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Whether your organization is still experimenting with AI or already building it into core processes, this story challenges three assumptions a lot of leaders still hold.

Trust has always been contextual. We trust people to perform specific responsibilities under specific circumstances, with safeguards appropriate to the level of risk.

For many finance and business leaders, the honest answer to "what's your AI governance plan?" is some version of: we know we need one, and we haven't started.

Artificial intelligence is unfolding differently.

Organizations are investing unprecedented amounts of capital in artificial intelligence.

My flight to Charlotte is delayed again while the airline works through a mechanical issue. I'd love to get home sooner, but I have zero interest in watching them rush the process or cut corners to improve an arrival metric. The delay is frustrating, but the priority is clear: make sure the aircraft is safe before it leaves the ground.

Start with an honest assessment of your oversight readiness. Not of the AI tools themselves, but of your controls around them.

In a global study of 48,000 people, 57% admitted they present AI-generated work as their own. Another 66% said they don't consistently verify whether that output is accurate.

I hosted an executive roundtable last week, and one of the topics that came to the forefront was the rising cost of AI. Most executives understand the upfront costs. Software licenses. Consulting support. Governance. Training. Implementation. What many are only beginning to realize is that AI costs aren't always fixed. As organizations move beyond simple productivity tools and begin embedding AI into workflows, decision processes, and business applications, cost becomes incre

Seventy percent of employees are already using AI at work without adequate governance in place. Not in some distant future state. Right now, across your organization. The question finance leaders are grappling with isn't whether AI is being used. It's whether anyone knows where, by whom, and with what consequences for the decisions that matter most. That's the operational trust gap. And closing it is quickly becoming one of the defining leadership challenges in finance. Here'

People often say money magnifies character. I think AI does the same thing. In the hands of a highly capable employee, AI can be transformative. Not just making them faster, but making them dramatically more effective. Better analysis. Better communication. Better execution. Better decisions. AI can easily 10x the output of someone who already has strong judgment and discipline. But the opposite is also true. AI in the hands of a poor performer rarely creates a superstar. It

During the pandemic, a fitness company had the data. The machine learning models clearly signaled that demand was cooling. Leadership saw the output and decided to override it and build more. The world reopened, demand collapsed, and the company spent the next year in damage control mode with big financial consequences. You may be familiar with that story, and there are also plenty of examples where AI was wrong and companies paid the price. But here's the part that gets

Most companies have budgeted for AI. They’ve budgeted for software licenses, pilots, consultants, training, automation, and data infrastructure. They’re making room for AI because they know it’s going to reshape how work gets done. What far fewer companies have budgeted for is AI failure. According to recent data from EY, 64% of enterprise organizations reported at least one AI-related incident that cost more than $1 million, with the average financial impact estimated at $4.