America's factories offer great examples of game-changing AI use cases. At large industrial plants, an hour’s downtime can cost more than $500,000. That means using AI to boost efficiency and reliability could deliver enormous bottom-line benefits.
Top predictive maintenance systems, for instance, deliver at least 250% ROI, powered by a 75% reduction in unexpected breakdowns. And with the US now actively reindustrializing — committing unprecedented capital to building out domestic manufacturing capacity — the upside of getting industrial AI right has never been larger, and the cost of getting it wrong has never been higher.
But there’s a long way to go. Today, only 27% of manufacturers use predictive tools; meanwhile, more than three-quarters of industrial AI initiatives fail, often dragged down by low adoption rates and poor quality data.
The demand for disruption is real, but most industrial facilities still depend on fragmented, analog datasets. That makes it all but impossible to create transformative AI tools.
We need a new approach. Instead of settling for subpar datasets, industrial organizations must start capturing data directly from the technicians who keep their machinery running. Frontline workers understand their equipment and their environmental context better than any algorithm ever could — and activating their insights at scale is the key to unlocking the potential of industrial AI.
Why we need frontline insights
Industrial facilities are already data-rich. With IoT sensors and sophisticated digital twins, industrial organizations will produce a staggering 4.4 zettabytes of data by 2030. Yet most firms still lack meaningful visibility into the root causes of downtime.
That’s because all those sensors track performance, not context.
They can detect vibrations, but they don’t know whether a machine was installed on a concrete pad or a metal floor, or how that changes the expected behavior. They can warn that a part is malfunctioning, but they don’t know its repair history or what technicians learned the last time it failed.
They also can’t track the countless other variables that human technicians notice and act on every day. The way a machine smells, sounds, or feels won’t show up cleanly in sensor data, but those are what human technicians routinely spot and act upon.
The most operationally valuable data in today’s industrial facilities is often never digitized. It lives in handwritten maintenance logs, unrecorded equipment fault codes, a technician's intuitions or a maintenance team’s tribal knowledge. These insights are subtle but powerful — and they’re missing from the data used to power current predictive tools.
How to unlock frontline insights
To capture those insights, industrial AI needs to be approached differently. Big outcomes with substantial ROI are tempting targets — but reaching them requires building from the bottom up and baking AI tools into frontline workflows.
That starts with digitizing maintenance. Instead of whiteboards and clipboards, technicians need tools to capture, share and act on maintenance data in real time.
That apparently simple step becomes a beachhead for bigger innovations. Once maintenance is digitized, LLMs can ingest messy, unstructured data: handwritten logs, voice notes and the other real-world data that technicians create as they go about their work.
This creates a meaningful new data layer — derived, crucially, from humans — that simply didn't exist two years ago. Because maintenance touches every person and asset across the organization, digitizing these workflows also forces organizations to do the hard integration work of connecting equipment data, technician knowledge and operational context into a single system. That integrated foundation enables downstream AI use cases, including:
- Process optimization,
- Quality traceability,
- Energy management,
- Product design feedback loops.
What makes this approach durable is that it’s self-reinforcing. Technicians spend one-fifth of their time on repetitive admin. Reducing that burden delivers immediate ROI through productivity gains and earns enthusiastic buy-in from frontline teams who are eager to eliminate administrative overhead.
Of course, as with any new technology, there are potential risks. It’s important to put safeguards in place to screen for hallucinations, control for errors and ensure data security. But done right, frontline data becomes an enduring strategic asset — driving not just maintenance gains, but major operational and technological improvements across the entire value chain.
Time for action
Capturing frontline insights will be vital as organizations deploy industrial AI. But there’s no time to waste.
Humans are not a long-term data-storage solution. When a worker leaves, the knowledge siloed in their head goes along with them. With more than one-quarter of technicians set to retire by 2030, that’s a serious challenge; for every month that passes without digitization, precious institutional knowledge is permanently lost.
To build effective AI tools, organizations need to start externalizing this human knowledge now. By embedding AI into maintenance workflows, teams can build a digital repository of institutional know-how, then use that repository to streamline onboarding and accelerate knowledge transfer, helping new recruits to quickly master the increasingly advanced equipment deployed on factory floors.
As we capture and structure this institutional knowledge, it also lays the groundwork for the next generation of AI tools. Agentic technologies will need to learn not just how equipment performs but also how decisions are made. The only way to master that, and understand how different actions translate into uptime and profitability for the organization as a whole, is by learning directly from the humans making those crucial operational decisions.
Consider a concrete example: an AI agent trained only on sensor data might flag a pump for immediate replacement based on vibration readings. One trained on human decision-making patterns would also know that the maintenance team has historically deferred that repair until a planned production shutdown, and that this choice has consistently avoided days of unnecessary downtime. That gap in outcomes is entirely explained by human knowledge that no sensor can capture.
Elevating industrial AI
As the US commits capital to building out industrial facilities, the need for predictive maintenance and industrial AI will only grow. America’s reindustrialization creates an opportunity (and a need!) to build a new generation of predictive tools that slash downtime, increase global competitiveness and empower technicians to thrive.
But this won’t happen by relying on current data sources. Just one in 10 industrial organizations uses next-gen prescriptive tools capable of identifying the specific steps needed to prevent breakdowns. To grow adoption, and turn AI insights into real-world downtime reductions, we need to start harnessing the power of frontline insights at scale.
By digitizing maintenance workflows and embedding human knowledge into AI systems, industrial organizations can drive transformative results. But the window of opportunity is closing — once people leave the workforce, their insights and experience are lost forever. If industrial organizations want to capture the value-add from their frontline teams, then the time to act is now.
