Section 01The Thesis
Our field is the study of business operations. AI is now the single largest force reshaping business operations. Studying it, applying it, and preparing students for it is not a side project for a business school, it is the job.
The Chief Technology Officer has proposed bringing Claude licenses to the cabinet for the School of Business Administration. This site lays out the case for that proposal in depth: why the moment matters, why SBA is the right place to do this, why Claude specifically, what it means for each group of people in the school, and how we recommend rolling it out.
The short version
- Operations. Administrative processes consume cognitive time that could go to students. Claude, working inside our existing Microsoft environment, lets us automate the routine and enhance the analytical.
- Faculty. Research, teaching, and service all have work that can be accelerated or deepened with the tool, without lowering standards.
- Students. Through our student consulting groups, students both use the tool on real client work and serve as consultants to the school itself, documenting the implementation as case studies.
- Discipline. The MIS discipline exists precisely to be the interface between emerging technology and business operations. We have the expertise, the infrastructure, and the track record already in house.
- Research. One initiative produces a multi lens research program, operational, pedagogical, and student development, with grant potential.
- Mission. Reducing process burden so faculty and staff can spend their time on people is a direct expression of cura personalis.
Section 02Why Now
The gap is widening
Technology is moving faster than any academic unit can absorb through traditional means. A business school has a double obligation that most units do not: we must understand what is shifting in the technological environment, and we must bridge that shift to students who will enter it within months of leaving us.
Right now, three pressures compound:
- Time scarcity. Faculty and staff have limited hours to explore new tools while carrying full teaching, research, service, and operational loads.
- Operational weight. The school runs like a business: admissions pipelines, program administration, advising, accreditation reporting, event logistics, communications, budgeting. These tasks are real and they do not shrink.
- Rising student expectations. Students arrive expecting fluency in the tools industry uses. Employers expect graduates who can work with AI, not just talk about it.
The opportunity
The same technology that creates the gap can close it. Modern AI tools do two things well:
- Automate. Take repeatable, rule based, document heavy work off people's plates.
- Enhance. Extend thinking and analysis: drafting, summarizing, synthesizing across sources, modeling scenarios, generating options.
Applied across the school, this creates a loop: operational pain points become the testing ground, faculty and staff gain skill through use, students learn by doing real work, and the whole thing produces documented knowledge the school can share.
The trigger
Why this document exists now
The CTO has reached out and would like to propose Claude licenses to the cabinet, scoped to the School of Business. That gives us a champion at the institutional level and a narrow window to shape how the licenses are used. This document is our answer to that window.
Section 03Why the School of Business
If AI adoption is going to succeed anywhere on campus, the strongest case for it is here. Not because business is more important than other fields, but because this is what a business school studies, teaches, and already does.
The MIS discipline is the interface
Management Information Systems is the discipline dedicated to how organizations adopt, implement, use, and keep using technology in operations. In industry, MIS professionals sit between the technology and the business. That is exactly the role this initiative requires. SBA houses an MIS discipline lead, an MIS and Business Analytics concentration (recently redesigned and launching Fall 2026), and faculty who study this specific problem.
The infrastructure already exists
We are not starting from zero:
- Student consulting groups with real clients. The Gonzaga Sport Consulting Group (GSCG) ran 11 client projects across 9 organizations last year, working with major professional sports organizations. Zag Business and Tech (ZBT) has worked with corporate partners including Microsoft and Amazon. The New Venture Lab adds an entrepreneurial channel.
- An operating platform built for this. GSCG runs on a purpose built institutional knowledge system with role playbooks, task templates, per role workspaces, contact management, and a full semester operating board. It was designed on the principle that knowledge lives in data, not in any single tool, which makes it a natural environment to layer AI into.
- Custom, AI aware course materials. Faculty have already authored custom course texts (including an AI in enterprise text at the graduate level) and built AI assisted grading and feedback workflows in live courses.
A track record of responsible AI in student work
GSCG has already implemented AI into its consulting operations and has worked through the hard question: how do you use AI to deliver for clients without hindering student learning? We have thought through the balance between delivery and development from the student's standpoint. This proposal builds on that experience rather than inventing it.
Faculty with live industry practice
Two of the three faculty leading this initiative run active technology consultancies alongside their academic roles, currently helping companies adopt and implement modern AI tools. This is not a theoretical exercise. The skill set and the use cases come directly from current industry work and can be applied immediately in the academic environment, growing our own capability while adding value to every actor in the school.
The bottom line
Expertise, infrastructure, track record, and mission alignment are all present. The School of Business is where a Claude pilot is most likely to work, most likely to produce transferable learning, and most defensible to the cabinet.
Section 04Why Claude
The proposal is for Claude specifically, and the reasoning should be stated plainly.
Enterprise trust through the Microsoft environment
The university runs on Microsoft. Claude's integration into that environment matters for two reasons: it puts the tool where the work already happens (email, documents, spreadsheets, Teams), and it inherits the identity, security, and data governance posture the institution already trusts. Our position is that when Claude's capability growth is combined with the trust of a Microsoft managed environment, this becomes the leading tool in industry, and the one our students are most likely to encounter after graduation.
Note: the specific licensing and integration details will come from IT. This section states our rationale, not the technical configuration.
Capability where it counts for us
- Long document reasoning. Accreditation reports, curriculum documents, policy manuals, research literature, client deliverables. Our work is document heavy.
- Structured, honest analysis. The tool is strong at reasoning through business problems, drafting options, and flagging uncertainty rather than fabricating confidence.
- Agentic and coding tools. Claude Code and related tooling allow faculty and students to build small automations and internal tools, not just chat. This is where operational efficiency actually comes from.
- Projects and shared context. Persistent workspaces let a program office, a research team, or a student consulting team keep shared context across sessions.
A safety and responsibility first vendor
For a Jesuit institution, the vendor's approach to responsible AI is not a footnote. Anthropic's public commitment to safety and to not training on enterprise customer data is aligned with how we want to introduce AI to students and staff.
It is the tool we already use
Faculty leading this initiative already use Claude in course infrastructure, grading workflows, consulting practice, and student group operations. Standardizing on it removes friction and lets us move immediately.
Lens 01 · Section 05Administration & Operations
The problem
The school runs a business inside a university. Program directors, staff, and faculty in administrative roles carry a steady stream of process work: scheduling, communications, reporting, data pulls, event logistics, forms, compliance, and the constant translation of information from one format to another. Each task is manageable; the sum consumes cognition that should go to judgment, relationships, and students.
What we would explore
Using Claude inside the Microsoft environment to automate the repeatable and enhance the analytical:
| Area | Automate | Enhance |
|---|---|---|
| Program administration (MBA, MAcc/MSTax, MSBA, undergrad) | Draft routine communications, assemble recurring reports, generate first pass schedules, summarize meeting notes into action items | Model enrollment scenarios, identify at risk cohorts from existing data, surface patterns across advising notes |
| Accreditation & assessment (AACSB) | Compile evidence from existing documents, draft assurance of learning summaries, maintain living documentation | Cross check standards against current practice, flag gaps early |
| Admissions & recruiting | First draft responses to common inquiries, structured summaries of applicant materials | Analyze funnel data, draft targeted outreach |
| Events & external relations | Logistics checklists, run of show drafts, follow up communications | Partner briefings, sponsorship proposals |
| Budget & planning | Consolidate line items from multiple documents, draft narrative justifications | Scenario comparison, variance explanation |
| Knowledge management | Turn tribal knowledge into written playbooks | Make the playbooks searchable and conversational |
How we would do it
- Start with the processes that hurt most, identified by the people who do them.
- Build small, documented workflows, not a grand system.
- Pair a staff or faculty process owner with a student consulting team (see Section 7) so the work is both delivered and studied.
- Measure hours saved, error rates, and critically, whether people keep using the workflow after the pilot.
Lens 02 · Section 06Faculty
Faculty carry three obligations, research, teaching, and service, plus whatever administrative processes flow through them. Claude touches each.
Research
- Literature synthesis and gap identification across large bodies of work
- Drafting and refining IRB protocols, grant narratives, and methods sections
- Data cleaning, exploratory analysis, and reproducible code with Claude Code
- Reviewer style critique of one's own drafts before submission
- Turning the school's own AI implementation into a research program (Section 9)
Teaching
- Course design and redesign: aligning outcomes, assessments, and materials
- Building custom course texts and materials (already done in house for multiple courses)
- Feedback workflows that give students richer, faster, mentor voice comments while preserving the instructor's judgment on grades
- Case development from real client projects
- Preparing students to use AI well, with disclosure, verification, and judgment, because they will use it regardless
Service
- Committee work: agenda synthesis, minutes to action items, policy drafting
- Program review and curriculum proposals
- Advising: faster preparation before student meetings, so the meeting itself is about the student
The faculty adoption question
Not every faculty member will adopt, and that is fine. The implementation paths in Section 10 are designed around this reality: give licenses to those who will use them, document what works, and let peer evidence, not mandates, drive expansion.
Lens 03 · Section 07Students
This is the lens that makes SBA's proposal different from a generic "give staff AI tools" request.
Students as practitioners
Our students already do real consulting for real clients through:
- Gonzaga Sport Consulting Group (GSCG). Analytics, operations, and strategy work for professional sports organizations.
- Zag Business and Tech (ZBT). Technology focused projects for corporate and regional partners.
- New Venture Lab. Entrepreneurial and startup adjacent work.
Giving these students Claude licenses means they learn the tool the way industry uses it: on live deliverables, under deadlines, with a client who cares about the output.
Students as consultants to the school
The sharper move: treat the School of Business as a client.
Student consulting teams already help outside organizations adopt and implement technology. Point that same capability inward. A student team works with a program office or administrative process owner to:
- Map the current process
- Identify where Claude can automate or enhance
- Build and test the workflow
- Train the process owner
- Document the engagement as a case study
This does three things at once: improves school operations, gives students a portfolio grade project, and produces published use cases the school can share with alumni, partners, and industry.
Students as researchers
Every engagement generates data on adoption, learning, and outcomes. Students can participate in the research program (Section 9) as co investigators, not just subjects.
Protecting learning
We have already worked through this in GSCG: AI must accelerate delivery without short circuiting learning. Guardrails include:
- Students must be able to explain and defend every output they use
- Verification and disclosure are part of the deliverable standard
- Skills progression is tracked, the tool is scaffolding, not a substitute
- Faculty review remains in the loop on client facing work
Lens 04 · Section 08Mission: A Jesuit Business School
Gonzaga is a Jesuit institution. That shapes how we introduce any technology, and it strengthens rather than complicates the case.
Cura personalis: care for the whole person
Faculty and staff who spend less time on process grinding spend more time on students, colleagues, and the substance of their work. Automating the routine is, in practice, an act of care for the people doing it. That is the frame we should use with administration: the point is people, not efficiency.
Discernment and responsible use
Jesuit education emphasizes discernment, thoughtful judgment about how to act. Teaching students to use AI with verification, disclosure, and humility is discernment applied to technology. A business school that models this is preparing ethical practitioners, not just efficient ones.
Magis: the more
The "more" here is reach: knowledge produced inside the school (playbooks, case studies, research) is shared with alumni, regional partners, and the broader academic community rather than held internally.
Service to the region
Faculty already consult with regional organizations on AI adoption. Documented school use cases become a resource for the Inland Northwest business community, extending the university's mission beyond campus.
Lens 05 · Section 09Research & Grants
One implementation, many research questions. This initiative is a natural laboratory across at least four lenses.
Research lenses
| Lens | Example questions |
|---|---|
| Operational | Which administrative processes yield the most time savings? What predicts sustained use versus abandonment? How does integration into an existing environment (Microsoft) affect adoption? |
| Pedagogical | How does AI assisted feedback affect student writing and analytical quality? What disclosure and verification norms actually change behavior? |
| Student development | Does AI use in consulting work accelerate or hinder skill acquisition? Which scaffolding approaches preserve learning? |
| Organizational | How do adoption paths (mandate vs. opt in vs. champion led) compare in a professional school? What role does peer evidence play? |
| Knowledge management | How does AI reshape personal, professional, and organizational knowledge management? What structures make knowledge usable by people and AI alike? Does it reduce knowledge loss across turnover and graduation? |
| Ethics & mission | How does a values based institution operationalize responsible AI? What governance structures emerge? |
Guiding research questions
Central question
How can a business school incorporate ethical, intentional use of AI into its own operations so that the school itself becomes a core case study for the study, teaching, and implementation of AI?
Everything below branches from that.
Adoption: how do we adopt AI with each group?
- Students. How do students adopt AI in real consulting work? What conditions produce skill growth rather than dependence? How does adoption differ between undergraduates and graduate students, and across disciplines (accounting, marketing, MIS, finance)?
- Faculty. What drives faculty adoption: peer evidence, mandates, time savings, or research value? How do research, teaching, and service uses differ in uptake and persistence?
- Administration and staff. Which operational processes are adopted first and which resist? What is the role of a student "internal consultant" in staff adoption?
- Alumni and partners. How do documented school use cases influence adoption in regional organizations and among alumni?
Intentional and ethical use
- What disclosure, verification, and judgment norms actually change behavior, versus norms that exist only on paper?
- How does a values based (Jesuit) institution translate mission into concrete AI governance, and does that governance improve outcomes?
- Where do people over rely on AI, where do they under use it, and what predicts each?
- How does explicit ethical framing at rollout affect long term use patterns compared to a tool first rollout?
Learning and pedagogy
- Does AI assisted feedback improve the quality of student analysis and writing, and does the effect persist without the tool?
- How should assessment change when students have professional grade AI available?
- What does "AI fluency" mean for a business graduate, and how do we measure it?
Operations and organization
- Which processes yield the largest sustained time savings, and what happens to the recovered time?
- How do the three adoption paths (school wide, open cohort, power user core) compare on use, persistence, and documented outcomes?
- How does integration into an existing environment (Microsoft) affect trust and adoption relative to standalone tools?
- What organizational structures (working groups, champions, playbooks) make adoption durable after the pilot ends?
Knowledge management: personal, professional, and organizational
- How does AI change personal knowledge management, how individuals capture, organize, and retrieve what they know, for faculty, staff, and students?
- What professional knowledge management practices emerge when a role's tacit know how (a program director's, an advisor's, a project manager's) is written into playbooks that AI can read and reason over?
- Does AI assisted documentation actually reduce knowledge loss when people leave roles or graduate?
- How should a school structure knowledge so it is usable by both people and AI, what belongs in prose (judgment) versus structured data (facts)?
- What strategies help students build durable personal knowledge systems they carry into careers, rather than disposable chat histories?
- How does shared context (project workspaces, living documents) change collaboration and handoffs across teams and semesters?
The school as a case
- Can a business school's own AI implementation be a replicable model for other professional schools?
- What does it take to turn internal implementation into published cases, teaching materials, and research, and what is lost or gained in that translation?
Why this is publishable
Business schools are simultaneously the subject and the study. An AI implementation inside a business school, run by the MIS discipline, with student consultants documenting it, is a rare and clean case. It is relevant to IS, management education, operations, and higher ed administration journals.
Grant potential
Categories worth pursuing, to be scoped once the pilot is approved:
- Higher education innovation and teaching with technology grants
- Workforce development and regional economic development funding
- Vendor and industry partnership programs focused on AI in education
- Internal university innovation or curriculum development funds
- Jesuit network and mission aligned funding for ethical technology
Doctoral and early career development
The initiative creates authentic research sites for doctoral students and junior faculty, tying into existing networks in the IS academic community.
Section 10Three Implementation Paths
Colleagues raised a real concern: if licenses go to everyone, a meaningful share will go unused. The paths below account for that. They are not mutually exclusive, the recommended approach is to sequence them.
Path 1: School wide
What: Every faculty and staff member receives a license, plus student allocations.
Pros
- Simplest to communicate and administer
- No one feels excluded
- Signals institutional commitment
Cons
- Adoption will be uneven; unused licenses are visible waste
- No structure to capture learning
- Hardest to measure impact
Path 2: Open pilot cohort
What: Licenses go to anyone who opts in, with light expectations: try it, share what worked, complete a short survey.
Pros
- Self selection ensures motivated users
- Broad enough to surface diverse use cases
- Natural data on who adopts and why
Cons
- Still variable engagement
- Requires coordination and follow up
- May miss the deepest, most transferable wins
Path 3: Power user core Recommended start
What: A small, targeted group (faculty, staff, and student consultants who are already experimenting) explores intensively, documents use cases, builds workflows, and shares with the school in phases.
Pros
- Highest depth per license
- Produces documentation and case studies
- Builds internal champions and peer evidence
- Easiest to evaluate rigorously
Cons
- Slower to reach the full school
- Risk of being seen as exclusive
- Depends on the core group's capacity
Sequencing options
- 3 to 2 to 1 (recommended). Start with the core, expand to an open cohort with the core as coaches, then go school wide with documented playbooks in hand.
- 2 to 1. Skip the core; open cohort first, school wide second.
- Straight to 1. Fastest, least structured.
Recommendation
Begin with Path 3, with a defined expansion trigger into Path 2 (for example, after one semester and a documented set of use cases). This maximizes learning per license, produces the case studies that justify broader rollout, and gives the cabinet evidence rather than hope.
Final recommendation to be confirmed by John, Vivek, and Andrew.
Section 11Governance & Responsible Use
Short and practical. Details to be developed with IT and the appropriate university offices.
- Data. Work stays inside the university's Microsoft managed environment. No student records or confidential client data in unmanaged tools. Follow existing FERPA and data classification policy.
- Disclosure. Faculty, staff, and students disclose AI assistance where it materially shapes a deliverable, consistent with academic integrity policy.
- Verification. AI output is a draft, not a decision. Humans verify facts, numbers, and citations before anything leaves the school.
- Client work. Consulting group use follows existing client agreements and university oversight; nothing changes about NDA and review processes.
- Equity. As paths expand, ensure access is not limited to those already technically comfortable. Training and coaching are part of the rollout.
- Review. A small working group (Section 13) reviews use cases, incidents, and policy questions each semester.
Section 12What Success Looks Like
Measured at the end of the first phase and again at expansion.
Operational
- Hours returned to staff and faculty per documented workflow
- Number of workflows still in use 90 days after handoff
- Error or rework reduction where measurable
Faculty
- Active use rate among license holders
- Course materials, feedback workflows, or research products created with the tool
- Faculty self reported confidence in teaching AI use
Student
- Number of consulting engagements (external and internal) using the tool
- Portfolio grade case studies produced
- Skill progression evidence: students can explain, defend, and extend what they built
Institutional
- Documented use cases and playbooks shared across the school
- Research outputs and grant submissions initiated
- Peer driven demand for expansion, the best signal that a broader rollout will stick
Section 13Team & Roles
| Person | Role | Contribution |
|---|---|---|
| John | MIS Discipline Lead; Faculty Director, GSCG; Faculty Advisor, ZBT | MIS expertise, student consulting infrastructure, existing AI implementation, active AI consultancy |
| Vivek Patel | Professor of Marketing; leads MBA and MIS/BA programs | Program level integration, active technology consultancy, graduate program use cases |
| Andrew Brassica | Professor of Accounting & Tax; Director, Master's in Accounting & Taxation | Accounting and tax program integration, compliance heavy process use cases, professional standards lens |
| CTO / IT | Institutional sponsor | Licensing, cost breakdown, Microsoft integration, security |
| Student consulting teams | GSCG, ZBT, New Venture Lab | Internal implementation engagements, documentation, research participation |
Section 14Timeline
To be completed after internal alignment.
- Internal alignment (John, Vivek, Andrew)
- Submit to SBA administration
- CTO presents to cabinet
- Decision
- Phase 1 (Path 3) launch
- Phase 1 review / expansion trigger
Section 15Open Questions
For Vivek and Andrew to weigh in on:
- Final license count and split across faculty, staff, and students
- Which administrative processes go first
- Who is in the Path 3 core group
- What the expansion trigger into Path 2 should be
- Which research questions to prioritize and which venues to target
- Whether to name a student "internal implementation" engagement for Fall or Spring
- Anything the cabinet is likely to push back on that we have not addressed
Appendix AUse Case Catalog
A starting inventory. Each entry is a candidate for a documented engagement.
Administration & operations
- Recurring report assembly from existing spreadsheets and documents
- Meeting notes to action items and follow up drafts
- Inquiry response drafting for admissions and program offices
- Accreditation evidence compilation and gap analysis
- Event run of show and logistics checklists
- Policy and handbook drafting and revision
- Converting tribal knowledge into written, searchable playbooks
Faculty: teaching
- Course redesign aligned to outcomes and assessments
- Custom course text development and revision
- Rubric guided feedback drafts in a mentor voice, with instructor final judgment
- Case development from anonymized client projects
- Discussion question and exercise generation tied to current events
Faculty: research
- Literature synthesis and gap mapping
- Protocol, grant, and methods drafting
- Reproducible analysis code with Claude Code
- Pre submission self review
Students
- Client deliverable acceleration with verification standards
- Data analysis and visualization for consulting projects
- Internal "school as client" implementation engagements
- Case study writing and portfolio documentation
- Research assistance on the initiative itself
Cross cutting
- Shared project workspaces for program offices, research teams, and consulting teams
- Small internal tools and automations built with Claude Code
- Training materials and onboarding for new users
Appendix BThe One Page Proposal
Draft of the short proposal that will point to this site.
Proposal: Claude Licenses for the School of Business Administration
Why Our field is the study of business operations, and AI is now the dominant force reshaping them. We face a widening gap between what is changing in industry and what we can absorb given time and operational load. The CTO has proposed Claude licenses for SBA; this is how we would use them.
What Claude, integrated into our Microsoft environment, applied across three groups: administration (automate routine processes, enhance analysis), faculty (research, teaching, service), and students (real consulting work, plus treating the school itself as a client and documenting the results as case studies). The initiative doubles as a multi lens research program with grant potential.
Why here MIS is the discipline that bridges technology and operations. We have the expertise, an existing student consulting infrastructure with real clients, a purpose built operating platform, faculty with active AI consultancies, and prior experience implementing AI responsibly in student work.
How Three paths, school wide, open pilot cohort, or a power user core, sequenced as 3 to 2 to 1. We recommend starting with a core group of roughly 30 to 50 faculty, staff, and student power users (up to 60 to 70 if warranted), documenting use cases, and expanding on evidence.
Ask Approve the licenses (cost breakdown provided by IT), endorse the phased approach, and support a semester end review to trigger expansion.
Mission Returning time to people so they can focus on students and colleagues is cura personalis in practice.
Full resource: this site.