differences rather than examined for insight. In this collaboration, conflict was treated as an opportunity to surface hidden assumptions and reconcile competing interpretations. This approach is consistent with evidence that constructive conflict improves learning and solution quality when psychological safety is present (Bergmann et al., 2021). Finally, many projects fail because accountability for decisions becomes diffused across disciplinary boundaries. In contrast, this work maintained clear accountability for interpretation and risk ownership with geotechnical engineers, while ensuring that analytical assumptions and limitations were transparent. This clarity supported trust and reduced the risk of inappropriate reliance on automated outputs. Collectively, these design choices mitigated known failure modes and enabled sustained collaboration under uncertainty. 5. DISCUSSION The outcomes of this work highlight that advances in early-warning capability depend as much on organisational design as on analytical innovation. Transdisciplinary research consistently shows that complex, high-stakes problems are best addressed when teams establish a shared language, build a collaborative culture, and integrate diverse expertise through structured interaction-conditions that have been shown to be critical for success in real-world transdisciplinary environments. The two technical studies underpinning this work demonstrate how Slow Movement Analysis (SMA), combined with multi-interval processing and structured feature analysis, substantially improves the detection of low-velocity deformation—revealing precursory trends well before conventional real-time radar systems can respond. In parallel, AI-supported movement-pattern grouping and geotechnical validation show that these slow movements can be reliably differentiated into stable, pre-failure, and failure patterns, enabling clearer operational interpretation. In this work, AI-driven data-analysis tools were used to interpret thousands of movement patterns that would be impossible to assess manually. By analysing long-term displacement, velocity, and acceleration behaviours across different geological domains, these tools identified areas with similar deformation characteristics and highlighted where slow, emerging trends aligned with known precursors to instability—providing earlier, more confident insight for geotechnical decision-making
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