Answer extracted from The Manufacturing IT Podcast — listen to the full episode below.
What survey data reveals the gap between digital investment intentions and advanced AI adoption in small manufacturers?
Wipfli's survey of approximately 350 small and medium manufacturers across nearly 30 states found that 99% prioritize digital investments, but only 30% have begun trialing advanced AI. Most manufacturers are still investing in foundational infrastructure—cloud platforms, servers, cybersecurity, networking, and equipment—before they can move toward AI implementation.
This gap reveals a critical reality: digital transformation is not a linear journey. Manufacturers recognize the importance of modernization, but the path to AI adoption requires several prerequisite layers of technological maturity that many organizations have yet to establish.
As Mo Abuali explains in the episode, the survey data shows that small and medium manufacturers—which make up more than 90% of the market in North America—are facing a chicken-and-egg problem: they want to adopt AI solutions, but the foundational work required to make AI effective remains incomplete for most organizations.
Why the infrastructure foundation matters before AI
The 69-percentage-point gap between digital investment intention and actual AI adoption is not a failure of commitment; it reflects the reality of what AI requires. Advanced AI systems demand clean, integrated, real-time data flowing from well-connected machines, secure networks, and cloud platforms that can process that information at scale.
Without these building blocks—enterprise resource planning systems that talk to each other, machines connected to networks, data flowing reliably into centralized platforms—AI becomes a tool deployed on weak foundations. This is why most manufacturers surveyed are correctly prioritizing foundational infrastructure first, even though the business case for AI may seem more compelling.
The survey captures a moment in the digital journey for small manufacturers: they have the intention and the budget, but they are still in the prerequisite phase. As discussed at length in this episode, this is not wasted investment—it is essential groundwork that makes future AI adoption possible.
"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, Digital Strategy at Wipfli, with nearly 25 years of manufacturing experience spanning shop-floor operations at companies like Toyota and digital solution delivery across MES systems, IoT platforms, and AI technologies. He holds a PhD in Industrial Engineering from the University of Cincinnati's Center for Intelligent Maintenance Systems, where he conducted applied research across manufacturing facilities globally.
This quote encapsulates the deeper message of the survey: manufacturers investing in foundational infrastructure are making the right call. The 30% who have begun AI trials are likely those who have already laid that groundwork with robust data systems, integrated platforms, and network connectivity.
For the remaining 70% prioritizing digital investment, the path forward is clear: complete the infrastructure phase first, as the podcast explores in detail, and the organization will then be positioned to move AI initiatives from pilot to production at scale.
See also
How will industrial AI and generative AI differ in their manufacturing applications over the next five years?
Industrial AI and machine learning have existed for decades, using sensor data and machine vision for predictive maintenance, quality tracking, and zero-defect manufacturing. Generative AI introduces a new capability layer on top of this foundational infrastructure.
What barriers prevent small manufacturers from successfully implementing Industry 4.0 solutions?
Technology challenges include old IT systems, legacy machines, lack of connectivity and security. Process challenges occur when companies jump to technology without first establishing the foundational data practices and organizational alignment required for sustainable digital transformation.
What are the primary technology trends small and medium manufacturers should prioritize?
Five major trends are industrial machine learning to collect real-time shop floor data, advanced planning and scheduling to improve standards and job optimization, real-time visibility into operations, cybersecurity infrastructure, and organizational change management to support digital adoption.