Harnessing AI to Overcome Enterprise Architecture Challenges and Drive Innovation
Bibliographic record
Abstract
This paper delves deeper into some of the challenges faced by enterprise architecture (EA) in modern organizations and highlights how artificial intelligence (AI) can provide transformative solutions to those.By leveraging AI, organizations can enhance governance, optimize costs, foster innovation, and improve overall efficiency.The paper emphasizes the critical role of EA in addressing organizational complexities, such as breaking down team silos, streamlining redundant business capabilities, and maintaining alignment across business units.AI-driven tools and strategies are presented as key enablers to simplify these complexities while ensuring robust security and operational excellence.Key challenges discussed include maintaining end-of-life software, managing competing tools and technologies, establishing software ownership, and fostering innovation while mitigating associated risks.The paper highlights how AI-driven solutions, such as Retrieval-Augmented Generation (RAG) workflows and knowledge graphs, can enhance API governance, streamline software ownership management, and provide detailed software dependency mapping.These tools reduce manual intervention, improve decision-making, and enable faster, more reliable processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".