AI Resilience Report forFinancial Managers

Many of a Financial Manager’s number-crunching tasks are already aided by software. For example, AI tools can quickly analyze budget data and draft reports. McKinsey describes companies using AI assistants to review budget variances and even create first drafts of risk reports, saving up to 30% of the finance team’s time ^[1].

Experts note this lets managers “focus on high-level” decisions instead of routine calculations ^[2]. By contrast, people-related tasks stay mostly human. Banks use AI chatbots for simple customer questions, but a recent survey found only about 27% of customers trust AI for financial advice ^[3].

Training programs and team coordination use tech to manage schedules or suggest courses, but managers still design the training and lead their teams. Recruiting tools can shortlist resumes, but a manager picks the hire. In short, tools handle data-heavy parts (matching the 50–60% “automation” tasks), while personal judgment and relationships remain for human managers.

AI tools for finance are widely available and many leaders plan to use them, but adoption varies. In one survey, 65% of finance firms said they will boost AI spending next year ^[1], reflecting high interest. Potential savings are big – one report estimated AI could cut $447 billion in bank costs by 2023 ^[2] – so companies see economic incentive.

Still, firms move carefully. New software and data projects can be expensive and complex, and financial data is highly regulated. For example, many early AI pilots never scaled up fully, so most organizations hadn’t fully rolled out AI across finance by 2025 ^[1].

Trust and ethics are also considered: as above, customers are still cautious with AI advice ^[3], which slows replacing human roles. In summary, finance teams have the tools to automate maths and reporting, but they balance costs, rules, and customer trust. Human skills — like judgment and people management — stay valuable even as AI augments many tasks.