligence to make decisions. Most organizations are stuck at L0. AI is confined to data science experiments and has no impact on real decision-making. The limitations of standard machine learning technology are to blame.
Core problems include a lack of adaptability to real-world dynamics, and a lack of explainability. Data scientists may attempt to implement “post hoc explainability” methods, but these methods do not produce actionable insights.
There are many other limitations of standard machine learning (see L1). However, these two mean that business decision-makers do not trust AI systems sufficiently to let them out of the lab. Read up on the shortfalls of current state-of-the-art AI here.
Organizations stuck at L0 are wasting resources on AI investment which has zero impact.