A domain track
CFOs, controllers and FP&A analysts who already use AI to draft the odd email, but still close the books and build the board deck by hand every cycle. This track puts Claude to work inside your actual Excel models and GL, so the close moves faster and the deck builds itself from the actuals.
25 topics
The monthly close is where you find out whether a finance team really works with Claude. In the workshop, people load a real close workbook, with its reconciliation tabs, pivot tables and years of accumulated fixes, and learn to have Claude read it, explain the variances and flag what looks odd before anyone opens the report.
The risk finance people lose sleep over is a number in the board pack that nobody can trace back to its source. So every exercise is built around the audit trail. Each answer Claude gives comes with the sheet, the cell and the ledger movement it was taken from, and participants practice rejecting any figure that arrives without one. It is a small habit, and it is what separates an assistant that saves hours from one that generates auditor questions.
The work runs on the artifacts your team already knows: the cash-flow forecast, the revenue bridge between quarters, opex analysis by department and the budget vs actuals breakdown. There is also formula archaeology: the inherited workbook with seven linked tabs and a formula nobody remembers writing. Claude walks the chain and explains what each cell pulls and from where. For each deliverable the group builds a small Skill that the team keeps and returns to at the next close, so the know-how does not stay with whoever sat in the room.
A live opening session built to shift the room, not lecture it: real demos of what Claude can already do inside your workflows, run in front of the group. Participants leave with a working sense of where AI helps and where it doesn't, and enough curiosity to start using it that afternoon.
What tokens, context windows and hallucinations mean, explained without the math and without the hand-waving. Once a team understands why a model sometimes gets things wrong, it stops either trusting it blindly or dismissing it after one bad answer. Every other module in the program builds on this one.
Before the work starts, we walk the Claude screen together: a new conversation, uploading files, choosing a model, and where Projects, Artifacts and Skills live. Face to face it is a live demo; in the online course it is a recorded tour. Ten minutes that save weeks of fumbling.
Claude Skills turn a procedure your team already does well into something Claude can repeat on its own: the same checklist, the same tone, the same edge cases covered every time. You build it once with the person who actually knows the process, and from then on every teammate gets that expertise on demand. For teams where quality depends on one or two people, this is how that stops being a bottleneck.
Structure, context and role framing: the craft of asking in a way that gets a usable result on the first try instead of the fifth. Participants practice on real prompts from their own work, not toy examples, so what they take home already fits the job. Every other module assumes this fluency, which is why it comes early.
Claude Projects hold your documents and instructions in one persistent workspace, so every conversation starts already knowing your company, your terminology and your rules. Teams that set this up once save the ten minutes of re-explaining context at the start of every session, every day. For anyone running the same kind of task repeatedly, this alone changes the math.
Artifacts are documents, dashboards and small interactive tools Claude builds live inside the conversation, editable on the spot instead of copied out and reformatted. The session covers static outputs and interactive artifacts alike, with an eye on what a non-technical teammate can actually ship without engineering help. It's the module that convinces people Claude is a builder, not just a chatbot.
The shift from asking Claude a question to handing it a multi-step task it completes on its own, checking in only where it matters. Participants learn what actually counts as an agent, when handing over work beats doing it turn by turn, and how supervision needs to change once Claude is running unattended. Get this wrong and you either babysit an agent that didn't need it or trust one that did.
In the Claude chat you can hand over a whole job instead of asking a question. Claude plans the steps, searches the web, reads your files, runs code and hands back a finished document, spreadsheet or deck. On a big task it can split the work between several sub-agents running at once. Participants learn which work is worth handing over and how to brief it the way they'd brief a colleague, then practice staying in control while Claude works. They finish by turning a weekly chore into a scheduled task that runs on its own.
A full pass across the Office suite: drafting and redlining in Word, tables and analysis in Excel, decks in PowerPoint, and inbox triage in Outlook, taught as one connected workflow rather than four separate tricks. Every exercise uses documents close to what participants actually produce at work. It's the broadest module in the program, and the one most teams come back to most after training.
Claude inside Excel: building and checking models, tracing a formula back through six sheets to find where the logic breaks, and producing outputs you can stand behind. This is for teams whose spreadsheets are the actual product, not a side task. Formula archaeology alone tends to pay for the session.
Tasks Claude runs on its own, on a schedule: a morning brief from your calendar and inbox, a weekly report, a competitor watch. Participants learn to tell a routine from its schedule, connect only the connectors it needs, and test every routine before it runs alone. They leave with one routine that works from the next morning.
Connecting Claude to the systems your team actually runs on: drives, CRMs, databases, through connectors and MCP, with the permission boundaries covered before the connection ever gets made. Participants leave knowing exactly what to expose and what to keep out of reach. This is the module that turns Claude from a smart typewriter into something acting on real, current data.
Real engineering workflows built agent-first: how a task moves from a prompt in the terminal to reviewed, shipped code. The module is built for developers, but the data-analysis half works just as well for a non-developer who lives in spreadsheets and needs that same rigor applied to a script. Teams leave with a working setup running in their own repo.
CLAUDE.md is a plain text file Claude Code reads at the start of every session, so whatever you'd explain to a new hire on day one, you write there once. Participants learn what goes in it and what stays out, when it's read, and where it lives: the organization, you, the project and a subfolder, all loading together. They leave with a draft from /init and one rule for growing it: what you corrected twice goes in the file.
How to build an agent without writing code: it gets a job description, a badge that limits what it can touch, and a bottom line it hands back. Participants see why the work goes to a separate room, take an agent file apart, and fill in the one-page charter that turns into the agent's file. They learn to hire for a role, not a task, and to tell when a job needs an agent rather than a skill. They leave knowing the hard part is the charter, and that the charter is their own expertise, written down.
A plugin carries the agents, skills and hooks a team built, so another team installs the same department instead of rebuilding it. Participants see what goes in the box and what stays home: CLAUDE.md and the permission rules belong to each team. Then they ask Claude to build one, try it in a fresh session, and share it through a marketplace.
How context windows, memory and cost interact, and what to do about it before a long working session starts drifting or the bill surprises someone. Participants learn to spot when Claude is still hauling a task that should have been closed an hour ago, and how to reset without losing what mattered. A short module with an outsized effect on both output quality and the invoice.
The rules every employee needs before their first real session: what data is safe to share, what isn't, and what org-level guardrails should exist so individual judgment isn't the only safeguard. This module is usually the one legal and IT ask for by name. It replaces vague anxiety about compliance with a clear, specific policy people can actually follow.
Where people belong in an AI-assisted workflow: which steps need a review gate, which need an approval, and how to calibrate trust so oversight isn't either everywhere or nowhere. This pilot module gives teams a concrete framework instead of a gut feeling about what to check. Get the calibration right and quality goes up without slowing anything down.
Permissions, prompt injection and data boundaries: what makes an agent that can act also one that can go wrong, and how to run it anyway without carrying the risk. This pilot module is built for organizations moving agents past read-only demos into things that write, send and change data. It's the module security teams should sit in on.
Treating tokens as a real budget: measuring current spend, predicting where it grows, and cutting it without cutting the quality of what Claude produces. This pilot module is built for teams past the experimentation phase, where AI cost is now a line item someone has to defend. Finance and engineering leave with the same numbers, which usually hasn't happened before.
Coming soon: the foundations under the products everyone's using, training, fine-tuning and embeddings, taught for the decisions a non-researcher actually has to make. This is not a path to becoming a data scientist. It's enough grounding to evaluate a vendor's claims or a build-versus-buy question without taking their word for it.
Coming soon: preparing and governing the data an AI system actually works from, structure, access and quality, before automation gets layered on top of it. This syllabus item is for teams that already tried an AI project and hit a data problem they weren't expecting. Skipping this step is the single most common reason automation projects stall.
Coming soon: writing the AI policy an organization actually needs, usage rules, risk tiers and rollout governance, instead of a generic template nobody follows. This syllabus item is for leadership and compliance stakeholders shaping how AI gets adopted company-wide. Built to produce a document teams can point to, not a discussion that goes nowhere.
Four things you can use in the next close, each one on your own files.
A working template where Claude goes through the reconciliations, spots gaps between the books and the bank, and drafts the open-items list. You approve every line before it enters the close.
A budget vs actuals exercise where every variance explanation carries its source. When the finance lead asks where a number came from, the answer takes a minute instead of a hunt through three files.
A draft pack built from your existing data, with an appendix listing each figure, its source and the assumptions behind it, ready before the meeting rather than during it.
A forecast model built in Excel with Claude, including formula checks, a sensitivity pass and notes inside the file, so whoever inherits the model can follow it without calling you.
Your next close
At the tailoring stage, the practice files give way to your own close workbooks, reports and models, with your account names and your assumptions. The team rehearses next month, not a textbook case.