{"id":"W4410501861","doi":"10.3390/info16050415","title":"Artificial Intelligence in SMEs: Enhancing Business Functions Through Technologies and Applications","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais; Université du Québec à Trois-Rivières","funders":"","keywords":"Business intelligence; Business; Knowledge management; Process management; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001502451,0.0001040038,0.0001016844,0.000465277,0.0001779639,0.0003249664,0.0001802687,0.0000888705,0.00003040555],"category_scores_gemma":[0.0002028816,0.00009831061,0.00001368147,0.001968226,0.00008606206,0.004998572,0.0001679144,0.0001125404,0.0002567204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002747161,"about_ca_system_score_gemma":0.00002684349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000359377,"about_ca_topic_score_gemma":0.0003902232,"domain_scores_codex":[0.9992471,0.00000191702,0.0003851482,0.0001227067,0.00009543037,0.0001476964],"domain_scores_gemma":[0.9994486,0.00003313008,0.0001171063,0.00019357,0.000205447,0.000002146908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001161131,0.00002603471,0.00116987,0.0002592242,0.000004158355,1.767311e-7,0.00006269704,0.0003102016,0.00008620024,0.274149,0.0004558543,0.723465],"study_design_scores_gemma":[0.0001171125,0.000005316964,0.0149356,0.0004448753,0.0000507406,0.000003890963,0.0135427,0.01774884,0.002939568,0.2734964,0.6761881,0.0005268515],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009116191,0.0001381137,0.9665816,0.00210192,0.0003147908,0.0004169479,0.000005774566,0.0003334116,0.02099127],"genre_scores_gemma":[0.9981922,0.00008978815,0.0006401885,0.0006216762,0.0001074649,0.00017955,0.0001184175,0.000003953017,0.00004679696],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.989076,"threshold_uncertainty_score":0.4008991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03562801299073627,"score_gpt":0.2822760577997157,"score_spread":0.2466480448089794,"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."}}