10 Apr, 2026
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10 mins

Causal AI – Moving Beyond Fluency to Accurate and Trustworthy Decision-Making

Causal AI – Moving Beyond Fluency to Accurate and Trustworthy Decision-Making
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Whether you're selling solar or streamlining supply chains, accurate decision-making can make or break your goals. While AI models can help automate some systems and streamline others, most fall short when it comes to complex, high-stakes decision-making scenarios. But why? 

Correlation doesn't always equal causation. We've all heard that before, yet it's something most AI models are still learning. That is beginning to change – and a growing body of data suggests causal AI is experiencing increased focus from investors and companies alike. 

 

What is causal AI and how is it different? 

 

Causal AI builds on techniques from causal inference and structural modelling, enabling systems to move beyond prediction and towards explanation and decision intelligence. 

Regular ML models – even deep-learning ones – are typically advanced pattern-recognition programmes. They ask, ‘What do I see?’ and, based on historical data, extrapolate an outcome. 

Causal AI refers to systems that model cause-and-effect relationships rather than relying solely on correlations. (Cambridge University Press) 

 

That system operates on three levels: 

  1. Association - What do I see? 

  1. Intervention - What if I do X instead of Y? 

  1. Counterfactual - What happens if I do Z? 

 

This new kind of decision-making will be vital in handling current and emerging challenges across industries, from energy to life sciences. It isn't a stretch to imagine causal AI rolling out across other industries too – but there are some hurdles to clear first. 

 

Which factors are holding causal AI back? 

 

As with all AI, causal AI requires significant energy and investment. Billions will be spent on its development in the next five years across industries. But the most important factor to overcome is the talent gap. This is a common story across all AI industries, but particularly acute in causal AI, where hiring premiums exceed 35% compared with traditional ML roles. (Mordor Intelligence). 

 

 

The causal AI talent gap 

  • 62% of C-suite executives cited a shortage of talent and AI skills. 

  • 6% of C-suite executives said they were upskilling their workforce in meaningful ways. 

  • Israel, Singapore, and South Korea showed some of the highest concentrations of AI talent globally, based on specialist density metrics. In Europe, Germany remains a leading hub for AI talent. (Keller Executive Search) 

 

There is some good news. The gap is bridgeable with the right help. 

For organisations looking to shift from predictive analytics to causal decision-making, access to the right expertise is essential and timely. 

CMC specialises in connecting businesses with experienced AI consultants who provide real-world impact in their fields, whether that is improving operational decisions, supporting regulatory compliance, or accelerating R&D. Talk to a consultant today or discover more about our expertise here. 

 

The current causal AI market and its forecast 

 

IBM, Google, Microsoft, and Amazon reflect the growing proliferation of causal AI. Smaller, specialist causal AI companies are also emerging: Causalens, a London-based pioneer, raised $45 million in a Series A round in 2022, led by Molten Ventures and Dorilton Ventures, to expand causal AI uptake across sectors. This alone may indicate that the market size lies towards the higher end of estimations. (Molten Ventures) 

The market size is disputed, but higher estimations put it at $81.4 billion in 2024 – the discrepancy arises from the variety of definitions of ‘causal AI’. 

A CAGR of 40%, however, is consistent across research firms exploring the space and is therefore a more reliable figure. (MarketsandMarkets & Fortune Business Insights) 

This is not news in itself. By now, it is obvious that AI generally, and in its specific forms, will have a huge impact on revenue and productivity. However, the challenge of finding the right people to implement new technologies and drive operational change is essential. 

 

Causal AI success stories 

 

To date, the most eye-catching examples of causal AI come from healthcare. Studies in San Diego (COMPOSER) and Switzerland (HERACLES) have illustrated the real-world effectiveness of causal AI-led systems, in some instances reducing 90-day mortality in selected hospitals by roughly five percentage points. With growing pressures on healthcare worldwide, these studies could point to a new way forward for efficient and effective care. (Nature) 

‘What took one scientific reviewer two to three days [before] now takes six minutes.’ 

Causal AI is also making strides in medical R&D, unlocking new possibilities and saving lives along the way. It is not just about accuracy, but speed. ‘What took one scientific reviewer two to three days [before] now takes six minutes,’ says FDA Commissioner Dr Marty Makary. (Forbes) 

Regulatory bodies require hard evidence – mechanisms rather than correlation – which makes causal AI particularly useful in advanced engineering, especially drug and food production. 

This subtle yet definite differentiator means causal AI is going to be a cornerstone in many tightly regulated industries, and those teams with expertise onboard will be positioned to take advantage. 

 

How can CMC help? 

 

At Coalesce Management Consulting, we are constantly increasing our AI capacity by finding new experts for our virtual workbench. So, whatever your needs, we are ready to deploy expertise as and when you need it. 

Get in touch with our AI experts today to discuss upcoming opportunities for new projects, and the resources you’ll need to ensure you can deliver on time and on budget.