AI in Production Planning: 4 Myths Holding Us Back
Artificial Intelligence has become one of the most hyped topics not only in manufacturing, but also in Planning & Scheduling. Everywhere you look, vendors and analysts are making bold promises:
- “AI will generate the perfect plan in seconds.”
- “Feed it enough data and it can predict the future.”
- “Human planners will no longer be needed.”
At first glance, these ideas are attractive. They offer a simple solution to the complexity of modern operations. But they’re also sometime misleading or exaggerated.
At planeus, we partner with some of the world’s largest and most innovative manufacturers to modernize their Planning & Control. From automotive to electronics to pharmaceutical, we see the same pattern: companies are tempted by AI promises, but stumble without the right foundation.
In this article, we are breaking down the four most common misconceptions about AI in production planning — and explain what’s really required to make AI deliver value.
Myth 1: AI can create the perfect plan with one click
The vision here is seductive: push a button, and an optimized plan for the entire factory appears instantly. Some go further, imagining a fully automated system that constantly rebalances production in real time, with no human intervention.
The Truth
We’ve heard this before. Advanced Planning & Scheduling (APS) and Master Planning & Scheduling (MPS) systems promised the same decades ago. The recipe was simple:
- Define some parameters.
- Press the magic button.
- Receive the “optimal” schedule.
In practice, companies spent years setting up these systems to match their unique processes — or worse, forced their processes to fit the software. The result? Expensive projects, brittle schedules, and constant workarounds just to keep things running.
AI doesn’t change this. If we treat it as another miracle button, we risk repeating the same cycle — only with higher expectations, bigger investments, and more frustration.
Myth 2: More data means better AI Planning
“Once you have the data you need, you can create schedules in seconds!”
Claims like this usually arise from real-time, execution-focused environments, where the sheer power of collecting and analyzing data would create the belief that they contain a perfect map of the future.
The Truth
No matter how big, granular, or complex, these are still historical data — records of what has already happened. Historical data can help us understand trends and improve transparency, but it cannot fully predict what comes next. The future is shaped by countless elements outside the reach of any monitoring system:
- A supplier going bankrupt.
- A sudden spike in customer demand.
- A machine breaking down at the worst possible moment.
Assuming that AI can simply “read the future” from historical data sets unrealistic expectations. Forecasting is valuable, but it is not fortune-telling. When we treat it as such, AI becomes just another disappointment in the long history of planning technologies.
Myth 3: AI will replace human planners
The third myth imagines AI as the new “brain” of the factory — orchestrating every decision, balancing resources, and reacting to disruptions without human involvement. In this scenario, the role of the planner disappears.
The Truth
This claim misunderstands the essence of modern planning.
Planning is not just about filling up machine capacities or meeting due dates. It is about navigating trade-offs across three objectives:
- Goals – aligning every decision with business outcomes, such as delivery reliability, cost efficiency, or shorter lead times.
- Constraints – working within real-world limits like machine availability, labor shifts, and material shortages.
- Improvements – adapting to change, setting new goals, and learning from past performance.
No AI system can weigh these trade-offs in the broader context of strategy, customer priorities, and long-term business goals. These are judgment calls — and judgment is human.
AI can support planners by surfacing better options, detecting risks earlier, and simulating scenarios. But the final decision, the balance of goals and constraints, will remain with people.
Myth 4: AI will fix broken planning
Finally, there’s the belief that current struggles — firefighting, late deliveries, bloated inventories — are simply the result of bad tools. Add AI, and everything will be solved.
The Truth
Most planning problems don’t start with tools. They start with mindsets and assumptions. Two patterns stand out:
- Goalless planning – where schedules are created without clear outcomes, leading to endless adjustments but no real progress.
- Rule-based planning – where stability is assumed, every detail is coded into parameters, and the entire plan collapses when unexpected changes occur.
Adding AI on top of these flawed approaches doesn’t fix them. It only magnifies the cracks.
Can AI Take Over Production Planning?
In short: no — at least not in the way many expect. AI cannot fix planning until we first fix our old way of thinking in Planning & Control. Modern Planning & Control requires a different mindset — one that accepts change as the norm, focuses attention where it matters most, and uses technology not as a replacement, but as an amplifier of human expertise.
At its core, modern Planning & Control rests on four principles:
- Exception-driven problem solving – focus attention only where it’s needed. Like a smoke detector, the system should remain silent when all is well, and alert you only when something requires action.
- Goal-oriented operation – every plan should tie directly to outcomes such as delivery reliability, shorter lead times, or better capacity utilization.
- Real-world planning – plans must reflect actual machines, skills, and capacities as they exist on the shop floor, not abstract spreadsheets.
- Knowledge sharing – expertise must not remain locked in individual heads. Capturing and sharing know-how strengthens the entire system.
Once this foundation is in place, AI becomes a powerful amplifier rather than a fragile substitute. Its real value is in supporting and enhancing planning across the 4 principles:
Enhancing exceptions
AI detects deviations and anomalies in production data earlier — and can even predict them before they occur. Example: spotting a machine drift in advance, or signaling a likely material shortage.
Sharpening goals
AI simulates scenarios and quantifies their impact on key KPIs, helping planners align actions with business outcomes. Example: a co-pilot suggesting: “With an extra shift, order X will ship on time.”
Grounding reality
AI refines planning parameters such as setup times, cycle durations, and equipment performance using real shop-floor data. Example: analyzing feedback to provide more realistic setup values instead of static estimates.
Scaling knowledge
AI helps capture tacit expertise and make it available for the entire team. Example: voice assistants or chatbots that guide planners through best practices, learned from past decisions.
In this way, AI does not replace Planning & Control — it elevates it.
The planner remains in charge, supported by systems that reduce noise, provide foresight, and propose actionable options.
The Right Way Forward
So if AI isn’t the silver bullet, what is the path to success?
The answer is maturity — built step by step.
Stage 1: Foundation — Data Quality & Transparency
- Clean and structure data across master records, shop-floor reporting, and machines.
- Use AI to identify anomalies, patterns, and gaps.
- Goal: a reliable base for all future planning.
Stage 2: Amplification — Better Results with AI
- Apply AI to refine predictions such as setup times or lead times.
- Make planning results more realistic, practical, and trusted by planners.
- Goal: higher-quality outcomes and stronger adoption.
Stage 3: Transformation — Autonomous, Goal-Oriented Planning
- AI agents respond automatically to disruptions or rush orders.
- The system generates proposals or executes re-plans.
- The planner oversees, validates, and decides.
- Goal: flexible, automated planning — with planners elevated from firefighters to strategic decision-makers.
Recommendations for Companies
- Check your digital maturity: Assess the quality of your data and feedback loops. Without a clean foundation, AI cannot deliver value.
- Start small: Identify a clear use case, launch a pilot project, and look for quick wins before scaling.
- Involve your people: AI is there to support, not replace. Success depends on acceptance, collaboration, and adoption across the workforce.
Conclusion
AI in production planning is not about removing people or promising one-click perfection. It is about amplifying human judgment, grounding plans in reality, and giving organizations the clarity to act with confidence.
The path forward is clear: build solid foundations, improve data quality, and introduce AI step by step.
AI won’t “solve” planning for us — but with the modern mindsets, it accelerates the transformation to the new generation of planning & control.