Big Tech’s AI infrastructure spending now rivals national GDPs, and concerns about a trillion-dollar bubble are mounting.
Yet amid the noise, there’s a more interesting signal: while most organizations struggle to extract value from AI projects, a small but growing group is seeing genuine returns. The difference?
Chief Product Officer at IFS.
They’re not chasing hype or deploying AI for its own sake. They’re applying it where it matters most – in the industries that keep the world running.
This is Industrial AI, applied: the use of artificial intelligence to optimize operations in manufacturing, energy, utilities and logistics. And it’s quietly proving what works.
The 70% of workers AI forgot
Much of the public conversation about AI is around the 30% of the workforce that sits behind a desk. That’s where most of the visible innovation has happened: content, copilots, and productivity.
But around 70% of the global workforce doesn’t sit in an office at all. These are the engineers, technicians and field workers who maintain, operate, and deliver the physical systems that our economies depend on.
For them, AI is a practical tool for helping work get done faster, safer and more efficiently. Industrial AI is being applied to the everyday reality of these roles. It connects machines, sensors, and systems. It analyses anomalies, predicts failures, and automates scheduling.
The new industrial workforce
This convergence has created the need to 10X the workforce: human experts elevated to orchestration and judgment roles, AI agents running diagnostics and workflows around the clock, and robotic workers handling hazardous tasks and precision work.
Together, they unlock productivity that was previously constrained by human availability alone. It is, in short, the intelligence behind the work most people will never see.
That shift is changing the nature of how work itself is organized. The businesses that succeed with AI won’t be the ones that adopt the most technology, but those that bring together the triptych of people, process and purpose to create measurable results.
Bridging the AI execution gap
The biggest challenge for most enterprises is how to make AI scale. Research across global industries shows a widening “execution gap” between the speed of adoption and the readiness of organizations to use AI effectively.
Leaders are realizing that AI success has less to do with model performance and more to do with change management. Technology is the easy part. The hard part is change: rethinking roles, retraining people, and rebuilding trust in decisions powered by data.
Trust, in fact, may be the most underappreciated factor in the next phase of AI maturity. Many executives remain hesitant to allow AI systems to make independent decisions. That’s an instinct, especially when the consequences of failure are operational and financial.
A more pragmatic view is to treat AI much like a new employee. No leader hires a graduate and gives them full authority on day one. They’re trained, supervised and gradually empowered as their understanding grows.
The same should apply to AI systems; they should be trusted through experience, not assumption. Over time, as confidence builds, approval processes will give way to autonomy. This evolution will define the next era of enterprise AI – the Agentic Age.
Hyper to hard value
If the current AI boom is defined by investment, the next will happen by integration. The most forward-looking companies are now embedding AI into workflows rather than bolting it onto existing legacy systems.
This is changing business models. One of the most profound shifts underway is servitization – the move from selling products to delivering outcomes. Instead of charging for a machine, organizations charge for its performance: uptime, efficiency, or availability.
That model makes AI indispensable because it provides the intelligence to predict issues before they happen and allocate the necessary resources to maintain the equipment’s service.
Industrial AI is also emerging as a key enabler of sustainability. As ESG reporting becomes mandatory across jurisdictions, AI is helping organizations monitor emissions, optimize resource use, and make data-driven decisions that reduce waste and costs.
What once felt like a regulatory burden is now a catalyst for transformation; the rising tide lifts all ships.
The quiet future of AI
There’s a growing sense that the AI sector is heading for a shakeout. Some companies will overextend, others will consolidate, and a few will thrive. But a correction in speculative investment is, ultimately, a sign of progress.
The reality is that Industrial AI is already being applied, and not because of hype or hope. It’s because it solves real problems for real people. It’s helping industries operate more efficiently, more sustainably, and more intelligently and will become a quiet, common norm.
While investors debate whether AI is a bubble, the industrial world is proving its value. This is not an Intelligent Age for its own sake – it’s an age of intelligent industry. And its impact will last long after the hype and bubbles have dissipated.
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