Post by XpertRule Software

𝗔𝗜 𝗜𝘀𝗻'𝘁 𝗙𝗮𝗶𝗹𝗶𝗻𝗴 𝗶𝗻 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗲𝗱 𝗮𝗻𝗱 𝗛𝗶𝗴𝗵-𝗦𝘁𝗮𝗸𝗲𝘀 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗲𝘀 𝗕𝗲𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗮𝗻𝗱 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀. 𝗜𝘁'𝘀 𝗙𝗮𝗶𝗹𝗶𝗻𝗴 𝗕𝗲𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲—𝗮𝗻𝗱 𝗛𝘂𝗺𝗮𝗻-𝗶𝗻-𝘁𝗵𝗲-𝗟𝗼𝗼𝗽 𝗪𝗼𝗻'𝘁 𝗙𝗶𝘅 𝗜𝘁. Leading research firms estimate that between 70% and 85% of corporate AI initiatives fail to reach production or deliver measurable ROI. The common explanation is that organisations attempt to layer advanced AI onto poor data foundations, broken processes, and unmanaged organisational change. That's certainly true in many cases. But in high-stakes and regulated domains, I believe the primary culprit is different. The fundamental problem is that the prevailing AI deployment models simply don't fit the requirements of consequential trusted decision-making. On one extreme, organisations are understandably reluctant to deploy autonomous GenAI to make decisions that affect customers, patients, financial outcomes, compliance obligations, or critical infrastructure. On the other, the fallback strategy of putting humans in the loop to validate opaque AI recommendations is often neither cost-effective nor scalable. If experts must independently reconstruct and verify every AI recommendation, much of the promised productivity benefit disappears. So how can organisations become AI-native while preserving trust, explainability, accountability, and economic viability? For regulated enterprises, the answer may lie in a different architecture / paradigm: 🔹 Use Generative AI at design time to extract knowledge from policies, regulations, business rules, procedures, and historical cases. 🔹 Use GenAI to synthesise decision models and accelerate knowledge engineering. 🔹 Keep humans in the loop at design time to validate and govern decision logic. 🔹 Execute decisions at runtime through deterministic, explainable, and auditable decision services. This may be the blueprint for trustworthy AI-native regulated enterprises: combining the adaptability and productivity of Generative AI with the transparency, control, and accountability demanded by mission-critical environments. AI projects in regulated industries are failing because we are trying to force regulated enterprises into two unsustainable extremes. Autonomous AI making consequential decisions or humans endlessly validating opaque AI outputs. The future likely lies somewhere in between. #AI #GenerativeAI #AINative #ResponsibleAI #Governance #DecisionIntelligence #AgenticAI #RegTech #FinTech #InsurTech #DigitalTransformation #TrustworthyAI #DecisionManagement #BusinessRules Read the latest whitepaper from our Chairman Akeel Attar https://lnkd.in/e8mVqVrV