From what I’ve seen, teams get better results when they don’t try to automate everything at once.
Common patterns that work:
- Start with high-volume tasks like data extraction, line validation, and basic GL suggestion to reduce manual effort.
- Keep human involved for exceptions, especially early on. Automation should highlight anomalies, not force decision.
- Leverage historical approval data no accuracy improves over time.
- Prioritize ERP compatibility - automation fails quickly if it disrupts existing workflows.
Some teams are using AI-driven finance automation tools(for example, Vic.ai) mainly for invoice intelligence and anomaly detection rather than full process replacement. Automation seems most effective as decision support , not just a rules engine.
Interested to hear how others handled rollout and exceptions.
