{"id":"W7160279464","doi":"10.1109/aixb65684.2025.00022","title":"GenAI Inside: A Practical Methodology for Embedding AI Across Enterprise Workflows","year":2025,"lang":"","type":"article","venue":"","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Mastercard Foundation","keywords":"Workflow; Embedding; Process (computing); Key (lock); Domain (mathematical analysis)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008066366,0.0009577762,0.0004142282,0.001559576,0.001014616,0.003512977,0.001904729,0.001134244,0.005694373],"category_scores_gemma":[0.01551427,0.0009689194,0.001345697,0.0007998088,0.002717737,0.003468531,0.006334529,0.002229125,0.002049962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009307975,"about_ca_system_score_gemma":0.002937368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001463608,"about_ca_topic_score_gemma":0.002920628,"domain_scores_codex":[0.9939704,0.002988483,0.0004551906,0.0009502569,0.001333169,0.0003024549],"domain_scores_gemma":[0.9901571,0.00404099,0.0007629433,0.003796317,0.0008622225,0.0003803049],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002048828,0.0004032068,0.006069741,0.001011343,0.0001801715,0.001102927,0.01185352,0.04818799,0.03617012,0.3339542,0.008085605,0.5527762],"study_design_scores_gemma":[0.0001378661,0.0006967554,0.003433886,0.0007277261,0.0001586747,0.001584048,0.003915858,0.3163154,0.05474962,0.3200774,0.297942,0.0002607814],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002436232,0.00002281735,0.9921432,0.0001560713,0.00002020422,0.0002163876,0.00004771858,0.002984618,0.001972865],"genre_scores_gemma":[0.03038204,0.00004388676,0.967304,0.00007194852,0.000007977852,0.0002695995,0.0001390745,0.0005051536,0.001276346],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008066366,"threshold_uncertainty_score":0.04265958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3222248450500721,"score_gpt":0.5699861437219467,"score_spread":0.2477612986718746,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}