Albert Lee

FCPA · ex-PwC · Founder, Axiom FP&A · AI Finance Coach & Corporate Trainer

Run 3 checks on any AI finance report

HKICPA Fellow · ex-PwC · Founder, Axiom FP&A · AI Finance Coach

Uniform segment misses, mismatched allocation bases, unlabelled assumptions — seen live in a real $255M variance.

Diagnose a variance before you explain it

Completeness, consistency, assumptions — a portable sequence for any dashboard, variance pack or model.

Run a 3-check review in under 20 minutes

Use the Explainability Test to decide if output is decision-ready — and find which layer your process stops at.

Apply a release gate before your name goes on it

Future-proof your finance career by mastering AI tools, predictive modeling, and autonomous agents that handle real finance workflows.

Professor & AI Expert (1M+ Students)

Microsoft

General Motors

Samsung

HSBC

Dr. Ryan Ahmed

Professor, AI expert, & YouTuber who has taught 1M+ learners worldwide.

Director of Engineering @Launchpad.AI

Nike

Levi's

Mr. Cooper

Launchpad

Kukesh Kodess

Director of Engineering with 7+ years building cutting-edge AI/ML systems

Cohort 3

Cohort 4

⭐Over 1 million students taught across 160 countries. Has trained teams inside RBC, HSBC, TD Bank, BNY Mellon, Jefferies &amp; Barclays. Capped at 50 seats. 90+ alumni rated it 4.8/5: Bank of America, American Express, UBS &amp; The LEGO Group. 📩 Questions? Email Ryan: <a target="_blank" rel="noopener noreferrer nofollow" class="underline underline underline-offset-2 decoration-1 decoration-current/40 hover:decoration-current focus:decoration-current" href="mailto:ryan.ahmed@stemplicityschool.com">ryan.ahmed@stemplicityschool.com</a>You already use AI. It still hasn't taken one recurring task off your plate.93% of finance teams use AI. Only 21% say it changed how they work. The gap isn't better prompts. It's that nobody taught you to build an AI Agent: something that opens your files, runs your process and hands you a finished deliverable while you're in a meeting.Four weeks from now, the work still gets done. You just stop being the one doing it.<ul><li>Finance: close, variance commentary, reconciliations, forecasts, board packs</li><li>Marketing: campaign reporting, competitor monitoring, content ops, attribution</li><li>Sales: prospect research, CRM hygiene, call follow-ups, RFPs, pipeline forecasts</li></ul>Built live on your own files in Cowork, Claude Code, Codex and Copilot. You leave with 8 working agents, a capstone you can demo and a certification. No coding or technical experience needed.

Build AI Agents That Run Your Finance, Marketing & Sales & Get Certified

Build your first AI agent in week 1 with Claude Cowork, with zero coding

Turn messy files into branded PowerPoint, Word and PDF deliverables

Build Excel models and financial analysis with AI agents, not formulas

Forecast revenue, cash flow and risk with AI agents, no stats degree needed

Connect agents to HubSpot and your systems with MCP, then run them nightly

Ship a real end-to-end agent system and earn your certification

If you can use Excel and write an email, you can build Agents. Most alumni are CFOs, controllers, FP&A managers & marketers, not engineers.

No coding or technical experience required

You build your own AI agents starting in week 1. Sample workflow and data are provided if you can't bring your own.

A real workflow that you want to automate

<blockquote>CFOs, Controllers &amp; FP&amp;A Teams who want to automate the close, variance commentary, reconciliations, forecasts and board-ready reporting.</blockquote>

Financial Analysts &amp; Accounting Teams who want to automate reconciliations, transaction reviews, tie-outs and recurring monthly reporting.

<blockquote>Marketing &amp; Sales Leaders who want to automate campaign reporting, competitor monitoring, prospect research, CRM hygiene and RFP responses.</blockquote>

Transform from consumer of analytics to independent operator - asking sharp questions, validating answers, shipping decisions.

Principal Data Scientist @ Ontra 

Stripe

Nextdoor

PwC India

Ontra

AppFolio

Shane Butler

Principal Data Scientist @ Ontra | Ex-Stripe, Nextdoor, PwC, Appfolio

Data Science Leader |Ex-Microsoft

eBay

Grammarly

Superhuman

Sravya Madipalli

Data Science Leader@Superhuman (Prev. Grammarly)| Ex-Microsoft, eBay, Nextdoor

Head of Data @ Ontra AI | Ex-LinkedIn

LinkedIn

Pinterest

Hai Guan

Head of Data @ Ontra | Ex-Nextdoor, LinkedIn, Pinterest, Meta

Cohort 2

As AI reduces the value of deep domain silos, the people who thrive will be the ones who can work across disciplines and ship. Analytical independence - the ability to validate your own ideas and make data-informed decisions - remains the missing skill in most builders' toolkits.AI can conduct the analysis, but it can't tell you what to ask or whether the answer makes sense. That requires analytical judgment.This course teaches you to think like a senior Product Data Scientist. You'll learn the frameworks used at FAANG companies to frame problems, choose the right metrics, diagnose root causes, execute the analysis, and turn insights into decisions.-&gt; <a target="_blank" rel="noopener noreferrer nofollow" href="https://join.slack.com/t/dataneighbor/shared_invite/zt-3ox4drc3r-3g0ptSthytkgv0sD6ovvqg">Join AI Builders Slack Community!!</a> &lt;-What makes this different: No SQL. No stats lectures. Five weeks of hands-on real scenarios. You'll build end-to-end, metric specs, deep dives, experiment plans, stakeholder readouts.The result: You become analytically self-sufficient. The last dependency removed from your toolkit. Make data-informed decisions in hours, not weeks.

Ask Better Questions, Make Faster Decisions - with AI as Your Analyst

Frame Problems Like a Senior Data Scientist

Design Metrics That Actually Drive Decisions

Diagnose Root Causes Without Guessing

Design Experiments That Ship

Turn Analysis Into Action With AI as Your Copilot

Synthesize Insights and Prioritize for Impact

Product Managers, engineers, and operators who need analytical independence and frameworks to measure success and drive decisions.

Designers and researchers stepping into strategy roles who need to quantify impact, run experiments, and influence with data-driven insights

Data professionals who want to frame better questions, delegate technical execution to AI, and drive impact with agentic analytics tools.

Become an AI-powered accountant who achieves 3-5x efficiency gains while maintaining trust, accuracy, and professional judgment.

CEO of Model Citizn

PwC South Africa

KPMG

National Australia Bank - NAB

Investec

Lance Rubin

Leading financial modelling expert, Founder & CEO of Model Citizn & Chief Excel Office of EXL Cloud

CEO, Co-Founder of TabAI

Columbia University

NeurIPS

ICLR 2026

TabAI

Andrew Siah

AI Researcher (NeurIPS, ICLR), 
Co-Founder TabAI,
Ex-Quant, Physics @ Columbia.

Co-Founder of TabAI

New York AI Labs

Iris Meng

AI Enablement in Finance, ex-Big 4 M&A Advisory, Master's Degree in OR@Columbia

This is the 2nd of many cohorts on the future of accounting and finance with the AI-Powered series

There is a lot of noise and theoretical concepts that lack sufficient practical hands-on application of AI, precisely where Accountants do their best work. In Excel!The landscape is moving fast, and tools are evolving quickly, but the training and adoption lag. This course fixes that.WHAT TO EXPECT9 sessions (6 Live , 3 Async ) Foundations + 10 modulesTotal estimated hours of CPD 20-32 hrsCertification includedFoundations for accounting and finance professionals.We cover key elements of the what and the how of AI in relation to accounting and finance workflows. The growth mindset and human skills are also key elements of this course, not just the technical aspects.Hands-on real-world examples Cases developed from actual workflows in the real world—anonymised and translated to training. You'll gain first-hand experience in the same scenarios.Digital artefacts you'll build and keep<ul><li>Automated Excel and Power BI solutions</li><li>Library of effective AI prompts for finance</li><li>Change management templates for considering AI for your team</li><li>Video recordings and sample datasets for continued practice</li></ul>Tools you'll play with TabAI, Claude, Microsoft Copilot, Power BI, EXL Cloud &amp; XeroCohort max 50

Learn to build AI finance workflows that deliver.

Excel Fluency: Master spreadsheet fundamentals for AI integration

Data & Process: Optimize workflows and data practices for automation

Tech Assessment: Select the right AI tools for finance tasks

AI Concepts: Understand AI capabilities, limitations, and value

Growth Mindset: Embrace continuous learning in AI technology

Human Skills: Maintain strategic thinking and ethical judgment

This program is centred around accounting and finance workflows, and therefore, this knowledge is important to maximise the benefit.

Accounting and finance knowledge

Most of the activities centre around Excel as the main user interface. A basic working knowledge of Excel is needed (no Gsheets)

Basic Excel knowledge

Accountants and finance professionals who want to use AI in their work with clients, stakeholders or for fun but don't know where to start

Management accountants and analysts drowning in manual data entry and reconciliation

Professionals skeptical of AI hype who want practical, battle-tested solutions

You stop arguing about whether the agent is good, and start showing the number, the rubric behind it, and the decision it justifies.

Author, AI Agent Evaluation (Springer)

Abdullah Mansoor

Co-author of AI Agent Evaluation (Springer 2026), written for non-coders.

Cohort 1

You shipped an AI agent. It demos beautifully. Then a board member, a customer, or your own gut asks the question you have been avoiding: how do you know it works? Most teams answer with more testing, more prompt tuning, and a dashboard nobody trusts. The gap does not close, because the missing piece is not effort. It is a system: a written standard for what good means, an instrument that measures against it, and a loop that runs on a schedule. This is the companion course to AI Agent Evaluation (Mansoor and Bokhari, Springer 2026), cut for the person who makes the release decision rather than writes the code. Six modules take you from naming the failure you are afraid of, to writing a contract your agent has to honor, to scoring it on four dimensions, to running a loop that improves it every week. Every tool, framework and metric is introduced at concept level. After a module you can hold your own with your engineers using these terms. No lesson asks you to open a terminal. You leave with seven artifacts built on your own agent: a contract, a rubric with anchors, a benchmark set, a failure taxonomy, a release gate, a cost and latency budget, and a seven-step playbook.

A demo proves it worked once. Production asks you to prove it works every time.

Name the failure pattern your agent is most likely to hit

Write a one-page agent contract

Score your agent on the four dimensions that decide readiness

Build a rubric two independent raters would score the same way

Run the Biruni Loop so quality improves on a schedule

Put cost and latency on the same row as quality

Every exercise is built on your own agent, so you leave with a contract and a rubric for something real rather than a worked example.

An AI agent in production, or one you are about to ship

Everything is taught at concept level. You will hold your own with your engineers using these terms without opening a terminal.

No coding required, and no prior testing or evaluation background

The templates only pay off against a decision you actually have to make, so bring a release you are genuinely unsure about.

Willingness to bring one real agent and one real release decision

Founders whose agent demos beautifully, then answers the same question two ways on a Tuesday. You need the rate, not the anecdote.

Product leads who own the AI feature but not the code. Engineers report a score. You cannot tell if the product improved or the test eased.

Technical founders who stopped coding. You read a system diagram fine. You want the vocabulary back at concept level, without a code tour.

Run 3 checks on any AI finance report

What you'll learn

Why this topic matters

You'll learn from

Albert Lee