Spring 2027

Artificial Intelligence and the Enterprise

Deep dive into the strategic, operational, organizational, and cultural implications of applying AI to the business enterprise, focusing on leadership dynamics.

offering recorded2 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 3

Recent recorded grades — Spring 2026: 3.93 GPA, 96.7% A/AB (n=30 letter grades).

What you’ll be able to do

  • Explain the business value chain involved in AI technology.

  • Articulate the implications of AI technologies within business functions including customer engagement, operations and enterprise support services.

  • Apply business strategy and economic frameworks to AI investment decisions.

  • Assess the implications of AI on organizational design, culture and change management.

  • Describe approaches to AI governance, including implementation and risk management.

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
GENBUS 708

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • Graduate/professional standing

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.

Meeting source records

No records available.

Student experience

the class

Recent recorded grades — Spring 2026: 3.93 GPA, 96.7% A/AB (n=30 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Business value chain involved in AI technology.
  • Implications of AI in customer engagement, operations, and enterprise support services.
  • Business strategy and economic frameworks for AI investment.
  • Organizational design, culture, and change management in the context of AI.
  • AI governance, implementation, and risk management.

Skills

  • Applying business strategy and economic frameworks to AI investment decisions.
  • Assessing the implications of AI on organizational design, culture, and change management.
  • Describing approaches to AI governance, implementation, and risk management.

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F
More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1280 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

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Selected offering source records
GENBUS 708 · Spring 2027

Artificial Intelligence and the Enterprise

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:231:027212",
    "course_id": "GENBUS 708",
    "course_uid": "course_8239f2fad2b03d7ee783869f",
    "term_id": "1274",
    "source_course_id": "027212",
    "source_subject_id": "231",
    "title": "Artificial Intelligence and the Enterprise",
    "credits_min": 2,
    "credits_max": 2,
    "typically_offered": "Not Applicable"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews

No records available.

Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "55543c7e0dee0ae8418af431edeb664fc810dab2c762729efd0d9570c0a64c8d",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}