Prosper Insights AI & Innovation Briefing 2026 E27

AI Has A Data Problem - Causal Data May Solve ItDespite massive investments, most artificial intelligence initiatives fail because they rely on transactional data that only tracks historical patterns and past behaviors. These traditional datasets explain what happened but fail to capture the underlying intentions or emotions that drive future actions. To overcome this, organizations must shift toward causal data, which identifies the specific mechanisms and reasons behind decision-making. By focusing on upstream signals like consumer expectations and sentiment, models can predict market shifts months before they appear in sales records. This transition from correlation to causation makes AI systems more robust, transparent, and capable of delivering meaningful outcomes in changing environments. Ultimately, the future success of AI depends less on improving algorithms and more on integrating high-quality causal insights.