3 Keys to Building Trust in AI: A Finance Leadership Imperative

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's what the organizations getting this right are doing differently.

1. They know where AI is already influencing decisions.

This sounds basic. It isn't.

AI isn't arriving in most organizations as a formal initiative with a kickoff meeting and a governance framework. It's arriving quietly, bundled into tools your teams already use every day. Microsoft Copilot. Salesforce Einstein. SAP Joule. Workday. Oracle.

Over time, employees begin relying on AI-generated summaries, recommendations, and prioritizations in ways leadership doesn't fully see. According to KPMG's 2025 Global AI Trust Study, one of the most comprehensive to date, surveying 48,000 people across 47 countries, 57% of employees are already concealing their AI use from employers. 66% aren't consistently evaluating AI outputs for accuracy. 56% have made errors in their work as a result.

That's not a technology problem. That's a visibility and governance problem. And you can't manage what you can't see.

The starting point for most organizations isn't deploying more AI. It's understanding where AI influence already exists across the business.

2. They've redefined the question entirely.

There's a version of the AI governance conversation that inadvertently slows organizations down. It draws bright lines, AI approved here, humans required there, and in doing so discourages some of the highest-value use cases.

Board reporting. Pricing strategy. M&A analysis. These are all areas where AI can create real value. The question was never whether AI should be involved. The question is what governance structure makes that involvement responsible.

The frame that works better for finance leaders is one they already know intuitively: tiered controls based on risk and materiality.

Two variables determine what oversight looks like for any given AI application: the consequence of error, and the interpretive complexity involved. As both rise, oversight shifts from checking outputs to owning conclusions. AI can play a meaningful role across that entire spectrum. What changes is the governance design, not the decision to use AI.

Finance already thinks this way about controls. AI governance should work exactly the same way.

3. They're engineering confidence, not assuming it.

The organizations scaling AI most effectively aren't simply trusting that their AI tools work. They're building operational systems to validate that they do, and to know precisely when human judgment needs to step in.

Consider how some organizations are approaching this in practice.

We've worked with companies deploying dual-agent validation, where one AI performs the analysis and a second independently reviews it for logic gaps and faulty assumptions before any human sees the output. Others are running AI and human review in parallel for a defined period, measuring error patterns, calibrating trust thresholds, and reducing manual review only when the data supports it. Others still are shifting human attention away from routine outputs entirely, directing review toward anomalies, outliers, and material variances where the stakes are highest.

The specific mechanism matters less than the mindset behind it. These organizations aren't asking "do we trust AI?" They're asking "how do we know when to trust it, and for what?" That's a fundamentally different question, and it's the one that leads to scalable answers.

McKinsey's 2026 AI Trust Maturity Survey makes the point well. Trust is what enables organizations to realize value from AI investments through sustained adoption and integration into core workflows. Without it, adoption stalls regardless of how good the technology is.

The bottom line

McKinsey's State of AI 2025 found that 88% of organizations are now using AI in at least one business function. Only 6% have achieved meaningful enterprise-wide impact.

That gap isn't primarily a technology gap. It's an operational trust gap.

The organizations that figure this out won't just be ahead on AI. They'll have built something harder to replicate than any tool: the operational discipline to scale it responsibly.

If this resonates and you'd like to talk through where your organization stands, I'm happy to have that conversation. Feel free to reach out or schedule time directly.

About Root Idea

Root Idea helps CFOs protect the business from AI decision risk. Root Idea works directly alongside finance teams to map AI influence, establishes decision governance controls that hold up to board scrutiny, and delivers training and change management to make governance stick. 

If your organization is scaling AI and governance hasn't kept pace, that's exactly the conversation we're built for. Learn more at rootidea.ai.