Who This AI Training Is For
This course is built for finance professionals who need practical AI skills -not computer science theory. Whether you're a CFO evaluating AI strategy or a controller trying to accelerate month-end close, you'll find role-specific guidance backed by real finance examples.
CFO / Finance Director
Your Current Reality
- Board and investors asking "what's our AI strategy?"
- Pressure to improve close speed, forecast accuracy, working capital
- Limited clarity on where to start, what ROI looks like
- Need to balance innovation with risk management (especially FCA-regulated entities)
What You'll Be Able to Do
- Articulate a documented AI strategy to the board with confidence
- Calculate specific ROI for your finance function (typical: £100K-£500K for 10-50 person teams)
- Evaluate AI vendors using the F.A.I.R. Framework
- Design AI Governance Frameworks compliant with FCA explainability requirements
- Establish controls for AI decisions (audit trails, human oversight)
- Deploy acceptable use policies and data classification schemes
Relevant Course Modules
- • Module 1: AI fundamentals, ROI calculation
- • Module 4: UK regulation (FCA, PRA, ICO), governance frameworks
- • Module 5: Business case development, board communication
- • Module 6: 90-day implementation roadmap
UK Professional Services CFO (£120M revenue): Approved AI pilot after Module 1 (identified £180K ROI), implemented FP&A forecast automation reducing cycle from 13 days to 6 days, presented results to board after 6 months: ±6% forecast accuracy (was ±12%), 2 FTE capacity redeployed to strategic work.
Finance Business Partner
Your Current Reality
- Spending 60-70% of time on data gathering, only 30-40% on analysis
- Manually drafting variance commentary and forecast explanations
- Time-consuming scenario modelling (2 days per scenario)
- Business unit leaders want faster insights and proactive recommendations
What You'll Be Able to Do
- Generate board-ready P&L variance commentary in 15 minutes (was 2 hours)
- Consolidate forecast data automatically reducing cycle time by 50%+
- Improve forecast accuracy 20-40% using ML pattern detection
- Free 10-20 hours per week from data prep to focus on strategic conversations
Relevant Course Modules
- • Module 2, Lesson 2.4: FP&A use cases (forecasting, scenario modelling)
- • Module 1, Lesson 1.3: RACEF prompt framework for variance analysis
- • Module 3: Power BI Copilot, ChatGPT, Claude for reporting
[R] You are a UK Finance Business Partner preparing monthly management accounts. [A] Analyse the P&L variance report (November 2024 actual vs budget). [C] Revenue £12.2M vs £11.8M (+£400K), Gross margin 38.2% vs 40.5% (-2.3pp), OpEx £3.8M vs £3.5M (+£300K), EBITDA £2.1M vs £2.3M (-£200K). [E] Professional, board-ready language. Focus on business drivers. [F] Provide: 1) Executive summary (2 sentences), 2) Top 4 variances with drivers, 3) FY forecast implication, 4) Recommended actions. Max 250 words.
FP&A Manager / Analyst
Your Current Reality
- 13+ day forecast cycles with 75% time spent chasing inputs
- Manual consolidation from disparate Excel files
- Forecast accuracy ±10-15% despite significant effort
- Scenario analysis requires copying entire models
What You'll Be Able to Do
- Auto-pull actuals from ERP, sales pipeline from CRM, headcount from HR
- Apply ML models that improve forecast accuracy to ±5-8% (30-50% improvement)
- Generate "What if revenue is 15% lower?" scenarios in 30 minutes (was 2 days)
- Draft forecast variance commentary in 15 minutes with AI
UK Professional Services FP&A Team (£120M revenue, 5 FTE): Implemented Anaplan PlanIQ + ChatGPT Enterprise. Forecast cycle reduced from 13 days to 6 days (54% improvement), forecast accuracy improved from ±12% to ±6% (50% improvement), FP&A time allocation shifted to 30% data prep, 70% analysis (was 75% data prep, 25% analysis).
Financial Controller / Management Accountant
Your Current Reality
- 8-10 day month-end close with 50-60 hour weeks
- 120+ hours per month on manual reconciliations
- Recurring journals manually posted every month
- 2+ days drafting variance commentary and management reports
What You'll Be Able to Do
- Reduce close from 10 days to 5 days (typical 40-50% improvement)
- Auto-match 90%+ of reconciliations using ML pattern recognition
- Post 60% of recurring journals automatically with anomaly detection
- Save 85+ hours per month on reconciliations (2 FTE equivalent)
Relevant Course Modules
- • Module 2, Lesson 2.3: R2R use cases (reconciliations, journal automation)
- • Module 4: UK regulation, AI governance frameworks, audit trails
- • Module 3: D365 AI Agents, BlackLine AI, SAP Joule
UK Manufacturing Group (£200M revenue, 4 entities, 12-person finance team): Implemented D365 AI Agents + BlackLine AI. Close duration reduced from 10 days to 5 days (50% improvement), reconciliation time: 120 hours → 35 hours per month (85 hours saved), audit findings: 8-10 per quarter → 2 per quarter, normalised working hours during close (no more 60-hour weeks).
Credit Controller / Collections Manager
Your Current Reality
- DSO sitting at 60-70 days when industry benchmark is 45-50 days
- Sequential collections approach (oldest first) misses high-risk accounts
- 10-15 hours per week manually matching bank remittances
- Manual email drafting for 200+ customers per month
What You'll Be Able to Do
- Implement AI risk scoring that prioritises by cash value × payment risk × days overdue
- Reduce DSO by 10-20 days (typical: £2M+ working capital release for £60M revenue business)
- Auto-match 85%+ of bank remittances using ML pattern recognition
- Generate personalised collections emails based on customer payment history
UK B2B Services Company (£80M revenue, 450 customers, £12M debtors): Implemented HighRadius AR Intelligence + D365 Copilot. DSO reduced from 68 days to 52 days (16-day improvement), £2.1M working capital released, overdue >60 days: £2.8M → £1.4M (50% reduction), collections team: 30% time redeployed from chasing low-risk customers to relationship management.
AP Manager / Procure-to-Pay Lead
Your Current Reality
- 500+ supplier invoices per week with 60% requiring manual intervention
- 5 days from invoice receipt to posting
- Missing 60% of early-payment discounts due to slow processing
- Duplicate payments discovered retrospectively (£30K-£50K annual issue)
What You'll Be Able to Do
- Achieve 80-90% touchless invoice processing using AI OCR + three-way matching
- Reduce processing time from 5 days to <1 day
- Capture 80-90% of available discounts (was 40%) with AI payment timing optimisation
- Save £50K-£100K annually (typical for £50M procurement spend)
UK Manufacturing Company (£50M procurement, 2,000 invoices/month): Implemented D365 AI Builder + Power Automate. Touchless processing: 40% → 87%, processing time: 5 days → <1 day, early-payment discount capture: 40% → 85% (£60K annual savings), duplicate payments: £45K annual → zero post-implementation.
Transformation / ERP Implementation Lead
Your Current Reality
- Leading D365, SAP S/4HANA, Workday, or Oracle implementation
- Under pressure to demonstrate AI capabilities and ROI
- Uncertainty about which AI features to activate vs build custom
- Need to balance innovation with change management
What You'll Be Able to Do
- Audit existing AI capabilities in your ERP (most organisations use <30% of embedded AI)
- Prioritise which AI features to activate based on ROI
- Design integration architecture for Generative AI tools with ERP data
- Position AI as "embedded in your new ERP" to reduce adoption friction
Relevant Course Modules
- • Module 3: D365 AI Agents, SAP Joule, Workday AI, Oracle EPM Cloud
- • Module 2: P2P, O2C, R2R, FP&A use cases mapped to ERP AI capabilities
- • Module 4: Governance frameworks for AI integration
- • Module 5: Change management, pilot design, scaling strategy
Other Finance Roles
If your role isn't listed above, this course is still relevant if you work in any finance-related capacity:
- Treasury Manager: Cash forecasting, payment optimization, FX risk
- Management Accountant: Reporting, variance analysis, budgeting
- Tax Manager: Tax calculations, compliance reporting, provision analysis
- Payroll Manager: Payroll processing, compliance, reporting
- Internal Auditor: Audit automation, risk assessment, controls testing
- Finance Analyst: Data analysis, reporting, forecasting support
- Procurement Manager: Supplier analysis, contract management, spend optimization
- Finance Systems Manager: ERP optimization, system integration, automation
Core principle: If you work with financial data, processes, or decisions, you'll find AI applications relevant to your role in Modules 2-3. The frameworks (RACEF prompting, F.A.I.R. tool evaluation, governance) apply universally across all finance functions.
What You'll Gain
- • Adapt P2P, O2C, R2R, and FP&A use cases to your specific responsibilities
- • Use RACEF prompt framework for your finance tasks (reporting, analysis, compliance)
- • Evaluate AI tools using F.A.I.R. Framework for your function
- • Build business case for AI adoption in your area
- • Navigate UK regulatory requirements (FCA, PRA, ICO) if applicable
Is This Right for You?
This course is right for you if:
- You're a UK finance professional (CFO, FP&A, Controller, Credit, AP)
- You want practical, implementable use cases -not theoretical computer science
- You need templates, prompts, and frameworks you can use tomorrow
- You're responsible for P2P, O2C, R2R, or FP&A processes
- You need to navigate UK regulation (FCA, PRA, ICO)
This course is not right for you if:
- You're looking for coding/Python/data science training
- You want generic AI training (not finance-specific)
- You're in accounts receivable only for US GAAP context
- You expect AI to "replace your team"
- You're not willing to experiment and iterate
What's Included for Every Role
Downloadable Resources
- ✓ AI Opportunity Calculator, ROI Calculators
- ✓ Process Assessments (P2P, O2C, R2R, FP&A)
- ✓ 64 finance-specific prompts
- ✓ RACEF Prompt Builder, F.A.I.R. Evaluation Scorecard
- ✓ Business Case Template, Pilot Design Template
- ✓ UK Regulatory Checklist, AI Governance Framework Builder
Time Commitment
- • Core lessons: 8-10 hours (6 modules)
- • Practical exercises: 2-3 hours
- • Download review: 2-3 hours
- • Pilot planning: 3-5 hours
- • Total: 15-20 hours over 3-5 weeks
FAQs by Role
For CFOs: "Do I need to take the whole course or can I delegate?"
Take Modules 1, 4, 5 yourself (AI fundamentals, regulation, business cases). Delegate Modules 2-3 to your FP&A/Controller team (process use cases, tool selection). Review Module 6 together (90-day plan). Total CFO time: 5-7 hours.
For FP&A: "Will this work with our planning tool (Anaplan/Workday/Planful)?"
Yes. Module 3 covers Anaplan PlanIQ, Workday Adaptive Planning, Planful, Board International, OneStream -plus Generative AI tools (ChatGPT, Claude, Copilot) that work alongside any planning platform. The course is vendor-neutral.
For Controllers: "How do I convince my CFO to invest in AI?"
Module 1 includes the AI Opportunity Calculator (calculate ROI in 10 minutes) and Business Case Template. Typical mid-size finance team ROI: £100K-£300K annual opportunity vs £99 course investment. Payback period: days to weeks. Present this to your CFO with the included template.
For Credit Controllers: "Can I implement collections AI without IT involvement?"
Many collections AI tools (D365 Credit & Collections, HighRadius, ChatGPT for email drafting) require minimal IT involvement. Module 2 teaches you how to assess your current AR system's embedded AI before buying new tools.
For AP Managers: "Do I need to replace our ERP to get AI invoice processing?"
Often no. D365, SAP S/4HANA, Workday already include AI invoice processing (OCR, matching). Module 2 teaches you how to audit what you already have. If your ERP lacks AI, Module 3 covers Esker, Coupa, and other bolt-on solutions.
Ready to Start?
Enrol today and receive immediate access to all 6 modules, downloadable resources, and the AI Opportunity Calculator.
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What Finance Professionals Say:
"This course gave me the exact frameworks and prompts I needed. I implemented FP&A automation within 90 days and cut our forecast cycle in half."
"The UK regulatory guidance in Module 4 was invaluable. We're FCA-regulated and needed FCA-compliant AI governance. The templates saved us weeks of work."