How AI is working its way into IBP
Introduction
S&OP has steadily evolved from short term demand-supply balancing to the main tactical business planning process, covering marketing, sales, demand, supply, and financial planning. Not many companies may have achieved this stage of true Integrated Business Planning yet, but the leaders demonstrate that it is perfectly feasible today and can add enormous value.
The role of technology
Technology has played an essential role in this journey. The vision of Integrated Business Planning existed for decades already, but without technology to support it, it was difficult to realise. Cloud computing, volume to value conversion, aggregation-disaggregation, scenario planning, working with incomplete master data, etc., proved essential for enabling IBP in practice.
The change aspect is still on the table of course, but with these essential enablers, IBP has come into reach of any professional organisation that wants to reap the benefits.
Will AI revolutionise IBP?
The obvious question today is to what extent AI will impact IBP. Will agentic AI take-over decision making, revolutionise the IBP process and finally enable the vision of “no-touch” or “lights-out planning”?
We don’t think so. Our view is that the complex cross-functional and tactical nature of IBP will require people to be in the lead for many years to come, making cross-functional trade-offs for the mid-term horizon and aligning these with the company’s strategy.
The impact of AI on IBP will be significant though, improving the decision support and potentially adding new topics to the agenda. AI will gradually work its way into IBP and specifically into the “feeding” and support processes. Let’s look at some important examples to demonstrate this statement, without even trying to be exhaustive.
1. AI in Advanced Planning Systems
A lot has been said about AI in Advanced Planning Systems (APS). Important topics are ML forecasting using causal drivers, exception management, automatic scenario analysis and LLMs enriching the user interface. These will help improve forecast accuracy, service levels and working capital, and increase the planners’ efficiency. We will explore the growing role of AI in APS in a separate article.
2. AI for continuous network optimisation in IBP
Dependent on the flexibility of your sourcing matrix, continuous network optimisation can generate massive savings, specifically in multi-national companies. The classic approach is to run a large study every five years, followed by a multi-year program to implement the recommendations. Modern AI supported tooling is rapidly changing this approach now, making it much more continuous, up to the level that it can be integrated into the monthly IBP process.
The use of AI-native tools like Optilogic is so much simpler than more “classic” tools, that companies can become largely self-supporting as well. The core OR platform may not even be so different from classic tools, but the way you communicate with it, the data handling engine, and the tools to analyse the output have been completely transformed using AI.
More on this topic can be found in this blog.
3. AI based assessments
An important aspect on the road to IBP maturity is measuring the quality via assessments. Without constant and consistent measurements, improving and even sustaining IBP maturity will be difficult. Ideally, a central team does these assessments, but this requires significant resources that in most cases are not available. Many companies therefore reside to annual self-assessments, at the cost of quality, consistency, and independence.
We have therefore introduced a fully automated AI based assessment tool that is also able to coach the S&OP / IBP practitioners and so help improve the process.
AI-Coded apps: filling the gaps
Since the end of 2025, AI-assisted software development has really taken off. This has created endless possibilities, including “filling the gaps”: build tailored solutions for unique, complex and missing functionality in the company’s systems landscape.
Given the cross-functional and complex nature of IBP, it is quite likely that not all required decision support is offered by the formal systems. AI coding may offer a structured way out here, whilst avoiding complex Excel-based solutions.
More on AI-coded apps can be found here.
Conclusion
Technology took decades before it could enable widespread IBP, and even now, most companies have not developed their tactical planning process beyond a solid S&OP. AI develops at such incredible speed that it will surely be an order of magnitude faster to seriously impact IBP. We believe that IBP decision making will remain human territory for years to come though, except for routine decisions that will be handled by AI agents.
The current AI-driven IBP evolution is mainly taking place in the feeding and support processes, enabling a quality and flexibility in decision support that was unthinkable until recently. Companies should not underestimate these developments but hop on the train to remain competitive. Everybody is experimenting, but a clear vision and strategy are required to ensure genuine business benefits before somebody else does. If you have not done so, you better get started!
