AI in Finance: Turning Hype into Real Value for CFOs

Artificial intelligence has become one of the most talked-about topics in finance. Yet for many CFOs, the journey from early experimentation to measurable business value is still underway. 

We believe that AI should be viewed not as a technology project but as an enabler of finance transformation. When applied thoughtfully, it strengthens insight, accelerates decision-making, and builds trust in data-driven strategy. 

From hype to practical value 

Over the past few years, many finance functions have launched AI pilots such as invoice automation or predictive forecasting. But while enthusiasm is high, tangible impact has been uneven. 

The teams that succeed focus on operationalising AI. They start small, identify clear use cases, measure results, and scale what works. In these organisations, AI is not an isolated experiment; it becomes part of how finance operates day to day.  

Where AI adds real value 

We believe AI creates the greatest impact when it removes repetitive work, improves accuracy, or generates insight faster than manual analysis can. 

Common examples include: 

• Predictive forecasting that identifies trends in revenue, cost, or demand 

• Anomaly detection that flags unusual transactions for review 

• Automated variance analysis and narrative generation for management reporting 

• Cash and working capital optimisation through predictive analytics 

Each of these applications allows finance professionals to spend less time on data gathering and more time on interpretation and business partnering. 

Building the right data foundation 

AI depends on clean, structured, and well-governed data. The most advanced algorithms will fail if the inputs are inconsistent. 

Creating a trusted data layer across ERP, FP&A, and operational systems ensures that data flows automatically and definitions remain consistent. We often see CFOs underestimate how much their data landscape limits AI progress. 

Organisations that invest early in master data management and integration build AI capabilities that are scalable and reliable.  

Governance and trust 

Finance leaders cannot afford to treat AI as a black box. Every model should be explainable and auditable. 

Strong governance includes validation of algorithms, transparent documentation of data sources, change control for updates, and clear human oversight. 

We believe governance and innovation go hand in hand. When decision-makers trust the process, they are more willing to rely on AI-generated insights.  

Integrating AI into everyday finance 

AI delivers its greatest benefit when it becomes part of the normal finance workflow. Linking AI outputs directly into dashboards, planning systems, and monthly reviews helps ensure the insights are used consistently. 

Automating data refreshes keeps models current without manual intervention. When finance teams use AI routinely, adoption grows naturally and value compounds over time. 

Empowering people, not replacing them 

Introducing AI should elevate the finance team’s role rather than threaten it. We believe that automation should free professionals to focus on analysis, storytelling, and strategy. 

Training analysts to question AI outputs, redesigning roles to combine technical and commercial skills, and encouraging curiosity all help create a modern, adaptive finance culture. 

Measuring success 

AI investments must be measured like any other. CFOs can track improvements in efficiency, accuracy, and insight quality. Once value is proven, scaling can follow, supported by central governance to maintain standards. 

A confident path forward 

AI will not replace finance professionals, but it is reshaping how they work. CFOs who combine technical curiosity with strong governance will lead the shift from hype to genuine business value. 

At Positive8, we believe that finance teams achieve the best outcomes when intelligent automation and sound process design move forward together.