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Course Description

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Title

Measuring GenAI ROI: Token Efficiency and Executive Financial Reporting

Course Overview

Generative AI is now embedded in software development and data science workflows — from code generation and test authoring to data labeling, document synthesis, and customer-facing chat interfaces. Yet most IT organizations cannot answer a basic question their finance leadership will ask: is this generative AI investment actually paying off, and where is the money going? This course teaches IT professionals — software engineers, data scientists, ML/AI engineers, DevOps/MLOps practitioners, and technical managers — how to measure the return on investment (ROI) of generative AI systems, with a particular emphasis on understanding token economics: how tokens are counted, priced, and consumed, and how token-level decisions translate directly into operating cost.

The course makes no assumptions about the student's prior exposure to generative AI, large language models, corporate finance, or executive communication. It assumes only a general data science background (basic statistics, familiarity with data workflows, and comfort reading quantitative reports). Building from first principles, the course moves from "what is a token and why does it cost money" through ROI and TCO fundamentals, into concrete cost-optimization techniques across every phase of the software development lifecycle (SDLC) and the data science lifecycle, and finally into a step-by-step method for producing a high-quality, extensive, and detailed ROI report written for executives with a financial (not technical) background. The capstone project requires students to produce a complete, executive-ready GenAI ROI report for a realistic organizational scenario.

Target Audience

Professional development / adult continuing education — practicing IT professionals, including software engineers, data scientists, ML/AI engineers, DevOps/MLOps practitioners, technical program managers, and IT leads who need to justify, govern, or report on generative AI spending within their organization. This is not an introductory data science course: students are assumed to already work in a technical role.

Prerequisites

General data science knowledge only, specifically:

  • Basic statistics (mean, distributions, correlation)
  • Familiarity with common data science and software workflows (data pipelines, model training/evaluation cycles, software release cycles)
  • Comfort reading tables, charts, and quantitative reports

No prior knowledge of generative AI, large language models, tokenization, corporate finance, accounting, or executive communication is assumed — these are taught from first principles within the course.

Main Topics Covered

  1. Foundations of generative AI systems for IT professionals (LLMs, inference vs. training, what a "token" is)
  2. Token economics — tokenization, context windows, and provider pricing models
  3. Cost drivers in generative AI systems (compute, API pricing, latency and throughput tradeoffs, infrastructure)
  4. Token-efficiency techniques (prompt compression, caching, batching, model right-sizing, quantization, distillation, retrieval-augmented generation tradeoffs)
  5. Financial fundamentals for technologists (ROI, TCO, NPV, payback period, cost-benefit analysis)
  6. Metrics and KPIs for measuring GenAI ROI (productivity gains, quality metrics, cost avoidance, time-to-value, revenue impact)
  7. Data collection and instrumentation for ROI analysis (usage telemetry, cost attribution/tagging, observability, baselining)
  8. Cost optimization across the software development lifecycle (requirements, design, coding/code-generation, testing, deployment, maintenance)
  9. Cost optimization across the data science lifecycle (data collection and labeling, feature engineering, model training, evaluation, deployment, monitoring)
  10. Vendor and model selection economics (build vs. buy, open-source vs. proprietary models, multi-model strategy, licensing)
  11. Risk, governance, and hidden costs (security, compliance, technical debt, model drift, retraining costs, vendor lock-in)
  12. Communicating technical cost data to financial stakeholders (financial literacy for a technical audience, translating jargon, audience framing)
  13. Designing an executive ROI report (structure, executive summary, methodology, findings, recommendations, appendices)
  14. Data visualization and storytelling for financial audiences (charts, dashboards, sensitivity analysis, scenario modeling)
  15. Producing the capstone GenAI ROI report (step-by-step drafting, peer review/critique, executive presentation)

Topics Not Covered

  • The mathematical foundations of transformer architectures or LLM pretraining (this course treats models as cost/performance systems, not as subjects of ML theory)
  • Hands-on implementation of model training or fine-tuning code
  • Vendor contract negotiation, procurement law, or licensing law
  • General corporate finance or accounting theory beyond what is needed to read and produce an ROI report (e.g., no GAAP/IFRS accounting standards)
  • Cloud infrastructure provisioning or DevOps tooling tutorials
  • Regulatory or compliance deep-dives (e.g., GDPR, sector-specific regulation) beyond a general awareness of governance risk
  • General project management methodology (Agile/Scrum ceremonies, etc.)

Learning Outcomes

After completing this course, students will be able to:

Remember

Retrieving, recognizing, and recalling relevant knowledge from long-term memory.

  • Recall standard financial terms used in ROI analysis (ROI, TCO, NPV, payback period, cost avoidance)
  • List the primary cost drivers of generative AI systems (compute, tokens, API calls, storage, latency)
  • Identify the phases of the software development lifecycle (SDLC) and the data science lifecycle
  • Recognize common token-pricing models used by major generative AI providers (input/output token pricing, context-window pricing, batch and caching discounts)

Understand

Constructing meaning from instructional messages, including oral, written, and graphic communication.

  • Explain how tokenization affects the cost and latency of a generative AI system
  • Describe the relationship between prompt design, context length, and token consumption
  • Summarize how generative AI can reduce or increase costs at each phase of the SDLC and data science lifecycle
  • Explain why financial executives require a different framing of technical data than engineers do

Apply

Carrying out or using a procedure in a given situation.

  • Apply token-efficiency techniques (prompt compression, caching, batching, model right-sizing) to reduce the operating cost of a generative AI workflow
  • Use ROI, TCO, and payback-period formulas to quantify the financial impact of a generative AI initiative
  • Apply data-collection and instrumentation methods to capture usage and cost telemetry for a generative AI system
  • Translate a technical cost analysis into financial terminology appropriate for an executive audience

Analyze

Breaking material into constituent parts and determining how the parts relate to one another and to an overall structure or purpose.

  • Analyze usage and billing data to identify the largest cost drivers in a generative AI deployment
  • Compare the cost-effectiveness of alternative token-efficiency strategies (e.g., caching vs. model right-sizing vs. retrieval-augmented generation)
  • Break down cost and risk factors across each phase of the software development and data science lifecycle
  • Analyze the tradeoffs between build-vs-buy and open-source-vs-proprietary model choices from a cost perspective

Evaluate

Making judgments based on criteria and standards through checking and critiquing.

  • Evaluate the quality and completeness of a generative AI ROI analysis against industry best practices
  • Critique an executive report for clarity, financial accuracy, and appropriateness for a non-technical, finance-oriented audience
  • Judge whether a proposed token-efficiency optimization is worth its implementation cost and risk
  • Assess the credibility of vendor claims about generative AI cost savings

Create

Putting elements together to form a coherent or functional whole; reorganizing elements into a new pattern or structure.

  • Design and write a comprehensive, executive-ready report that measures and communicates the ROI of a generative AI initiative
  • Develop a step-by-step cost-optimization plan covering all phases of the software development and data science lifecycle
  • Construct data visualizations and financial summaries that communicate token-efficiency and ROI findings to a financial-background executive audience
  • Capstone project: produce a complete GenAI ROI report package (executive summary, methodology, findings, recommendations, appendices) for a realistic organizational scenario, suitable for presentation to a finance-background executive audience