Beyond the Balance Sheet: Why CFOs Need an AI Readiness Plan for Predictive Finance

This blog was written in collaboration with Holly DeSantis, Executive Vice President and Chief Financial Officer, Konica Minolta

For Fortune 1000 CFOs, AI adoption in finance moved quickly for a couple of years and then slowed almost to a stop. Gartner’s 2025 AI in Finance Survey shows a sharp rise from 37% in 2023 to 58% in 2024, followed by only 59% in 2025. A one-point increase after such a steep jump could technically be described as “steady,” but anyone who has lived through an enterprise rollout can read between the lines. The first wave of AI experiments was easy to launch. Expanding that work in a way that delivers real financial impact is where most teams started to hit resistance. In many enterprises, the hidden cost is fragmented data: forecasts that lag reality, working capital surprises, and decisions that arrive too late to manage.

Even with that slowdown, confidence hasn’t slipped. The same survey found that 67% of finance leaders are more optimistic about AI this year. That level of optimism is meaningful because it signals trust in the long-term potential. What hasn’t kept pace is the readiness underneath the ambition. Teams are learning that running a pilot is simple, but scaling anything connected to forecasting, cash, supply chain, or working capital requires stronger data foundations than most organizations currently have.

Most CFOs can feel this gap growing. Finance is being asked to provide sharper forward visibility, tighter cash discipline, and more reliable guidance in a business environment that moves faster than traditional planning cycles. When inputs come from systems that aren’t aligned or the timing of data isn’t consistent, it becomes harder to trust the forecast. And when that trust weakens, financial judgment becomes slower and more cautious than the business needs.

Where Financial Pressure Shows Up in Performance

You see the effects across several areas of the financial engine:

• Forecasts that move too slowly to keep up with real shifts in demand
• Margin pressure that becomes visible only after it’s already hurting results
• Inventory that sits longer than expected, tying up capital
• Cash timing that shifts unpredictably from month-to-month
• Last-minute operational spending that could have been avoided with earlier signals

None of these challenges are new. They are just more noticeable now because the pace of change is higher and the margin for delay is lower. Without consistent, connected data, even well-designed models struggle to keep up.

Why the Data Layer Matters

The most effective predictive finance work starts with the same thing: stronger data foundations. Not bigger models, not more tools. Just better alignment of how information moves through the organization.

An AI data layer helps by creating a shared structure for the signals that shape most financial outcomes. Think of it as a governed layer that standardizes and connects data across core systems (ERP, CRM, and supply chain), so forecasting models run on consistent inputs. It reduces the amount of reconciliation required and provides a clear view of what’s happening operationally. And it gives CFOs something they don’t always have today — the confidence that every team is using the same definitions and the same version of the truth.

A dependable data layer usually delivers a few practical advantages:

• More transparency across systems

Operational data from sales, supply chain, fulfillment, and production feeds into finance consistently.

• Cleaner definitions of key metrics

Teams stop debating whose number is right and focus on why the number moved.

• Better auditability and control

Data lineage becomes easier to follow, which helps with risk, compliance, and board discussions. This defensibility is what makes forecasts board ready.

• Reusable building blocks

One investment in structure supports many use cases instead of one-off analytics projects scattered across the business.

It isn’t elegant work, but it’s the part that determines whether AI becomes a core part of planning or stays in the “pilot” stage indefinitely.

“In my view, the true power of AI lies in the rigor and discipline in how you approach your data foundations. Lasting success doesn’t come from deploying more tools or bigger models – it begins with establishing trustworthy, well-aligned data before you even think of scaling. When you get the data right, everything else becomes possible.” – Holly DeSantis, Executive Vice President and Chief Financial Officer, Konica Minolta.

How Predictive Finance Actually Works in Practice

When the underlying data is reliable, predictive finance becomes much easier to operationalize. You start to see:

• Revenue and cost forecasts that adjust as soon as new signals come in
• Earlier identification of where working capital could tighten or loosen
• Faster recognition of margin risks from pricing or supply changes
• Scenario models that reflect the real state of the business, not last quarter’s assumptions
• Planning discussions that focus on actions instead of data validation

This doesn’t replace judgment. It strengthens it. Finance teams can spend more time understanding the implications and less time stitching data together.

Where the Improvements Show Up

Here is a more practical breakdown of where CFOs usually feel the impact first and what enables it.

What a CFO-Led Readiness Plan Usually Looks Like

CFOs who have moved beyond pilots tend to follow a familiar pattern. It’s not complicated, but it is consistent:

1. Start with the areas where volatility hurts the most (demand movement, inventory swings, supply disruptions, or cash timing).
2. Map the data that influences those outcomes (where it lives, how it flows, and how clean or aligned it is).
3. Build the data layer before expanding the use cases to prevent each pilot from becoming an isolated project.
4. Choose early use cases that show measurable impact (forecasting, cash visibility, inventory optimization, and margin analysis).
5. Bring risk and audit teams into the process early so outputs can be explained, not just calculated.
6. Scale to other teams once early wins are stable (treasury, FP&A, supply chain, and pricing).

This is how organizations shift from early excitement to durable results.

The Opportunity Ahead

AI can strengthen the finance organization in very practical ways. It improves visibility, reduces uncertainty, and helps teams respond to change with more confidence and less delay. But it only works when the foundations are in place. The organizations that invest in those foundations now will be in a better position to guide the business through whatever comes next.

A thoughtful AI readiness plan turns data into a strategic asset and makes finance more forward-looking without adding complexity. To see a roadmap for building the data layer and starting with a targeted pilot in a high-impact workflow, download The Executive Framework: AI Readiness Roadmap for Fortune 1000 Enterprises.

Got questions now? Let’s connect for a 15-minute call today.