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Mastering AI for the CFO Function
A practitioner-first curriculum for CFOs and finance leaders — governing AI costs, procuring models, running agentic finance workflows, and navigating compliance in a world where every AI call is a metered line item.
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About this program
AI is now a finance problem. When an engineering team enables an AI feature in a customer support tool and the cloud bill jumps from $28,000 to $312,000 in a single month, CFOs discover they have no model for a cost that scales with cognitive work rather than provisioned infrastructure. This cohort builds that model from first principles. It covers the full stack of what a CFO needs to govern AI spend and extract strategic value: token economics and why they break legacy cost controls; application-layer instrumentation and chargeback frameworks that actually work; prompt and context cost engineering; model procurement — per-token vs. provisioned pricing, commitment risk, on-prem vs. cloud trade-offs; AI applications in FP&A — predictive cash flow, variance automation, scenario modelling, and the fast close; agentic AP/AR and treasury workflows with the trust ramp that lets finance teams increase AI autonomy safely; NIST AI RMF, SOX, GDPR, audit trails, model drift as a financial control, and shadow AI risk; building a 90-day AI roadmap from an honest audit of what you already have; SaaS vendor negotiation — Microsoft, SAP, Salesforce, ServiceNow pricing mechanics and the contract clauses that protect you; and the full CFO tech stack from FP&A to ERP challengers to treasury and AP tools. 10 modules, 42 lessons, fully self-paced — no live sessions required.
Who is this for?
CFOs, finance directors, FP&A heads, and finance business partners who need to govern AI costs, evaluate vendors, and build agentic finance workflows
What you'll actively build & learn
Understanding Fundamentals
Grasp the core mechanics of AI systems, from transformers to retrieval algorithms, moving beyond superficial APIs.
Production-Ready Architecture
Learn how to architect scalable, resilient generative AI applications that handle edge cases and high throughput.
Hands-on Engineering
Write custom PyTorch models, build multi-agent swarms using LangGraph, and deploy to Kubernetes.
Verifiable Execution
Complete rigorous capstone projects that serve as a proof-of-work portfolio for your next AI engineering role.
Time Commitment & Schedule
Self-Paced Modules
Flexible
No live sessions — work through all 10 modules whenever suits you, in any order you need.
Hands-On Application
~15 hrs total
Each module ends with a practical exercise: cost audits, chargeback model design, vendor scorecard, 90-day roadmap, and more.
Module-Based Syllabus
Each module is structured around three things: what you'll cover, what capability you'll walk away with, and the concrete deliverable that moves you toward a working system of your own. Work through them in any order, at any pace.
10 self-paced modules, 42 lessons — work through them in order or jump to what your organisation needs most
A working AI cost governance framework, a 90-day AI roadmap grounded in your current stack, and the negotiation and compliance knowledge to protect your organisation as AI spend scales
Practitioner-first lessons covering both technical mechanics and finance leadership decisions, with concrete frameworks in every module
The Token Economy
- What tokens are, why AI costs scale with cognitive work rather than infrastructure, and how to read your first AI invoice.
A working mental model for AI cost mechanics — the foundation for every governance and procurement decision in the course.
A cost anatomy breakdown of your organisation's current AI spend by token type and model tier.
AI Cost Visibility
- Why resource tagging fails for AI, how to instrument at the application layer, cost-per-outcome metrics, and chargeback models that actually allocate AI costs to business units.
The ability to see where AI money is going at the use-case level — not just the cloud bill line item.
A chargeback model design for at least one AI use case in your organisation.
Prompt & Context Cost Engineering
- How prompt design choices affect token costs, compression and caching tools that reduce spend, and governance gates that prevent runaway prompts.
The ability to evaluate prompt design decisions as cost decisions and build governance that keeps prompt costs predictable.
A prompt governance policy for one high-volume AI workflow in your organisation.
Model Procurement & Pricing
- Per-token vs.
- provisioned pricing, model routing strategy for cost vs.
- capability trade-offs, commitment risk management, and on-prem vs.
- cloud economics.
The ability to evaluate model procurement options and build a routing strategy that balances cost, capability, and commitment risk.
A model procurement scorecard for your organisation's top three AI use cases.
AI in the Finance Function
- Predictive cash flow forecasting, workflow debt from legacy FP&A processes, variance analysis automation, AI-assisted scenario modelling, and the fast close.
Practical fluency in which FP&A processes are highest-leverage AI targets and how to sequence their automation.
A workflow debt audit for one FP&A process, with an AI automation sequencing recommendation.
Agentic Finance Workflows
- Deterministic vs.
- probabilistic agents, the trust ramp for increasing AI autonomy safely, agentic AP/AR, and multi-bank cash positioning.
The ability to design an agentic finance workflow with appropriate trust controls, audit trails, and escalation paths.
A trust ramp design for one candidate agentic workflow — AP reconciliation, cash positioning, or expense exception handling.
Risk, Compliance & Governance
- NIST AI RMF applied to finance systems, model drift as a financial control risk, SOX and GDPR requirements for AI audit trails, and shadow AI risk.
A compliance framework for AI systems in the finance function that satisfies auditors and protects the organisation from regulatory exposure.
A shadow AI risk register and remediation plan for one finance process.
Building Your AI Roadmap
- The right sequencing for AI initiatives, how to audit what you already have before buying more, and building a credible 90-day plan.
A grounded, sequenced 90-day AI roadmap built from an honest audit of current AI usage, not vendor aspirations.
A 90-day AI roadmap for the finance function, built on the inventory from the module audit exercise.
Vendor Negotiation & Contracts
- Microsoft's 2026 pricing reset, SAP, Salesforce, and ServiceNow AI add-on pricing mechanics, the data access tax, utilisation as leverage, and contract clauses that protect you.
The ability to negotiate AI vendor contracts with a clear understanding of pricing mechanics, leverage points, and the clauses that matter.
A vendor negotiation brief for the next AI contract renewal or expansion in your organisation.
The CFO Tech Stack
- Evaluating native vs.
- augmented vs.
- AI-washed tools, the FP&A landscape, ERP challengers, treasury and AP tools, and migration risk.
A framework for evaluating CFO tech vendors that distinguishes genuine AI capability from feature-washed incumbents.
A tech stack evaluation scorecard for one planned or under-review finance tool procurement.
The syllabus builds toward a final proof of work.
The weekly syllabus is designed to stack toward a capstone that demonstrates what you can actually build. By the end of the cohort, you are not just finishing modules. You are presenting a concrete output that ties the learning arc together.
View Alumni CapstonesIndustry-Grade Certification
Earn a credential that actually matters. Every certificate is tied to your Capstone Project repo, valid for life, and optimized for your professional technical profile.
View Certification TiersYour instructor
Anubhav Srivastava
Anubhav has spent the past two decades building machine learning and AI systems across startups, large enterprises, and high-scale consumer platforms. He has worked on patented AI technologies, authored books, and founded multiple ventures, and is currently building a deeptech startup focused on physical AI. Known for combining technical depth with practical thinking, he enjoys breaking down complex ideas into clear, accessible insights and is driven by a curiosity for how technology can solve real-world problems.
From our students
Engineers at different levels share what they built and what changed.
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Alumni network
Alumni at
“The most technically rigorous program I've attended. No fluff — just pure deep-dives into transformer blocks and swarm logic. It's about understanding how LLMs actually work.”
Siddharth S.
Staff Engineer · Build Your Own LLM
“LangGraph and multi-agent orchestration was the missing link for our production pipeline. Essential for developers who need to move beyond single-prompt engineering.”
Elena R.
Senior AI Engineer · Agentic AI
“Direct access to instructors who are actually shipping AI products. The focus on evals-driven development is unique — we implemented their RAG evaluation approach across our entire startup.”
Arjun R.
Tech Lead · Claude Code
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