Artificial intelligence adoption is on the rise everywhere—throughout industries and in businesses of all sizes.  And while generative AI was much hyped in 2023, the deterministic quality of causal AI—which determines the precise root cause of an issue—is a key foundation for reliable recommendations that emerge from generative AI technologies.

Further, not every business uses AI in the same way or for the same reasons. So, it’s important for organizations to choose the AI type that best meets their needs. While traditional AI relies on finding correlations in data, causal AI aims to determine the precise underlying mechanisms that drive events and outcomes.  As a result, causal AI use cases are key to enabling organizations to identify the root cause of problems and determine remediation.

Deriving cause-and-effect relationships is a key way for causal AI to reason, explain itself, and make reliable predictions. That’s why causal AI use cases abound for organizations looking to build more reliable and transparent AI systems.

Making the case for causal AI

Causal reasoning capabilities mimic human understanding of cause-and-effect relationships—deriving explanations for why things happen, predicting what would happen if circumstances were different, and gaining insight into how complex systems work. Therefore, causal AI is a useful deterministic AI technique that provides concrete answers about the source of events, not probabilistic outputs.

Most AI today uses association-based machine learning models like neural networks that find correlations and make predictions based on them. However, correlation does not imply causation. So, these models are limited in their ability to explain why outputs occurred or to make reliable predictions in new situations. They’re essentially informed guesses. The growing recognition of these limitations is driving increased interest and research into causal AI use cases.

Understanding complex systems

Causal AI holds great importance for achieving full-stack observability in complex systems. Traditional monitoring provides correlations between events, but causal AI goes further by inferring the probabilistic causal relationships between them. This representation of how different components influence one another enables intelligent reasoning about the root causes of issues.

Causal AI use cases can complement other types of AI. For example, machine learning can identify correlations in data and generate predictions, while causal AI determines which ones are the true drivers. Causal AI and reinforcement learning are useful for understanding interventions’ effects because the cause-and-effect relationship is well understood.

Causal AI use cases span myriad industries

Causal AI use cases are increasing across many fields, including the following:

Marketing. Causal AI can analyze customer data to identify connections between variables such as buying habits, demographic details, and product preferences. Marketers can use these insights to better understand which messages resonate with customers and tailor their marketing strategies accordingly. Additionally, causal AI can identify the impact of ads and promotions on sales to enable better marketing optimization.

Finance. Financial services companies can scrutinize market data to uncover complex links among stock prices, economic indicators, and overarching market trends. This information can enable more educated investment choices. Financial companies can also discover causal risk factors for loan defaults and simulate the effects of interventions. These insights can reduce loan costs and protect against cascading defaults.

Healthcare. Clinicians can use causal AI to discover a patient’s drug interdependencies that could prompt a negative reaction. Causal reasoning can also help pinpoint the best patient treatments by modeling how people with similar conditions have reacted to such interventions in the past.

Software development. Software developers can use causal analysis to identify the root causes of bugs or application performance issues and to predict potential system failures or performance degradations. Software project managers can optimize development processes by analyzing workflow data, such as development time, code commits, and testing phases.

Government. Government agencies can learn from cause-and-effect relationships to make more evidence-based policy decisions. For example, causal AI can help public health officials better understand the effects of environmental factors, healthcare policies, and social factors on health outcomes.

Retail. Algorithms can mine customer behavioral data to understand the underlying factors driving purchasing decisions. Organizations can also identify the root causes of supply chain delays, inefficiencies, and disruptions. Retailers can analyze how factors such as demand, competition, and market trends affect pricing.

Causal AI use cases for modern observability

Integrating causal AI into observability systems can significantly advance organizations’ understanding of their environments. Traditional monitoring tools can alert organizations to issues, but causal AI can precisely identify the underlying causes. This facilitates quicker and more effective problem solving, reducing downtime and improving reliability.

More generally, causal AI can contribute to explainable and fair AI systems. That’s important as regulatory scrutiny and demands for responsible AI are growing. According to a recent Dynatrace survey of 1,300 CIOs, CTOs, and other senior technology leaders, 98% of technology leaders are concerned that generative AI could be susceptible to unintentional bias, error, and misinformation. AI systems’ ability to explain the reasons for their recommendations could go a long way in resolving these trust issues.

How causal AI in a data lakehouse can transform your business

IT organizations are buried under growing amounts of data for the complex systems they manage across an estate, including on-premises, cloud, and software-as-a-service applications.

The logs, metrics, traces, and other metadata that applications and infrastructure generate have historically been captured in separate data stores, creating poorly integrated data silos. Cleaning, normalizing, and ingesting all that data into traditional data warehouses is costly and time-consuming. It also doesn’t give IT organizations the instant insights they need for troubleshooting. That’s why many organizations turn to data lakehouses.

Data lakehouses combine a data lake’s flexible storage with a data warehouse’s fast performance. Data lakehouses can store and query structured, semi-structured, and unstructured data on low-cost infrastructure. They use a schema-on-read approach to classify information as it’s accessed, rather than the data warehouse’s more restrictive schema-on-write technique. That means data doesn’t need to be cleaned, structured, and converted into the predefined format before being stored. This enables much faster performance even on a mix of structured, semi-structured, and unstructured data.

Data lakehouses play a pivotal role in facilitating causal AI by providing a versatile data management infrastructure for vast amounts of diverse data—a requirement for AI training models. Some incorporate features for data governance and quality control, which is important for ensuring the accuracy of causal inferences. Their scalability, comparatively low cost, and support for advanced analytics and machine learning have helped fuel AI’s rapid enterprise adoption.

For more information on how data lakehouses can seamlessly store data, check out this free guide, “Data lakehouse architecture stores data insights in context.”

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