Integration of AI Supported Risk Management in ERP Implementation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The objective of this paper is to show the possibilities for the implementation of artificial intelligence (AI) in risk assessment methodology for ERP projects. Both AI and ERP being solutions built around data, it is of great importance how this data is organized and processed, and how it can be used on the one hand to manage the business process in a more efficient way and on the other to address risk factors that might compromise the ERP system in a way, which standard risk assessment methodologies might miss. AI can add value to such risk assessment methodology as it can process large amounts of data and even automize repetitive and heavy load risk management steps. AI can allow risk managers to respond faster to new and emerging threats in an ERP project. By acting in real time and with some predictive capabilities, AI supported risk management could reach a new level in improving the managers’ decision-making for building the ERP system of the company. The literature review is given of the main AI and machine learning techniques of benefit to risk management and ERP projects. Then an analysis, using current practice and empirical evidence, is carried out of the application of these techniques to the risk management fields in implementing an ERP system. The paper also presents a showcase of how Bulgarian companies address the issues of risk assessment and AI implementation in it to build ERP systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.009 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it