The latest industrial AI readiness findings are painting a sobering picture that many of us in the automation trenches have suspected all along. While boardrooms are buzzing with talk of predictive maintenance, digital twins, and agentic operations, the 2026 Industrial AI Readiness Report confirms what plant engineers know too well: there’s a massive canyon between AI ambition and actual implementation capability.
The AI Implementation Reality Gap
Manufacturing companies across energy, transportation, and smart cities are feeling the pressure to “do something with AI,” but here’s what the report doesn’t need to tell us – most facilities are still struggling with basic data quality and integration challenges. You can’t build sophisticated industrial AI readiness on a foundation of siloed systems and inconsistent data streams. Before we get starry-eyed about autonomous operations, maybe we should focus on getting our SCADA systems talking properly to our MES platforms.
The rush toward AI-powered solutions reminds me of the early days of Industry 4.0, when everyone wanted IoT sensors but forgot about the network infrastructure to support them. Now we’re seeing the same pattern with AI – lots of enthusiasm, but not enough attention to the unglamorous groundwork that makes these technologies actually work in harsh industrial environments.
Hardware Innovations That Actually Matter
Speaking of practical progress, Excelitas just launched their PYD 2597 ultra-low-power DigiPyro sensor, and this is the kind of incremental innovation that often gets overlooked but can make a real difference on the factory floor. Battery-powered motion detection systems that can run longer without maintenance? That’s solving actual problems for remote monitoring applications and wireless sensor networks.
Meanwhile, Neural Concept’s Dr. Pierre Baque continues pushing the envelope on AI-driven design optimization. Their approach to using neural networks for engineering simulations represents the kind of focused, practical AI application that might actually deliver ROI instead of just PowerPoint slides.
The Servitization Journey
The ongoing discussion about servitization maturity models also deserves attention. As automation systems become more sophisticated, the shift from selling equipment to selling outcomes is accelerating. This trend forces us to think differently about industrial AI readiness – not just as a technology challenge, but as a business model transformation that requires new organizational capabilities.
The step-by-step approach to servitization success makes sense, especially when you consider how many companies are still figuring out remote monitoring basics. You can’t jump straight to outcome-based contracts if you don’t have reliable condition monitoring and predictive analytics in place first.
What strikes me most about these developments is how they highlight the importance of building solid foundations before chasing the latest trends. Are we setting ourselves up for another round of overpromised and underdelivered automation initiatives, or are we finally learning to crawl before we run?