What two recent AI events tells us about Manufacturing AI

This month, our team attended two AI events — Ai4 and AIMST — and there were some common threads among both of them. These events reinforced the fact that manufacturers span the entire AI spectrum. Many are still trying to figure out how to get started with AI while some use it in pockets, very few we spoke with have deployed it at scale. But, across the board, everyone understands the value of manufacturing-specific AI solutions.

Ai4: The case for industry-specific solutions

What we kept hearing at the event, especially at our booth, was a real interest for industry-specific AI solutions, and attendees were glad to see Viaduct represented at the show. A common thread in these conversations was the importance of understanding the entire manufacturing lifecycle. One attendee put it simply: "It's great to talk with an AI company that understands our manufacturing and quality challenges and knows what we need."

During the event, David Hallac, Viaduct's founder and CEO, participated in the Smarter, Faster, Leaner: AI in Modern Manufacturing panel, which was focused on how leading manufacturers are using AI to boost productivity and resilience.

When asked about what a successful AI implementation actually looks like, David pointed to the work Viaduct has done for Sumitomo Rubber Industries (SRI). 

SRI produces over 124 million tires per year, and Miyazaki is one of its largest manufacturing facilities, responding to equipment maintenance issues every single day. In just 3.5 months, the Miyazaki plant stood up a unified data foundation, gained real-time operational visibility, and launched AI-powered repair guidance, cutting repair time by 45% and monthly analysis time by 80%. Having proven the model on a specific use case, SRI is now looking to replicate it at scale, with Miyazaki's improvements serving as the starting point for what's possible across its global plant operations.

Sumitomo's results show what's possible when purpose-built solutions are deployed in a manufacturing environment. 

AIMST: Getting to predictive with equipment maintenance

At AIMST, I participated in the AI-Enabled Continuous Improvement & Operational Excellence in Industrial Operations panel, which discussed the decisions that will shape manufacturing over the next decade. A few themes came up.

When looking to incorporate AI into your operations, trust is something that needs to happen upfront. For the people who've been running operations for years, it's imperative that they are part of the process and are being heard. Many plant workers have decades of experience and expertise, and executing processes still requires that knowledge. That's why human-in-the-loop architecture matters; it's about putting better tools in experienced people's hands versus replacing them altogether.

Even with all of that knowledge, most organizations are still reactive but manufacturers don’t have to jump straight from reactive to predictive. The first step is becoming proactive by understanding what is actually causing issues inside their operations and identifying the leading indicators that signal problems before they escalate. This alone can be a significant improvement over how decisions are made in organizations today. 

Nowhere did the gap between reactive and predictive show up more than around equipment downtime; this topic was discussed more than any other during the event. Manufacturers want end-to-end visibility into their equipment health — insights into when something is likely to fail, and clear repair guidance when it does. It's less about "can we use AI" and more about "we need to know before it breaks."

The common thread

Both events reinforced the fact that the AI journey is a spectrum, and organizations are at different points along it. Many need guidance on which data sets to use in building a data foundation, while others need guidance on which use cases to apply AI.

What's clear is that organizations are hungry for guidance and answers - not just on where to start, but on what success looks like. The best approach is to partner with an industry-specific provider who understands the workflows, challenges, and the KPIs necessary for tracking success.

Learn more about Viaduct's approach by visiting www.viaduct.ai.