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Planning with AI: From Uncertainty to Advantage

Where AI Already Moves Corporate Planning Forward, and What Comes Next

About this survey

Planning with AI: From Uncertainty to Advantage

Start: Q2/2027
Publication: Q4 2027
Region: Global Research
Authors: Dr. Christian Fuchs and Robert Tischler

Volatility has become the norm in planning, rather than the exception. Many organizations recognize that they need to transform their planning processes, and they see AI as the most important lever for planning more efficiently. Meanwhile, vendors are promising AI agents, predictive forecasts, anomaly detection, and self-adjusting plans. In practice, however, many teams are still working on the basics: data, skills, processes, and responsibilities. The question of how AI can support achieving these efficiency objectives remains unanswered.

This BARC survey examines which planning tasks are measurably improved by AI, from data preparation and scenario analysis to commentary and reporting. It also analyzes how much autonomy successful organizations grant AI and how far they trust its results. Additionally, it examines which responsibilities organizations have defined for data, processes, and AI output. Building on previous BARC evaluations, the survey provides an updated view of the market by analyzing the progress made since then. The survey looks at what sets successful organizations apart, including their skills, data foundation, governance, and technology support. Readers will gain an independent, empirical benchmark of where AI is helpful for planning under uncertainty and where it is not.

Core questions:

  • Planning practice under permanent uncertainty: how planning and forecasting cycles are changing, including the shift from annual budgeting towards continuous planning and steering, and where the pressure to reduce effort and gain speed is highest.
  • Where AI measurably improves planning tasks: data preparation, forecasting, scenario modeling, variance analysis, commentary and reporting, plus the building and maintenance of planning models, and where results still fall short of expectations.
  • Progress and payback since the previous BARC studies: what has changed in adoption and results, and which AI use cases in planning already justify the investment today.
  • Autonomy, trust and responsibility: how far organizations let AI go, from suggestion via approval to automated execution within set limits, how far they trust AI results, and which responsibilities they have defined for data, processes and AI output.
  • What sets successful organizations apart: how data foundation, skills, governance, processes and technology support determine whether AI improves planning under uncertainty, and what separates successful adopters from laggards.

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