Ethics & Trust: What Responsible AI Looks Like in Accounting

AI is moving into more parts of the accounting profession. Research, summaries, drafting, workflow support, meeting follow-up. And increasingly, the relationship-driven advisory work that firms are trying to grow.

That’s exciting. For a lot of accountants, it also sets off alarm bells. Honestly, both of those reactions make sense.

The excitement comes from seeing what is possible. The caution comes from caring deeply about doing the work right. Those are not opposites. They are the same professional instinct, looking at a new tool from two different angles. So, how do we do this right? How do we use AI responsibly? 

Trust Your Instincts 

You already know what is at stake. When a client sits down with you, they are sharing more than numbers. They are sharing decisions that affect their business, their livelihood, sometimes their family. You’ve probably felt the weight of that.

All of a sudden, AI enters the relationship, and your instinct is to ask hard questions. To see risk before opportunity. Where is the data going? Who can access it? Can the output be reviewed? What happens when context is missing? Who is responsible if something goes wrong?

Those questions come from the values that drive the profession. Confidentiality is the reason a client tells you things they would not tell anyone else. Objectivity is what makes your judgment worth trusting in the first place. Due diligence is the discipline of getting it right even when no one is watching. And those same values tell you exactly how to approach a new tool.

Three Questions Worth Asking 

Responsible AI use in accounting tends to come down to three questions. Let’s unpack them. 

1. Is client information protected? This is about security. Firms need to understand where data goes, how it is stored and who can access it. If you cannot answer those questions, the tool probably doesn’t belong in client work yet.

2. Can we trust the output? AI does not need to be perfect to be useful. But it does need to be treated as a draft, not a conclusion. That means bringing the same professional skepticism to AI-generated content that you would bring to any other source. Checking it, questioning it and filling in what it may have missed.

3. Who is responsible for the final result? This is the most important question. AI can assist with the work, but it cannot carry the professional responsibility that comes with it. Someone still decides what the tool is used for, what requires human review and what gets shared with a client.

This might look like a manager reviewing AI-generated meeting notes before a client follow-up goes out, catching a missed nuance, adjusting the tone, adding context the tool could not have known. AI got you 80% of the way there. But human judgment made it ready to send. 

Judgment Still Belongs to You

The division of labor between humans and AI deserves a closer look. We know that AI is not an oracle. It does not know your client the way you do. It has not sat across the table from them, heard what they did not say, or carried the responsibility of getting it right. What it can do is act as a second set of eyes, a tool that helps you see something you might have missed.

That is genuinely helpful. But it doesn’t get the final word, even when its output sounds convincing. AI can sharpen your thinking. It cannot replace your judgment.

Think of it the way you already think about tax software. It does the math, but you decide whether the position is supportable. AI belongs on that same side of the line. It can inform the work. You still decide what matters and what happens next.

Where to Start

If you want to use AI responsibly, start small.

Pick one meeting. Upload the transcript to a tool like Navi, built specifically for accounting advisory work. It analyzes the conversation, finds what got left on the table and flags where a follow-up might add value. Use what you find to shape the next conversation.

That’s it. One meeting. One insight. That’s how responsible AI use develops across a firm, by learning what acceptable use looks like in your own work. We have always figured out new tools that way. This one is no different.