Answer extracted from The Manufacturing IT Podcast — listen to the full episode below.
What technology trends should small and medium manufacturers prioritize?
Small and medium manufacturers should focus on five core technology trends: industrial machine learning for real-time shop floor data, advanced planning and scheduling to improve standards and profitability, ESG and sustainability initiatives to reduce carbon footprint, advanced analytics and AI for labor efficiency, and connected worker applications using augmented reality to reskill workers rather than replace them.
Why these five trends matter now
The manufacturing landscape is shifting rapidly, and SMMs face unique constraints compared to larger enterprises. 99% of small and medium manufacturers surveyed across nearly 30 states prioritize digital investments, yet many struggle to know where to start. The five trends identified represent a strategic roadmap rather than a scattered technology shopping list.
Industrial machine learning sits at the foundation because it transforms raw shop floor data into actionable intelligence. Without this capability, manufacturers operate largely on historical patterns and reactive troubleshooting, missing opportunities for continuous improvement. As explained in this episode, the quality of that underlying data determines everything downstream—garbage inputs yield garbage outputs, even with advanced AI tools like ChatGPT.
Balancing automation with human capital
A critical insight often overlooked: these technologies don't eliminate jobs—they elevate worker roles. Connected worker applications and augmented reality work instructions represent a deliberate choice to reskill people rather than replace them, addressing a looming workforce challenge. The average age of skilled operators at major aerospace and defense facilities is now 55, meaning retirement and knowledge transfer must occur within five to ten years.
Advanced planning and scheduling systems improve profit margins by standardizing production workflows and reducing waste. ESG investments, meanwhile, respond to both regulatory pressure and customer demand—sustainability is no longer optional. Only 30% of surveyed SMMs have begun to trial advanced AI solutions, revealing that most manufacturers are still in the early stages of adoption.
"If you don't have the data or the data quality or the data integrity, putting your stuff in ChatGPT is garbage in, garbage out."
Mo Abuali — Director of Digital Strategy at Wipfli, with 25 years of manufacturing experience split between shop floor operations at companies like Toyota and delivering enterprise digital solutions including MES, IoT platforms, and AI systems to manufacturers globally. He holds a PhD in Industrial Engineering from the University of Cincinnati's Center for Intelligent Maintenance Systems, where his research focused on predictive maintenance and AI when sensor costs were prohibitively expensive and cloud infrastructure did not exist.
The path forward isn't about adopting every technology at once. Wipfli works with more than 7,000 manufacturing, retail, and distribution accounts ranging from $20 million to $250 million in annual revenue, and the most successful implementations follow this prioritized sequence: first, collect and clean your data; second, use analytics and planning to optimize processes; third, enable workers with augmented reality and connected applications; fourth, address sustainability metrics; and fifth, scale AI capabilities as your data infrastructure matures.
Key takeaways
- Industrial machine learning and real-time shop floor data collection form the foundation for all other digital initiatives.
- Advanced planning and scheduling systems directly improve production standards and job profitability.
- Connected worker applications with augmented reality reskill employees rather than eliminate jobs, addressing imminent workforce transitions.
- Only 30% of surveyed SMMs are trialing advanced AI, indicating most manufacturers are still in early adoption phases.
- Data quality and integrity are non-negotiable—poor data undermines even the most sophisticated AI solutions.
See also
What is digital maturity and how do manufacturers assess it?
Wipfli assesses digital maturity across technology, people, and process through innovation workshops and digital assessments, scoring capabilities in each dimension to identify gaps and priorities.
What career path led to expertise in both shop floor operations and digital manufacturing solutions?
Mo Abuali spent almost 25 years in manufacturing, half on shop floors serving companies like Toyota in automotive, and half delivering digital solutions including MES, IoT platforms, and AI to manufacturers worldwide.
How can AI-driven predictive insights improve proactive decision-making on the manufacturing plant floor?
Rather than after-the-fact analysis, AI can provide plant floor operators with proactive information about what to expect in production based on equipment patterns and operational history.