If you’re exploring AI in manufacturing, you’re not alone—and you’re definitely not late to the game. Most manufacturers have already tested AI in some form. But here’s the truth many industry leaders now acknowledge: pilots don’t move the business. Scale does.
That said, there is no shortage of opinions or industry reports that seem to provide a lot of conjecture but are not always actionable.
For example, in 2023, McKinsey estimated that generative AI could contribute between $650 billion and $1.1 trillion in annual productivity improvements for manufacturing and supply chain-related activities globally. Then, just two years later, their 2025 global AI survey reports that only a small group of “high performers” are capturing outsized value, implying most firms are still struggling to operationalize/scale. Interesting. But not helpful.
If we get past the noise, it usually boils down to two things. First, there are no quick fixes. A realistic timeframe for ROI can be anywhere from 1 to 4 years, depending on your starting point and how ambitious you are. Second, it must help you deliver on a specific business goal, not simply reduce cost or headcount. If AI doesn’t improve availability, throughput, or cost per unit, it won’t earn stakeholder buy-in or funding for growth. That’s the gap to close.
Shift the Question: From “What can AI do?” to “What should AI improve?”
For AI to deliver real value, it must solve tangible operational problems and move the metrics that matter. That might mean shifting your focus from what AI promises to what it can prevent: costly downtime, quality escapes, and supply chain disruptions.
In practical terms, it can look like:
| Priority |
AI capability |
| Asset uptime |
Predictive maintenance |
| Throughput & OEE |
AI scheduling + microstop analysis |
| Quality |
Automated defect detection |
| Supply chain stability |
AI demand sensing |
| Workforce productivity |
AI-guided workflows |
What are the SAP solutions that can deliver these AI capabilities now?
| SAP solution |
AI use case |
Result |
| SAP Asset Performance Management |
Predictive maintenance |
Prevent unplanned downtime |
| SAP Digital Manufacturing |
Root-cause analytics |
Optimize OEE at bottlenecks |
| SAP IBP |
Demand sensing & supply simulation |
Improve service resilience |
| SAP Analytics Cloud |
Predictive planning |
Protect margins in real time |
| SAP AI Core (BTP) |
Custom models at the edge |
Build plant-specific intelligence |
The 5 steps to scaling AI without getting stuck
If you want repeatable success—not one-off AI wins—build around this model:
- Start with a constraint, not a tool
Pick one proven bottleneck: downtime, scrap, changeover loss, or plan stability.
- Unify operational and financial signals
Use SAP Cloud ERP + SAP Analytics Cloud to link AI actions to cash impact.
- Operationalize AI—not just visualize it
Embed decisions into SAP workflows that planners, engineers, and supervisors already use.
- Build once, deploy everywhere
Use SAP BTP to package AI logic as reusable templates across sites.
- Track hard KPIs
AI credibility grows when you show monthly gains in OEE, MTTR, OTIF, or contribution margin.
How to tell your AI is delivering results, not just reports.
- It’s adopted by the frontline: Your maintenance and production teams actively use it to make decisions and have more time to work on high-value tasks.
- It changes how work gets done: Teams shift from reactive fixes to proactive improvements.
- It closes the insight-to-action loop: Automatically triggers work orders or alerts based on AI predictions.
- It drives measurable outcomes: Directly improves availability, quality, and throughput.
Related reading:
- For a deeper dive into AI that works for operations and IT, download this e-book.
- Or continue exploring related topics:
