When foundation models commoditize AI capabilities, competitive advantage shifts to how systematically you encode organizational intelligence into your systems. Nicholas Clarke, Chief AI Officer at Intelagen and Alpha Transform Holdings, argues that enterprises rushing toward ”AI first” mandates are missing the fundamental differentiator: knowledge graphs that embed unique operational constraints and strategic logic directly into model behavior. Clarke’s approach moves beyond basic RAG implementations to comprehensive organizational modeling using domain ontologies. Rather than relying on prompt engineering that competitors can reverse-engineer, his methodology creates knowledge graphs that serve as proprietary context layers for model training, fine-tuning, and runtime decision-making—turning governance constraints into competitive moats. The core challenge? Most enterprises lack sufficient self-knowledge of their own differentiated value proposition to model it effectively, defaulting to PowerPoint strategies that can’t be systematized into AI architectures. Topics discussed: - Build comprehensive organizational models using domain ontologies that create proprietary context layers competitors can’t replicate through prompt copying. - Embed company-specific operational constraints across model selection, training, and runtime monitoring to ensure organizationally unique AI outputs rather than generic responses. - Why enterprises operating strategy through PowerPoint lack the systematic self-knowledge required to build effective knowledge graphs for competitive differentiation. - GraphOps methodology where domain experts collaborate with ontologists to encode tacit institutional knowledge into maintainable graph structures preserving operational expertise. - Nano governance framework that decomposes AI controls into smallest operationally implementable modules mapping to specific business processes with human accountability. - Enterprise architecture integration using tools like Truu to create systematic traceability between strategic objectives and AI projects for governance oversight. - Multi-agent accountability structures where every autonomous agent traces to named human owners with monitoring agents creating systematic liability chains. - Neuro-symbolic AI implementation combining symbolic reasoning systems with neural networks to create interpretable AI operating within defined business rules.