New insights from the SAP Concur annual CFO Insights Survey reveal a clear shift in how finance leaders are adopting AI—not as a replacement for human expertise, but as a tool to stress test assumptions, improve accuracy, and support decision-making.
The findings point to a finance function in transition, where AI is being embedded into specific workflows such as forecasting and risk analysis, while human judgement continues to play a central role in interpreting outcomes.
A major challenge emerging from the data is cost forecasting reliability. Around 40% of finance leaders rank inaccurate forecasting among their top internal concerns. Interestingly, confidence levels differ across leadership roles—39% of finance leaders consider forecasts reliable, compared to only 31% of CEOs and managing directors. This gap reflects differing levels of proximity to financial data and accountability for outcomes.
Despite advances in technology, manual processes remain deeply entrenched. More than half of finance leaders (51%) still rely on manual methods alongside digital tools, while a smaller segment continues to depend heavily on manual workflows. The persistence of these processes is driven by factors such as data integration challenges, the need for flexible modelling, limitations in existing tools, and legacy systems.
This reliance comes at a cost. Nearly 59% of finance leaders and CEOs report that manual data entry errors require correction before use, slowing down decision-making and reducing operational agility. The findings suggest that while human oversight is critical, excessive manual intervention can hinder the efficiency of modern finance teams.
Against this backdrop, AI adoption is becoming widespread but targeted. Only a small minority (3%) report not using AI in forecasting and analysis. Instead, organisations are applying AI in high-impact, value-driven use cases, including revenue forecasting, risk analysis, demand forecasting, and scenario planning. These applications allow finance teams to test assumptions, simulate outcomes, and improve the reliability of projections.
AI is also being used for real-time updates, expense forecasting, and working capital management, although adoption in these areas reflects a more cautious approach due to operational dependencies and integration challenges.
The overarching trend is clear: AI is not displacing finance professionals but enhancing their capabilities. By automating repetitive tasks and improving analytical depth, AI enables finance teams to shift focus toward strategic decision-making.
As organisations move into 2026, the emphasis will be on striking the right balance—leveraging AI for speed and precision while retaining human judgement for context, interpretation, and accountability.