Before diving into building, I need to be honest about what AI can actually do today — and where it still falls short. This is the reality check every finance professional needs before starting their AI journey.
This might be the most important entry I'll ever write.
What AI Does Brilliantly in Finance
Let me be clear — AI is genuinely transformative for certain finance tasks: - Pattern matching across thousands of transactions - Extracting data from invoices, receipts, and documents - First-draft financial commentary and variance analysis - Anomaly detection and fraud flagging - Categorising and coding transactions - Generating routine reports from structured data
These tasks share common traits: they're repetitive, rule-based, and benefit from processing speed rather than judgment.
Where AI Still Falls Short
And here's where honesty matters: - Complex judgment calls (going concern, impairment triggers, provisions) - Interpreting ambiguous contracts and side agreements - Understanding business context that isn't in the data - Navigating regulatory nuance across jurisdictions - Client relationships and trusted advisor conversations - Ethical reasoning when numbers could be presented multiple ways
The Critical Insight
AI is not replacing accountants. It's replacing the parts of accounting that accountants never should have been doing manually in the first place.
The future belongs to finance professionals who can: 1. Use AI to handle the mechanical work 2. Apply professional judgment to the exceptions 3. Spend their time on strategy, relationships, and decisions
That's exactly what I'm building toward.
My Principles for This Journey
- Honesty over hype — I'll share what actually works, not what sounds impressive
- Practical over theoretical — Every entry will be grounded in real workflows
- Progressive complexity — Start simple, build up
- Always human-in-the-loop — AI assists, humans decide
With this understanding in place, I'm ready to start building. The foundation is set.