Scaling Production AI Series
Part 2 of 6
Editor’s Note: This is the second in a weekly series unpacking the strategic insights from our new playbook, “Scaling Production AI.” Read last week’s installment about the vertical AI architecture required to move from pilot to production.
Running AI in production doesn’t feel like running a model.
It feels like running a workflow where nothing happens in isolation. A fraud alert lands, and suddenly investigation queues shift. A pricing change looks small until it ripples through the supply chain. A maintenance recommendation makes sense on paper, but collides with scheduled safety checks once it hits the floor.
This is the point where many teams realize something important: the challenge isn’t getting AI to generate insights. It’s getting those insights to hold up once they touch the rest of the business. The transition from pilots to production marks a shift from learning what AI can do to measuring how much business value it delivers.
That’s where context starts to matter—not as documentation or metadata, but as shared infrastructure that lets AI operate inside real workflows instead of alongside them.
Why context becomes the differentiator in production
In early AI experiments, context is less critical because a model can summarize a document or answer a question without understanding the broader system it sits within.
In production, that illusion disappears.
Real business workflows are messy. Every fraud alert affects investigation capacity and regulatory reporting. A pricing adjustment influences supply planning, promotion execution, and margin targets. A maintenance recommendation changes production schedules, safety checks, and inventory availability.
As AI moves into these environments, value comes from understanding those connections—not just generating outputs. This shift is already shaping how enterprise leaders think about what comes next.
This is where vertical AI platforms separate themselves from general-purpose tools. Horizontal models provide raw intelligence, but they don’t carry the domain knowledge required to operate inside live workflows. Context provides that logic.
What we mean by the context layer in AI
In production AI systems, context isn’t a feature or an interface. It’s foundational AI architecture.
At its core, the context layer is a domain knowledge graph: a structured, industry-specific model of how a business works. It encodes the entities that matter—like customers, accounts, SKUs, assets, services—and the relationships that govern how decisions propagate across the system.
Crucially, it also captures state and constraints. What’s normal versus exceptional. What’s already been investigated. Which rules apply in which situations, whether operational thresholds or regulatory requirements.
This shared context becomes the reference point for every AI component. Instead of inferring meaning from scratch each time, AI systems operate against a consistent, persistent map of the business.
That’s the shift from AI that responds to signals to AI that operates within workflows.
Why context is where real enterprise AI work lives
Building the AI context layer is not trivial—and that’s precisely why it’s such a durable advantage.
Enterprise complexity doesn’t live in data volume. It lives in meaning. Different systems encode the same concept in different ways, optimized for local tasks rather than end-to-end decisions.
In financial services, “risk” looks different in transaction monitoring, customer onboarding, sanctions screening, and investigations. Without shared context, each system sees a partial picture.
In retail, merchandising, supply chain, pricing, and store operations often disagree on what’s happening at the SKU level in real time. Context aligns planning with execution.
In industrial environments, sensor data, asset hierarchies, maintenance logs, and safety protocols all describe the same physical system from different angles. Context unifies them into a single operational view.
The teams seeing the fastest path to ROI aren’t reconstructing this meaning one use case at a time. They’re treating context as shared infrastructure—built once, reused everywhere —which is a defining trait of AI systems designed to scale.
What changes when context is shared
Once a context layer is in place, AI systems begin to behave differently.
Signals are no longer treated as isolated events. Decisions are evaluated within an ongoing business narrative.
In financial services, this means alerts are assessed with full awareness of customer relationships, historical behavior, and prior investigations. Analysts spend their time on the cases that matter most.
In manufacturing, AI understands how asset behavior, operating conditions, and past failures connect—allowing teams to move from reactive response to proactive planning.
In retail, recommendations reflect not just demand signals, but supply constraints and store-level realities. Strategy and execution finally stay aligned.
Across industries, shared context turns AI into a system that compounds value. Each decision feeds back into the graph, refining future actions without rebuilding the architecture. Context isn’t something you add once AI “works.” It’s what allows AI to work as part of the business.
The shift that defines the ROI phase
As AI becomes infrastructure, the competitive advantage compounds.
Teams that invest early in shared domain context move faster, adapt more easily, and spend less time maintaining bespoke integrations. Their systems learn from real outcomes, not just abstract metrics.
In the ROI phase, this difference matters. As more teams deploy agents into live workflows, the industry is converging on the same lesson: scaling AI isn’t about autonomy alone, but about the underlying systems that support it—a theme echoed in recent analysis on what it takes to scale AI agents in production.
Understanding why context matters is the first step. Designing it into your architecture is the next.
Next up: Scaling AI in industrial operations
Understanding the role of shared context is step one. Applying it inside high-stakes, real-world operations is step two. Next week, we’ll take a deep dive into how industrial leaders are scaling AI across asset performance, maintenance, and operations—turning intelligence into measurable uptime and reliability.
Get the full blueprint for scaling AI
Go deeper on the architecture leaders use to move AI from pilots to production — including context, orchestration, and governance built for real-world workflows.
