Enterprises bank on different technologies to complete crucial tasks. Among all the technologies, AI has a special place. Different AI-based technologies are used to achieve the required result, from AIOps platforms to generative AI. Among different technologies, causal AI has a special place in the corporate world. Causal AI has been around for many years but is continuously evolving. It has already surpassed the capacities of traditional rule-based models. Often, enterprises must make informed decisions to thrive in this competitive era. They cannot trust other AI technologies for high-risk decisions. Causal AI has proved to be a vital solution for decision-making in the corporate world. Read on to understand the use cases of causal AI for enterprises.
Understanding the root causes of different events
Event correlation is a must for enterprises. Without event correlation, enterprises cannot determine the root cause. The root cause is usually determined for IT incidents that hamper the organisation’s service availability. Without knowing the root cause, IT professionals cannot start fixing the incident. Traditional rule-based models use the correlation method to determine the relationship between different events. An AI-automated root cause analysis solution that runs on traditional rule-based models will identify the patterns and anomalies. The process can be speeded up with the help of a causal AI solution.
Causal AI determines the exact cause of an event or incident. It knows that an incident does not occur out of the blue. It defines a cause-effect relationship which is easier for humans to understand. Let us say that event A has occurred within an organisation. Causal AI will know that B, C, D, E, and F have not caused event A. In such a case, it concludes that the remaining variable G must have caused event A. Humans might fail to understand the results produced by traditional rule-based models. On the other hand, causal AI offers a human-like analysis which is easier to understand. In the coming years, more enterprises will use causal AI in AI-automated root cause analysis solutions.
Enterprises also use causal AI for event simulation. Often, enterprises do not know how their infrastructure will react to a change. They might not know the outcomes of an event in advance. When enterprises know the outcomes of an event in advance, they can make informed decisions. Causal AI can allow enterprises to simulate events and know their outcomes in advance. Not many technologies can simulate “what if” events with high accuracy. Causal AI allows enterprises to save it from physical or manual tests. Physical or manual tests might cost more than simulated tests.
Many enterprises use causal AI to enhance the customer retention rate. Traditional rule-based models can already link customers according to their likings. However, causal AI takes it a step further and provides detailed insights. For example, causal AI can determine which customers are likely to respond to follow-up messages and which aren’t. Enterprises can allocate resources to those customers who are likely to use the services/products. It will help enterprises decrease the churn rate and enhance the customer retention rate.
Corporate entities also invest in different initiatives expecting higher returns. However, investment decisions cannot be taken without any prior research. Manual research is not an option considering the size of market data and multiple variables. Causal AI can help researchers understand the relationships between different investment factors like stock rates, inflation, and market trends. Detailed analysis by causal AI will uncover several insights for investors. It will help enterprises invest their funds for higher returns.
Enhanced business operations
Enterprises can use causal AI to optimise business operations and improve productivity. With the help of causal AI, enterprises can find bottlenecks and inefficiencies that are preventing growth. Operations like supply chain and customer support can be improved with the help of causal AI. It analyses the data related to different business operations and detects anomalies. Enterprises will know the anomalies that result in lower service availability and productivity. It will lead to reduced costs, increased productivity, and high competitiveness for the enterprise.
Traditional rule-based models might determine incorrect relationships between data sets. They are prone to bias and can produce incorrect results at times. An incorrect result by a traditional rule-based model can cost a fortune to the company. The enterprise might experience a sudden decrease in service availability due to incorrect results produced by traditional rule-based models. These models require human intuition at times to produce correct results. What’s the point of automation when you guide the rule-based model manually?
Compared to traditional rule-based models, causal AI is not vulnerable to bias. Since it determines the exact cause of an event, there are no chances of bias. There is no need to manually guide causal AI for determining the right relationship between data sets. Since the bias level is lower, enterprises can trust causal AI for high-risk decisions. Also, there is no need to offer human intuition to causal AI at regular intervals.
Many enterprises are actively using causal AI for fraud detection and risk management. Frauds within the company are a serious threat to service availability. Causal AI can determine the abnormalities within data sets and notify the concerned teams. Experts can’t analyse large amounts of data without any external help. Causal AI reduces the burden on experts by automating the data analysis process. Causal AI does not simply determine the relationships between data sets or variables. It determines a cause-effect relationship that leads to more informed insights. Causal AI is capable of handling complex data sets and drawing inferences. Enterprises have also started to include causal AI into their cybersecurity tech stacks.
In a nutshell
Causal AI is revolutionising the way businesses used to operate. It is actively being used to remove bias from data-based decisions. Enterprises are optimising their business operations with the help of causal AI. The cause-effect relationship offered by causal AI is easy for professionals to understand. In the coming years, more enterprises will use enhanced AIOps platforms and causal AI in place of traditional rule-based models. Implement causal AI solutions in 2023!