Data First, AI Second: What CFOs and Finance Leaders Need to Know About AI Readiness

AI is firmly on the boardroom agenda. But for CFOs and Finance Leaders, the real challenge is understanding where it can deliver value, what investment is required and whether the business is ready to make it work.

With pressure to improve efficiency, manage costs and deliver measurable returns, Finance has a critical role in shaping how organisations approach AI.

The opportunity is significant. But where should Finance start, and how do you move from AI ambition to measurable business impact?

1. Is your Finance function ready for AI?

Before investing in new technology, Finance leaders need to understand whether the foundations are in place. AI readiness goes beyond having the right tools. It depends on the quality of your data, the maturity of your processes, the systems you rely on and the capabilities within your team.

The most common gap is not technical. It is data. Organisations that have underinvested in data quality, governance and ownership consistently find that AI amplifies the problem rather than solving it – producing outputs that look credible but cannot be trusted.

CFOs should be asking:

• Is our data accurate, complete and useful for the decisions we need to make?
• Are our Finance processes and systems fit for purpose?
• Do we have the right skills and internal capabilities?
• Is ownership clear across data, systems and governance?

If the honest answer to the first question is no, that is where the work starts – not with the AI platform.

2. Where can AI deliver genuine value?

Not every AI use case will deliver the same return. The priority should be identifying where technology can solve real business problems, improve decision-making and create measurable efficiencies.

For Finance, the highest-value use cases tend to cluster around the same themes: reducing manual reconciliation and reporting, improving forecast accuracy, strengthening cash flow visibility and enabling faster, better-informed decisions for non-finance stakeholders.

Chasing more exotic applications before these foundations are working well is one of the most common ways Finance AI programmes lose credibility with boards – spending is visible, but the return is not.

The key questions are:

• Which use cases offer the greatest potential value?
• How will success and ROI be measured?
• What investment is required, and when will benefits be realised?
• How should investment evolve as the organisation matures?

For CFOs, connecting AI investment to tangible business outcomes will be essential.

3. Data first – but do you need to fix everything?

Data quality and governance are fundamental to successful AI implementation. But one of the most paralysing mistakes Finance leaders make is concluding that everything needs to be perfect before anything can start.

It does not. The more practical approach is to pick a specific, high-value use case, better cash forecasting, for example, or automated variance analysis, and improve data quality in that area first. That creates visible progress, builds internal confidence and generates evidence of what works before committing to broader investment.

Finance leaders need to understand not only whether their data is reliable, but whether it is relevant, complete and accessible for the intended use case. This means establishing clear ownership, identifying gaps and making governance practical enough to support progress rather than slow it down.

4. Finding the balance between investment, speed and risk

CFOs must balance the pressure to adopt AI with the need to manage costs, maintain controls and demonstrate value. The two failure modes are well established: moving too quickly without the right foundations creates risk and erodes trust when outputs prove unreliable; waiting until every process and system is perfect means competitors move first and the board loses patience.

The answer is sequencing – being deliberate about what has to come before what, rather than trying to do everything at once or nothing until conditions are ideal.

This involves:

• Mapping existing skills and identifying capability gaps.
• Deciding what to build internally and where to bring in external expertise.
• Phasing investment in line with business priorities.
• Establishing clear accountability and appropriate controls.
• Moving from experimentation and pilots towards implementation at scale.

The organisations getting this right are not necessarily the ones moving fastest. They are the ones who are clearest about where they are starting from.

5. Turning AI strategy into action

For CFOs and Finance Leaders, AI readiness is ultimately about more than technology. It is about ensuring Finance can use it to improve performance, support better decisions and deliver measurable business value – and that means Finance needs to lead the conversation, not just respond to it.

The CFO is often the only person in the room who can connect the AI ambition to the financial reality: what it costs, what it requires, what it will and will not deliver, and when. That is not a constraint on progress – it is exactly the contribution Finance should be making.

The question is not simply whether your organisation is using AI. It is whether you have the foundations, capabilities and roadmap to make it work.

Join Cedar for our AI Business Breakfast

These questions do not have easy answers, but they have better and worse starting points, and the experience of organisations that have already worked through them is genuinely useful.

Cedar’s upcoming executive breakfast: Data First, AI Second: The Executive’s Guide to AI Readiness, brings together CFOs, Finance Directors and senior Finance Transformation Leaders to explore the practical realities of AI adoption and how Finance can help turn ambition into results.

Led by Anup Juneja, AI Operating Model Design Lead at IAG and former Bain Capital consultant, the discussion will cover:

• Readiness: Assessing data, processes, systems and organisational maturity.
• Value and ROI: Identifying where AI can make a measurable difference.
• Capability: Understanding the skills, ownership and expertise required.
• Data and governance: Making data fit for purpose without creating unnecessary bureaucracy.
• Investment and execution: Phasing budgets and moving from pilots to scale.

The session is designed to be practical rather than theoretical, drawing on real examples of what works, where organisations typically struggle and how Finance leaders can determine their most valuable next steps.

Data First, AI Second: The Executive’s Guide to AI Readiness

Join fellow CFOs and Finance Leaders for a practical discussion on what to fix, where to invest and how to turn AI into business value. Register your interest here.