Most enterprise AI projects stall after proof of concept because models lack business context, break under real-world logic, and quietly rack up scaling costs.
David Grannum, an AI Architect at VectisONE, explains that generic models and open-ended systems fail without structured context, human oversight, and alignment to specific use cases.
His approach uses local, context-rich data, deterministic rules, and governed infrastructure to produce reliable, scalable outputs that actually hold up in production.
Most AI proofs of concept work. The problem is what happens next. Organizations invest in models and staff teams only to watch systems stall at the production threshold, producing generic outputs and burning through API budgets. The gap between a working demo and a working deployment is where the majority of enterprise AI programs go to die.
David Grannum is an AI Architect building VectisONE, a sovereign AI engine designed to close what he calls the "Truth Gap" between probabilistic models and real-world operations. His system routes LLM outputs through a deterministic control plane that enforces policy using structured facts, seals each decision cryptographically, and fails closed when inputs are missing. Grannum's work spans critical infrastructure, construction safety, and maritime edge environments.
"The failure point is usually context," Grannum says. "These models don't understand your business or your use case out of the box, so when you try to scale them, things start to fall over." That context gap is the first domino. Organizations chase adoption without grounding models in business-specific knowledge, producing chatbot-style deployments with no connection to operational reality.
Generic models, generic failures: Grannum describes testing multiple POC deployments and watching them break as soon as they encountered real business logic. "You start scratching the surface and realize that various bits fall over," he says. "The models don't have your bespoke understanding or your use case." He moved away from general-purpose chatbot wrappers early, finding they produced erroneous results and "led to more problems than they solved."
Hidden costs escalate fast: API-dependent SaaS wrappers, forgotten Jupyter notebooks, and unmonitored cloud instances generate surprise bills that scale dangerously with user volume. "I've been stung in all the shortfalls," Grannum says. "Luckily they're only small amounts, but if something scaled, that money would rapidly escalate."
Open-ended systems underperform: Rather than relying on autonomous agents that run for hours with unpredictable outputs, Grannum keeps a human in the loop at every stage. "I'd rather do structured research for a specific gap," he says. "Then I put that into my model, and it's tailored specifically to that problem."
The solution Grannum is building moves intelligence closer to the point of use. His approach pairs edge hardware with structured, sovereign data infrastructure to keep processing local, latency low, and outputs grounded in domain-specific rules. A compact GPU device tokenizes sensor data locally, pushes it over fiber, and runs every decision through hard-coded rules on FPGAs.
Sovereign context beats centralized scale: Distributed, localized models trained on region-specific or company-specific data outperform generic LLMs for production workloads. "If you can pass all that data, history, and email threads into a local model, it understands your voice and your company ethos," Grannum says. "By definition, it's going to give a better response."
Infrastructure is the enabler: A company with 30 years of institutional knowledge and a local GPU has more to gain from a well-structured local model than from a frontier LLM that has never seen its data. "You've got your marketing team doing one job, your HR doing another," Grannum says. "You want a consistent voice. That's where local AI makes sense."
The throughline across every failure Grannum has encountered is the same: AI success is not a function of model capability. It is a function of data quality, structural clarity, and purpose. Organizations that treat AI as a layer on top of fragmented operations will keep watching pilots stall. Those that invest in context, governance, and infrastructure first are the ones that make it to production.
"Success with AI comes down to data quality, clear structure, and knowing exactly what you want the system to output," Grannum says. "It's definitely going to change the world. It's just how people are going to approach it and how we're going to harness that intelligence."
