Manufacturing Company Wastes $200K on AI Before Finding What Actually Works
Manufacturing
The Challenge
An established manufacturing company jumped on the AI bandwagon, investing heavily in a custom AI solution that promised to revolutionize their operations. After 18 months and $200,000, they had a solution that worked in the demo but failed in production. They came to us frustrated, burned, and skeptical of any technology promises.
What We Didn't Do
We didn't immediately pitch them another AI solution. We listened, analyzed their actual workflows, and asked hard questions about what problems they were really trying to solve.
The SenseSys Approach
Through our discovery process, we found that their core issue wasn't lack of AI—it was data quality and process standardization. We implemented a practical data pipeline that cleaned and standardized their manufacturing data, created automated reporting that replaced manual spreadsheet work, integrated their existing systems (which were working fine), and only then added targeted ML models for predictive maintenance where it made clear ROI sense.
The Results
- Reduced downtime by 35% through predictive maintenance
- Eliminated 20 hours/week of manual reporting work
- Improved inventory forecasting accuracy by 60%
- Total investment: $45,000 (versus the previous $200K)
- Achieved ROI in under 6 months
Sometimes the best AI solution is no AI at all—until you've solved the foundational problems. Technology should serve your business, not the other way around.