Work Smarter, Not Harder: Top 5 AI Prompts Every Finance Professional in Austria Should Use in 2025

Austria's finance teams should treat AI prompts as a practical toolkit in 2025: national funding schemes like aws Digitization and SME.DIGITAL make pilot projects affordable and easier to start - see a concise overview of 2025 funding options at KI Company - while studies show generative AI could add EUR 35–40 billion to Austria's GDP and have 62% of jobs working alongside AI, creating a big productivity upside for corporate finance.

Finance already leads AI uptake in Europe and experts predict a rapid rise in agentic AI use, so prioritizing prompt-driven workflows for forecasting, cash reforecasting and AR aging can cut errors, save weeks of work, and align with Austria's green data-center growth.

For teams that need practical, job-focused training, the 15-week AI Essentials for Work course offers prompt-writing and workplace AI skills to move from pilot to scale quickly (course details below).

Selection focused on prompts that are practical for Austrian finance teams today: each candidate had to demonstrably map to core tasks (forecasting, short‑term cash reforecasting, expense categorization, AR aging and deal evaluation), align with Austria's national push to increase trustworthy AI projects, and respect emerging compliance expectations.

To keep choices grounded, the team cross‑checked economic and workforce signals from the Implement Consulting Group report (which estimates a EUR 35–40 billion potential GDP uplift and that roughly 62% of Austrian jobs will work with generative AI) and matched them to programmatic support and readiness aims described by Austria's aws AI adoption initiative.

Prompts were then filtered through a privacy-and-governance lens using Austria's DSB FAQs on AI and data protection to ensure minimal-risk data use and traceable outputs.

The result: five prompts that balance immediate ROI and pilotability, clear inputs/outputs for finance users, and a compliance-aware design that makes scaling from a proof‑of‑concept to routine workflows realistic for Austrian teams.

Prompt 1 - “Revenue Forecasting” turns historical sales, billing and churn data into an operational early‑warning system that's especially useful in Austria's 2025 landscape: with GDP expected to contract again and the government deficit projected above 4% of GDP, finance teams need rolling, scenario‑rich forecasts to protect cash and guide tight budgets (see the European Commission economic forecast for Austria 2025: European Commission economic forecast for Austria 2025).

Start with pragmatic models - moving averages and bottom‑up pipeline builds for recurring streams, regression or multivariable models when marketing spend or energy costs matter - and combine them with scenario planning and monthly refreshes so a small slip in revenue shows up long before month‑end reconciliations.

Practical playbooks such as the “10 revenue forecasting models” guide explain when to use pipeline, usage‑based or renewal/expansion approaches, and why hybrid models reduce blind spots.

Pair forecasts with strong data pipelines and governance so outputs are auditable and actionable; the payoff is concrete: faster decisions on hiring, CAPEX and collections, and clearer reports for stakeholders or lenders.

For Austrian teams piloting forecasts, low‑code tools and a short prompt that specifies inputs, cadence, confidence bands and stress scenarios can deliver a usable next‑quarter projection in days, not months.

“With the right strategy, CFOs can create substantial benefits by deploying emerging technologies such as AI.”

Prompt 2 - “13‑Week Cash Reforecast” turns routine AR/AP activity into a weekly lifeline: map recent accounts receivable and payable flows, bank balances and ERP feeds into a rolling 13‑week model so teams can spot looming shortfalls and act before a crisis arrives.

Use the direct, weekly approach favored in models like GTreasury's 13‑week guide to capture receipts, supplier payments, payroll and one‑offs at the transaction level, then refresh the roll forward each week and run best/expected/worst scenarios to stretch that extra time into options (delay non‑critical payables, accelerate collections, draw or repay a short line).

Automation and AI reduce manual churn - J.P. Morgan's playbook shows how daily positioning, automated data pulls and pattern recognition improve accuracy - yet success still needs a clear owner (a “cash office” or treasurer) and executive sponsorship so inputs arrive on time.

For a vivid rule of thumb: treat cash like oxygen - when it's cut off, operations stop - so a tight weekly reforecast, linked to AR/AP and bank feeds, gives Austrian finance teams practical, immediate control over liquidity.

GTreasury 13‑Week Cash Flow Model Guide for Treasury Teams • J.P. Morgan Cash Forecasting Tips for Treasury and Finance Professionals • Business of Architecture: Why Cash Is Like Oxygen - 13‑Week Cash Flow Forecast

“The ‘special sauce' of forecasting is the human element: knowing how to interpret the data and anticipate market uncertainty.”

Prompt 3 -

Expense Categorization

turns a pile of receipts into a compliance-safe, audit-ready ledger by automatically sorting transactions, matching VAT codes and flagging outliers that need human review - a must in Austria where correct invoicing dictates input‑VAT recovery and missing proof can block claims for months.

Use rules that reflect local law (standard VAT 20% with reduced rates 13% and 10%), per‑diem and mileage conventions, and archival requirements so the model understands which items are deductible, which need a fiscal representative, and when to apply reverse‑charge logic; practical references include Klippa's Austria spend‑control guidance and Yokoy's compliance rundown on per diems, proof of receipt and VAT extraction.

Add anomaly detection to spotlight duplicate or unusually large reimbursements, apply the 12/12 or 3/3 per‑diem rules for travel allowances, and surface receipts that lack invoice essentials (VAT ID, invoice date, line descriptions) so teams can reclaim input VAT faster - think of it as a triage nurse for spend: catching a single bad claim early keeps liquidity flowing and audits calm.

Prompt 4 -

AR Aging Summary

equips Austrian finance teams with a clear, auditable snapshot of open invoices so collections can move from guesswork to precise action: compile customer and invoice details, sort outstanding amounts into standard buckets (0–30, 31–60, 61–90, >90 days), and surface top overdue customers for prioritized follow‑up - an accounts receivable aging report is your financial early‑warning system (accounts receivable aging report guide - Stripe).

Review the report weekly or biweekly for high‑volume books, calculate DSO and collection KPIs, and link notes on disputes or payment plans so collectors and credit managers share a single source of truth; practical templates and automation playbooks show how to move from Excel to real‑time dashboards (AR aging report template and automation tips - Zenskar).

Remember the hard statistic: invoices lingering past 90 days have a much lower chance of collection (only about 18% paid), so flagging and escalating those customers early preserves cash and reduces bad‑debt risk.

For teams still on spreadsheets, a simple Excel build‑out gives instant visibility and a repeatable process (how to create an aging report in Excel - Sage), while automation scales the same rules across ERPs and multicurrency billing.

Prompt 5 - "M&A Target Evaluation" (Concourse M&A Analyzer) packages a practical, audit‑ready pre‑deal checklist so Austrian finance teams can run fast, repeatable diligence that maps to the disciplines PwC treats as essential: red‑flag scans, financial due diligence (P&L, cash flow, working capital and quality of earnings), plus tax, legal, operational and technology checks that reveal integration risk or hidden upside; use the prompt to request standard inputs, define tolerance thresholds and return a short red‑flag summary and recommended next steps, mirroring PwC's emphasis on speed, digital tools and cross‑discipline analysis (PwC business diligence services and methodology).

In Austria's 2025 market backdrop - where macro uncertainty means price assumptions are fragile - a Concourse prompt can capture the same critical questions a human diligence lead would ask and surface issues early (for example, a single overlooked working‑capital item can turn a clean term sheet into weeks of renegotiation).

For teams without a data‑science bench, pilot the prompt with beginner‑friendly, low‑code tooling to validate signals before scaling (beginner-friendly low-code AI tools for finance teams) and keep the conclusion grounded in current market themes (J.P. Morgan 2025 global market outlook).

“Business Diligence” is part of our transaction process. An overview of all our transaction processes you can find here.

Start small, govern wisely, scale faster: pick one high‑value prompt from this list (for many teams the 13‑week cash reforecast or AR‑aging summary), run a short low‑code pilot to prove accuracy and owner accountability, measure one clear KPI (DSO, cash runway or forecast error) and only then expand the scope; Austria's fast‑moving AI scene and the DACH finance findings from PwC both show pilots are where business value and compliance meet, so bake explainability, data lineage and audit trails into every rollout (PwC analysis of AI in financial services).

Use beginner‑friendly tooling and checklists to keep risk low while moving fast - Nucamp's roundup of low‑code options helps finance teams pilot without a data‑science bench (Top 10 AI tools every finance professional in Austria should know in 2025) - and invest in prompt and governance skills through a structured program like the 15‑week AI Essentials for Work so prompts translate into repeatable, auditable workflows (AI Essentials for Work syllabus - Nucamp).

Treat each pilot like a surgical test: one owner, one metric, short feedback loops - and the result is steady, governed scale rather than brittle, rushed adoption.

“AI is going to be a key competitive factor for financial institutions in the future, but it also offers other applications far beyond process automation.” - Michael Berns