Finance leaders have access to vast amounts of data, yet getting clear answers when they need them most remains a challenge. Despite the information available, finding the right insights can still be a slow and complicated process.
Say you are asked by your board to explain why margins have fallen in a particular region. The answer is unlikely to be found on a single dashboard. Revenue figures might sit in one system, contract details in another, while the context is held by regional teams.
Piecing this information together, often means turning to IT or relying on analysts to pull data from multiple systems. In fact, nearly 78% of finance leaders cite waiting on cross-system data as the primary reason their monthly close gets delayed.
These delays can leave CFOs without the answers they need until the opportunity to act has passed. This is where AI can make a real difference.
Giving every finance leader access to AI
Business leaders should be able to access valuable insights directly through AI, yet most tools are still designed for engineers and data teams. CFOs really shouldn’t need a middleman just to ask a question about their own business.
The opportunity lies in giving finance leaders access to AI that behaves more like a colleague. One that understands the business behind the numbers, knows which data to trust and can convert real-time insight into action.
With direct access to company data, CFOs can get the answers they need faster, leaving analysts with more time to focus on higher-value, strategic work.
Bridging the gap between insights and action
The way executives interact with financial performance data is being transformed by AI-powered conversational interfaces. Instead of looking at retrospective end-of-month PDFs or static dashboards, a CFO can ask questions in plain English during a meeting and pull up answers immediately.
AI can go a step further, acting more like a trusted colleague who understands the organisation’s data, business terms and KPIs. Crucially, it should also understand how those concepts relate to each other and which sources the organisation considers authoritative.
Take a finance leader in a board meeting who needs to understand why margins have fallen in a particular market. They can instantly analyse the latest data through an agent and ask follow-up questions in real time. A process that once took days and the involvement of multiple parties, can now be completed in a couple of minutes, allowing leaders to make more informed decisions.
Beyond answering questions, autonomous agents can continuously monitor data, identify potential issues early and trigger actions across connected software.
Why reliable AI starts with the right business context
Quick answers mean little if the numbers behind them can’t be trusted.
The greatest challenge for many enterprises is the lack of accurate business context, not the underlying algorithm. AI doesn’t automatically know how your specific company defines a KPI, which revenue metric takes precedence when systems disagree, or which accounting rules apply to a region. So, when an AI model encounters those missing pieces, it makes statistical guesses. But in finance, a statistical guess is a liability.
Gartner, for example, recently reported that 38% of leaders who faced setbacks with their AI initiatives said poor data quality and missing context were key reasons for failure.
Unreliable or incomplete information can quickly undermine a CFOs confidence in the decisions that follow. The challenge is that business knowledge still sits across operational, analytical and departmental systems. Bringing these sources together on a unified data foundation, while preserving the business definitions and relationships around them, gives AI a richer understanding of how the organisation actually works.
Making AI trustworthy for CFOs
Governance cannot be an afterthought as AI takes on more tasks to achieve clear business outcomes.
The same permissions and access controls governing existing systems must also apply to AI colleagues, with clear lineage and auditability around the information they use.
CFOs need confidence not only in the answer itself, but in where it came from and whether the AI was authorised to access it.
The bottom line
Success of AI in the long-term success will depend less on how quickly companies deploy new tools and more on whether they have the right data foundations in place.
Without the right business context, poor-quality, fragmented data can lead to unreliable outputs and undermine confidence in the decisions that follow. By investing in secure, unified and well-governed data and business context, CFOs can gain access to the tools they can trust, allowing them to move beyond simply talking to their data to acting on it.
Rich Radley
Rich Radley is VP of Field Engineering at Databricks. An experienced technology leader with a broad background spanning IT delivery, strategy, procurement and governance, Rich combines deep technical expertise with strong analytical and strategic capabilities. He has a proven track record of delivering innovative technology solutions to complex business challenges. His experience includes leading large-scale projects across both the public and private sectors, translating business needs into practical, effective technology outcomes.
