November 3, 2025

Generative vs predictive AI in mineral processing plants

Artificial intelligence might be making headlines in every industry, but in mineral processing, its story is just beginning. Between the buzzwords and the bold claims, it can be hard to know what “AI in the plant” actually looks like or where to start.

At The Path to Progress: Get Your Plant AI Ready, we’ll be unpacking exactly that. But before we do, let’s demystify two of the most common AI terms you’ll hear: predictive and generative AI, and why understanding both matters for the future of your operation.

Predictive AI: the plant’s crystal ball

Predictive AI is designed to forecast what might happen next. It learns from historical data such as sensor readings, maintenance logs and production outcomes to identify patterns and predict future events.

In mineral processing, that can mean:

Predicting when equipment is likely to fail, enabling maintenance to be planned before unplanned downtime occurs

Identifying optimal process conditions to maximise throughput or recovery

Comparing real-time operating conditions to historically high-performing “golden batches” to guide adjustments in the plant

According to Mipac’s Solutions Manager, Dominic Stoll, the industry is starting with what’s achievable:

“Predicting flotation performance goes from complicated to complex, so most operations aren’t doing it yet. The focus right now is on predictive maintenance to maximise uptime because it’s complicated, not complex, and it’s already delivering tangible value. The best we’re seeing in performance prediction is golden batch, where algorithms compare current inputs and variables against statistically significant historical data to suggest the most effective conditions.”

Predictive AI gives engineers and operators the power to see ahead but relies on people to decide what to do with that insight.

Generative AI: the plant’s creative problem solver

While predictive AI forecasts outcomes, generative AI creates something new such as text, code, images or insights based on patterns in existing data.

In mineral processing, it’s easy to imagine practical uses:

Automatically generating daily shift reports from control system data and lab results

Drafting SOPs or maintenance notes using equipment logs and OEM manuals

Summarising complex plant data into clear, human-readable insights for supervisors

Where predictive AI crunches the numbers, generative AI tells the story. Together, they can transform how information moves across the plant, from control room to management dashboard, reducing the time spent writing, interpreting and sharing reports.

Agentic AI: taking the next step

There’s also a new kid on the block: agentic AI. While predictive and generative AI provide insight and information, agentic AI can take action within clearly defined limits.

Imagine an AI “agent” that monitors plant data in real time, predicts a potential issue, drafts a corrective action using generative AI, and then automatically applies that change to the control system, all while keeping the operator in the loop.

But the real shift is that agents aren’t just autonomous—they’re collaborative. Where predictive and generative AI focus on producing outputs, agentic AI is about producing coordinated behaviour—among multiple agents, humans, and tools. An agent isn’t valuable simply because it acts alone, but because it can work with others toward shared or complementary goals.

That might sound futuristic, but it’s where AI in process industries is heading. And it’s why now is the right time to start building the data foundations and digital maturity that make this possible.

Myths vs reality

AI can sound like magic, but in practice it’s a set of tools that depend on data quality, context and human judgment.

Myth: AI will replace operators.

Reality: AI amplifies human capability. It handles repetitive analysis so engineers can focus on optimisation and improvement.

Myth: You need a full digital transformation before you can use AI.

Reality: Many predictive and generative AI tools can integrate with existing historian and control data. You can start small and scale graduall

Myth: Generative AI isn’t relevant to heavy industry.

Reality: When trained on plant-specific data, it’s already improving report accuracy and knowledge transfer.

The path to progress

AI in mineral processing isn’t about replacing expertise; it’s about empowering it. Predictive, generative and agentic AI each play a role in helping plants move from reactive to proactive to adaptive operations.

That’s the journey we’ll explore at The Path to Progress: Get Your Plant AI Ready, where leaders from across mining, academia and technology will share what’s really happening behind the buzzwords.

If your goal is to make your plant smarter, safer and more efficient, this is the place to start.