Responsible AI adoption is a critical issue for finance leaders: AI can accelerate analysis and improve efficiency, but inaccurate outputs, weak controls, and insecure data handling can put financial reporting and trust at risk. In this webinar, Kevin Appleby leads AAB‘s Andrew Webster and Iplicit’s COO Olivia McMillan through the practical challenge of balancing AI’s potential with the accountability finance requires.
The discussion emphasizes that AI should support finance professionals—not replace their judgment or bypass controls. The speakers highlight clear ownership, appropriate governance, reliable data, audit trails, and human review as foundations for responsible use. They also explore practical applications in credit control, invoice processing, and reporting, while urging finance teams to build AI literacy and critical-thinking skills.
Highlights:
- AI adoption is advancing faster than many organizations’ controls and policies.
- Finance teams should keep humans responsible for reviewing outputs, especially where financial judgment or ledger entries are involved.
- AI systems and processes need clear owners, security checks, and traceable audit trails.
- Reliable data and consistent processes are prerequisites for useful AI results; AI cannot fix weak foundations.
- Practical finance use cases include credit control, invoice processing, and management reporting, provided outputs are checked.
- Finance leaders should develop team members’ AI literacy, curiosity, and ability to critically question AI-generated analysis.
