CFO in the age of AI

How finance leaders turn AI ambition into business impact?

The finance function is entering a decisive decade. Artificial intelligence is expected to fundamentally reshape how CFOs plan, steer performance, and support business decision-making. BearingPoint’s global CFO study shows that expectations for AI’s impact are already high, yet most finance organisations remain at an early stage of adoption.

Based on a global survey of 221 finance leaders and more than 30 in‑depth interviews with CFOs and senior finance executives, the study examines how AI is reshaping finance today, why adoption is struggling to scale, and what it will take to turn AI ambition into sustainable business impact.

A clear AI execution gap in finance

The study reveals a pronounced gap between expected impact and current maturity. While AI ambition is high, 73% of finance leaders describe their current AI adoption as minimal or basic, and only 9% report that AI has been scaled in line with expectations. Most organisations have progressed to isolated pilots but have yet to achieve enterprise‑wide deployment.

This gap is visible across core finance activities. CFOs expect the strongest AI impact in planning, budgeting, and forecasting (77%), followed by accounting and reporting (74%). However, these same areas continue to rely on fragmented data, legacy system landscapes, and manual processes that limit scalability.

Together, the findings point to a structural challenge: AI ambition is widespread, but execution capacity remains constrained.

Data quality emerges as the dominant barrier, cited by 74% of respondents, followed by data silos and fragmentation (63%). Legacy system architectures and inconsistent master data limit the reliability of AI outputs and make enterprise‑wide deployment difficult

Governance remains underdeveloped. Many finance organisations lack clear ownership, prioritisation mechanisms, and lifecycle management for AI initiatives, keeping AI at the margins rather than embedding it into business‑as‑usual operations.

Skills and confidence also lag behind expectations. While CFOs widely expect role changes, finance teams often lack the analytical, data, and AI literacy required to work confidently with AI‑generated outputs. Resistance to change, insufficient training, and concerns about job displacement further slow adoption.

Investment priorities are shifting, but foundations lag

CFO priorities for AI investment are evolving. Early efforts focused on efficiency and automation, freeing capacity in transactional processes. Looking ahead, the emphasis is shifting toward AI‑enabled planning, predictive analytics, and decision support, reinforcing finance’s strategic role.

However, the study shows that foundational enablers such as harmonised data models, operating model redesign, and workforce transformation often trail behind use‑case experimentation. This imbalance risks reinforcing the “pilot trap”, where AI delivers local value but fails to reshape the finance function at scale.