Before AI delivers results, CFOs need to rethink their data strategies

AI needs good data. After all, it's the fuel that powers every prediction, insight, and automation your finance function relies on. Incomplete, siloed, or data riddled with errors simply will not cut it. You need clean and reliable data if you want your AI tools to accurately identify patterns, make predictions, and support decision-making.

If you're eyeing up introducing AI to your finance function, then you will need a high level of data hygiene.

What does poor data management look like?
If you're looking to get your data AI-ready, the first step is to identify and address existing data challenges.

When departments work in silos, their data often remains trapped within separate systems, creating a fragmented view of your organisation. If you're a finance professional, you've likely felt the impact of this - making critical decisions without seamless access to accurate, up-to-date information is like navigating in the dark. Breaking down these silos is essential if you want to shift your finance function from reactive to proactive, strategic decision-making.

Errors, inconsistencies, and inaccuracies also run rife in many organisations' datasets. Whether it's duplicate records, incorrect entries, or missing information, poor data quality can erode trust in the insights you're trying to leverage.

Clear data policies are essential, too. Without consistent rules around how data is collected, stored, and maintained, creating a unified strategy becomes nearly impossible. And in finance, inconsistent governance opens up compliance risks in a sector where accuracy and privacy are non-negotiable.

Legacy systems are another common barrier. If your systems weren't designed to integrate or process the large volumes of data required for AI, they will hold you back from unlocking AI's potential.

Finally, there's the challenge of system integration. Finance platforms often don't communicate effectively with other operational tools, leaving gaps in your data flow. This can lead to time-consuming manual consolidation processes, which not only slow you down but also introduce further risks of error.

By addressing these issues, you can lay a strong foundation for leveraging AI to its full potential.

Want to dive deeper into building an AI-powered finance strategy? Download Annexa's free guide, Building an AI strategy for finance, for practical insights and actionable steps.

If you want AI in finance, you need to be a data champion
Finance leaders are uniquely positioned to drive data strategy, making them critical players in preparing for AI adoption. Often, it is the financial systems that become the engine for other systems, acting as the central hub where data from various functions - operations, sales, HR, and more - converges. By ensuring the accuracy and accessibility of this data, finance leaders lay the groundwork for AI initiatives that can deliver actionable insights across the organisation.

Here are some steps to begin your AI in finance journey.

Audit your data environment
Begin by assessing the current state of your data. Is it complete, accurate, and well-structured? Identify gaps and inconsistencies that could prevent AI from performing at its best.

Centralise your data
Integrated systems, like cloud-based ERPs, help to eliminate silos. These business suites are integrated platforms that centralise and streamline an organisation's data and processes. Unlike smaller accounting platforms or legacy on-premises systems, which might operate in silos, a cloud ERP connects different business functions - like finance, operations, sales, and HR - into one unified system. This creates a single source of truth, ensuring that all departments work with the same accurate, up-to-date information. As a bonus, modern cloud ERPs often come equipped with built-in AI capabilities, such as predictive analytics, intelligent process automation, and anomaly detection – outing powerful AI capabilities right into the systems you use every day.  

Establish strong governance
Effective governance involves setting clear roles and responsibilities for data management, defining access levels to ensure only specified employees can view or modify sensitive data, and regularly auditing for compliance. It also means standardising data entry and validation processes to minimise errors and discrepancies. With clear policies on data access, accuracy, and security your organisation's data will be more trustworthy, consistent, and compliant with regulatory requirements.

Invest in AI-ready tools
The tools you choose can make or break your efforts to integrate AI into your finance operations. Platforms already embedded with AI are built to process and analyse vast amounts of data to support advanced capabilities such as predictive analytics, anomaly detection, and intelligent automation. For example, the latest AI features integrated into popular cloud ERP NetSuite offer capabilities such as cash flow trend analysis, automated reconciliation, and the generation of narrative reports to improve the pace of financial analysis and action. 

Ready your AI-powered data strategy today
Ultimately, your chosen tools will only be as effective as the data they work with. Clean, structured financial data will give you the confidence to know that the final outputs are accurate and actionable. Investing in platforms that prioritise data integration and scalability will not only meet your current needs but also position your organisation to take advantage of future advancements in AI. 

For more insights on creating an AI-powered finance strategy, download Annexa's guide, Building an AI in finance strategy.

Our free guide will help you discover practical strategies and real-world insights on finance-led AI initiatives that will transform the finance function into a true powerhouse of data-driven insight and efficiency. 

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