Proposed Title (catalog/branding): BADM 350: Technology & AI Strategy for Competitive Advantage
Credits: 3
Level: 300 (Undergraduate)
Proposed Positioning: Gateway course to the Information Systems major + core course option for the business undergraduate curriculum (recommended).
Prerequisites: None. (BADM 210/211 helpful but not required.)
Term Length: 15 weeks + final presentations (16 instructional weeks).
Gies’ AI Integration Strategy (Dec 2025) sets a clear objective: every business graduate should have practical AI literacy and applied competency, with Gies positioned to lead in Agentic Systems & Workflows and to strengthen Human-Centric AI (governance, ethics, policy, and workforce impacts).
BADM 350 is designed as a gateway course: for many students it will be their first structured introduction to Information Systems and applied AI in organizations. It builds basic literacy and practical competency that transfers across majors (inside and outside business) and prepares interested students for deeper IS coursework.
- From concepts → to decisions: Without assuming prior IS coursework, BADM 350 teaches students to translate technology and AI into clear business/organizational decisions (strategy, economics, operating models).
- Agentic systems in business (Track 3): The strategy explicitly calls out a campus demand for non-engineer-friendly agentic workflow education. BADM 350 can be the business-school anchor that teaches students how to design, evaluate, and govern AI-enabled workflows.
- Human-centric governance (Track 4): As regulation and organizational risk rise, core business education must include responsible AI, security, privacy, and governance—not as an elective afterthought, but as a baseline competency.
- Measurable outcomes: The course is designed around assessment artifacts that can support Assessment-of-Learning (AoL): a board memo, an AI-assisted web app/pitch project, and a data-storytelling deliverable.
Core promise: After BADM 350, a student can (1) evaluate technology/AI investments, (2) design an AI-enabled operating model or workflow with clear ROI and controls, and (3) communicate recommendations credibly to executives.
Organizations increasingly compete on technology and AI—not merely as tools, but as strategic capabilities that reshape products, processes, and business models. BADM 350 examines how firms create and capture value through data systems, platforms, cloud, cybersecurity, and (especially) AI-enabled workflows and agentic systems.
Students will use frameworks from strategy and information systems to analyze cases, build data-backed narratives, and develop technology/AI transformation recommendations. The course emphasizes responsible adoption: governance, risk, privacy, security, and workforce implications.
By the end of the course, students will be able to:
- Explain how technology and AI can create sustainable competitive advantage (e.g., Porter, RBV, complements, network effects).
- Describe the major categories of enterprise technology (data, cloud, platforms, security, AI) and how they shape organizational capability.
- Analyze a firm’s technology/AI strategy and diagnose where value is created, captured, and at risk.
- Evaluate tradeoffs in AI adoption (speed vs. control; automation vs. augmentation; innovation vs. governance).
- Use business intelligence and visualization to support an executive recommendation.
- Design a simple AI-enabled workflow or prototype concept (including guardrails, data governance, and measurement).
- Communicate a board-level recommendation that integrates strategy, value logic (including ROI where feasible), risk, and an implementation plan.
Campus framework alignment:
- Track 1 (AI Basics/Fundamentals): Outcomes 1–5 (core literacy/competency for broad audiences)
- Track 3 (Agentic Systems & Workflows): Outcomes 3, 6, 7 (practical workflow design and evaluation)
- Track 4 (Human-Centric AI): Outcomes 4, 6, 7 (governance, responsibility)
- Data analysis & visualization tools (instructor-approved) for data storytelling (e.g., BI platforms, spreadsheets, or Python notebooks)
- Canvas for submissions
- GenAI tool access (institutionally approved) for structured activities
Note on AI tools: Students may be required to use AI tools in specified assignments. All AI use must be disclosed per the course AI policy.
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Exams (2 × 15% = 30%)
- Strategic frameworks, technology economics, platform dynamics, governance concepts, and applied case reasoning.
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Case Memos (4 × 5% = 20%)
- Short written analyses; at least 2 must include explicit AI strategy and governance considerations.
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Data Storytelling Assignment (15%)
- Visualization/dashboard (using an instructor-approved tool) + 1–2 page executive narrative connecting data to a strategic recommendation.
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Signature Project: Venture Pitch + AI-Assisted Web App Prototype (25%)
- Team deliverable designed for students with no prior IS coursework. Students go from idea → requirements → prototype → pitch, using AI tools responsibly.
- Required components:
- Idea + problem statement (who, what, why now)
- PRD-lite (key features + 6–10 user stories; scope to an MVP)
- Prototype: a working web app (or high-fidelity interactive prototype) published via a shareable link
- expectation: multiple pages/screens demonstrating core user flows; basic data capture is encouraged
- Execution narrative: what AI tools were used, how outputs were validated, and what was learned
- Risk & governance: privacy/data handling, reliability, and guardrails appropriate to the app
- Pitch deck (public link): concise story of value proposition, users, differentiation, and rollout plan
- Optional extra credit (instructor-defined): add a small AI feature (e.g., chatbot/assistant) and provide a link or short demo.
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Executive Board Memo + Presentation (10%)
- Board memo (max 2 pages) + 6–8 minute presentation.
Participation (optional add-on / instructor discretion up to 10%)
- If included, participation replaces a portion of exam weight or case memo weight.
This course assumes AI is part of modern business work. Students must learn to use it responsibly.
- Allowed/encouraged AI use may be specified for particular assignments (e.g., brainstorming, outlining, drafting).
- Disclosure required: Any use of AI must be disclosed in an appendix describing: tool used, prompts (or summary), what was accepted/edited, and how results were validated.
- Verification expectation: Students remain responsible for correctness, citations, and reasoning. “The AI said so” is not acceptable.
- Prohibited: Submitting AI-generated work as original without disclosure; fabricating citations; sharing confidential data into non-approved tools.
- Week 1: Course intro; what is an information system; how AI fits; what counts as evidence
- Week 2: Competitive advantage basics for non-specialists (Porter/RBV/network effects—conceptual, applied)
- Week 3: Disruption & digital transformation case (e.g., Netflix or a non-tech industry equivalent)
- Week 4: Digital economics (marginal cost, scale, switching, bundling)
- Week 5: Platforms, marketplaces, and network effects; governance of platforms
- Week 6: Digital markets, pricing, experimentation, and measurement
- Week 7: Data as strategic asset; data quality; measurement and incentives
- Week 8: BI + visualization for executives (tools lab)
- Week 9: Analytics-to-action: decision-making under uncertainty; KPI design
- Week 10: Enterprise systems + cloud as operating model choices
- Week 11: Midterm + integration workshop (strategy × data × operating model)
- Week 12: Cybersecurity, privacy, ethics; AI risk categories; governance design
- Week 13: AI fundamentals for strategy: where AI works/doesn’t; evaluation; build vs. buy
- Week 14: Agentic workflows: process mapping; human-in-the-loop design; controls
- Week 15: AI operating model: adoption, change management, workforce implications
- Week 16: Final board memos + project presentations
To keep this a business core course (not “tech giants only”), cases should span:
- Digital disruption: Netflix (or equivalent)
- Platform economics: Airbnb/Uber (or equivalent)
- Enterprise transformation: a traditional firm modernizing data/cloud (e.g., retail, healthcare, manufacturing)
- AI-native strategy: OpenAI (business model) or comparable
- Responsible AI / governance: regulated domain case (banking/health)
Instructors may substitute cases while preserving the portfolio balance.
The course uses tools that can improve accessibility (captioning, structured templates). Students with accommodations should contact the instructor early. Tool access will be designed to minimize cost barriers.
- This syllabus is designed to be shareable across instructors while leaving room for case substitutions.
- The signature artifacts (data story + web app/pitch project + board memo) are the key standardization points and provide AoL evidence.
This course directly supports the strategy’s aims:
- “100% AI-Ready” graduates: Makes AI literacy, responsible use, and applied decision-making a baseline capability.
- Track 1 (AI Basics/Fundamentals): Establishes durable AI/IS literacy suitable for students across majors.
- Track 3 leadership (Agentic Systems & Workflows): Introduces workflow thinking (human-in-the-loop, controls, implementation planning) without assuming advanced technical prerequisites.
- Human-Centric AI (Track 4): Governance, privacy/security, ethics, and workforce impacts are taught as decision constraints.
- Identify–Implement–Impact cycle: The project mirrors the strategy’s implementation logic: identify opportunity → implement design → measure impact.
BADM 350 sits in an IS sequence that also includes BADM 352 (databases) and BADM 353 (systems analysis/design). However, many students will take BADM 350 first (or concurrently), and BADM 350 also serves non-IS audiences via the business minor.
Accordingly, this syllabus is written to be self-contained and does not require students to have prior familiarity with database design, UML, or formal systems development artifacts.
A synthesis of five existing BADM 350 syllabi also shows strong consensus on:
- Strategic framing of IT
- Case-based pedagogy
- Data/decision support and visualization
- Enterprise systems and governance themes
This proposed syllabus does not attempt to be a superset of all topics; instead it defines BADM 350 as the gateway that builds transferable AI/IS literacy and decision-making capability aligned to the college AI strategy.