Heidi Bullock

AI Needs Real‑Time Context, Not Just Bigger Models

The AI industry doesn’t have a model problem. It has a supply chain problem. Across the enterprise, we see a consistent pattern: sophisticated models operating in isolation from customer reality — think of it as “Brain in a Jar.” The model can reason. It can predict. It can generate. But without real-time, trusted context, it can’t truly operate. It’s intelligence without grounding. To move from experimentation to enterprise-scale deployment, we need to focus less on model novelty and more on operational fundamentals: Reliable Facts & Real-Time Context AI breaks down when it lacks grounding. At the moment of inference, models need structured, identity-resolved, real-time context — behavioral signals, unified profiles, consent states. Without that, even the most advanced system is making highly sophisticated guesses. Deterministic Guardrails Governance can’t sit downstream. Consent, compliance, and policy controls must be embedded directly into the data loop before a model is invoked. When guardrails are upstream, AI decisions become not just intelligent, but accountable. I think we'll see this trend increase. Integrated Memory Without persistent knowledge of the customer, AI treats a VIP like a first-time visitor. Enterprise AI requires a continuous feedback loop — where identity resolution informs every interaction, and every interaction refines the profile. Execution Infrastructure Insight without activation is just strategy theater. A model might suggest the “next best action,” but it needs connected systems to execute that action — and capture outcomes in real time so the system actually learns. While much of the industry debate centers on compute and model size, the real bottleneck is often data latency. Batch pipelines and fragmented architectures separate the model from the live customer interaction. The result? Smart AI making decisions on stale information. When identity-resolved data is delivered in milliseconds instead of hours, AI moves from unpredictable to deterministic. Outcomes are trusted and there is more control. We’ve invested heavily in the “brain.” Now we need to invest in the nervous system. How are you thinking about context in your AI strategy?