Case USA62: Why a National AI Research Lab Equated Model Pipelines with Enterprise Architecture Strategy

Sunil Dutt Jha
Aug 27
1 min read

Updated: Nov 3

Overview:

This case is part of a 100-diagnostic series revealing how US research institutions have mislabeled advanced project outputs as “Enterprise Architecture progress.”

In national AI labs, a recurring pattern is treating machine learning model pipelines as evidence of architectural maturity.

Models moved from experimentation to deployment faster, GPU utilization was optimized, and research-to-production time shortened — yet the enterprise structure linking research goals, data governance, compute allocation, compliance, and cross-project reuse was never modeled.

P1–P6 Insight Preview:

P1 (Strategy): Pipelines prioritized research velocity, not alignment to institutional objectives or mission impact.

P2 (Process): Training and deployment workflows varied wildly between projects, with no common operating process.

P3 (System): Data lakes, training clusters, and deployment platforms lacked unified behavior models for security, versioning, or reproducibility.

P4 (Component): Each team’s models, feature stores, and pipelines were built and maintained in isolation.

P5 (Implementation): Sprint priorities were driven by project deadlines, ignoring cross-project integration opportunities.

P6 (Operations): Business ops could demonstrate rapid AI results, but tech ops fought scaling issues, dependency conflicts, and compliance gaps across the environment.

Stakeholder Impact Mapping:

CEO/Director: Feels P1 — research outputs look impressive but don’t ladder up to mission-aligned outcomes.
CIO: Feels P3 & P4 — infrastructure is fragmented, creating high overhead to manage resources.
Sales Head (Government/Industry Partnerships): Feels P2 & P5 — hard to promise repeatability or timelines to partners without standard processes.
Chief EA: Feels P1–P6 — no enterprise anatomy governing research, infrastructure, and delivery.
Head of AI Engineering: Feels P3, P4, & P6 — spends more time fixing environment issues than building scalable models.