How Agentic AI is tackling the mundane and essential tasks industry relies upon
Agentic AI is here and it's here to stay, tackling the inefficiencies and operational gaps that teams have been quietly working around for years. Only the form and function alter across industries, while the agentic AI we know today is not the one we will know tomorrow as it continues to evolve.
And evolve it will: recent research estimates that the Agentic AI market in Australia is expected to grow to more than US$435.1 million by 2030, up from just US$36.9 million last year – at a compound annual growth rate of 51.6 per cent. Meanwhile, companies like Schneider Electric are already announcing initiatives to ramp up the rollout of agentic AI for industry, saying that "this technology allows us to create a force multiplier effect where complex data analysis and tasks are automated, freeing our clients to focus on the strategic initiatives and innovations that lead to greater impact". With that level of spend and competitive spirits pushing AI to the envelope, evolution is a given.
But what is agentic AI, you ask?
Simple: an AI agent is a kind of software application that sits and autonomously works behind the industrial operating technology platform, acting as something of an AI "colleague" that performs a task by accessing and interpreting system data such as operating temperatures, pressure specifics or safety metrics.
On the surface, that sounds like something a human should do. But what's actually landing on factory floors and in control rooms today is specifically designed to augment workers' abilities to optimise output and production. In other words, it makes humans better at what they do. It looks like software, specific, contained, and built to solve the kinds of problems that no one ever had time to fix.
Industrial, Agentic AI enhancing jobs
Across energy, utilities, and manufacturing, the pattern repeats: complex systems, thinly stretched teams, and too much time spent reconciling, reporting, or reacting after the fact. AI agents offer something less dramatic but far more useful: autonomy designed for uptime, consistency, and scale without the overhead.
Here's what that could look like in practice. A feed pump starts to behave oddly. A standard system flags the anomaly, maybe sends an alert. But a predictive maintenance AI agent goes further, providing early detection of the issue before it becomes a real problem, and then another agent could take it further to assess maintenance history, suggest a root cause and steps to fix, and even automatically trigger a work order – while flagging what'll happen if you ignore the issue.
And that's just one of many practical examples of what AI can achieve. As agentic AI is a technological approach to AI, the concept can be applied to many use cases. Think of monitoring or report summarisation and analysis – agentic AI can take that and allow that worker to do, well, anything else that isn't the day-to-day minutiae.
Reducing the workarounds
Most Australian operators aren't starved for data. What they lack is the connective tissue, the ability to clean, classify, and act on that data before it becomes stale. Half the time, teams are stuck resolving mismatched IDs or cleaning sensor inputs just to get a usable baseline.
This is where agentic AI earns its keep. For example, if one system identifies a piece of equipment as Pump 101 and another labels it PMP 101, a worker must manually establish that connection. Agentic AI can automate most of this work, resolving asset identities with up to 80 per cent accuracy. That doesn't grab headlines, but it does unlock hundreds of hours in reclaimed engineering time and cuts weeks off digital twin rollouts.
None of this is glamorous. But it's the kind of behind-the-scenes friction that quietly drains progress, and the kind of task AI agents are purpose-built to smooth out.
What's changing fast isn't just what AI can do, it's who can use it. Until recently, building and managing machine learning models meant specialist skills and months of tuning. Now, with natural language interfaces layered on top, the barrier's collapsing.
Operators can simply say, "track condenser performance on Line B and flag drops below 90 per cent" and the system just does it. No scripting, no escalation, no fuss.
It's a usability shift that matters more than any algorithm. And in a country like Australia, where geography stretches teams thin and finding extra hands isn't always an option, tools that work out of the box aren't just helpful, they're essential.
AI a partner, not a silver bullet
That doesn't mean these systems are infallible. AI agents are still only as good as the data they're fed, and mistakes at the input stage can spiral fast. That's why the most effective deployments treat AI as a partner, not a panacea.
Let it handle the pattern recognition and 24/7 monitoring. Bring in humans for edge cases, judgment calls, and anything requiring context. The goal isn't full automation. It's cleaner, faster decisions, without burning out the people making them.
PwC estimates AI could add $15.7 trillion to global GDP by 2030, with productivity gains making up a third of that. But we won't get there by tinkering around the edges. The real value shows up when AI stops being an experiment and starts becoming part of the plumbing.
And that's already happening: not with fanfare, but with form fills and fault predictions.
Agentic AI won't make a splash. It'll make a dent in downtime, in inefficiency, in the endless backlog of tasks teams never had the bandwidth to tackle.
It's here to do the boring bits brilliantly, reconcile systems, spot faults before they cascade, and surface insights before they're lost in noise.
If that sounds unglamorous, good. Because in industries where even small failures ripple wide, progress rarely looks like a grand unveiling. It looks like fewer delays, tighter tolerances, and teams that can finally spend less time fixing and more time thinking.
This is what transformation actually looks like: autonomous, unobtrusive, and already underway.
