#306 Feel Behind on AI? Where CFOs Should Start Gavin McGahey CTO & Co-Founder at AccountsIQ

As finance teams face mounting pressure to adopt AI, CFOs need a practical starting point – not a rushed, organization-wide rollout. Gavin McGahey, CTO & Co-Founder at AccountsIQ, examines the gap between finance professionals’ interest in AI and their need for confidence in its accuracy, security, and business value. The episode references a survey of more than 1,000 senior finance professionals and reports that 81% mostly or fully trust AI-driven outputs.

McGahey recommends beginning with limited, measurable use cases, such as accounts payable automation and anomaly detection. He also stresses the importance of well-structured data, careful review of vendors’ data-protection practices, and human oversight of consequential actions. A deliberate approach can help finance leaders test AI’s potential while maintaining review and audit controls.

Key topics covered:

• Finance leaders see AI’s potential but need confidence in its accuracy, data handling, and verifiability.
• Initial AI projects should be small and matched to a finance team’s readiness.
• AP automation and anomaly detection offer practical starting points for saving time and flagging unusual transactions.
• Well-structured data is essential for useful AI analysis; poor inputs can lead to convincing but incorrect results.
• Focused AI agents can assist with finance workflows while people retain control over approvals and posting.
• Finance teams should examine vendors’ data practices, security documentation, and audit trails before adopting AI tools.

Links

• Gavin McGahey on LinkedIn
• Kevin Appleby on LinkedIn
• GrowCFO Mentoring

Timestamps: 

• 00:31 — Finance professionals see AI’s potential but feel pressure to adopt it quickly.
• 04:11 — Questions to ask about AI solutions, including data use, transparency, autonomy, and traceability. 
• 05:40 — Why well-structured data matters when using AI tools for finance analysis.
• 09:26 — Start small, choose carefully, and adopt AI at the organization’s own pace.
• 11:17 — AP automation and anomaly detection as practical early use cases.
• 20:21 — AI agents supporting finance workflows while users retain control.
• 31:14 — Reviewing data-protection documentation and supplier practices.
• 35:03 — Building on sound finance processes when introducing AI.

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