By Juanjo Mestre, CEO & Co-founder, Dcycle
CFOs now operate in an environment that’s overwhelmed by data that sits beyond the traditional financials. Despite being armed with ways to report on insights faster than ever, businesses still lack the complete visibility to view the full operational picture, and identify the factors driving financial outcomes. The problem is that the CFOs are seeing the results in the numbers, but not the underlying causes.
There are an amalgamation of challenges in play: margins are getting squeezed, it’s harder to stay on top of costs and forecasts aren’t landing like they used to. Ongoing market volatility sits at the heart of this, and continues to cause havoc across customer demand and supply chain stability.
The intelligence CFOs need to make the critical decisions at times of volatility are buried deep in procurement records, supplier performance data, logistics workflows, and sustainability metrics, split across teams, platforms, and reporting systems. They don’t have a centralised view of the entire operational layer of the organisation. This fragmentation is has led to closer collaboration between CFOs and operational teams to help maintain control over business performance. As part of this activity, finance leaders are now turning to integrated, actionable intelligence that connects financial outcomes to the operational realities driving them. And this is where AI tools bring a new layer of value: by allowing organisations to connect operational, supply chain, and ESG data in real time, AI can detect hidden patterns and surface them for CFOs to view the cause-and-effect across the business.
Why CFOs are moving closer to operations
Greater operational visibility is redefining the CFO’s remit. Finance leaders are moving beyond just retrospective reporting and are now expected to interrogate and influence the day-to-day decisions that drive business outcomes. In practical terms, this requires CFOs to move beyond the data and understand the operational factors driving the cost and efficiency trends. They need to identify why supplier costs are rising, locate site-level energy consumption patterns, and expose operational bottlenecks that are eating into margin. These are the operational realities that have immediate financial impact to the organisation.
The lines between finance, procurement, and operations are now less defined. CFOs are stepping into domains once considered outside their scope, not to overlap but to integrate fragmented data. Their mandate is shifting toward orchestrating cross-functional intelligence, consolidating supplier, logistics, and operational data to deliver an accurate view of business performance. Without clear operational insight, finance teams are forced to make decisions on partial data, undermining their ability to manage risk, control costs, and respond to regulatory or market pressures with confidence.
Why are CFOs now connecting finance with operational data?
Core financial information is typically stored within centralised systems thanks to decades of process development. Operational data, on the other hand, is a few years behind so still sits across various departments like procurement, sustainability teams and external supply chains. This lack of integration makes it difficult to establish a consistent, view of business performance or understand how operational changes are impacting financial results.
As the commercial risks of fragmented data become impossible to ignore, CFOs are stepping in to shoulder the responsibility of mitigating these operational data challenges. Previously, management of operational or sustainability data was siloed within compliance or reporting teams, with limited input from the wider business. Now, CFOs are recognising that detailed operational datasets – such as supplier performance, logistics costs, and energy usage – directly influence the bottom line.
When finance teams do not have visibility into the operational drivers behind cost, they are forced to take a reactive stance in their decision making rather than a proactive one. Cost increases, hidden inefficiencies in supply chains, and unreliable forecasts become the norm. In this environment, investing in reliable operational data infrastructure becomes a foundation for financial control and risk management. But aggregating this amount of data into a centralised hub and analysing it to uncover the intelligence needed to make informed decisions is no mean feat.
How AI unlocks operational intelligence
AI has the potential to connect financial and operational data more effectively to provide finance leaders with a clearer picture of what’s happening across the organisation. The data that AI relies on is still divided across different functions. For example, supplier records might sit with procurement, logistics data with supply chain teams, and energy usage information in separate systems altogether. When that data is inconsistent, AI models struggle to produce insights that accurately reflect day-to- day operations.
The more forward-thinking CFOs are taking the next step, using AI to populate and manage centralised systems of record that house all operational data from across the entire organisation. By arming themselves with robust data infrastructure today, these CFOs are building long-term resilience advantages, paired with genuine transparency, and the means to enforce all decision making with credible and accurate operational intelligence.