- Operational questions are increasingly targeted at the chief financial officer.
- Every department records information in ways that suit its own objectives, so it’s problematic when organisations try to combine everything into one view.
- Leading organisations are beginning to treat operational information with the same discipline that finance applies to financial reporting. They view operational data as something to guide decisions every day.
Every function inside a business believes it has a view of how the company is performing. Finance understands the numbers and operations understands how the business actually runs. Each perspective is valid, but built on its own data and its own timeline while being interpreted through its own lens. And most of the time, they never come together. That becomes a tracking problem, and one that’s been solved before.
Finance went through something similar twenty years ago, when every team had its own spreadsheets and versions of the numbers. The fix was finance sorting out its own tracking. Once it had a single set of numbers it could stand behind, the rest of the business followed suit.
Finance and operations are becoming inseparable
Not long ago, a CFO’s role was largely retrospective. Close the books, report the numbers and explain what had already happened.
Today, finance leaders are expected to answer a very different set of questions:
- Why are supplier costs creeping up?
- What’s driving energy consumption?
- Where are operational bottlenecks eroding margins?
- Which parts of the business are creating unnecessary risk?
Really, these are operational questions, but they are increasingly targeted at the CFO.
The problem is that the information needed to answer them doesn’t tend to sit with finance. Financial data lives in ERP systems, accounting platforms, payroll and procurement records. Operational data lives somewhere else entirely: energy consumption, carbon emissions across Scopes 1, 2 and 3, water use, waste, logistics and business travel. It is spread across operations, procurement and sustainability teams, often collected for compliance rather than decision-making.
As long as those worlds remain disconnected, leadership is always working from an incomplete picture.
Operational data often reveals problems long before they become visible in the financials. But if those signals are trapped in separate systems, businesses don’t recognise the issue until quarterly reporting exposes the consequences. By then, the underlying problem may have been building for months.
In today’s market, that delay means that margins are tighter, supply chains are harder to predict, and costs keep moving in ways that are difficult to plan for. Businesses already have much of the intelligence they need to navigate that uncertainty, but it’s locked inside disconnected operational systems that weren’t designed to work together.
AI is only as good as the data behind it
There’s another challenge that becomes obvious when organisations try to solve this with AI. Financial data has benefited from decades of standardisation, and most organisations have agreed definitions, established governance and centralised systems.
Operational data is very different. Every department records information in ways that suit its own objectives. Procurement measures suppliers one way, operations track performance another, and sustainability is collecting whatever it needs for regulatory reporting. Half the time, the suppliers themselves are working to a completely different set of standards on top of that.
That becomes a problem when organisations try to combine everything into one view.
Ask three departments what qualifies as an “active supplier” or when a delivery is considered “complete”, and you’ll often receive three perfectly reasonable but entirely different answers. None of those definitions is necessarily wrong. They were simply created for different purposes.
Within each function, that inconsistency often doesn’t matter. Across the organisation, however, it creates exactly the kind of ambiguity that undermines AI. AI is exceptionally good at processing vast quantities of operational data, identifying patterns and surfacing insights that no individual team could detect. What it cannot do reliably is decide which conflicting definition is the correct one. Feed it inconsistent data, and it will confidently produce inconsistent conclusions.
The quality of AI is ultimately limited by the quality of the operational foundations beneath it.
Turning operational data into operational intelligence
The businesses pulling ahead are the ones making far better use of the data they already have. Leading organisations are beginning to treat operational information with the same discipline that finance has always applied to financial reporting. They view operational data as something to guide decisions every day.
AI makes that possible at a scale that wasn’t doable before. It connects information across functions, identifies relationships that would otherwise remain hidden and gives leadership the ability to intervene while a problem is still operational, before it develops into a financial one.
In a volatile market, success increasingly depends on seeing issues earlier and responding faster. This is a board-level issue now.
The businesses that build this capability now will have better visibility and they’ll be able to act on it before their competitors have even finished pulling the data together.
Juanjo Mestre is the CEO of Dcycle.
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