Money Matters: Finance in the New Age
22 September, 2025
Artificial Intelligence: From manual grind to measurable gains
AI is changing how finance teams operate. Think of verifying a Da Vinci sketch: clear criteria, hard evidence, confident decisions. That same precision now applies to payables, receivables, forecasting, and risk-at scale.
Automation that clears the deck
RPA and AI remove repetitive work across invoice processing, transaction categorization, and bank reconciliations. Error rates drop, close cycles shorten by days, and exceptions become the only items that need attention.
- Accounts payable: touchless invoice capture, 2/3-way match, auto-approvals within policy
- Bank recs: continuous matching and exception routing
- GL coding: learned patterns improve accuracy and reduce rework
Result: finance shifts time from keystrokes to decisions.
From reporting to real insight
Machine learning spots outliers and waste in real time. Expense anomalies, duplicate payments, and unusual vendor behavior surface before they become losses.
- Fraud flags: abnormal spend patterns, new vendors near approval limits, weekend activity
- Procurement: supplier scoring that factors quality, delivery, and price trends
- Unit economics: product, channel, and cohort profitability refreshed continuously
Forecasts you can act on
Models use historicals and current signals to project cash, revenue, and risk ranges. Scenario planning moves from quarterly ritual to weekly habit.
- Cash: receipts timing, seasonality, and customer behavior improve short-term views
- Revenue: pipeline, pricing, and macro signals feed rolling forecasts
- Budgets: variance drivers quantified, not guessed
Skills: the line is blurring
Classic finance strengths are still essential, but technical fluency is now baseline. Teams that pair domain judgment with data skills deliver more insight, faster.
- Data basics: SQL, spreadsheets with Python add-ins, data modeling
- Automation: RPA design, workflow thinking, controls
- AI literacy: prompt writing, model limits, evaluation methods
- Storytelling: crisp visuals, clear recommendations, business impact
Guardrails: keep AI useful and compliant
AI without human judgment produces noise. Set intent, measure outcomes, and keep an audit trail.
- Start with clear questions tied to KPIs (DPO, DSO, forecast accuracy, cost-to-serve)
- Data readiness: cleanse vendors, normalize SKUs, define golden sources
- Bias and quality: test on known cases, monitor drift, document decisions
- Controls: approvals, segregation of duties, immutable logs, reproducible outputs
- Compliance: privacy, model risk, and record retention policies
For a solid framework, see the NIST AI Risk Management Framework.
Quick wins finance leaders can deploy this quarter
- AP automation: OCR + matching + policies for touchless processing
- Expense anomaly alerts: real-time flags for out-of-policy spend
- Cash forecasting: 13-week model using collections history and pipeline data
- Close acceleration: automated reconciliations and variance explanations
- Vendor rationalization: spend clustering to cut tail suppliers
Metrics that prove impact
- Close time: days to close and reconciliations completed per day
- Touchless rate: percent of invoices processed without human intervention
- Exception rate: percent of transactions requiring manual review
- Forecast accuracy: MAPE for cash and revenue at 1, 4, and 8 weeks
- Working capital: DSO, DPO, inventory turns
- Loss avoidance: prevented duplicates, blocked risky payments
- Time redeployed: hours shifted from processing to analysis
Operating model: bring finance and data closer
Create small squads that own a metric and a workflow end to end. Pair a finance lead with data, engineering, and process peers; ship in two-week increments.
- One backlog per workflow (AP, AR, FP&A, treasury)
- Standard interfaces: data contracts, APIs, and naming conventions
- Documentation by default: definitions, tests, and lineage
- Change management: short training, office hours, and clear playbooks
What won't change
AI will do heavy data lifting. Finance leaders still set direction, ask sharp questions, and keep context front and center. Blend AI with judgment and leadership, and your team will stand out.
Level up your finance stack
- Explore practical tools for finance: AI tools for Finance
- Upskill by role: Courses by Job
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