The CFO’s Playbook for Leading AI Across the Enterprise

Most CFOs are being told that artificial intelligence will redefine productivity. The problem is, few conversations explain how finance leaders should actually guide that change across the business, beyond automating a few accounting tasks or forecasting models.

AI is no longer a side project for the IT or data team. It’s quickly becoming the backbone of how organisations plan, operate and compete. That means finance leaders are now being called to play a central role: deciding where AI makes sense, how to measure its value, and what risks and returns are acceptable.

This is a new chapter for the CFO, one that blends financial discipline with operational judgment. In my conversations with CFOs, CIOs and strategy leads over the past months, one pattern is clear: the best outcomes come when finance, technology and corporate strategy work in lockstep.

Over the past few months, I’ve spoken with CFOs who are deep into AI implementation, and with senior executives at several global IT services firms running AI programs for clients. Those conversations all come back to one theme: everyone’s experimenting, but few are extracting consistent value. The gap isn’t in technology, it’s in ownership, measurement, and execution.

Here’s how the most effective CFOs are closing the gap between activity and value:

Shift the focus from hype to accountable delivery

We’ve all seen the hype: board slides full of grand claims. But underneath, many companies are still running pilots with uncertain outcomes.

CFOs are uniquely positioned to bring discipline to this process. They can separate genuine transformation from technology theatre.

That starts with three questions:

• What business problem are we solving?
  
• How will we measure impact — in dollars, time or productivity?
  
• Who owns the result if it doesn’t deliver?

When these questions aren’t asked early, pilots drift into permanent experiments. Insisting on investment logic with defined outcomes, measurable milestones, and an exit point, cuts waste and forces clarity.

Treat this as business transformation, not another system purchase

Too many organisations treat AI like another software rollout. They buy tools, plug in data, and expect results. But this changes how work is done: forecasts are produced differently, customer inquiries are handled differently, pricing can be dynamic.

That’s why CFOs should shape AI investments around enterprise outcomes, not system features. A few examples:

• Predictive maintenance in manufacturing can lower unplanned outage costs and improve production yield.
  
• Customer service automation can reduce repeat calls and improve retention.
  
• Smarter demand planning can reduce inventory and free up working capital.

These benefits rarely sit inside a single function. They cross finance, operations and commercial teams. The CFO’s job is to ensure those benefits are quantified, prioritised and baked into the forecast, not merely captured in slide decks.

Build governance that accelerates, not restricts

Good governance doesn’t slow innovation; it gives it credibility. CFOs already manage capital programs and risk frameworks, AI requires the same mindset.

Effective governance means:

• Clear approval criteria for projects, including financial justification and measurable KPIs.
  
• Ongoing checks on data quality, ethical exposure and control risks.
  
• Post-implementation reviews that compare promised benefits to realised outcomes.

One more practical point: treat AI models like any material financial model, they need validation, monitoring, and a lifecycle. Boards and regulators are focused on this now, so insist on model-performance reporting (drift checks, accuracy trends) and post-deployment validation as standard. It isn’t technical theatre, it’s basic risk control that protects value when models are in use.

This isn’t red tape. It’s trust-building. With structured oversight, boards and CEOs are more comfortable funding expansion, and IT leaders can move faster because the business has confidence in the results.

Redefine productivity around people and insight

AI often gets framed as a headcount play. In my experience, the real advantage is in how people spend their time. When routine tasks disappear, skilled staff can focus on analysis, supplier negotiation, and commercial partnering, activities that actually grow margin.

Consider finance close cycles. Teams using smarter reconciliation tools spend less time fixing data and more time explaining movement and risk to the business. That supplies better decisions, faster.

To make this stick, CFOs must invest in change management: training, role redesign, and clear expectations about redeployment. If you don’t budget for the human side, productivity gains evaporate as people revert to old habits.

Partner early, not after the fact

A common failure is finance showing up only after AI pilots are funded. The CFO’s leverage is strongest when involved from the outset: helping shape the business case, defining success metrics, and designing how benefits flow to the P&L.

This partnership extends to vendors. Many enterprises will engage large service providers to build and operate AI solutions. When finance is at the table early, contracts can be structured around outcomes, shared savings, improvement targets, or milestone payments, instead of meters of labour. That aligns incentives and makes vendor relationships true partnerships rather than cost centres.

Measure what the business actually feels

Every CFO knows that what gets measured gets managed. But AI benefits are often indirect: faster decisions, fewer exceptions, less rework. Translate those into financial impact:

• Faster planning means earlier product launches or reduced time-to-cash.
  
• Improved forecast accuracy lowers safety stock and working capital needs.
  
• Fewer service failures increase customer retention and lifetime value.

Also, demand operational observability: set up a simple dashboard that tracks a handful of running indicators, model accuracy, exception rates, and the volume of human overrides. If those trends go the wrong way, you’ll know before the P&L shows it. That early warning is what keeps pilots from quietly failing in production.

By turning AI outcomes into metrics the business understands, CFOs make progress visible, and credible. That visibility builds momentum and future investment support.

Final thought: The CFO as Catalyst

AI will change how every function works. But it will be the CFO who decides whether those changes create sustained value or simply generate noise.

Finance leaders don’t need to be data scientists. They need to be enablers, applying capital discipline, operational judgment and governance so that every dollar spent on AI is likely to return measurable business value.

This isn’t about chasing technology trends. It’s about leading the enterprise into a new era of disciplined innovation, where capital, capability, and confidence move in sync. That is the new playbook for the modern CFO.

About the Author

Ben Dodds is a senior finance leader with CFO and executive leadership experience, helping complex organisations accelerate growth, improve profitability, and deliver enterprise-wide transformation. In senior roles at Tabcorp, DXC Technology, and Hewlett Packard, Ben has shaped finance into a strategic business partner, driving commercial performance, optimising cost structures, and enabling better decision-making.

Ben is known for building leadership teams that deliver results, creating clarity in complexity, and working directly with executive teams to unlock sustainable performance. He shares his perspectives on financial leadership and transformation as a contributor to CFO Magazine A/NZ.

Follow Ben on LinkedIn: https://www.linkedIn.com/in/benrdodds