Ep. 18 | Is Your Data Ready for AI?

For finance leaders, poor data quality can have consequences far beyond inaccurate reporting. It can affect decision-making, operational efficiency, compliance, customer relationships and, in extreme cases, the financial stability of the business.

In Episode 18 of Finance Chief Uncut, host Matt High is joined by James Briers, Founder and Board Member at Intelligent Delivery Solutions (IDS), to explore why organisations need to get their data foundations right before scaling AI.

Drawing on a career spanning software, data quality and enterprise transformation, James explains why businesses need greater visibility over their data and AI, how poor information can translate into financial risk and why governance must become an ongoing business process rather than a periodic exercise.

His message to finance leaders is clear: AI may offer significant opportunities, but businesses cannot expect reliable results if the data feeding it is inaccurate, fragmented or poorly governed.

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In this episode Matt and James explore:

• Why data quality has become critical to successful AI adoption
• How poor data can affect financial reporting, billing and business decisions
• Why organisations need continuous visibility over data and AI
• How CFOs can balance AI opportunities against cost and risk
• Why responsibility for AI cannot sit solely with technology teams
• How finance leaders can create greater accountability around AI and data

Understand the financial cost of poor data

For James, data is not simply a technology issue. It provides the operational view on which organisations make decisions.

When that information is inaccurate, incomplete or duplicated, those decisions can quickly become unreliable.

The consequences can range from inefficient processes to much more serious financial problems.

James points to situations where inaccurate data can leave organisations significantly further away from their expected financial position than leaders realise, potentially affecting operating capital and forcing businesses into recovery or turnaround scenarios.

For CFOs, that makes data quality a financial priority.

Even where the consequences are less severe, fragmented information creates additional work. Finance teams can spend significant time consolidating information from multiple systems while increasing the opportunity for human error.

Make data the foundation of AI

As organisations expand their use of AI, James argues that strong data foundations have become non-negotiable.

AI systems rely on the information businesses provide them. If that information is inaccurate or poorly governed, organisations risk generating unreliable outputs and making decisions based on flawed information.

The data itself is only one part of that foundation.

Organisations also need to understand the processes surrounding their data, the workflows AI interacts with and the governance structures controlling how those systems operate.

For CFOs assessing AI investment, that means looking beneath the technology and understanding whether the organisation is actually prepared to use it effectively.

Know where AI actually adds value

The pressure to adopt AI can create another problem: businesses deploying it where it is not needed.

James warns against an “AI everywhere” strategy.

Instead, organisations should understand their existing workflows and identify where AI can create the greatest benefit.

A relatively small proportion of business processes could potentially generate the majority of the value, meaning organisations do not necessarily need AI embedded across every operation.

This is particularly relevant for CFOs.

AI usage carries a cost, and uncontrolled adoption can cause those costs to increase quickly. James says finance leaders are becoming increasingly aware of this as they consider the financial implications of expanding AI across their organisations.

Make AI a business issue, not a technology issue

One of James's key arguments is that organisations should stop viewing AI purely as a technology challenge.

AI affects finance, operations, governance, compliance, strategy and people. Responsibility for its adoption therefore cannot sit exclusively with the technology team.

James advocates bringing together stakeholders who understand both the business and the technology.

That means creating ownership and accountability across different functions while ensuring boards have enough visibility to make informed decisions.

CFOs can help organisations understand the financial implications of AI, challenge whether investment is generating value and ensure risk remains visible at board level.

Give boards the visibility to act

For finance leaders wondering where to begin, James returns repeatedly to one principle: visibility.

Organisations need to understand what they have, the current state of their data, where AI is already operating and where the risks and opportunities sit.

From there, leaders can make more informed decisions about where AI should be deployed, what controls are required and where investment will generate the greatest value.

James also stresses that every organisation is different. An AI use case that creates significant value for one business could introduce unnecessary cost or risk for another.

That makes cross-functional oversight essential.

Boards need people who understand the business, people who understand the technology and a governance structure capable of bringing those perspectives together.

For CFOs, the opportunity is to help connect those decisions with financial outcomes.

AI adoption will continue to accelerate, but organisations that understand their data, maintain visibility and establish clear accountability will be better placed to turn that technology into sustainable business value.

Explore more from Finance Chief Uncut

Catch up on Episode 17, featuring Rod Freeman, VP of Customer Success at Globality, exploring how finance leaders can use AI to drive ROI, unlock savings and transform procurement.