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Specialised Track · Finance Function

Re-engineer the finance function with AI.

A four-day, hands-on programme for finance leaders, controllers, and FP&A teams. Redesign R2R, P2P, Q2C, and FP&A end-to-end with applied AI and agentic automation. Every delegate leaves with a deployable use case and a working agent built in their own Codespaces lab.

4 days 23 modules 60% hands-on Codespaces lab included STOA certified
Daniel Sto
Why this programme exists
"Forecasting is a model. Closing is a process. Both are agents waiting to be built."
Daniel Sto · Director of Faculty, STOA Institute
Duration
4 days
Format
Live online or on site
Next open date
Private cohorts
Fee
On request
includes Codespaces lab & all materials
For teams
Private cohort from 8 delegates
Schedule a TNA call
Programme overview

Built around the four core finance value streams.

This four-day programme equips finance professionals to redesign the finance operating model with AI. Finance work runs on control and auditability, so finance AI is built the same way: deterministic calculations, model-written commentary and triage, evidence behind every figure, and a named approver before anything is released. The programme is built around the four core finance value streams, Record to Report, Procure to Pay, Quote to Cash and FP&A, and takes each of them from diagnostic and process re-engineering to applied AI and agentic automation.

Day one is the diagnostic: the finance operating model and the Finance & AI Maturity Diagnostic Scorecard, value-stream analysis across the four flows, generative AI essentials, prompt engineering with the C.L.E.A.R. framework on month-end work, the finance AI opportunity map, and a first agent run in the Codespaces lab. Day two applies AI across Record to Report, Procure to Pay and Quote to Cash: the group close and consolidated reporting, invoice-to-purchase-order matching, collections prioritisation, and the controls, audit trail and explainability that automated finance processes need. Day three covers FP&A and decision intelligence: AI-assisted forecasting and cash flow prediction, variance analysis and management commentary, scenario modelling, management reporting and the board pack, connecting finance data, and the design of a finance agent. Day four is the build: participants build a finance agent on their chosen value stream, test it, read its traces, run the Agent Health Check, and close with governance, emerging methods and the capstone.

Every module follows the TEA™ framework, twenty percent theory, twenty percent examples, sixty percent hands-on application. Prompt work runs on participants' own finance scenarios. Labs run on synthetic finance datasets with planted edge cases in a pre-configured Codespaces environment, where a coding agent does the building and no participant writes code. Participants leave with a prioritised AI use-case roadmap for their function, a finance prompt library, working agents, and a capstone they can hand to their team on Monday morning. The programme closes on where agentic AI in finance is heading: context engineering, agentic retrieval-augmented generation, and the Model Context Protocol connecting agents to finance systems.

Who should attend

This programme is designed for the finance function and the teams that report into it:

  • CFOs and Finance Directors
  • Controllers and Heads of FP&A
  • Process owners across R2R, P2P, Q2C
  • Finance transformation leads
  • Internal audit and SOX leaders
  • Shared services and GBS managers
  • Finance Managers and Finance Business Partners
  • FP&A, Treasury and Cash Management Managers and Analysts
  • Accounts Payable and Accounts Receivable Managers
  • Management Accountants and Financial Analysts
  • Finance systems, ERP and business solutions professionals

Learning outcomes

  • Diagnose finance operating model maturity
  • Re-engineer R2R, P2P, Q2C and FP&A flows
  • Apply the C.L.E.A.R. framework and K-shot prompting to finance tasks
  • Prioritise AI use cases by ROI and risk
  • Apply AI to reconciliation, the close and narrative reporting
  • Design AP, AR and working capital automations with their control maps
  • Build forecasts, scenarios and variance commentary with AI
  • Design a finance agent with boundaries, approval points and human accountability
  • Build and test a working finance agent in Codespaces
  • Evaluate a finance agent's traces, metrics, costs and failure modes
  • Apply governance, data protection and audit requirements to finance AI
  • Build a prioritised AI use-case roadmap and a ninety-day plan for the finance function
R2R

Record to Report

Intelligent reconciliation, journal anomaly detection, agentic close orchestration, narrative reporting.

P2P

Procure to Pay

Invoice capture, three-way match exceptions, duplicate detection, payment fraud screening.

Q2C

Quote to Cash

Credit risk scoring, collections prioritisation, cash application, dispute resolution agents.

FP&A

Plan, Forecast, Decide

Driver-based forecasting, variance commentary, scenario modelling, decision-intelligence dashboards.

Programme outline

Twenty-three modules. Four days. One working agent.

A progressive structure from operating-model diagnostic, through process re-engineering, to applied AI and agentic automation across the finance value chain.

Day 1: Operating Model Diagnostic, Process Re-Engineering and AI Foundations

01
Day 1 · Module

Finance Operating Model and AI Maturity Diagnostic

  • Diagnostic frameworks for finance maturity and effectiveness
  • Structural realignment options (Shared Services, GBS, Centres of Excellence, hybrid models)
  • Redefining finance roles for business partnering and value creation
  • Linking operating model decisions to enterprise strategy and cost structure
  • The Finance & AI Maturity Diagnostic Scorecard
Diagnostic tool the Finance & AI Maturity Diagnostic Scorecard applied to participants' own function
02
Day 1 · Module

Process Re-Engineering Across R2R, P2P, Q2C and FP&A

  • End-to-end value-stream analysis across the four finance flows:
    • Record to Report (R2R)
    • Procure to Pay (P2P)
    • Quote to Cash (Q2C)
    • Plan, forecast and decide (FP&A)
  • Standardisation vs localisation strategy in multi-entity environments
  • Control optimisation in automated and semi-automated processes
  • Efficiency and cost-to-serve metrics for finance
  • AI insertion points in a value stream
Hands-on exercise the P2P value-stream teardown, mapping, measuring and identifying AI insertion points
03
Day 1 · Module

Generative AI Essentials for Finance

  • What is a large language model?
  • Tokens, context windows and temperature
  • Hallucination and non-determinism
  • The finance AI tool landscape (ChatGPT, Claude, Gemini, Microsoft Copilot)
  • Assistants, workflows and agents
  • What is an AI agent?
  • Agent anatomy (trigger, task, tools, memory, output)
  • Control and auditability as the finance standard for AI
  • Data privacy, confidentiality and organisational policy
Live walkthrough the calculator test
04
Day 1 · Module

Prompt Engineering for Finance Work

  • The C.L.E.A.R. prompt engineering framework (Command, Layer, Examples, Assess, Refine)
  • K-shot prompting (zero-shot, one-shot, few-shot, multi-shot)
  • Chain-of-thought prompting for calculations and reconciliations
  • Finance prompt patterns:
    • Variance commentary, flux analysis and month-end narrative
    • Reconciliation support and exception summaries
    • Executive summaries and board papers
  • Structured outputs (tables, journal proposals, memos)
  • Verification of AI output before it enters a finance deliverable
  • The finance prompt library
Hands-on exercise the month-end commentary pack, built with C.L.E.A.R. on participants' own scenarios
05
Day 1 · Module

The Finance AI Opportunity Map

  • Use cases across the value streams (close, reporting, FP&A, AR, AP, treasury)
  • Deterministic and probabilistic finance processes
  • High-value work types (reconciliation, triage, reporting, commentary, correspondence)
  • The Finance Agent Opportunity Matrix (value, risk, readiness)
  • Quick wins and agent candidates
  • Prioritising AI use cases by ROI, risk and strategic impact
Individual and pair work the Finance AI Opportunity Map scored on participants' own processes
06
Day 1 · Module

The Finance Lab Environment

  • The lab environment (GitHub Codespaces, browser-based, pre-configured)
  • Synthetic finance datasets with planted edge cases
  • Working starter agents to fork and modify
  • The build track (coding agent with Claude Code, no code written by participants)
  • Acceptance criteria and verified end states
  • Reading what an agent did (run log, output files)
Lab launching the lab environment and running a first finance agent

Day 2: Applied AI Across Record to Report, Procure to Pay and Quote to Cash

07
Day 2 · Module

Applied AI in Record to Report

  • Intelligent reconciliation and journal anomaly detection
  • Close acceleration with agentic checklists and exception routing
  • AI-generated variance commentary and narrative reporting
  • Ledger cleaning, normalisation and account mapping
  • Deterministic calculations and model-written commentary
  • Audit trail, explainability and SOX considerations
08
Day 2 · Module

The Group Close and Consolidated Reporting

  • Multi-entity ledger consolidation
  • Intercompany eliminations and reconciliation controls
  • Fraud and control screening with risk scoring and reviewer triage
  • Working-capital risk and the quarterly projection
  • Consolidated statements and the close dashboard
  • The CFO approval gate and controlled distribution
  • Structured outputs with evidence references and exception flags
Live demonstration the Group Close Leadership Pack, from ledger to statements, control screen, projection and approval gate
09
Day 2 · Module

Applied AI in Procure to Pay

  • Invoice capture, three-way matching and exception triage in AP
  • Payment fraud detection and duplicate-invoice pattern recognition
  • Exception types (price discrepancy, quantity mismatch, missing purchase order reference, duplicate invoice)
  • Tolerance thresholds and approval routing
  • Human review of flagged invoices
Lab an invoice-to-purchase-order matching agent that flags price, quantity, missing-PO and duplicate exceptions
10
Day 2 · Module

Applied AI in Quote to Cash

  • Credit risk scoring, collections prioritisation and cash application in AR
  • Dispute resolution and customer correspondence with agents
  • Ageing analysis, payment history and disputed amounts
  • Priority tiers and collection actions
  • Human approval for credit decisions and customer contact
Lab a collections prioritisation agent on an AR ageing and customer profile dataset
11
Day 2 · Module

Controls, Audit Trail and Explainability in Automated Finance Processes

  • Human-in-the-loop controls for every automation
  • Preventive, detective and compensating controls in AI-assisted processes
  • Segregation of duties and approval thresholds
  • Evidence, logging and the audit trail for AI-assisted work
  • Explainability requirements for auditors and regulators
  • The control map (input controls, processing controls, output controls, approver)
Individual and pair work one finance automation and its control map

Day 3: AI for FP&A, Forecasting, Reporting and Decision Intelligence

12
Day 3 · Module

AI-Assisted Forecasting and Cash Flow Prediction

  • Driver-based forecasting with AI assistance
  • Cash flow forecasting and working capital drivers
  • Natural language analysis over finance data
  • Regression and scenario analysis in Excel
  • AI-assisted Excel formulas and Power BI DAX measures
  • Reviewing model output (reasonableness tests, reconciliation to source)
Hands-on exercise a twelve-month cash flow forecast with regression scenarios in Excel
13
Day 3 · Module

Variance Analysis and Management Commentary

  • Automated variance analysis (budget, forecast, prior period)
  • Movement, driver and attention flags
  • Materiality thresholds and exception lists
  • Management commentary and the month-end narrative
  • Narrative consistency with the numbers
  • Commentary review and sign-off
Hands-on exercise variance commentary and forecast narrative on participants' own management accounts
14
Day 3 · Module

Scenario Modelling and Sensitivity Analysis with LLMs

  • Sensitivity tables and driver ranges
  • Base, upside and downside cases
  • Scenario commentary and recommended actions
  • Decision-intelligence dashboards
  • Model choice and settings for analytical work (temperature, reasoning models, context)
Live demonstration the same financial task run across models and settings
15
Day 3 · Module

Management Reporting, Dashboards and the Board Pack

  • Management reporting with AI (first drafts, human judgement, review)
  • Dashboard generation from raw data
  • The board pack automation pattern
  • Approval and delivery (sign-off routing, controlled distribution)
  • Narrative quality in the board pack
Live demonstration the Management Reporting Workflow, from ledger and spreadsheet data to a management report with commentary
16
Day 3 · Module

Connecting Finance Data

  • Multiple data sources (spreadsheets, databases, documents, business systems)
  • ERP exports, Excel and CSV pipelines
  • Data quality and mapping (cleaning, validation, account mapping, currency conversion)
  • Structured JSON outputs for finance analysis
  • Multi-step analysis agents (plan, analyse, check, escalate)
  • Credential boundaries and data security in connected workflows
Individual and pair work the data map for participants' own reporting process (sources, quality rules, mappings, outputs)
17
Day 3 · Module

Finance Agent Design

  • Boundaries, approval points and human accountability
  • The structured output pattern:
    • Controlled source data
    • Structured schema
    • Evidence and source references
    • Exception flags and missing-information handling
    • Human escalation route
  • Read-only and write permissions
  • Choosing the value stream (R2R, P2P, Q2C, FP&A)
Individual and pair work the finance agent design brief (goal, data, tools, controls, approver, output)

Day 4: Building, Evaluating and Governing the Finance Agent

18
Day 4 · Module

The Finance Agent Build

  • The build brief and the supplied data
  • Inputs, processing logic, controls and outputs
  • Building with the coding agent
  • Iteration against the acceptance criteria
  • Planted edge cases (missing, conflicting and out-of-policy data)
  • Peer review of another participant's build
Lab building the finance agent on participants' chosen value stream
19
Day 4 · Module

Testing and Evaluating the Finance Agent

  • Completeness, omission and calculation error checks
  • Test sets and reference answers
  • Groundedness and source citation
  • Agent failure modes (wrong data, fabricated figure, silent failure, loops)
  • Traces, runs and run inspection (LangSmith)
  • Trace contents (prompt, data, tool calls, model response, tokens, latency, cost)
Lab instrumenting the finance agent and inspecting its traces
20
Day 4 · Module

The Agent Health Check

  • The Agent Health Check measures:
    • Groundedness and source citation
    • Classification, escalation and abstention results
    • Missing-information handling
    • Hallucination and omission rates
    • Prompt-injection resistance and sensitive-data leakage
    • Cost, token use, latency and reliability
    • Error and failure rates
    • Prompt, data and model drift
  • Ownership, thresholds, escalation and remediation
  • Reporting agent performance to finance leadership
Hands-on exercise running the Agent Health Check on the finance agent
21
Day 4 · Module

Governance, Risk and Audit for Finance AI

  • Model risk and validation for finance AI
  • Audit evidence packs for agent output
  • Data protection, confidentiality, retention and residency
  • Approval thresholds and human accountability for agent actions
  • The agent register and change control
  • The regulatory and standards direction (ISO/IEC 42001, the NIST AI Risk Management Framework, the EU AI Act, SOX)
Controlled group discussion governance guardrails for finance agents in participants' organisations
22
Day 4 · Module

Emerging Methods

  • Context engineering
  • Agentic retrieval-augmented generation (RAG)
  • Model Context Protocol (MCP)
  • Multi-agent patterns (planner and executor, critic and reviewer)
  • Computer use and browser agents
Live demonstration an agent using external tools through the Model Context Protocol
23
Day 4 · Module

Capstone, Finance AI Roadmap and Ninety-Day Plan

  • Peer showcase and governance review of the finance agent
  • The prioritised twelve-month AI use-case roadmap for the function
  • The control map for each prioritised use case
  • Pilot scope, ownership and success criteria
  • The ninety-day plan (next steps, priority use case, timeline, owners)
Personal action planning each participant's ninety-day finance AI plan
Day 4 · Course closing

Course closing

  • Course debrief and key takeaways
  • Final Q&A
  • Official closing ceremony and award of certificates
  • Group photo
Four-day path

Diagnose. Apply. Design. Build.

Each day moves further from theory and closer to a working agent in your own environment. Everyone, regardless of technical background, can follow along.

Day 1
Diagnose
Operating model & processes

Finance maturity diagnostic, operating-model design, and end-to-end re-engineering across R2R, P2P, Q2C and FP&A.

Day 2
Apply
AI across R2R, P2P & Q2C

The group close walkthrough, then Codespaces labs: invoice-to-purchase-order matching and collections prioritisation, each with its control map.

Day 3
Design
FP&A & agent design

AI-assisted forecasting, variance commentary, scenario modelling and the board pack. Design your own finance agent.

Day 4
Build
Build, evaluate & govern

Build and test your finance agent, inspect its traces, run the Agent Health Check and set governance guardrails. Leave with a use-case roadmap and a ninety-day plan.

Inside the programme

A live Codespaces lab. No setup. No friction.

Every delegate gets access to a pre-configured GitHub Codespaces environment with finance datasets, working notebooks, and reference agents already wired up. You see agentic AI run end-to-end, then build your own.

  • Pre-loaded R2R, P2P, Q2C and FP&A datasets
  • Working starter agents you fork and modify
  • Reconciliation, fraud-screening and forecasting notebooks
  • Connectors for ERP exports, Excel and CSV pipelines
  • Lab access remains active for 30 days post-programme
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Built for finance

Designed for the people who own the numbers.

A practitioner-led programme, calibrated for the pace and language of the finance function. Not a generic AI course wrapped in finance examples.

For finance leadership

CFOs, Finance Directors, and Heads of Transformation who need a clear AI roadmap, governance posture, and ROI thesis for the next 12 months.

For process owners

Controllers, Heads of FP&A, AP/AR managers, and shared-services leads who want to redesign their value stream around agentic automation.

For audit & control

Internal audit, SOX, and compliance professionals who need to evaluate AI-enabled controls, explainability, and risk in finance workflows.

Our training methodology

Three frameworks behind every programme.

Whatever programme you choose, it follows three proven instructional frameworks, from design to delivery to evaluation.

Delivery

TEA™ Delivery Framework

  • T Theory, core concepts & frameworks
  • E Examples, live demos & case studies
  • A Application, hands-on exercises & practice

Our signature approach structures every module into three phases for maximum retention and practical application.

Design

ADDIE Instructional Design

  • A Analyse, assess learner needs
  • D Design, structure the learning path
  • D Develop, build materials & activities
  • I Implement, deliver the programme
  • E Evaluate, measure outcomes & refine

The industry-standard model for course design and learning alignment.

Evaluation

Kirkpatrick Model

  • L1 Reaction, participant satisfaction
  • L2 Learning, knowledge & skills gained
  • L3 Behaviour, on-the-job application
  • L4 Results, business impact & ROI

The globally recognised four-level framework for measuring training effectiveness, from initial reaction to long-lasting post-training effects.

Upcoming sessions

Choose your date.

Open-enrolment dates below. Need a private session for your finance team? Schedule a TNA call, custom proposal within 48 hours, delivery in as little as three weeks.

Date
Format
Duration
Price
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What every participant gets

You keep everything.

No vendor lock-in. No paywalled materials. Every delegate leaves with the working files, the templates, the lab access, and the post-programme support to actually use them.

Working finance agent

A live, deployable agent built on your own R2R, P2P, Q2C or FP&A use case during the capstone.

Codespaces lab access

Pre-configured GitHub Codespaces environment with finance datasets, starter agents, and notebooks.

Maturity scorecard & roadmap

Finance & AI Maturity Diagnostic Scorecard plus a prioritised 12-month AI use-case roadmap.

Finance prompt library

Curated prompt and agent templates for close, reconciliation, AP, AR, forecasting and variance commentary.

STOA certificate

Certificate of Completion plus a guided pathway to the Google AI certificate as an additional credential.

30-day support

Post-programme Q&A, lab access, and implementation guidance for your finance use case.

Delivery options

Choose how your finance team learns.

In-Person

Instructor-led on-site

Delivered at your offices or venue. Maximum engagement for finance teams of 8 – 20 participants. All exercises use your team's actual close, AP, AR and FP&A scenarios.

Live Online

Virtual instructor-led

Real-time delivery via Zoom, Teams, or Meet. Interactive breakout rooms, the same Codespaces lab, same depth as in-person. Ideal for distributed finance organisations.

Hybrid

Blended approach

Combine in-person and virtual sessions. Flexible scheduling across time zones. Pre-work, capstone reviews and follow-up resources included.

Training catalogue

104 courses in nine families, overview level, methodology up front.

Download the catalogue
Ready to build

Stop talking about AI in finance. Start running it.

Join the next open session, or book a private programme for your finance team. Custom proposal within 48 hours. Delivery in as little as three weeks.

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