A Strategic Plan for 2026–2028
Terry College of Business
February 2026
| Name | Department / Unit |
|---|---|
| Chris Cornwell (Chair) | Economics & Ivester Institute |
| Margaret Christ | Accounting |
| Susan Cohen | Management |
| Jerry Kane | Management Information Systems |
| Son Lam | Marketing |
| Jim Metcalf | Office of Information Technology |
| Nikhil Paradkar | Finance |
| Marc Ragin | Institute for Leadership Advancement |
The shift is from teaching students to do the work to teaching students to direct, verify, and take responsibility for work that agents perform.
15 Months of Transformation
88%
of organizations use AI regularly in at least one business function
McKinsey 2025
23% scaling agentic AI
39% actively experimenting
McKinsey 2025
40%
of enterprise apps will embed task-specific AI agents by end of 2026
Gartner 2025
The Gap
79% of companies report AI adoption, but only 21% have mature governance models
Brynjolfsson, Chandar & Chen 2025
Hosseini & Lichtinger 2025
The traditional career ladder is being compressed from the bottom.
P&G Field Experiment Findings (Dell’Acqua et al. 2025)
One analyst with well-configured AI agents may outperform a traditional team.
Organizations will need:
Graduates will need:
If agents perform the tasks that built junior expertise, how do newcomers develop judgment?
Academic honesty cases at UGA nearly doubled from AY24 to AY25
The business school must ensure graduates arrive with enough foundational expertise that firms find value in hiring them—not as cheaper alternatives to agents, but as the professionals who can direct agents, verify their work, and make decisions that agents cannot.
| Finding | Result |
|---|---|
| Use at least one AI tool for instruction | 85% |
| ChatGPT specifically | 76% |
| AI has NOT changed assessment approach | 56% |
| Willing to pilot new approaches | 49% |
| Top barrier: Accuracy concerns | 78% |
| Time to learn as barrier | 57% |
| Prefer quick guides over workshops | 68% |
Note
Faculty are engaged but need practical support—not mandates. The barriers you cited are solvable: accuracy concerns, time constraints, and tool access.
Our Core Commitment
Terry will become the business school known for producing graduates and partners who can use AI to make better decisions—with integrity, transparency, and measurable impact.
In a fast-takeoff world, this means graduates who can direct and oversee autonomous AI agents, not merely prompt chatbots.
Moves slowly and deliberately
Moves fast and learns from failure
Without a stable core, experiments cannot build on each other and institutional credibility erodes. Without a flexible frontier, we cannot adapt to rapid change.
Foundational Track (all faculty)
Applied Track (teaching with AI)
Advanced Track (research enablement)
Rotating cohort with course release/stipend:
Why Fellows?
Converts the 49% willing to pilot into visible leadership that drives adoption by example.
The Teaching & Learning Hub
A centralized resource that reduces duplication and protects your time
The Hub should feel like an enablement resource, not a compliance checkpoint.
Every Terry graduate should demonstrate competence in:
Verification is the highest-value human skill in a fast-takeoff world.
| Zone | AI Expectation | Example Assessments |
|---|---|---|
| Green (Encouraged) | AI agents allowed and encouraged, with disclosure | Brainstorming, drafting, research synthesis, agent-assisted analysis |
| Yellow (Constrained) | AI allowed for specific steps, with process evidence | Research papers with interaction logs, analysis with validation documentation |
| Red (Restricted) | AI not permitted; individual demonstration required | In-class exams, oral defenses, proctored assessments, live presentations |
The Framework’s Purpose
Normalizes AI use where appropriate, creates structured skill-building, and preserves spaces where students demonstrate genuine mastery.
Terry AI in Business Practice Summit
Applied Project Marketplace
Executive Education
Build the Core
Months 1–6
Pilot to Scale
Months 7–12
Institutionalize
Months 13–24
The sequencing reflects dependencies: governance before pilots, pilots before embedding.
| Domain | Key Actions |
|---|---|
| Governance | Establish AI Steering Committee; designate AI Strategy Lead; negotiate enterprise licenses |
| Faculty Development | Launch Hub with minimum viable offering; recruit first Fellows cohort; deploy foundational training |
| Student Learning | Finalize agent-ready outcomes; develop tiered assessment guidance; pilot defense-based evaluation in 3+ departments |
| Industry Engagement | Plan Advisory Council; approach founding partners |
Critical Deliverable
Clear, publicly communicated decisions about AI tool use and assessment expectations within 60 days. Faculty and students cannot plan if the rules are uncertain.
Phase 3 is when the strategy must prove its durability by surviving a leadership transition, a budget cycle, or a technology shift.
| Risk | Likelihood | Impact | Mitigation |
|---|---|---|---|
| Assessment credibility erosion | High | High | Tiered framework; defense-based evaluation; process evidence |
| Agent-enabled academic dishonesty | High | High | Agent interaction logging; oral defenses; real-time demonstration |
| Faculty adoption gaps | Medium | High | Peer-led development; Fellows model; protect faculty time |
| Student AI dependency | Medium | Medium | Red Zone assessments; metacognitive exercises; reflection requirements |
The Deeper Risk
The cost of investing too cautiously—moving at academic pace while the world restructures at technology pace—exceeds the cost of investing too aggressively.
The Core Argument
In a fast-takeoff world, the graduates who thrive will not be those who can use AI most skillfully—that skill will be ubiquitous.
They will be those who can:
This is what Terry must teach. This is the signal a Terry degree must send.
Your Input Matters
This strategy succeeds only with faculty engagement. We want your questions, concerns, and ideas.