AI workshop for finance leadership: from strategy to implementation
When a finance working day brings your 8-day close down to 3 days, not tomorrow, but within 90 days
A day with the CFO, the financial controllers, the treasury lead and the finance operations manager. No generic AI explainers, no tool demos for staff. Instead, a structured working day in which your finance leadership team determines where AI genuinely adds value for you: financial close, rolling forecast, AP/AR, audit trail, CSRD reporting. It also covers the compliance conditions (DNB/AFM, the AI Act, SOX controls, IFRS, ITGCs) you need to secure before going live.
Plan the finance leadership day View the day's agendaWhy finance, and why now
Finance has the highest ROI potential of any corporate function for AI: a high frequency of repetitive transactions, structured data in the ERP and general ledger, and clear quantitative output. At the same time, finance is also the department with the strictest compliance requirements: DNB and AFM supervision, AI Act classification, SOX controls for listed companies, ITGC requirements for the external auditor, and IFRS foundations that directly affect what you publish in your annual accounts.
The danger is that under pressure from the COO or CIO, finance teams experiment with generative AI in spreadsheets and ad hoc tooling, without the CFO knowing what happens to ledger data, how training models are maintained, or whether the control environment (COSO) remains in order. When an external auditor finds during the annual audit that key controls are partly performed by an opaque model, it immediately results in a management letter.
At the same time, waiting is not an option. At many organisations, the monthly close still takes six to ten working days, while AI-driven peers already close in three to four days. Variance analysis is still done in spreadsheets, while models could generate 80% of the commentary in seconds. CSRD reporting goes live this year, and the workload on finance has structurally increased. This workshop is for finance leadership teams who want to establish within one day which AI applications are responsible, scalable and auditable for them.
What this is not. It is not training on "ChatGPT for finance staff". It is not a SaaS tool demo, nor abstract AI strategy talk. It is a working day in which your finance leadership examines its own close, forecast and compliance processes and leaves with concrete next steps.
Six finance domains where the workshop makes sharp choices
For each domain we discuss: the current turnaround time, which data quality is available, which AI approach is realistic, which controls must be involved, and the next pilot of six to twelve weeks.
Financial close acceleration
From seven to ten days down to three to five. Anomaly detection on ledger postings, automated subledger reconciliation (AP/AR/intercompany), AI-supported GR/IR clearing, journal entry classification and flux explanations. We discuss which close steps you can automate without affecting SOX controls, and where human-in-the-loop remains mandatory.
Rolling forecast and variance analysis
From an annual budgeting exercise to a rolling forecast that adapts monthly, or even weekly, to operational drivers. We build an approach to predictive forecasting based on leading indicators (order book, pipeline, conversion, OWC trends) and discuss when a statistical model is sufficient and when you should move towards machine learning.
AP automation and GR/IR
Invoice recognition has become a commodity, but the real gains lie in three-way matching, automatic GR/IR clearing, fraud detection on supplier changes, and cash flow forecasting based on payment behaviour. We map where your current AP tool falls short and what is realistic to build in-house or integrate.
AR AI: predictive collections and DSO
Which debtors are going to pay late, and how do you steer proactively rather than reactively? Predictive collection models based on payment history, sector signals and order patterns measurably shorten your DSO. We discuss which data fields you need and how to calculate ROI against your existing credit management process.
Audit trail AI and ITGC
The external auditor wants an end-to-end traceable chain: who booked what, on what basis, and where the control sits. AI models can increase audit readiness through automatic sampling, exception detection and evidence collection. We discuss how to build an AI component so that it passes the ITGC and COSO testing.
CSRD/ESG and ESEF/iXBRL
Data points that are not held in the ERP: scope 3 emissions, supplier due diligence, social KPIs. AI helps with collecting, validating and tagging (ESEF/iXBRL) CSRD data. We discuss which parts can be automated and which decision structure you need to obtain a CSRD assurance report "with reasonable assurance".
The day in detail: from inventory to a 90-day roadmap
A working day from 09:00 to 16:30 at your location or at Appfront, with the CFO, the financial controllers, the treasury lead, a representative from the business control side and ideally someone from IT/risk. No slide-deck marathon: 70% of the time is structured work on your own processes, 30% knowledge input on specific decision points.
- 09:00 – 09:45Current state of the finance stack. Inventory of ERP, consolidation tool, EPM/CPM system, treasury platform, BI. Which data flows exist, where is the single source of truth, and where are the breaking points in the close.
- 09:45 – 10:45Close process analysis. We map out your month-end close step by step, quantify the lead time per step and mark AI candidates. Where are the manual journal entries, reconciliations and intercompany eliminations that can be automated without breaching SOX controls.
- 11:00 – 12:00Forecast and variance analysis. How you currently roll your forecast, how long variance analysis takes, and which leading indicators you have available for a driver-based model. We sketch a rolling forecast architecture on your own data.
- 12:00 – 13:00Lunch + informal conversation about peers, sector benchmarks and first impressions.
- 13:00 – 14:00Compliance layer. AI Act classification of the chosen use cases, DNB/AFM conditions where applicable, SOX controls (key vs non-key), ITGC and COSO mapping, IFRS impact on the annual accounts. We build a compliance checklist that evolves alongside your planned AI applications.
- 14:00 – 15:00Build, buy, integrate. For each chosen use case: do you build it yourself, buy a SaaS module, or integrate an AI layer into your existing stack. We discuss total cost of ownership, vendor lock-in and the auditor's perspective on black-box models.
- 15:00 – 16:0030/60/90-day roadmap. Together we prioritise which two or three pilots to start first, which datasets are needed, which stakeholders you should involve (controllers, external auditor, IT, risk), and how you measure success.
- 16:00 – 16:30Wrap-up and ownership. Who takes which actions, which follow-up sessions we schedule, and which decision goes to the Executive Board or the audit committee.
The compliance layer: why your AI must get past the auditor
An AI implementation in finance that delivers impressive numbers but makes the external auditor uneasy will ultimately cost you more than it returns. We build finance AI from the auditor's perspective: traceability, reproducibility and evidential strength are not an afterthought.
AI Act and risk classification
Many finance applications fall under limited risk or high risk in the EU AI Act. Which documentation obligations apply (technical documentation, conformity assessment, post-market monitoring), who is the provider and who is the deployer, and how you arrange this within internal governance. We map your use cases by risk level.
SOX controls and ITGC
At listed groups (NYSE/Nasdaq, AEX, AMX), AI components quickly become key controls. How do you maintain segregation of duties, change management on models, and logging that passes the PCAOB review? We discuss which controls remain user-driven and which become automated controls with a human-review layer.
DNB and AFM for financial institutions
Banks, insurers, asset managers and pension administrators face additional requirements around model validation, governance and explainability. We discuss how an AI tool for, for example, AML monitoring, fraud detection or solvency reporting fits within your existing model risk framework and SREP, Solvency II or IORP requirements.
IFRS, ESEF and CSRD
AI outputs that flow into IFRS figures, for example for IFRS 9 expected credit losses, IFRS 16 lease measurement, IFRS 17 insurance liabilities, or CSRD reporting with ESEF/iXBRL tagging, must be reproducible and auditable. We ensure model outputs land in your consolidation tool with an audit trail.
From workshop to implementation: how Appfront builds finance AI
The workshop is not a standalone product. For finance teams who want to go further after the day, Appfront is the implementation partner: we build the chosen pilots, integrate them into your existing ERP, EPM and BI stack, and ensure the first results are visible within 90 days, without the external auditor or the DPO pulling the plug.
We are not a management consultancy that delivers a report and leaves. Our developers and finance functional architects build the actual integrations, models and dashboards. We work iteratively in sprints of two to three weeks, deliver usable functionality along the way, and calibrate with your controllers what is and is not production-ready.
For finance organisations that want to take their first AI pilot seriously, the bottleneck is not the tooling but the discipline to address process, data and compliance simultaneously. That is what the workshop is for. And Appfront is there if, after that day, you are looking for implementation partners who speak finance.
Technology and integration points
We work with the finance stack you already have. No rip-and-replace, but AI components added to your existing general ledger, consolidation and EPM environment. Native integrations where possible, custom integrations where needed.
Three scenarios finance management teams typically work through during the workshop
These are not invented examples but illustrations of how structured discussions can unfold on the day. Which scenario is relevant to you will become clear during the discovery phase in the morning.
Scenario A — Listed holding company dealing with IFRS reporting and SOX
A listed holding with several operating subsidiaries. The month-end close takes eight working days, intercompany reconciliation is a bottleneck, and the external auditor raised comments last year about manual journal entries. The workshop delivers: a priority mapping of close steps, a SOX impact assessment for each proposed AI component, and a pilot for automated intercompany matching that goes live within 90 days without PCAOB issues.
Scenario B — Scale-up with rapid growth and CSRD requirements
A mid-market organisation of 200 to 800 employees, growing quickly, with a relatively small finance team. Forecasting is annual with a quarterly review rather than rolling. CSRD reporting is approaching and the team knows it cannot manage without automation. The workshop delivers: a rolling forecast approach based on driver-based modelling, an ESG data architecture with validation controls, and a 90-day pilot on variance analysis automation.
Scenario C — Financial institution under DNB supervision
A bank, insurer or asset manager. AI applications must pass through model validation, second-line-of-defence review and, where applicable, notification to DNB. Existing tooling for AML, fraud detection or solvency reporting has limitations. The workshop delivers: a classification of use cases against the model risk framework, governance mapping (first, second and third lines) and a decision between an external AI vendor and an in-house build with external model validation.
Scenario D — Family-owned or private equity-backed company
Not listed, no SOX, but growing demands from owners for faster reporting and greater cash flow visibility. Treasury still runs on spreadsheets and the FX position is monitored manually. The workshop delivers: a treasury AI approach with FX prediction and cash pooling optimisation, plus an accounts payable AI pilot that steers DSO and DPO simultaneously.
Why finance management teams run this workshop with Appfront
Finance terminology, not just AI terminology
We speak the language of GR/IR, OWC, DSO, three-way match, SOX key controls and flux explanations without needing translation. Our finance functional architects come from controllership and internal audit, and they know where your daily work gets stuck.
An auditor's perspective from day one
We build finance AI as though the external auditor were arriving tomorrow. Traceability, reproducibility, version-controlled models, evidence trails and human-in-the-loop oversight on key controls are standard, not optional extras.
An implementation partner, not a report supplier
After the workshop, our developers build the chosen pilots. No handover to a third party, no advice that gathers dust in a drawer, but working integrations, models and dashboards in your stack within 90 days.
Frequently asked questions from finance management teams
A working day that measurably changes your finance function
Schedule a no-obligation one-hour intake call. We will discuss your close, forecast and compliance context, and see whether the workshop is worthwhile for your management team. Not a sales pitch, but an honest assessment.
Schedule the intake call