Let's strip back the hype: A look at real AI Concerns & Some Pragmatic Views

It's 2025, and if you're running an industrial business, you can't escape the noise about Artificial Intelligence. You’re being told it will revolutionise everything, but that hype can sound distant and, frankly, terrifying.

For any mid-sized industrial business, every dollar and every minute counts. You don't have the luxury of a massive development or even a 'nice to have' budget. You have wages to pay and orders to fill. So, when someone mentions "AI," it's natural for some very real fears to surface.

Let's tackle those fears head-on and explore a cautious, practical way forward that doesn't involve betting the factory.

The Fear: "The cost is astronomical and the ROI is a fantasy."

You see the presentations promising the world, but your mind immediately goes to the real cost: consultants, new hardware, endless integration fees, and business disruption. For a growing business, it feels like a capital sinkhole with a return on investment that's based more on hope than on hard numbers.

The Pragmatic Step: The "One-Problem Pilot."

Forget transforming the entire company. Instead, find one small, contained, and genuinely annoying problem that costs you time or money every week.

• Think small: Is it manually checking invoices against purchase orders? Is it the frustrating amount of sheet metal wasted on suboptimal cuts? Is it guessing your inventory needs for spare parts?

• The experiment: Dedicate a small, fixed budget to a targeted software tool for a 3-month pilot. The goal isn't to change everything; it's to answer a simple question: "Did we spend less on this tool than we saved in wasted material or admin hours?" This gives you a crystal-clear, "unquestionable" ROI you can trust, and use as an example to gain support for the next steps.

The Fear: "AI will replace my most experienced people."

You have loyal staff who have forgotten more about your machinery than a new employee will ever learn. The idea of bringing in a system that makes them feel obsolete is not only bad for morale, it risks destroying irreplaceable institutional knowledge.

The Pragmatic Step: AI as the "Super-Toolkit."

The goal should never be to replace your experts. It's to make them even better. Frame AI as a powerful new tool in their toolbox.

• Think augmentation, not automation: Give your veteran maintenance supervisor a tablet with an AI-powered thermal camera. They still use their years of experience to diagnose a problem, but now they have a tool that can instantly show them a heat map, confirming their suspicion or highlighting something they couldn't see.

• The expert is still in charge: The AI provides data; the human makes the decision. This respects their expertise and empowers them to do their job better, faster, and more safely.

This concern about people is absolutely real and justified. The advice I give business owners is this one thing 'It's your choice!':

1. Yes, you can absolutely go all in on cost cutting, if you choose. But it drives your business into being part of the low cost provider market, which is already crowded, and your single differntiang feature is cost! or;

2. Look at AI as a growth lever. Spend the time to examine what's holding the business back from growing and target that area. There are more than enough AI solutions to grow your business, through innovation, customer enrichment, icreased throughput and removing menial and repetitive work from people, re-directing them to strategic growth work. A growth focus will have you likley needing more people not less, or rewarding existing folks based on success.

The Fear: "My data is a mess. It's garbage in, garbage out isn't it?"

AI runs on data. But if your reality isn't clean spreadsheets; if it's a decade of handwritten logs, inconsistent part numbers, and critical knowledge stored in your team's heads. You know that feeding bad data into a smart system just gets you a bad answer, faster.

The Pragmatic Step: Start Clean, Start Now.

Don't even attempt to clean up the past. It's a massive, expensive project with diminishing returns, cherry pick anything solid, otherwise for the most part, start fresh on a small scale.

• Pick one critical asset: Choose one of your most important pieces of machinery, for example.

• Instrument it now: Install modern sensors to start collecting clean, high-quality data from today onwards. In a few months, you will have a small but perfect dataset. You can then test a simple predictive maintenance model on that single machine, proving the concept in a controlled environment before you even think about scaling.

There's a pattern building here.

Pick a critical aspect of your operation, only one! Do it right, test, measure the results, learn and adjust.

Build out as confidence and buy-in grows!

The Fear: "If the AI gets it wrong, who is to blame? It's a huge liability."

This is one of the biggest hurdles. If an AI optimises a process that leads to failure, or worse, fails to spot a critical fault that becomes an OH&S incident, who is responsible? You can't put an algorithm in the dock.

The Pragmatic Step: The AI is an Advisor, Not a Manager.

Keep your existing chain of command and accountability perfectly intact. The AI's role is purely advisory.

• It alerts, it doesn't act: The AI's job is to be the world's best smoke detector. It can send an alert saying, "Warning: Vibration analysis indicates an 85% probability of bearing failure within 48 hours."

• A human has to decide: That alert goes to your human supervisor. They use their experience to verify the issue and make the call to schedule maintenance or even shutdown the system or plant. The AI gives them better information to make a better decision, but the final call - and the accountability - remains with your team.

You will hear the term AI Guardrails more and more. Put simply, just as your HSEQ System provides the guardrails for employee Health and Safety, and your QA System your products and services, you will need to embed AI Guardrails in your Governance processes and SOP's so it's clear what AI can and cannot be allowed to do in everyday work activity.

Conclusion: From Fear to First Step

AI doesn't have to be a giant leap of faith that risks your business or your career if you decide to chamion it. By shifting your perspective, you can see it for what it can be: a series of small, cautious, and intelligent steps.

Instead of "implementing AI," think about "solving one problem."

Start there. Prove the value on a small scale, build confidence within your team, and grow at a pace that makes practical and financial sense for you. The future of industry might be smart, but the path to get there can be, and should be, incredibly pragmatic.

https://www.linkedin.com/pulse/lets-strip-back-hype-look-real-ai-concerns-some-pragmatic-m2tac