Accelerating enterprise AI delivery

Enterprise AI delivery slows when every team has to negotiate access, connect data, assemble a runtime, invent telemetry, and repeat security review for each system. Faster model or code generation does little for the time spent in those queues.

DORA reports that gains in coding speed are often absorbed by bottlenecks in testing, security, and deployment.¹ BROCS helps locate those constraints across Build, Run, Observe, Control, and Secure.

Shared capabilities reduce repeated work

A governed delivery path lets teams reuse the operating model. Measure fulfillment time, adoption, failed requests, and workarounds to confirm that teams use it.

Measure the whole flow

DORA defines change lead time from a version-control commit to production.² For enterprise AI, measure that interval for versioned code, prompts, tool instructions, retrieval configuration, evals, and policy.

Also measure the broader value stream from an approved idea to an operating change. DORA's value-stream guidance treats idea-to-production flow separately from the narrower delivery metric.³ For an enterprise AI value stream, that broader measure can include access requests, procurement, data approval, environment setup, security review, and operating handoff.

Useful measures include:

• time to obtain an approved workspace, model, and data connection;
• time from approved change to a running version;
• deployment failure and recovery measures;
• time to detect and reverse a behavioral regression;
• the share of changes using the supported path;
• operating cost per application or business unit.

Improve the limiting scope

Use the BROCS assessment to identify the scope constraining an active system or delivery path. Assign the smallest shared improvement that removes the constraint while preserving required evidence.

Review the result as an operating change. Check whether delivery time improved, whether teams use the path, and whether the required controls still produce evidence.