Mumbai’s manufacturing leaders gathered at Sofitel BKC with a lot to say — and most of it wasn’t about technology. It was about trust.
Trust that AI-driven insights are grounded in plant reality, not generic models. Trust that a recommendation made at shift start is still valid by shift end. Trust that what worked on one line will work on the next, and the one after that.
Digital Transformation Summit India 2026 made one thing obvious: India’s process manufacturers aren’t sceptical of digital transformation. They’re sceptical of digital transformation that doesn’t execute.
Key takeaways
Behind every polished case study on stage, the hallway conversations told a more honest story.
🧠 AI must be grounded to be trusted
Insight without context creates debate. Context creates decisions. Leaders aren’t anti-AI. They’re anti-AI-that-hallucinates-its-way-through-a-shift-handover. They want models grounded in their plant’s actual data, their assets, their batch structures — not generic manufacturing benchmarks.
⚡ Speed-to-value beats perfect architecture
Leaders want focused use cases that prove value fast—without a transformation hangover. Not six months to a dashboard. Not a year to a POC debrief. Manufacturers at DTS want focused use cases that prove value fast, then build out — not grand architectures that arrive too late and cost too much.
📌 Execution is the competitive edge
Real-time only matters when it changes what happens before the shift ends. When performance dips, teams can see it. They often can’t explain it fast enough to act on it — and by the time the root cause is found, the moment has passed. Visibility without operational context is expensive noise.
How we showed up at DTS
Not with promises. With a loop.
Connect → Contextualise → Act.
The conversation at our sessions kept coming back to the same question: “How quickly can we see something that matters?” Our answer: weeks, not months. Because Brabo connects to what already exists — no rip-and-replace, no middleware chaos — and contextualises it using ISA-88/95 models and industrial knowledge graphs that make the data meaningful before it hits an analyst.
Brabo AIQ — the GenAI co-pilot embedded in the platform — then closes the loop: turning contextual insight into decision-ready recommendations, in natural language, grounded in your operational reality.
This isn’t AI on top of chaos. It’s AI that understands your process, your assets, and your priorities — so the recommendations feel less like algorithms and more like your best plant engineer, available 24/7.
📍 Next step
Get your personalised “Connect → Contextualise → Act” use-case map in 15 minutes. OEE, downtime, yield, or energy — tell us your priority, and we’ll share the fastest path to a first win.
