The IT/OT Insider Podcast with David and Willem

From Platforms to Outcomes: Vatsal Shah on What Industrial AI Actually Needs

The IT/OT Insider Podcast with David and Willem

Show Notes

(Our topic, Our tone, Sponsored by Litmus.io *) Two years ago, roughly every second booth at Hannover Messe had the word “AI” on it. This year the tone has shifted. Less about the layer you buy, more about the outcome you’re trying to deliver. That shift is what Vatsal Shah, co-founder and CEO of Litmus, came on the podcast to unpack. It’s also the first time he’d talked publicly about how Litmus rethought its own strategy around this idea. Coming from the CEO of one of the more established industrial data platforms, that’s worth paying attention to. (If Litmus is new to you, we covered them earlier with COO John Younes in our Industrial DataOps series, and wrote about their approach to consolidation in Fighting Entropy.) The $1 trillion question nobody’s asking Here’s the observation that reframed Litmus’ entire 2025 strategy. “If you look at the manufacturing software market, it’s what, like $15 billion? How much is the manufacturing labour market? A trillion dollars plus. It’s a hundred times the software market.” — Vatsal Shah Vatsal’s numbers, not ours, but the ratio is what matters. Every industrial data vendor has been fighting for a share of that small software pie. Meanwhile the vastly bigger prize sits behind a wall we’ve barely tried to cross. To be clear: this isn’t about replacing labour. “In no shape or form can AI replace human workers right now,” Vatsal said, and we agree. We’d add something we’ve argued for a while: in most manufacturing environments today, the constraint isn’t too many operators, it’s not finding enough skilled ones. So the question isn’t “how do we cut headcount?” It’s “how do we make the people we have more effective?” Thanks for reading The IT/OT Insider! Subscribe for free to receive new posts and support our work.

The Industrial Data Catalog: an old idea, new to the plant floor You can’t get to outcomes without the plumbing. Ask a CIO how many databases they run and you’ll get an answer. Ask a plant leader how many PLCs, which historian versions, which SCADA systems — and past fifty sites, the honest answer is often I don’t know. Data catalogs are a well-worn concept in IT. Vatsal’s claim is that Litmus is the first to bring one to the operational side: every data logger, historian, database, CNC machine — listed on a single interface, for humans on compliance duty and for AI agents as a source of context. Without the catalog, an LLM answering questions on top of plant data was landing at around 85% accuracy in Vatsal’s internal tests. With the catalog providing metadata and context, it jumped to roughly 97%. We haven’t independently benchmarked those figures, but the direction is what everyone building agents on industrial data will run into: garbage in, garbage out — and “garbage” here mostly means no context. The most concrete example in the conversation came from pharma. A C-level exec, asked what burns his people out, named documentation — batch changes, recipe adjustments, quality fixes, all audit-critical, all soul-destroying. Offer them an agent that generates auditor-ready documentation from the underlying data continuously, and the response is “shut up and take my money.” Vatsal was honest about the follow-up: the agent doesn’t yet work to the 30–40% quality bar it needs, because the data, context and integration aren’t there yet. That’s the case for putting the foundation right. Rush the agent, and you’ve got another disappointed pilot. You can check the catalog offering here: litmus.io/litmus-data-catalog. Stop buying tools. Start buying outcomes. Roughly halfway through, we asked the crystal-ball question: what should the industry actually do next? Vatsal’s answer was refreshingly non-vendor-y for someone running a leading platform: “I’m CEO of one of the leading data technology companies out there, but even I’m thinking we need to get out of platform mindset and start thinking outcomes mindset.” His three non-negotiable foundations: * Own your data. Edge or cloud, but you

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