Causal AI is an artificial intelligence system that can explain the cause and the effect. You can use casual AI to interpret the solution given the AI Machine learning model and the algorithm. In different verticals, casual AI can help explain the decision making and the causes for a decision.
SwissCognitive Guest Blogger: Bhagvan Kommadi, CEO, Quantica Computacao
“After decades of watching great companies fail, we’ve come to the conclusion that the focus on correlation — and on knowing more and more about customers — is taking firms in the wrong direction. What they really need to home in on is the progress that the customer is trying to make in a given circumstance—what the customer hopes to accomplish.”
Clayton Christensen
Casual AI is popular in different areas and many companies are applying it in the business process. In the area of meteorology, causal AI can help analyze the weather patterns to predict the cyclones and storms. There are many areas where we hit obstacles to understand the weather predictions. Many occasions, we need to know the causes that led to the weather changes or storm intensity effects. This will help the citizens and the weather department react fast when necessary. Casual AI applications are increasing as more number are seeing its value. Health Care sees the value in applications related to worker productivity and efficiency measurements. Education vertical is seeing the benefit of usage in student ability enhancements and course delivery modernization. Project managers, policy creators, program managers, and experts are also embracing the concept. You can read in detail about the concept here.
Casual AI and Predictive Analytics are interplaying in enterprises and making AI applications powerful and valuable to different stakeholders. Casual AI is helping in inferring the results produced by the algorithms and AI techniques. Human intervention with casual AI help in cutting down the errors, bias, and issues with AI in the enterprise. Decisions with explanations, important factors driving the decision, and the possible actions help in the success of AI. Many of the verticals require solutions with less budget. These solutions can be developed if automation is possible. Human actions need to be captured and automated to make these solutions perform within the budget. Casual AI helps measure the key factors through indicators and also finds correlations between the factors. Some of the questions enterprises have can be answered by casual AI. Casual AI can answer some of the enterprises’ questions. Most of the time, the questions are related to customer retention, loyalty, churn, requirements, renewals, campaigns, next back action, and transactions.
Explainable AI and casual AI typically go together while deriving meaningful information from AI models. AI models typically produce results related to trends, features, following best action, prediction, and correlations. Explainable AI helps provide explanations to the results and casual AI focuses on the causes and the effects. It also helps in inferring the relations between the cause and the effect. Using causal AI, enterprises can personalize the campaigns, products, services, and other offerings to the customer. Casual AI blended with goals, obstacles, issues, objectives, and the constraints helps in the decision making for enterprises. It can be used to identify the key customer journey performing and the customer journeys which are not performing. Applying causal AI across the customer’s journey helps to understand which products and services are working and which are not working. Customer’s actions and transactions can be analyzed to make the relationship better.
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On the other hand, we need to avoid thinking the causation is the same as correlation. Causation focuses on the causes and the effects which are related to the results of root cause analysis. In future, many solutions can be blended with deep learning, casual AI, and explainable AI to make the enterprises successful in their business execution. You can see the ongoing research here.
About the Author:
Bhagvan Kommadi is the Founder of Architect Corner – AI startup and has around 20 years of experience in the industry, ranging from large-scale enterprise development to helping incubate software product start-ups. He has done Masters in Industrial Systems Engineering at Georgia Institute of Technology (1997) and Bachelors in Aerospace Engineering from the Indian Institute of Technology, Madras (1993). He is a member of the IFX Forum, Oracle JCP, and a participant in the Java Community Process.
Causal AI is an artificial intelligence system that can explain the cause and the effect. You can use casual AI to interpret the solution given the AI Machine learning model and the algorithm. In different verticals, casual AI can help explain the decision making and the causes for a decision.
SwissCognitive Guest Blogger: Bhagvan Kommadi, CEO, Quantica Computacao
“After decades of watching great companies fail, we’ve come to the conclusion that the focus on correlation — and on knowing more and more about customers — is taking firms in the wrong direction. What they really need to home in on is the progress that the customer is trying to make in a given circumstance—what the customer hopes to accomplish.”
Clayton Christensen
Casual AI is popular in different areas and many companies are applying it in the business process. In the area of meteorology, causal AI can help analyze the weather patterns to predict the cyclones and storms. There are many areas where we hit obstacles to understand the weather predictions. Many occasions, we need to know the causes that led to the weather changes or storm intensity effects. This will help the citizens and the weather department react fast when necessary. Casual AI applications are increasing as more number are seeing its value. Health Care sees the value in applications related to worker productivity and efficiency measurements. Education vertical is seeing the benefit of usage in student ability enhancements and course delivery modernization. Project managers, policy creators, program managers, and experts are also embracing the concept. You can read in detail about the concept here.
Casual AI and Predictive Analytics are interplaying in enterprises and making AI applications powerful and valuable to different stakeholders. Casual AI is helping in inferring the results produced by the algorithms and AI techniques. Human intervention with casual AI help in cutting down the errors, bias, and issues with AI in the enterprise. Decisions with explanations, important factors driving the decision, and the possible actions help in the success of AI. Many of the verticals require solutions with less budget. These solutions can be developed if automation is possible. Human actions need to be captured and automated to make these solutions perform within the budget. Casual AI helps measure the key factors through indicators and also finds correlations between the factors. Some of the questions enterprises have can be answered by casual AI. Casual AI can answer some of the enterprises’ questions. Most of the time, the questions are related to customer retention, loyalty, churn, requirements, renewals, campaigns, next back action, and transactions.
Explainable AI and casual AI typically go together while deriving meaningful information from AI models. AI models typically produce results related to trends, features, following best action, prediction, and correlations. Explainable AI helps provide explanations to the results and casual AI focuses on the causes and the effects. It also helps in inferring the relations between the cause and the effect. Using causal AI, enterprises can personalize the campaigns, products, services, and other offerings to the customer. Casual AI blended with goals, obstacles, issues, objectives, and the constraints helps in the decision making for enterprises. It can be used to identify the key customer journey performing and the customer journeys which are not performing. Applying causal AI across the customer’s journey helps to understand which products and services are working and which are not working. Customer’s actions and transactions can be analyzed to make the relationship better.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
On the other hand, we need to avoid thinking the causation is the same as correlation. Causation focuses on the causes and the effects which are related to the results of root cause analysis. In future, many solutions can be blended with deep learning, casual AI, and explainable AI to make the enterprises successful in their business execution. You can see the ongoing research here.
About the Author:
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