Artificial Intelligence Applied to Public Management: Towards an Operational Digital Maturity Model for an Intelligent State
Bibliographic record
Abstract
This paper proposes an applied and operational framework for the adoption of Artificial Intelligence (AI) in public management, addressing current challenges in institutional modernization, digital governance, and decision-making capacity in the public sector. The study introduces two original models: the MIIBE model (Modernization Institutional Model Based on Evidence) and the ODMM (Operational Digital Maturity Model), designed to guide public institutions through structured stages of digital transformation, from initial digitization to intelligent governance supported by AI. Using a qualitative and analytical approach, the paper integrates international references from OECD, BID, CAF, and UNDP frameworks, adapting them to the Latin American and Peruvian public sector context. The proposed models emphasize institutional capacity, interoperability, process automation, advanced analytics, and AI-assisted decision-making as core elements for achieving an Intelligent State. The contribution of this work lies in offering a practical, replicable, and scalable roadmap for public organizations seeking to implement AI responsibly, improve operational efficiency, and strengthen governance outcomes.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".