Artificial intelligence and records management in contemporary organizations: what cultural aspects are required? Insights from the information culture framework (ICF)
Bibliographic record
Abstract
Purpose This study aims to examine how artificial intelligence can be effectively integrated into records management practices by identifying the key cultural aspects that should be aligned with the prerequisites of automation. The author also discusses the new roles that are to be played by records managers in this context. Design/methodology/approach To identify those cultural aspects, the author has used the Information culture framework (ICF) developed by Oliver and Foscarini (2020). For each of the three levels of the ICF (i.e. visible, intermediate and invisible), the author has analyzed the cultural aspects serving as the prerequisites of AI for records management purposes. Findings The results of our theoretical reflection reveal that for AI features to be integrated into records management practices, many cultural aspects are to be taken into consideration. AI-powered technologies use, collaboration practices and horizontal communication are some visible cultural aspects contemporary organizations should have in place to meet the requirements of automation. Furthermore, policies and strategies should define automation purposes, identify actors that will be involved in records management practices and describe their respective roles. Finally, attention should be given to individual perceptions and personal traits to ensure that AI technologies are embraced by organizational actors. All those aspects should support the development of a common AI-related language in the organization and influence the extent to which actors trust AI-powered technologies. In this context of automation, records managers will have to assume new roles in change management and promoting information competencies, to assess organizations’ readiness to integrate AI into its records management practices and make the appropriate use of it. Originality/value To the best of the author’s knowledge, this study is the first to use the ICF model suggested by Oliver and Foscarini (2020) to identify the main cultural aspects to target for the effective use of AI in records management practices. Furthermore, the author confirmed the relevance of the expression “augmented records management”, referring to AI-assisted records management practices in contemporary organizations, by highlighting the fact that AI will not replace human work but can, rather, be used as a tool to support it.
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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 | 0.021 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.044 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".