When it comes to AI in the workplace, I think most people fall into one of two camps:
- “AI is handy for sorting my inbox and taking meeting notes, but that’s about it.”
- “AI will soon take my job, run the world, and I’ll wake up in 2040 being used as a battery.”
The truth? It’s far more nuanced. Many people are steadily building their AI skills, some even experimenting with creating agents to handle small, repetitive tasks. Yet, most still struggle to imagine concrete ways AI could help in their day-to-day work. In fact, many are more comfortable using AI at home than in the office — but that’s about to change.
So, I thought I’d (with a little help from AI) highlight practical, real-world examples of how AI can make a tangible difference in business. We’ll break it down by department, starting with the financial heart of any organisation: Finance.
Cashflow keeps suppliers happy, credit control keeps debtors in check, and payroll keeps the team smiling — but which finance processes are ripe for AI improvement?
Let’s dive in.
1. Accounts Payable & Invoice Processing
Traditionally, data extraction from invoices is a laborious manual process — even when OCR is involved. The challenge? OCR alone needs the layout to be consistent. AI-powered document understanding doesn’t.
AI can recognise “Total” and “Sub-total” regardless of where they appear, validate spelling, and learn from user corrections. Over time, it becomes faster and more accurate than any manual or rules-based system.
2. PO Matching
With AI-enhanced OCR, purchase order numbers are identified even if they’re hidden in unusual places or slightly misread. AI can also use fuzzy matching to handle typos, formatting differences, and cross-check supplier details before confirming the match.
3. Fraud & Anomaly Detection
This is a big win for finance teams.
AI continuously learns spending patterns — by supplier, department, or transaction type — and spots outliers in real time.
Unlike static, rules-based fraud detection, AI adapts to new tactics, catches subtle anomalies, and flags suspicious transactions for extra approval before payment.
4. Expense Management
Plenty of SaaS tools exist for this, but AI can add automation without extra licensing costs. For example:
- Scan and categorise receipts (travel, entertainment, etc.) automatically
- Populate claim forms and submit for approval in Teams or via workflow
- Auto-approve low-value, low-risk claims
The result? Less admin for both claimants and managers, higher compliance, and lower processing costs.
5. Centralised Data & Predictive Modelling
AI thrives on integrated data. Moving finance data into a central platform like Microsoft Fabric unlocks real-time insights and predictive analytics.
Imagine pulling sales spreadsheets from multiple international offices, adding external data like seasonality or political events, and generating forecasts for staffing, stock, or budget planning — all visualised in Power BI for instant decision-making.
6. Footfall & Revenue Correlation
For retail or location-based businesses, AI can combine footfall data (from services like Countwise) with sales, staffing, weather, or even parking costs. The result: clear, data-driven insight into why some locations thrive and others lag — enabling smarter staffing and marketing decisions.
Why Finance Is a Great Place to Start
Finance often sees the fastest ROI from AI because inefficiencies in payment processing, approvals, and reporting are common — and easily fixed with automation.
So, is Finance the best place to start with AI adoption? Or do other departments deserve the spotlight first? I’ll explore more areas in future posts.
And about that whole “AI will take my job, run the world, and turn me into a battery” thing — if it’s anything like The Matrix, it’ll be brilliant for the first instalment… and steadily downhill until you stop caring by episode four.
