Enterprise AI operations

Enterprise AI operations cover the shared services, responsibilities, and controls behind AI systems already in use. BROCS organizes that work under Build, Run, Observe, Control, and Secure.

The same scopes can define requirements before deployment. The starting enterprise AI guide uses BROCS as an onramp for a first tool, pilot, supplier, or platform. After adoption is underway, the same review applies to the active operating estate.

AI can arrive before the operating model

An organization's first AI dependency may be purchased, embedded, configured, or built. Microsoft treats these routes as separate adoption models with different degrees of control and required skill.¹ Deep model development is one route among several.

Every route creates operating questions. Someone has to manage identity and data access, understand runtime dependencies, record behavior, change configuration, contain cost, and produce evidence after an incident. Those responsibilities exist even when a supplier runs the model or the application arrives through an existing contract.

Build covers the sanctioned ways people configure assistants, connect data, create agents, write prompts, evaluate behavior, publish work, train models, and write application code.

Adoption and operations answer different questions

AI adoption frameworks help an organization set ambition, choose use cases, develop skills, and decide which technical paths to support. Google Cloud describes its adoption framework as a map between current and desired AI capability.²

For an active-system review, BROCS uses current operating evidence. AI may already be present in several parts of the organization while the support behind it varies by team, supplier, or use case. The framework asks whether each active system has a supported delivery path, an owned runtime, useful telemetry, safe change mechanisms, and enforced requirements.

The approaches can be used together:

• Use an adoption or transformation framework to set direction, investment, and the desired organizational capability.
• Use BROCS to inspect the operating coverage behind current tools, production pilots, and deployed systems.
• Use the BROCS assessment to record the gaps that can be demonstrated today.
• Use the buyer questions to test supplier responsibilities and the evidence available in production.

Five operating scopes

These scopes form a production AI operating model. Ownership may be distributed across platform, application, infrastructure, security, data, finance, and compliance teams. The interfaces and evidence between those teams have to remain clear.

Operational excellence for AI

Google's current architecture guidance describes AI and ML operational excellence in terms of deploying, managing, and governing production systems while reducing operating complexity.³ DORA's platform engineering guidance adds shared, self-service delivery paths and warns that faster development can be absorbed by testing, security, and deployment bottlenecks.⁴

BROCS applies those practices across the enterprise AI lifecycle. Use the operational coverage guide to assess current evidence, the delivery guide to find delays, and the adjacent-practices guide for the source material behind the framework.