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You’re probably sitting on content gold—and not using it well. Not because your content lacks value, but because it never appears when and where it’s needed most: in the right deck, at the right moment, for the right decision-maker. In industrial tech, I stopped treating content as just output. Instead, I treat it as infrastructure. This shift changed how I handle content. Every internal discussion gets noted and tagged. Buyer language is mapped and modeled. Customer proof be

Every year, Industrial AI brings a new buzzword that reshapes the conversation. From Digital Twins to Unified Namespace, then MCP, and now Agentic AI, the pace of change feels relentless. As a founder in this space, I find myself asking: are we surviving to win, or winning just to survive? This question matters because unlike industries such as automotive, pharma, or oil and gas, which have grown steadily before reinventing themselves, Industrial AI startups often chase the l

Something is changing in how Industrial AI buyers talk to us. Over the past few months, I have noticed a shift in the kind of enquiries we receive. They are sharper, more to the point, and almost too well-qualified. This change reflects not only better targeting but also a maturing audience. Five years ago, early conversations with prospects were mostly exploratory. We spent a lot of time helping them understand what Industrial AI could do, why data readiness mattered, and ho

When I first started using customer stories in our outreach, I believed they would open doors. After all, a 10% yield boost sounds impressive. But in industrial tech, that number often missed the mark. Buyers would hear it and think, “That’s not my main problem.” The stories didn’t connect because they didn’t speak to the real challenges buyers face in the moment they receive the message. I realized I needed a new approach. Instead of leading with polished success stories fro

When I first stepped into the world of industrial tech marketing, I quickly realized one thing: everyone is chasing the same prospects. Whether it’s the plant head in a remote facility, the maintenance manager keeping a critical production line alive, or the quality lead ensuring every batch passes audit before shipment, these roles are the holy grail for many marketers. The problem? They are not just my prospects—they are everyone’s prospects. This crowded chase creates a no

When your industrial platform grows wider, your go-to-market (GTM) focus must get narrower. This sounds simple, but it’s a tough balancing act. Industrial platforms often cover a broad range of solutions—from AI and data engineering to predictive maintenance and scheduling. Each of these solves a real problem, but not every buyer wants to tackle all of them at once. This mismatch creates confusion. Pitching to too many personas and mapping too many use cases leaves buyers won

In Industrial AI, I often see a frustrating pattern. The technology works well. The industrial problems it targets are real and pressing. Yet, adoption stalls. Why? Because buyers struggle to connect the dots between AI capabilities and real-world plant outcomes. This gap slows down decision-making and leaves many AI projects stuck in pilot mode. I call these gaps the 3 missing bridges in Industrial AI go-to-market (GTM). When these bridges are missing, meetings end with pol