There’s a particular kind of loss that never makes it into a board report. It’s not a single catastrophic failure or a headline-grabbing outage. It’s the maintenance window that slipped by only an hour that delayed everything after that, the production plan that was already obsolete by the time it reached the people executing it, or the senior engineer who retired last spring and took thirty years of undocumented knowledge with them.
Individually, it seems simple to fix; a single occurrence that only impacted that one product plant, right? But these things happen across every plant, in every team. Collectively, these slippages are why most industrial operations never get anywhere near their true capacity – sure bad strategy or difficult market conditions have a hand, but this solvable friction is so deeply embedded in daily operations that most organizations have simply stopped seeing it as a problem to solve all together. Running at 50-70% of your full capacity has been accepted as the status quo.
We all know unplanned downtime costs the industry $1.4 trillion a year; nearly two million production jobs will go unfilled by 2033; and remaining environmentally conscious comes not just with cost implications, but reputational as well.
However, the real cost isn’t in any single statistic; it’s in the cumulative weight of a thousand small inefficiencies that compound, every single day, on every single team, at every single plant, rig, yard, and office.
What follows is an honest look at five places where that cost is hiding. We know the problems because we’ve seen them up close; we’ve watched what happens when organizations finally decide enough is enough. So, let’s get into it.
Job 1: You need to predict the failure before the floor feels it
The oldest problem in industrial operations is also the most expensive one – unplanned downtime. Nobody notices degradation until failure. But by that time, the cost – in downtime, emergency repair, lost production, and reputational exposure – is already locked in.
The job the industry is hiring AI to do here is deceptively simple: see what’s coming before it arrives. Sensor data, maintenance history, failure patterns, and environmental context are processed continuously so that emerging faults surface before they become unplanned downtime.
What makes this harder than it sounds is that the signals are usually already there. Sensor feeds, SCADA outputs, maintenance logs – the data exists. It just lives in separate systems, owned by different teams, with no single view that connects it to a decision in time. The AI’s job is to close that gap. Not to replace the engineer’s judgment, but to make sure the engineer isn’t the last line of defense.
This is where the human stays firmly in the loop. The AI surfaces the signal. The engineer decides what to do with it. The best engineers in any facility carry decades of pattern recognition that no system has fully captured – the sound a pump makes before a seal fails, the reading that looks normal but isn’t on this particular asset. AI doesn’t replace that judgment. It makes sure the engineer isn’t the last line of defense, scrambling to catch something that the data could have flagged days earlier.
The result at William Grant & Sons, the world’s first AI-powered distillery, was £8.4 million in estimated annual savings – and a shift from a maintenance model where 38% of all repairs were emergency responses to one where failures are anticipated and scheduled before the line stops.
Job 2: Your team needs to fix it right the first time, every time
First-time fix rate is the number field service organizations live and die by. Every return visit burns capacity twice and customer trust once. And yet for most organizations, first-time fix is still the exception rather than the standard. The gap isn’t effort; it’s information. The technician arrives without the full picture – wrong parts, incomplete asset history, no visibility into what the last three engineers did on this exact unit.
The job AI is hired to do is load every job with the full context before the technician leaves the depot. Skills match, parts availability, SLA priority, asset history, known failure patterns, guided resolution steps – all factored in before the job is assigned.
The technician is still the one doing the work; what changes is that they arrive informed rather than under-equipped. A junior technician guided by the actual service history of a specific asset can perform at a level that previously took years to develop. The experience gap narrows without waiting for the workforce to catch up on its own.
TOMRA North America moved from 84% to 97% first-time fix across 85,000 installations. That’s not a marginal improvement. That’s a structural shift in how a service organization operates.
Job 3: You need the plan to remain current when reality shifts
Every COO has approved a production plan that was already wrong by the time it reached the floor. A supplier delivers short. A machine flags for maintenance. A demand signal comes in from an unexpected market. The plan becomes a historical document before it’s even executed.
The job industrial businesses are hiring AI to do here is continuous replanning – not rebuilding the schedule from scratch every time something shifts, but reassessing interdependencies in real time and surfacing the best available options under today’s constraints.
The planner doesn’t get replaced; they get an always on assistant doing the deep data analyses. Instead of spending the majority of their day manually repairing what changed since the last version of the plan, they spend it on the decisions that actually require their judgment – sequencing calls, supplier negotiations, trade-offs that no algorithm should be making alone. The AI handles the complexity of recalculating. The human handles the consequences of choosing.
Suzuki Garphyttan is forecasting a 50% productivity increase from AI-driven demand planning and scheduling across six countries of operation.
Job 4: You want to be able to defend your sustainability goals
Sustainability commitments are public. The scrutiny on them is increasing. CSRD, SBTi, CDP – the frameworks are all converging on one requirement: not just the headline figure, but how you got there.
The job enterprises are hiring AI to do is connect the operational data to the sustainability outcome in real time, at the point of decision. Not assembling estimates from utility bills and supplier projections three months after the fact, but recording the environmental context of every operational decision as it’s made.
The sustainability leader still owns the strategy, the commitments, and the relationships with regulators and investors. What AI gives them is the receipts – an auditable, continuous record that means when someone asks how you got to that number, there’s an actual answer. The human makes the commitment. The system makes it defensible.
PHS Group reduced travel time by 35% across 700 engineers in the UK through scheduling optimization. The carbon reduction followed automatically – not as a separate initiative, but as a byproduct of running more efficiently.
Job 5: You need to make the next generation as capable as the last one
Half the skilled industrial workforce is set to exit within five years. The knowledge they carry – the failure patterns that never made it into any manual, the thirty years of pattern recognition that tells an experienced engineer something is wrong before any diagnostic tool confirms it – leaves with them. Recruitment alone doesn’t close this gap. The pipeline of experienced people to replace what’s leaving simply doesn’t exist at the scale required.
The job AI is hired to do is capture that institutional knowledge before it walks out the door, and make it available to the people who need it most. But the goal was never to bottle an expert and replace them with software. It’s to make sure that when a veteran engineer retires, what they knew about a specific asset, a specific failure mode, a specific site quirk doesn’t disappear with them. The new technician still has to show up, diagnose, and make the call. They just do it with the benefit of everything the last person learned, rather than starting from scratch.
Cheer Pack/CDF Corporation anticipates $1.5 million in annual savings from this shift – and every employee affected is being redeployed to higher-skilled work, not shown the door.
The common thread
None of these five jobs are about replacing the people who run industrial operations. They’re about giving those people a fighting chance against the scale, speed, and complexity of what they’re being asked to manage. The engineer, the technician, the planner, the sustainability lead – they’re all still in the room; IFS Industrial AI just makes sure they’re working with the full picture.
The organizations getting results aren’t the ones that ran the most impressive pilots. They’re the ones that chose a specific problem, deployed a solution built for industrial conditions, and measured the outcome in production – not in slides.
SOURCE: Jillian Koskosky (2026 July 9) The 5 Jobs Industrial Enterprises Are Hiring AI to Do. IFS Blog. https://blog.ifs.com/the-5-jobs-industrial-enterprises-are-hiring-ai-to-do
