When it comes to data-driven decision-making, businesses often have mission-critical questions they want answered. Questions like, 'Why aren't customers completing their transactions?', 'What's causing customer churn?' or 'Why is this application sluggish at certain times of the day?'

All of these questions have one thing in common: they relate to the kinds of business and product challenges that keep leaders awake at night.

Business leaders have thrown successive generations of data science tools to answer these kinds of questions.

Artificial intelligence is the latest such tool to show promise in producing accurate answers and data-driven directions to move in to resolve some of these kinds of business challenges.

But even those who've only scratched the surface of artificial intelligence will know it's a fast-growing space, with many available options and flavours of algorithms or models to consider, not to mention experiment with.

Out of this fast-growth space has emerged causal AI, also known as deterministic AI. This is quickly setting itself apart from other variations of AI for its ability to get to the root of a question or problem and to provide a practical response.

When paired with other emerging forms of AI, such as generative AI, organisations can get access to even better-quality data and answers as they make key decisions.

The growing use of causal AI reflects the need for businesses and leaders to remove guesswork from data-driven decision-making, meaning the correct or most optimal decision in any given circumstance is apparent and can be taken with a higher degree of certainty.

The limitations of correlation AI
Conventional data science approaches and analytics platforms can predict the correlation between an event and possible sources, but they often fall short when it comes to understanding why an event occurred.

There was certainly a time when running historical data through a machine learning algorithm to predict likely future outcomes based on past patterns was a pinnacle for what data science could achieve.

Predictions, generated through a class of AI that may be collectively termed 'correlation AI', are useful in many scenarios. However, the shortcomings of correlation-based AI become evident when teams need to determine how an action they propose to take would affect an outcome.

A correlation AI model can observe an event and predict an outcome will occur, but it can't show that the outcome occurred because of the event. In addition, while it can identify the likelihood of certain positive or negative events happening, it's unable to explain how it arrived at that forecast. It's also unable to identify the underlying factors and cause-and-effect relationships.

This limits the utility of correlation AI and makes it largely unsuited to answering the kinds of business- and mission-critical questions that leaders would like data-driven guidance on.

Enter causal AI
Causal AI, on the other hand, identifies the underlying cause of an event and its precise relationship to the outcome.

Unlike correlation-based AI, which calculates probabilities based on statistics, causal AI uses fault-tree analysis and draws on supporting data, such as relationships, dependencies, and other contexts among network entities and events, to determine the precise root cause of an issue.

Causal AI essentially works in two steps. First, it collects information and discovers problems within the dataset. Then, it looks for causal relationships that help explain those issues using a plan devised from the collected data.

Organisations can use causal AI frameworks and algorithms to ask questions and gain a deeper understanding.

It is also well-suited to underpin, for example, AIOps approaches. AIOps - artificial intelligence for IT operations - is a model to help IT teams analyse metrics, traces, log data, user behaviour data, and other details to locate and resolve incidents immediately to prevent disruptions in service.

Pairing generative AI with causal AI
A key strategy in the future will be to pair generative AI with causal AI, providing organisations with better-quality data and answers as they make key decisions.

Because generative AI is probabilistic in nature, its value depends on the quality of data that trains its algorithms and prompts. Combining generative AI and causal AI can provide a way to increase the impact and value of ChatGPT and related technologies. The combination of natural language queries with causal AI-powered answers can provide accurate and clear context and lessen the risk of getting back highly generic or misfitting answers to questions. In other words, the power of generative AI can be amplified by causal AI, making the GPT's proposals more precise and actionable.

This combined approach promises to unleash GPT's full potential for software delivery and productivity use cases. DevOps and platform engineering teams will be able to use causal AI to verify the output of their generative AI – such as code snippets – to ensure they don't introduce reliability or security problems. They will also use intelligent automation to execute their reliable and secure code automatically.

This promises to build lasting competitive advantages, productivity gains and speed-to-innovation for teams that invest in AI today.