Yes, You Need AI Governance

Welcome to this week’s edition of Balanced AI Insights—where we turn AI complexity into practical clarity for finance leaders.

This week, we're tackling a topic that often sparks more fear than it should: AI governance. Yes, you need it. No, it doesn’t have to derail your progress. I’ll share simple ways to get started, plus a real-world example of how one professional services firm handled client transparency in an AI rollout—with minimal friction and maximum trust.

Let’s dive in.

The Balanced View: How to Build a Safe, Scalable Foundation for AI

While championing AI initiatives, it's easy to get caught up in automation successes and time savings. However, before scaling AI across your organization, one final pillar requires attention: governance.

Without a thoughtful governance approach, even the most promising AI efforts can stall—or worse, backfire. And yet, governance doesn’t have to mean slow, bureaucratic policy-making. In fact, the best AI governance strategies are practical, contextual, and collaborative.

Let’s break down what it really means to be governance ready—and how you can get there without killing momentum.

✅ 1. Start With Your Reality: Not Every Company Needs the Same Governance Model

When I started working with a new client recently, we were excited to jump into AI use cases. I suggested we begin with a governance policy. Their response? “Oh no, that’ll take us six months. Let’s not even start then.”

That kind of reaction is common—and understandable. The word "policy" often triggers visions of lengthy legal reviews and red tape. But here’s the truth: governance doesn’t have to be heavy-handed to be effective.

In this particular case, we launched with a simple three-point policy:

1. Don’t use AI tools with confidential or sensitive company data.

2. Don’t upload personally identifiable information (PII).

3. Use your best judgment when experimenting with AI.

That was enough to unlock experimentation safely—and avoid derailing innovation with premature complexity. Is this perfect for the long term? Probably not. But for a fast, responsible start? Absolutely.

Every organization will require a different approach. For some, that simple policy may be enough. For others—especially those subject to internal audits, regulatory scrutiny, or handling customer data—more robust governance is non-negotiable.

Before you scale, ask:
👉 What level of liability or auditability applies to our AI use?
👉 Do our AI systems need to pass internal or external checks?

Because AI systems can behave like black boxes—difficult to audit or explain—knowing your risk level upfront will shape how deeply you need to invest in governance from day one.

🧭 2. Build Governance That Matches the Stage You’re In

Governance isn’t one-size-fits-all. Here’s a simple way to think about it:

You don’t need enterprise-level rigor to get started—but you do need to be intentional about where you are on the maturity curve and build accordingly.

🤖 3. Ethics Might Not Be Obvious in Finance—But It Matters More Than You Think

In most finance tasks, governance focuses on things like data security, accuracy, and compliance. But as AI use cases evolve, you may find yourself intersecting with people-related decisions—and that’s where ethics comes into play.

Think about:

• Using AI to benchmark compensation

• Budgeting for headcount allocation

• Scoring vendors or employees using performance data

These are sensitive areas where bias can creep in, and outputs—however statistically “accurate”—can have real-world human consequences.

CFOs don’t have to become ethicists overnight. But when AI touches anything people-related, it’s essential to ask:

• Is this decision fair and explainable?

• Could it amplify bias?

• Would we be comfortable defending this output in a meeting—or in public?

Responsible AI isn’t just about tech hygiene. It’s about protecting people and reputation, especially in use cases where the stakes are personal.

🤝 4. AI Governance Is a Team Sport

Yes, you can implement AI tools in finance independently. But when it comes to governance? Go together, or don’t go far.

True AI governance spans departments:

• Finance knows the risks and workflows

• IT ensures systems and security are up to par

• People/HR flags ethical concerns and bias risks

• Compliance/Legal ensures regulatory alignment

Even if you’re piloting AI in a single workflow, loop these teams in early. Not only will this surface blind spots—it will also accelerate buy-in and prevent roadblocks later.

The biggest misconception about AI governance is that it slows you down. But when done right, it actually speeds you up—by removing ambiguity, reducing risk, and creating clarity on what’s safe, scalable, and responsible.

You don’t need a 20-page policy. You need a clear direction, a shared understanding, and the right conversations happening at the right time.

When AI Meets Clients: A Real Governance Decision

When professional services firms start experimenting with AI—especially on client deliverables—the conversation changes.

One of the firms I’m working with wanted to roll out AI for drafting client reports, analyzing inputs, and generating early-stage recommendations. The finance team was ready. The tools were tested. But there was one big open question:

“Do we need to tell our clients?”

Contractually? No.

But ethically—and strategically? Maybe.

The leadership team decided to notify clients even though they weren’t required to. Why?

• They believed in transparency and trust as a competitive edge.
  

• They didn’t want AI use to come as a surprise later—especially with clients in conservative industries.
  

• They felt that even though AI was only being used to accelerate drafts (not replace expert review), being upfront would reinforce their reputation for quality and care.

They sent a simple, well-crafted email to all active clients. It outlined:

• How they were starting to use AI tools.

• The nature of the tasks (drafting, summarizing—not decision-making).

• Their ongoing human oversight process.

• Reassurance that client data would be handled with strict safeguards and never shared with public AI tools.

💡 The result?

Some clients appreciated the transparency and even asked for a demo of the AI-assisted process for their own teams. A few, however, expressed concerns and requested not to have their data processed with AI-assisted tools. Because the company had anticipated this, they had clear protocols in place to honor those preferences—ensuring all client data flagged for opt-out remained fully human-handled.

By offering a choice rather than a surprise, the company positioned itself as a responsible, client-first advisor in a rapidly changing tech landscape.

📄 Want to do the same?

I've prepared a ready-to-use client communication template you can adapt for your firm.

😄 Funny side effect?

Since I helped this firm implement their AI workflows and write the client communication, they mentioned me in the demos. Turns out… several of their clients reached out asking if I could help them do the same. So, this project eventually turned into a pipeline full of AI-curious leads.

Closing Thoughts

That’s a wrap for this week! Remember: governance isn’t about slowing down—it’s about setting smart boundaries so you can scale with confidence.

If you’re navigating similar questions in your firm, I’d love to hear how you’re approaching governance—or help you think it through.

👉 Reply to this email or connect with me on LinkedIn.

See you next week,
Anna

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Until next Tuesday, keep balancing!

Anna Tiomina 
AI-Powered CFO