Although most companies now recognize the potential of AI in planning, only a few are seeing the impact they hoped for. This is the conclusion reached by the BARC study “Agentic AI in Corporate Planning: The Dawn of a New Era.” According to the study, 98 percent of the companies surveyed see potential for AI in planning, and 83 percent are investing primarily to make their planning faster and more productive. Yet only 10 percent currently see a noticeable effect on their planning.
According to the study, the gap between expectations and impact rarely lies in the AI itself. More often, the problem is the data foundation, integration, and organization. This means that for AI-powered planning to deliver measurable results, it takes more than a language model. This article shows what AI can do in planning, which pitfalls to avoid, and how Bissantz helps you integrate AI into your corporate planning reliably, transparently, and on a solid data foundation.
How AI can make planning processes more efficient
AI plus planning equals efficiency? Unfortunately, it’s not that simple. In practice, several factors determine whether AI can actually boost efficiency in corporate planning. Before applying AI to planning, companies should first identify where their current processes consume the most time and coordination effort.
AI-generated suggested values instead of manual calculations
Based on historical trends, seasonal patterns, and other influencing factors, AI can generate consistent suggested values for KPIs, taking existing structures, hierarchies, and KPI logic into account. Planners only step in where action is needed.
Automatically incorporating external context data
Manually factoring in market trends, competitor behavior, or sentiment indicators takes time and specialist knowledge. With AI, text sources, market data, and sentiment can flow into planning automatically. If relationships with the relevant key metrics are mapped in the planning model, their impact on revenue, costs, and margins can be factored into the proposed values, which the business units can then review and adjust.
Forecasting and scenario simulation
AI can identify patterns and dependencies in the data and thereby improve forecast quality. Scenario simulations use AI to explore alternative futures, identify the underlying drivers, and assess their consequences. This way, AI-powered planning can show which measure works under which conditions before figures are locked in and resources are committed.
A seamless end-to-end planning process
AI realizes its full potential in planning when it is seamlessly integrated into existing processes and systems. When AI is embedded directly in the planning model, it works with the same data foundation as reporting and analysis. As a result, it understands the company’s structures and delivers suggested values that fit seamlessly into the plan, where they can be reviewed, adjusted, and approved right away. In this way, individual AI results become part of an end-to-end planning process without breaks in the flow of information.
Why efficiency alone isn’t enough
Planning faster is useful, but it isn’t a competitive advantage in itself. If you use AI merely to follow the same path more quickly, you are wasting potential. The far greater value comes from AI helping you identify relationships more quickly and prepare courses of action.
For planning, this means: a plan only becomes effective when it is clear which drivers are behind the numbers and which measures influence them. Driver-based planning makes this possible by answering questions such as “What happens if …?” and “Which decision is the right one under these conditions?”
Pitfalls: Why AI-powered planning often fails
When AI initiatives fail, the reasons are often conceptual rather than technical. Typical pitfalls include:
- AI comes into the picture too late: A language model only supports better decisions if consistent KPIs, clean data structures, a robust business logic model, and clear responsibilities are already in place.
- Poor data quality: Without reliable data, AI can’t deliver reliable results. Raw data from different, inconsistently maintained sources must first be cleansed and structured.
- Planning silos and Excel workarounds: When sales, procurement, production, and finance plan in isolation, inconsistencies are almost inevitable. Manual planning in Excel and disconnected systems also ties up resources in the controlling department.
- No AI strategy: Many companies don’t know where to start with AI. Without prioritized use cases and measurable goals, AI quickly becomes an end in itself rather than a productivity lever.
- The pilot never leaves the pilot stage: Many AI initiatives never make it past the pilot project because responsibilities, realistic roadmaps, and an implementation plan are missing.
- AI without guardrails: If companies do not clearly define what AI may and may not do, they risk acting on hallucinated outputs, misinterpreting results, or relying on findings that cannot be reproduced.
What companies should keep in mind when planning with AI
| Aspect | What matters | How Bissantz helps |
| Data foundation | Consistent KPIs, clean data structures, reliable data quality | Analysis of the data landscape, identification of data quality issues, measures to ensure a consistent data foundation |
| Business logic | Correctly modeled KPIs and cause-and-effect relationships | Modeling of relevant business drivers and driver-based forecasting models |
| Accountability and guardrails | Clear definition of what AI may do and who grants approvals | Defined roles and policies for transparent, reproducible results |
| Data privacy and hosting | Language model and data storage according to your own requirements | Flexible choice of language model and hosting, for example Microsoft Azure with data residency in the EU; AI is only activated when you want it to be |
| Rollout | Start small, prove the value, scale up | Step-by-step rollout for individual business units or KPIs |
| Operations | Access to the language model, ongoing costs, usage rights | Support with selecting, setting up, and integrating LLM access |
How Bissantz supports efficient AI-powered planning
Bissantz combines software and consulting so that AI-powered planning can reach its full potential in your organization.
The AI planning tool: AI as an integral part of planning
An integrated planning model instead of silos, suggested values instead of manual calculations, context data instead of manual research, forecasts and scenarios instead of gut feeling: The Bissantz solution brings all of these productivity levers together in one powerful AI planning tool. Controllers save time while staying in control.
AI consulting: from strategy to implementation
Bissantz AI consulting works with you to develop an AI roadmap, prioritize use cases, and define measurable goals. The team assesses data quality, develops AI models for planning, forecasting, and simulation, and supports a secure implementation, including data security and compliance.
Corporate planning consulting: the methodological foundation
In its corporate planning consulting, Bissantz analyzes your planning landscape, uncovers weak spots, and develops tailored concepts for planning, budgeting, and forecasting. The services cover process redesign, rolling forecasts, integration across planning areas, and the use of AI in planning—including in Power BI and Microsoft Fabric.
Conclusion: AI-powered planning works when the foundation is right
AI-powered planning makes your planning both faster and more effective when AI is used not as an afterthought, but as part of a well-designed planning model. This requires clean data, a robust business logic model, and clear guardrails. With an AI planning tool and tailored consulting on AI and corporate planning, Bissantz supports your company’s AI-powered planning every step of the way, from the data foundation to the decision.
