What is this course about?
Per-seat AI licensing scales with headcount regardless of use, so an organization above a few thousand employees pays many times over for a workload it could meter directly. This course builds the three-scenario model on the learner's own numbers, including the utilization problem, the hidden line items, and an honest account of when self-hosting loses.
Who is this course for?
- CFOs, finance business partners, and FP&A staff
- CIOs and IT finance leads
- Procurement and vendor management
- Heads of AI and transformation programs
What do I need before starting?
- Bring headcount figures and any current AI vendor quotes
- Comfort with a spreadsheet
What will I be able to do afterwards?
- Classify any AI quote by pricing shape and name its hidden assumption
- Model per-seat cost at 1,000, 5,000, and 50,000 employees
- Estimate token cost bottom-up from workload rather than headcount
- Compute a defensible self-hosting break-even including operations
- Present a three-year TCO with sensitivity analysis to a finance committee
What does each module cover?
What are the three pricing shapes?
35 minPer-seat, usage-based, and owned — and what each assumes about your organization.
Objectives
- Classify a quote by underlying pricing shape
- Name the assumption each shape embeds
- Recognize hybrid pricing designed to prevent comparison
Topics
Activity. Classify three real quotes and state each one's hidden assumption.
What does per-seat cost at 50,000 employees?
45 minThe headcount multiplication that makes per-seat structurally wrong above a threshold.
Objectives
- Build the multiplied bill at three organization sizes
- Identify the headcount at which the shape becomes indefensible
- Model tiered and volume-discounted pricing accurately
Topics
Activity. Build the multiplied bill at 1,000, 5,000, and 50,000 seats for a real quote.
How many seats never log in?
45 minThe utilization problem, and the effective cost per active user it produces.
Objectives
- Measure real utilization from existing tool data
- Compute effective cost per active user
- Project utilization for a new deployment honestly
Topics
Activity. Compute effective cost per active user from a real deployment's login data.
How do you model token cost from workload?
50 minBottom-up cost estimation from what the work actually consumes.
Objectives
- Characterize representative workloads in tokens
- Account for retrieval and context overhead
- Model cost against volume growth
Topics
Activity. Measure token consumption for three real workflows and extrapolate annually.
When does self-hosting actually break even?
50 minGPU capital, utilization, and operations staffing, modeled without optimism.
Objectives
- Model hardware capital and amortization
- Include realistic operations headcount
- Identify the conditions under which self-hosting loses
Topics
Activity. Compute break-even volume and state the assumptions it depends on.
Which costs does nobody budget?
45 minIntegration, evaluation, monitoring, retraining, and exit.
Objectives
- Estimate integration and change management
- Budget ongoing evaluation and monitoring
- Quantify exit cost and its negotiating consequence
Topics
Activity. Add all five forgotten line items and observe how the comparison shifts.
How do you present this to a finance committee?
45 minA business case built on risk and sensitivity rather than a single confident number.
Objectives
- Frame the case around risk and optionality
- Present sensitivity rather than point estimates
- Pre-answer the standard finance objections
Topics
Activity. Present your model to a cohort playing a hostile finance committee.
Building your three-scenario model
50 minThe workshop module: a complete model on the learner's own figures.
Objectives
- Complete all three scenarios with real numbers
- Run sensitivity on the decisive variables
- Produce a one-page decision summary
Topics
Activity. Finish the model and write the one-page summary.
What is the capstone project?
Three-year enterprise AI TCO model
Build a complete three-scenario cost model on your organization's real headcount, utilization, and workload data, including all hidden line items and a sensitivity analysis, with a one-page summary for an investment committee.
Deliverable: A working model with documented assumptions and a one-page decision summary.
How are learners assessed?
- Model reviewed for arithmetic and explicitly stated assumptions
- Sensitivity analysis must identify the genuinely decisive variables
- Committee presentation assessed on objection handling
What ships with the course?
Facilitator guide
Session-by-session running order, discussion prompts, and the questions that reliably derail a room.
Learner workbook
Exercises, checklists, and the templates each module's activity produces.
Hands-on lab environment
A sandboxed ibl.ai deployment so exercises run against real agents, not screenshots.
Assessment bank
Scenario questions and rubric criteria mapped to each stated learning outcome.
Source bibliography
Every primary regulation and standard cited on this page, linked and dated.
Which AI agents does this course use?
The hands-on modules run against agents already deployable on the ibl.ai platform for enterprise.
Where does the course material come from?
Every module is grounded in primary sources — the regulation, standard, or research itself, not a summary of it. Each was resolved at authoring time.
- AI Index Report
Stanford HAI
Inference cost and compute trend data underpinning token modeling.
- Ideas Made to Matter
MIT Sloan
Research on enterprise AI adoption economics and pilot outcomes.
- Transformers documentation
Hugging Face
Technical reference for self-hosting requirements in Module 5.
- Embeddings guide
OpenAI
Token accounting mechanics for the bottom-up model.
Delivery notes
Binding guidance for anyone preparing and delivering this course.
- Ship the spreadsheet with working formulas as the primary artifact. It is the most-reused deliverable in the enterprise catalog.
- Module 5 must state honestly when self-hosting loses — low volume, no existing ops capability, spiky workloads. A model that always favors self-hosting will be discounted entirely by finance.
- Date every price used. AI pricing moves quarterly and an undated model looks stale within months.
- The hostile-committee role-play needs someone with real finance experience. A sympathetic facilitator produces a useless rehearsal.
- Keep the per-seat framing consistent with the rest of the ibl.ai corpus, and cross-link the existing cost calculators rather than rebuilding them.
Why run AI training on a platform you own?
You own the course, not a licence to it
Course content, learner data, and the platform run inside your perimeter — you own all the code and the data.
Model-agnostic delivery
Run the course's AI components on any LLM — Claude, GPT, Llama, Gemini, Command — and switch anytime.
No per-seat training licences
Usage-based or self-hosted, so cost tracks actual use rather than headcount.
Deploy anywhere
Cloud, private VPC, on-premise, or fully air-gapped — including for cohorts that cannot use public AI tools.
Frequently asked questions
What does the The Enterprise AI Cost Model: Per-Seat vs Token vs Owned course cover?
Per-seat AI licensing scales with headcount regardless of use, so an organization above a few thousand employees pays many times over for a workload it could meter directly. This course builds the three-scenario model on the learner's own numbers, including the utilization problem, the hidden line items, and an honest account of when self-hosting loses. It runs 5.5 hours across 8 modules across 8 modules, at foundational level, and closes with a capstone: Three-year enterprise AI TCO model.
Who should take The Enterprise AI Cost Model: Per-Seat vs Token vs Owned?
It is written for CFOs, finance business partners, and FP&A staff, CIOs and IT finance leads, Procurement and vendor management, Heads of AI and transformation programs. Prerequisites: Bring headcount figures and any current AI vendor quotes; Comfort with a spreadsheet.
Can we run this course on our own infrastructure?
Yes. ibl.ai is model-agnostic and deploy-anywhere — cloud, private VPC, on-premise, or fully air-gapped — and you own all the code and the data. Cohort data, submissions, and any material learners upload stay inside your perimeter, which matters for enterprise teams that cannot send work to a public AI tool.
How do we get access to The Enterprise AI Cost Model: Per-Seat vs Token vs Owned?
Request access and we will set it up for your cohort — hosted by ibl.ai, or running against your own deployment. Tell us the group size and timing you need, and whether it should run inside your own perimeter.
How much does AI training for enterprise cost on ibl.ai?
There is no per-seat pricing — you pay for usage or self-host and pay only for the infrastructure, so a 5,000-person rollout does not cost 5,000 licences. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.