# GENBUS 708: Artificial Intelligence and the Enterprise | UW–Madison [View on UW Courses](https://uwcourses.com/courses/GENBUS_708) ## Dataset ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ### repository twangodev/uwcourses ### schema version 6 ### importer version 7 ### projection id 52a78527ff09011d88cb04c7d43867d9fbfec2930bf1dd2ec359ebd2844e0b07 ### observed at 2026-09-07T15:55:43.033547+00:00 ### built at 2026-09-10T19:50:42.743001+00:00 ### courses 8951 ### current instructors 5754 ### limited false ### terms * 1272 * 1264 * 1262 * 1254 * 1252 * 1244 * 1242 * 1234 * 1232 * 1224 * 1222 * 1212 * 1204 * 1202 * 1194 * 1192 * 1184 * 1182 * 1174 * 1172 * 1164 * 1162 * 1154 * 1152 * 1144 * 1142 * 1134 * 1132 * 1124 * 1122 * 1114 * 1112 * 1104 * 1102 * 1094 * 1092 * 1084 * 1082 * 1074 * 1072 ### term 1272 ### departments | subject | count | | --------- | ----- | | AAE | 90 | | ABT | 20 | | ACCTIS | 36 | | ACTSCI | 14 | | AFAERO | 10 | | AFRICAN | 87 | | AFROAMER | 71 | | AGROECOL | 21 | | AMERIND | 53 | | ANAT\&PHY | 7 | | ANATOMY | 2 | | ANESTHES | 8 | | ANSCI | 57 | | ANTHRO | 95 | | ART | 125 | | ARTED | 10 | | ARTHIST | 121 | | ASIALANG | 129 | | ASIAN | 109 | | ASIANAM | 28 | | ASTRON | 38 | | ATMOCN | 69 | | BIOCHEM | 51 | | BIOCORE | 10 | | BIOLOGY | 14 | | BIOMDSCI | 18 | | BME | 66 | | BMI | 41 | | BMOLCHEM | 10 | | BOTANY | 69 | | BSE | 44 | | C\&ESOC | 73 | | CBE | 48 | | CHEM | 101 | | CHICLA | 52 | | CIVENGR | 133 | | CLASSICS | 47 | | CNP | 10 | | CNSRSCI | 50 | | COMARTS | 137 | | COMPBIO | 15 | | COMPLIT | 11 | | COMPSCI | 138 | | COUNPSY | 78 | | CRB | 19 | | CS\&D | 71 | | CSCS | 40 | | CURRIC | 193 | | DANCE | 93 | | DERM | 8 | | DS | 88 | | DYSCI | 35 | | ECE | 155 | | ECON | 145 | | EDPOL | 114 | | EDPSYCH | 97 | | ELPA | 70 | | EMA | 53 | | EMERMED | 17 | | ENGL | 206 | | ENTOM | 40 | | ENVIRST | 148 | | EP | 15 | | EPD | 67 | | ESL | 16 | | F\&WECOL | 60 | | FAMMED | 22 | | FINANCE | 50 | | FOLKLORE | 40 | | FOODSCI | 43 | | FRENCH | 63 | | GEN\&WS | 145 | | GENBUS | 72 | | GENECSLR | 17 | | GENETICS | 60 | | GEOG | 113 | | GEOSCI | 83 | | GERMAN | 82 | | GLE | 48 | | GNS | 38 | | GREEK | 27 | | HDFS | 41 | | HEBR-BIB | 13 | | HEBR-MOD | 10 | | HISTORY | 233 | | HISTSCI | 56 | | HONCOL | 13 | | ILS | 43 | | INFOSYS | 9 | | INTEGART | 7 | | INTEGSCI | 22 | | INTER-AG | 18 | | INTER-HE | 11 | | INTER-LS | 22 | | INTEREGR | 16 | | INTLBUS | 21 | | INTLST | 50 | | ISYE | 83 | | ITALIAN | 53 | | JEWISH | 56 | | JOURN | 88 | | KINES | 128 | | LACIS | 26 | | LANDARC | 61 | | LATIN | 24 | | LAW | 120 | | LEGALST | 48 | | LINGUIS | 35 | | LIS | 89 | | LITTRANS | 78 | | LSC | 52 | | M\&ENVTOX | 7 | | MARKETNG | 62 | | MATH | 153 | | MDGENET | 8 | | ME | 130 | | MEDHIST | 43 | | MEDICINE | 60 | | MEDIEVAL | 32 | | MEDPHYS | 39 | | MEDSC-M | 29 | | MEDSC-V | 41 | | MHR | 68 | | MICROBIO | 45 | | MILSCI | 16 | | MM\&I | 22 | | MOLBIOL | 6 | | MS\&E | 59 | | MUSIC | 165 | | MUSPERF | 126 | | NAVSCI | 20 | | NE | 44 | | NEURODPT | 11 | | NEUROL | 7 | | NEURSURG | 4 | | NTP | 11 | | NURSING | 111 | | NUTRSCI | 65 | | OBS\&GYN | 18 | | OCCTHER | 39 | | ONCOLOGY | 14 | | OPHTHALM | 5 | | OTM | 43 | | PATH | 34 | | PATH-BIO | 29 | | PEDIAT | 26 | | PHARMACY | 31 | | PHILOS | 77 | | PHMCOL-M | 11 | | PHMPRAC | 45 | | PHMSCI | 60 | | PHYASST | 37 | | PHYSICS | 88 | | PHYSIOL | 3 | | PHYTHER | 37 | | PLANTSCI | 52 | | PLPATH | 35 | | POLISCI | 192 | | POPHLTH | 58 | | PORTUG | 33 | | PSYCH | 101 | | PSYCHIAT | 22 | | PUBAFFR | 54 | | PUBLHLTH | 23 | | RADIOL | 11 | | REALEST | 41 | | RELIGST | 90 | | RHABMED | 9 | | RMI | 24 | | RP\&SE | 102 | | S\&APHM | 17 | | SCANDST | 73 | | SLAVIC | 89 | | SOC | 149 | | SOCWORK | 87 | | SOILSCI | 46 | | SPANISH | 83 | | SRMED | 21 | | STAT | 94 | | STDYABRD | 52 | | STS | 8 | | SURGERY | 25 | | SURGSCI | 33 | | THEATRE | 91 | | URBRPL | 60 | | UROLOGY | 5 | | ZOOLOGY | 89 | ## course ### run id 20260907T155543-ce3781c4 ### semester 1272 ### observed at 2026-09-07 15:55:43.033547+00:00 ### record version id 2180f29d2c05b05b74e73d13bfce595533d5edd64c02f2ac5a23fb0dc9c647b1 ### course id GENBUS 708 ### course uid course\_8239f2fad2b03d7ee783869f ### catalog version id 4f416eda1b7dfb253451276f35604f9037258b6ae1dc06eae8ae06c4ef1a7806 ### course number 708 ### subjects * GENBUS ### title ARTIFICIAL INTELLIGENCE AND THE ENTERPRISE ### description A deep dive into the strategic, operational, organizational and cultural implications of applying Artificial Intelligence (AI) to today's business enterprise. Addresses the key dynamics required for individuals to provide leadership in AI within their organizations. ### requirements text Graduate/professional standing ### credits min 2 ### credits max 2 ### credit offering ids * 1272:231:027212 ### llm job id enrich-8b774950c2b6adfdc46d1b82 ### llm output id f2220bcbab613d2f9ab19e6b3cd137f699e558ac77ac9c71491df1ccec20e714 ### llm model nvidia/Qwen3.6-35B-A3B-NVFP4 ### llm model revision 1355db6a052410cfd62085d94b58866fd0f2c3c5 ### llm task version 14 ### llm search status valid ### llm summary GENBUS 708 explores the strategic, operational, organizational, and cultural implications of applying AI to business enterprises, focusing on leadership dynamics. ### llm topics * Strategic, operational, organizational, and cultural implications of AI * Dynamics of AI leadership ### llm skills * Leadership in AI within organizations * Analyzing strategic, operational, organizational, and cultural implications of AI ### llm assumed background None recorded. ### llm search phrases * AI business strategy * enterprise AI leadership * organizational AI implications * AI operational management ### llm requirements status needs\_review ### llm student summary status valid ### llm experience status insufficient\_evidence ### catalog variants None recorded. ### student summary #### context hash 649ed9887581d6bc4df19a6e5d7e9cb8d0ee9c1066b52bf39918bad633b50106 #### course id GENBUS 708 #### current instructors None recorded. #### difficulty workload None recorded. #### errors None recorded. #### historical context None recorded. #### message No course-specific reviews available #### offered true #### profile hash 5cb4dabf887cdbcd8c00d5a1312e10828b95c63f30bc3ea76aea199565390d02 #### quick take Recent recorded grades — Spring 2026: 3.93 GPA, 96.7% A/AB (n=30 letter grades). ```json { "citations": [ { "course_id": "GENBUS 708", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "fa44d8b8-24da-3732-8861-7ccd8b6daa0f", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1264", "type": "grade" } ] } ``` #### student experience None recorded. #### task hash 74fb0997943e888960bbc9e47db8c4fd12e4292d55c20509d8d89db6a9910f68 #### teaching history None recorded. #### term id 1272 #### term name 2026 Fall #### version 2 ### requirements #### nodes ```json [ { "children": [], "condition": "Graduate/professional standing", "course": null, "evidence": "Graduate/professional standing", "id": "n0", "kind": "condition" } ] ``` #### notes * Reference 'Graduate/professional standing' is a general standing requirement not linked to a specific course in linked\_courses. #### root n0 #### status needs\_review ### instructors None recorded. ### offerings ```json [ { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "offering_id": "1272:231:027212", "course_id": "GENBUS 708", "course_uid": "course_8239f2fad2b03d7ee783869f", "term_id": "1272", "source_course_id": "027212", "source_subject_id": "231", "title": "Artificial Intelligence and the Enterprise", "credits_min": 2, "credits_max": 2, "typically_offered": "Not Applicable" } ] ``` ### sections None recorded. ### grade instructors ```json [ { "instructor_uid": "instructor_4ef3705fc1cf2a91948ea45a", "source": "madgrades", "source_instructor_id": "6800214", "identity_basis": "source_id", "identity_status": "source_identified", "name": "MATT SEITZ", "email": null, "first_observed_at": "2026-09-06 23:14:58.172943+00:00", "last_observed_at": "2026-09-07 15:55:43.033547+00:00", "grade_statistics": { "gpa": 3.933, "graded": 30, "counts": [ 29, 0, 0, 0, 1, 0, 0 ], "sections": 1 }, "instructor_url": "/instructors/MATT_SEITZ--instructor_4ef3705fc1cf2a91948ea45a" } ] ``` ### grades ```json [ { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "GENBUS 708", "course_uid": "course_8239f2fad2b03d7ee783869f", "term_id": "1264", "term_name": "Spring 2026", "instructors": [ "MATT SEITZ" ], "a": 29, "ab": 0, "b": 0, "bc": 0, "c": 1, "d": 0, "f": 0, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 30, "source_aliases": [ "GENBUS 708" ] } ] ``` ### statistics #### gpa 3.933 #### graded 30 #### counts * 29 * 0 * 0 * 0 * 1 * 0 * 0 ### grade conflicts None recorded. ### evidence #### history * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course\_8239f2fad2b03d7ee783869f-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course_8239f2fad2b03d7ee783869f-0.json) #### traces * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course\_8239f2fad2b03d7ee783869f-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course_8239f2fad2b03d7ee783869f-0.json) #### results * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course\_8239f2fad2b03d7ee783869f-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course_8239f2fad2b03d7ee783869f-0.json) ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ## context ### all #### term #### gpa 3.933333333333333 #### count 30 #### university ##### size 1283 ##### gpa Percentile 84 ##### count Percentile 0 ##### median Count 66 ##### histogram | range | count | current | | ------- | ----- | ------- | | 0.0–0.4 | 0 | false | | 0.4–0.8 | 0 | false | | 0.8–1.2 | 0 | false | | 1.2–1.6 | 0 | false | | 1.6–2.0 | 0 | false | | 2.0–2.4 | 1 | false | | 2.4–2.8 | 20 | false | | 2.8–3.2 | 130 | false | | 3.2–3.6 | 343 | false | | 3.6–4.0 | 789 | true | #### departments ```json [ { "subject": "GENBUS", "comparison": { "size": 30, "gpaPercentile": 86, "countPercentile": 0, "medianCount": 79, "histogram": [ { "range": "0.0–0.4", "count": 0, "current": false }, { "range": "0.4–0.8", "count": 0, "current": false }, { "range": "0.8–1.2", "count": 0, "current": false }, { "range": "1.2–1.6", "count": 0, "current": false }, { "range": "1.6–2.0", "count": 0, "current": false }, { "range": "2.0–2.4", "count": 0, "current": false }, { "range": "2.4–2.8", "count": 0, "current": false }, { "range": "2.8–3.2", "count": 1, "current": false }, { "range": "3.2–3.6", "count": 7, "current": false }, { "range": "3.6–4.0", "count": 22, "current": true } ] } } ] ``` ### terms #### 1264 ##### term 1264 ##### gpa 3.933333333333333 ##### count 30 ##### university ###### size 1283 ###### gpa Percentile 84 ###### count Percentile 0 ###### median Count 66 ###### histogram | range | count | current | | ------- | ----- | ------- | | 0.0–0.4 | 0 | false | | 0.4–0.8 | 0 | false | | 0.8–1.2 | 0 | false | | 1.2–1.6 | 0 | false | | 1.6–2.0 | 0 | false | | 2.0–2.4 | 1 | false | | 2.4–2.8 | 20 | false | | 2.8–3.2 | 130 | false | | 3.2–3.6 | 343 | false | | 3.6–4.0 | 789 | true | ##### departments ```json [ { "subject": "GENBUS", "comparison": { "size": 30, "gpaPercentile": 86, "countPercentile": 0, "medianCount": 79, "histogram": [ { "range": "0.0–0.4", "count": 0, "current": false }, { "range": "0.4–0.8", "count": 0, "current": false }, { "range": "0.8–1.2", "count": 0, "current": false }, { "range": "1.2–1.6", "count": 0, "current": false }, { "range": "1.6–2.0", "count": 0, "current": false }, { "range": "2.0–2.4", "count": 0, "current": false }, { "range": "2.4–2.8", "count": 0, "current": false }, { "range": "2.8–3.2", "count": 1, "current": false }, { "range": "3.2–3.6", "count": 7, "current": false }, { "range": "3.6–4.0", "count": 22, "current": true } ] } } ] ``` ### benchmarks #### all ##### school ###### size 1283 ###### gpa 3.6229729166451925 ###### top Share 80.13669149396227 ###### count 66 ##### GENBUS ###### size 30 ###### gpa 3.6817668874579623 ###### top Share 83.39332566820664 ###### count 79 #### terms ##### 1264 ###### school ###### size 1283 ###### gpa 3.6229729166451925 ###### top Share 80.13669149396227 ###### count 66 ###### GENBUS ###### size 30 ###### gpa 3.6817668874579623 ###### top Share 83.39332566820664 ###### count 79 ## instructor Trends ```json [ { "uid": "instructor_4ef3705fc1cf2a91948ea45a", "name": "MATT SEITZ", "count": 30, "terms": [ { "term": "1264", "count": 30, "sections": 1, "gpa": 3.933333333333333 } ] } ] ``` ## following None recorded.