What Caused This
The Challenge
Regulated industries—such as healthcare, aviation, and energy—face mounting complexity in incident analysis, compliance reporting, and root cause identification. Traditional systems often fail to capture contextual human intelligence or adapt to complex, evolving datasets. This gap leaves critical insights buried in silos, resulting in preventable incidents, inefficiencies, and compliance risks.
The Solution: What Caused This
What Caused This (http://whatcausedthis.com) is a human and AI collaborative platform designed to modernise the way regulated sectors capture, investigate, and learn from critical incidents. At its core, the platform solves the problem of fragmented incident data and disconnected human insight by integrating advanced AI tools with real-world domain expertise. It turns complex human experiences and event narratives into structured intelligence that organizations can act on.
Our Approach
We engaged with What Caused This, using a co-design model that flipped the script on traditional development. Rather than build features in isolation, we embedded innovators and AI experts into the What Caused This Team, working closely with them to visually conceptualise nuances and complexity behind their workflows and compliance requirements. This human-first, systems-thinking approach ensured that our AI was trained not just on data, but on context and intent.
Breaking Traditional Intelligent Silos
Where legacy tools treated data capture and analysis on a per-case basis, What Caused This brought them together. Our strategy fused data ingestion, contextual AI, and decision-making support into a visual problem statement. The result: faster, more accurate insights that scale human intelligence without replacing it. This integration helped organisations not just meet regulatory expectations—but exceed them, transforming incident analysis from a checkbox exercise into a strategic advantage.
By aligning human insight with machine intelligence, What Caused This is setting a new standard in how regulated industries learn, adapt, and prevent the preventable.