February 18th brought sobering news from the industrial automation world: we’re experiencing what industry analysts are calling “The Great Divergence.” The ARC Industry Leadership Forum 2026 in Orlando revealed that only 12.9% of manufacturers have successfully scaled industrial AI automation, creating a massive gap between AI leaders and laggards that’s reshaping our entire sector.
The Trust Problem No One Saw Coming
What’s particularly striking isn’t just the slow adoption rate—it’s the emergence of an entirely new category of risk that has nothing to do with cybersecurity. As manufacturers deploy AI for quality analysis, anomaly detection, and predictive maintenance, we’re discovering that trusting what these models have learned presents unprecedented challenges. Unlike traditional automation systems where you can trace every decision back to coded logic, AI systems learn patterns that even their creators can’t fully explain.
The successful “Pacesetters” identified at ARC Forum have cracked the code by implementing Industrial Data Fabrics that decouple intelligence from hardware. This approach allows them to scale industrial AI automation across multiple systems without being locked into vendor-specific solutions. Meanwhile, the remaining 87% of manufacturers are struggling with fragmented implementations that can’t achieve enterprise-wide intelligence.
Hardware Innovation Continues Despite AI Struggles
While the AI adoption story remains mixed, traditional automation hardware continues advancing rapidly. RS’s expanded partnership with ABB for conveyor system solutions comes at a perfect time, given that the global conveyor belt market hit $5.68 billion in 2025. More interesting is the democratization of advanced manufacturing through initiatives like Project DIAMOnD, which now offers industrial additive manufacturing services to non-members, potentially disrupting traditional supply chains.
The embedded systems world is also pushing boundaries. Microchip’s collaboration with Hyundai on 10BASE-T1S Single-Pair Ethernet technology represents a significant step toward unified industrial networks, while Silanna Semiconductor’s new laser driver ICs are cutting power losses by 70% in LiDAR applications—crucial for autonomous manufacturing systems.
What concerns me most about today’s developments is the widening gap between AI leaders and followers. The companies that figure out trustworthy industrial AI automation will gain such competitive advantages that catching up may become nearly impossible. Are we witnessing the birth of a two-tier manufacturing economy, or will the laggards find ways to leapfrog the current leaders?