Causal AI is an artificial intelligence system that explains the cause and the effect of events through deep and insightful analysis. It can be used to understand the solution in situations where the AI Machine learning model and the algorithm are provided.
Causal AI can give grounds for decision making and the causes for a decision.
The uses of Causal AI
Causal AI is gaining widespread applications in various areas. Today, companies in different sectors are using it in their business processes and workflows.
Meteorology
Here, causal AI can aid in debugging patterns that may assist us in predicting cyclones and storms. Methodology represents a serious challenge to our abilities to use technology to predict natural phenomena as common as weather. More often than not, our predictions go awry, for our failure to take into account one factor, variable or the other. Causal AI holds the promise of helping the citizens and the weather department be stirred into necessary action promptly. Causal AI applications are gaining ground slowly but surely.
Healthcare
Healthcare is using Causal AI in creating and running applications on worker productivity and efficiency measurements.
Education
Education sector is deploying the Causal AI technology in driving student ability enhancements and modernization of course delivery and pedagogy. Every stakeholder, including the project managers, policy creators, program managers, and experts are welcoming the adoption of this technology.
Augmenting AI, Data analytics and Predictive analytics
The real magic of Causal AI is being witnessed as it complements Predictive Analytics in enterprises for making AI applications powerful and valuable to all stakeholders. Causal AI is aiding in effectively understanding the results generated by the algorithms and AI techniques. Many important factors are behind driving the decision making ability of causal AI.
Causal AI helps measure the key factors through indicators and also finds correlations between the factors.
Causal AI can help find the answers to some of the key questions businesses and organizations are struggling with. Most of the time, these questions concern customer retention, loyalty, attrition, requirements, renewals, marketing campaigns, transactions, etc.
Differences between Explainable AI and Causal AI
Typically, the two complement each other in helping derive actionable insights from AI models. AI models give results in terms of trends, features, following best action, prediction, and correlations. While Explainable AI helps provide explanations to the results, Causal AI pitches in with the analysis of causes and the effects for a deeper insight. Another benefit of Causal AI is that it also helps in inferring the relations between the cause and the effect.
By deploying causal AI, businesses can achieve a lot in terms of driving personalized marketing campaigns, product and services delivery to the end customer. As a useful business aid, Causal AI can take into account your company goals, obstacles, issues, objectives, and the constraints to aid in the decision making for your company.
Causal AI works like an A/B test to determine customer choices and preferences. By using causal AI across the customer’s journey, it is possible to get an idea of which products and services are working and which are not getting the customers’ attention. Causal AI can help understand actions and transactions of customers to help businesses smartly improve their sales and conversions.
Conclusion
As the decision science and data science and AI matures further, we can await more pleasant surprises from Causal AI technology as its adoption increases widely. The future holds the promise of blending many solutions with deep learning, Causal AI, and explainable AI to make the enterprises successful in their business execution.
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