Is Your Data Ready for AI?

Welcome to this week’s edition of Balanced AI Insights.

When organizations start exploring AI in finance, there’s often excitement about tools, automation, and bold use cases—cash flow forecasting, auto-generated dashboards, AI-driven reporting.

But I haven’t seen a single implementation that could go straight to AI without first fixing the data.

This issue focuses on the third pillar of AI readiness: data.

📢 Upcoming Events for Finance Leaders – Save the Dates!

🔹 State of AI in Finance – April 6 (Houston, Free In-Person Event)

A practical executive briefing on AI adoption in finance featuring expert speakers and real-world case studies for CFOs and finance executives.

🔹 How To Break Digital Transformation Barriers And Accelerate AI Adoption – April 24 (Free Online Event Hosted by Accounting Advocate)

In this session, we’ll break down the biggest barriers holding finance back and explore real-world strategies to accelerate AI adoption.

The Balanced View: Data Readiness Is the Quiet Power Behind Successful AI

The AI audits I conduct evaluate readiness across four key areas: processes, people, data, and governance. While each plays a role, data readiness is where I most often put on my CFO hat. Because the data I assess isn’t just about format or consistency—it’s deeply financial. I ask questions like: Do you have a proper close process, so pulling data from QuickBooks today won’t yield a different result tomorrow? Or, Is revenue recognized consistently across entities and subsidiaries?

I often work closely with the client’s CFO, and almost always, the data issues we uncover are not new. They’ve usually been on the radar for years. But without urgency, they’ve stayed unsolved. 

However, AI projects can’t move forward with these data gaps and inconsistencies.

So, if your organization is considering an AI implementation project, this might be the perfect time to address that inconsistent, clumsy data—the same issues you’ve been meaning to fix for years.

A Story That Says It All

I recently started working with a company that has been around for 15 years. It operates across multiple countries, has opened and closed legal entities, bought and sold business units, and never implemented an ERP.

The company was tired of an inefficient budgeting process and wanted to introduce AI to make it smoother. Well, it's no surprise that the budgeting process was a mess. Guess where 15 years of consolidated financial data lived?

In a tab inside an Excel file—with more than fifty other tabs surrounding it, many obsolete or referencing sheets that no longer exist. The funny thing is that everyone knew how to find it. 

Before we could even talk about AI, we had to rebuild the foundation. We spent time cleaning the data, aligning definitions, and building consistent mapping across business units. Only then could we move forward with anything AI-related. The fix wasn’t overwhelming—it just required focused attention.

Three Layers of Data Readiness 

1. Structure

AI needs structure—reliable, repeatable logic across all inputs.

A recent client was eager to automate expense reporting using an AI tool. But when we reviewed the setup, we discovered that the same vendor might be coded under different names across departments—sometimes with typos, sometimes with completely different categorizations.

The tool couldn’t reconcile them without help. We introduced naming conventions and a master vendor mapping table. It took just a few focused days—and unlocked automation that had been stalled for months.

Things to check:

• Are your chart of accounts and cost centers consistent across entities or divisions?
  

• Do your teams follow the same reporting formats?
  

• Are naming conventions enforced?
  

You don’t necessarily need to implement an ERP. Sometimes, a simple mapping exercise is enough. And if you didn’t know, AI can actually assist with this cleanup work.

2. Stability

AI models need to be built and trained on stable data.

One company I worked with was pulling data directly from QuickBooks into an AI-powered forecasting tool—but the outputs kept shifting. As it turned out, the accounting team had no month-end lock process. They were retroactively changing prior period data for weeks after the close. AI, of course, treated those changes as new inputs—completely distorting the forecasts.

We had to hit pause, build a proper close process, and implement version control. Only then could we trust the outputs.

And like I said before, the CFO knew the close wasn’t working; it just never hurt enough.

Ask yourself:

• Are past periods truly locked after close?
  

• Are financial statements subject to post-close adjustments?
  

• Does your AI pipeline reflect a frozen set of data?

You don’t need every answer today. But you do need stability if you want AI to produce reliable results.

3. Access

Even perfect data isn’t useful if AI tools can’t find or trust it.

In one case, a company was using legacy accounting software that couldn’t be connected to modern AI tools via API. Every time the team needed data for reports or analysis, they had to export CSV files manually, clean them up, and re-upload them into another tool. This not only introduced delays but also created versioning issues and increased the risk of human error.

We eventually recommended creating an interim staging environment—a cloud folder with clean, structured exports from the accounting system—to bridge the gap until they could transition to a more modern, integratable system. It wasn’t perfect, but it allowed the AI implementation to move forward.

Things to consider:

• Do you have a single source of truth for financial reporting?
  

• Is data stored in a place where AI tools (and your team) can access it?
  

• Do you know who owns each dataset?
  

None of this requires heavy IT infrastructure. Sometimes, it’s just a matter of digital housekeeping—and a few well-placed access controls.

There’s a misconception that AI can only work once your data is perfect. That’s not true. What AI needs is clarity, consistency, and access. Most of the foundational fixes—like aligning revenue structures or building a basic month-end close control—can be handled in days or weeks, not months.

So if AI is on the roadmap at your company, start here: go back to the data. Look at what your CFO already knows needs fixing.

AI Data Readiness: How I Spot the Red Flags in AI-Powered Forecasting & Budgeting Projects

One of the most common requests I receive is to help finance teams implement AI for forecasting. It’s easy to see why: the potential for faster, smarter, more adaptive forecasting is compelling.

But the reality is that a very high percentage of AI forecasting projects fail. Not because the tools are weak, but because the data they rely on simply aren’t ready.

To uncover this early, I start every project with a 30-minute discovery session—usually with the CFO and FP&A lead. Below are the exact questions I use to assess data readiness for AI-powered forecasting and budgeting.

🧠 My Go-To Questions for Data Readiness in Forecasting & Budgeting Projects

1. What does your month-end close process look like?
   I want to understand timing, controls, and whether the numbers change after the fact. Stable actuals are essential for AI.
   

2. How are revenue recognition rules applied across your entities or business units?
   Inconsistent logic across subsidiaries is one of the biggest reasons AI forecasts become unreliable.
   

3. How aligned are your actuals and planning structures?
   Do you use the same account groupings, dimensions, and categories in forecasts as you do in actuals? If not, AI struggles to reconcile the two.
   

4. Where does your forecast model live, and how many versions are circulating at any given time?
   This helps me gauge the maturity of your collaboration process and version control.
   

5. How do you collect and structure compensation or headcount data for planning?
   FTE and payroll data often lives in HR or operations, not finance—and it’s often messy.
   

6. What does your reforecasting process look like?
   I want to hear about timing, data triggers, and whether it's a refresh or a rebuild each time.
   

7. Can your team easily export a clean, structured dataset of actuals and budgets?
   If every report requires manual reformatting, AI ingestion becomes time-consuming and error-prone.
   

8. How are assumptions documented and maintained across forecast cycles?
   I’m looking for whether they live in a shared location, have owners, and are versioned—or if they disappear into spreadsheets.
   

9. What happens when someone spots an error in a forecast?
   This surfaces how fixes are handled—whether there’s a clear process or it’s done ad hoc without tracking.
   

10. Who owns the forecasting process—and do they own the data, too?
    Forecasting ownership is often fragmented. AI needs clarity around responsibility for inputs and outputs.

Closing Thoughts

Data readiness plays a critical role in successful AI adoption—and for many organizations, it’s the necessary first step. A large percentage of AI projects stall or fail because the data is too clumsy, inconsistent, or fragmented to support it.

Often, the issues are already known. If you’re a CFO, chances are you’ve been aware of the weak spots in your data—across reporting structures, revenue classifications, and version control—for years.

Now is a good time to address them, while you still have the space to make foundational improvements without pressure.

Next week, we’ll close out the AI readiness series with the final topic: AI governance. We’ll talk about the oversight structures, policies, and human safeguards that make AI responsible—and auditable—in a finance environment.

See you then. Stay Balanced!

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Until next Tuesday, keep balancing!

Anna Tiomina 
AI-Powered CFO