What happens when AI allows finance teams to work at unprecedented speed, but enables mistakes to travel just as quickly?
In this episode of Consulting the Future, I speak with Graeme Fleming, Industry Principal for Governance, Risk and Compliance at Workiva, about how CFOs, CIOs, auditors, and risk leaders can capture the productivity benefits of AI without sacrificing governance, data integrity, security, or human judgment.
Graeme brings more than 25 years of experience spanning Big Four consulting, internal audit, risk management, and GRC. He explains why the current AI wave presents a different challenge from previous technology changes. Adoption is moving quickly, employees already have access to powerful AI tools, and companies that fail to provide approved options risk creating a growing problem with shadow AI and sensitive corporate data being entered into systems they do not control.
The conversation examines how AI is changing the role of the CFO. By automating data gathering, reconciliation, analysis, and reporting tasks, finance teams can spend less time managing spreadsheets and more time interpreting information and supporting business decisions. But faster analysis only creates value when leaders can trust the data going in, understand what AI systems are doing, and critically review the results coming out.
As Graeme puts it, companies also risk "making mistakes at the speed of AI."
We discuss why governance and controls should not be viewed as barriers to AI adoption. Graeme compares controls to the brakes on a bicycle: they do not exist simply to make you stop, but to give you the confidence to move faster because you know you can remain in control.
That becomes increasingly important as businesses introduce AI agents and give automated systems greater autonomy. Graeme explains why companies need clarity around what an AI system is designed to achieve, where its data comes from, what happens to that information, how the model operates, and who remains accountable for reviewing its output.
We also examine automation bias and the danger of trusting authoritative-looking AI responses when employees are already under pressure. Human review cannot become a box-ticking exercise. Finance professionals, auditors, and risk teams need to retain the ability to question outputs, identify anomalies, understand context, and take ownership of decisions.
Graeme shares practical examples of where AI is already delivering value in financial reporting and audit, including reviewing lengthy annual reports against disclosure requirements, checking consistency across documents, benchmarking reporting, automating reconciliations, and conducting compliance testing at scale. These applications can remove hours of repetitive work while allowing experienced professionals to concentrate on exceptions, insight, and judgment.
The conversation also looks at the emerging role of the AI auditor, both using AI to support audit work and auditing the AI systems businesses increasingly depend upon. Model governance, security, data provenance, controls, and accountability are becoming business concerns that CFOs and risk leaders can no longer leave solely to technology teams.
For CFOs, CIOs, auditors, consultants, and risk leaders, this episode provides a practical perspective on AI governance, financial reporting automation, shadow AI, automation bias, data integrity, and maintaining meaningful human oversight.
The opportunity is not to choose between AI-driven productivity and strong governance. It is to design controls that give companies the confidence to use AI at greater scale while remaining accountable for the decisions and information their systems produce.
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