Robert Tischler
Senior Analyst Data & Analytics
Robert's focus is on research and advisory. He is also responsible for BARC's business in Austria.
Free study
Agentic AI in Corporate Planning: The Dawn of a New Era
This BARC study is based on a quantitative online survey of 312 professionals working in finance, FP&A, and corporate planning. It shows where AI and agentic AI can relieve planning teams of manual work and what this requires: a reliable data foundation, integration with existing systems, and clear limits on autonomous decisions. It also explains how to transform your pilot projects into an integral part of your regular planning processes.
What this BARC study answers forprofessionals working in finance, FP&A, and corporate planning.
- Which parts of the planning process offer the greatest efficiency potential, and when do organizations expect AI and agentic AI to have a significant impact on planning and forecasting?
- Which proven use cases provide a practical starting point before organizations add generative and agentic capabilities?
- What requirements must organizations put in place across data, processes, roles, and governance, and where do they stand today?
- Which decisions remain with humans, and how can organizations move successful pilots into the regular planning process in a controlled way?
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Further information
AI and agentic AI are meant to relieve planning teams of manual work. Yet their tangible impact is still limited today. This BARC study shows which parts of the planning process offer the greatest efficiency potential, which use cases organizations prioritize, and where people remain responsible.
The study is based on a quantitative online survey of 312 professionals working in finance, FP&A, and corporate planning.
The findings show that the leading investment driver is increased efficiency and productivity. The greatest potential lies at the data- and analysis-intensive ends of the planning process: data processing and aggregation, scenario analysis, forecasting, reporting, and commentary. Anomaly and risk detection, scenario and what-if analysis, and ML-based forecasting rank ahead of autonomous, agentic applications.
At the same time, data privacy and security concerns, poor integration with existing systems, inadequate data quality, and unclear responsibilities hold back broader adoption. What sets more mature organizations apart is not better technology, but stronger processes, clearer governance, and a dependable data foundation. Strategic business decisions and final approval of plans, budgets, and forecasts largely remain human responsibilities.
Four recommendations set out the next steps: start with a tightly scoped pilot, fix the data foundation where it is needed first, begin with proven use cases, and measure value across several planning cycles before moving successful applications into the regular planning process.
Author(s)
Kelley Lynn Kassa
Senior Analyst Data & Analytics
Dr. Christian Fuchs
Senior Analyst Data & Analytics
Dr. Christian Fuchs is Senior Analyst and Head of Data & Analytics Research at BARC.
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The study shows which conditions organizations should put in place now so AI can reliably reduce manual work in planning in the future.