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Adapting skillsets for Industry 4.0 (No Data Science required)

As a facilities engineer, you might be concerned that automation will replace your job. Good news: Experts say fears that Artificial Intelligence (AI) will leave most of us unemployed are unfounded. In fact, the management consulting firm, McKinsey, predicts that while some jobs might disappear and new ones will emerge, most jobs will only experience a change in task composition.

Research also asserts that most automation efforts will focus on complementing human labour by removing tedious tasks, allowing you to focus on more complex aspects of your job, such as creativity, critical thinking, and decision making. These are all tasks where Artificial Intelligence fares poorly.

“Automation doesn’t generally eliminate jobs. Automation generally eliminates dull, tedious, and repetitive tasks. If you remove all the tasks, you remove the job. But that’s rare.” - Hal Varian, Chief Economist at Google, at Stanford’s Future of Work forum

So rather than wondering whether a machine will replace you, here's the real question you should ask: What changes can I make to adapt to a new and more efficient way of working?

To be or not to be a data scientist

If you like computer programming and statistics, a career change towards data science is certainly a good option. But if you’re more interested in facility design, operations, and maintenance, then data analysis is just a means to an end.

In that case, you’re better off focusing on becoming a smart user of AI, augmented with your domain expertise. There’s a couple of reasons for this.

  1. Identifying the correct problems to solve
  2. Understanding domain-specific data pitfalls and requirements for preparation
  3. Designing the appropriate solution architecture
  • Data science teams have limited bandwidth. They can’t tackle all the problems you might be facing, and they shouldn’t. Instead, you should find existing AI tools that don’t require any machine learning expertise to help you take care of tedious daily tasks, and identify areas where no solutions exist for your data science or machine learning teams to explore.

So what criteria should you use to find the right AI tools out of the plethora of existing options? These tools should be:

  • User-friendly and not require deep knowledge of the underlying tech
  • Catered for your domain—general solutions will perform poorly on your data
  • End-to-end so that you don’t need to mash together multiple solutions from multiple providers

One example of such a solution is a System of Intelligence (SOI). Systems of Intelligence are powered by a combination of domain expertise, deep learning algorithms, and human-in-the-loop machine learning to deliver a seamless experience in a single platform. They are designed to execute a series of workflows spanning several disciplines for large volumes of data, and are operable by subject matter experts who have no knowledge of machine learning or data science. For example, Cenozai is developing an SOI to span several aspects of Facility Maintenance, including building a hierarchical asset register using AI to help facility management teams embark on their Industry 4.0 journey.

Use the machine learning transition process to your advantage

Studying theory is great and all, but there’s nothing better than real life experience. Work closely with the data specialists helping your team or department transition to a system that’s AI-ready. By observing and understanding their mindset and concerns with data, you’ll gain valuable insight into what’s required for overseeing implementation of AI tools, as well as how to get the most out of them.

So how do you get your data prepared for AI? Funnily enough, you can actually use AI to help with that. Digitizing unstructured data is a tedious task, but computer vision and natural language processing algorithms are excellent for helping you extract and organize information for subsequent analysis.

It’s a crucial step, and requires knowledge of a bundle of ISO standards that incorporate facility domain expertise. Your data engineers will need your input for that, and you can gain deeper understanding through them as to how that system can feed future machine learning projects—such as automatically generating work orders, or predicting operator intervention on a control loop when an alarm triggers.

Then, when you’re engaged in an actual machine learning project, whether with an in-house team or sourcing a tool like a System of Intelligence, pay close attention to how the providers are delivering the solution architecture. That’s key knowledge that you can transfer to future projects.

Humans are underrated

Artificial Intelligence can give you a major efficiency boost if you integrate well with it. Soon, it’ll be part of the daily routine, just like how Waze or Google Maps has become part of our daily commute. Yes, there will be changes, but they’re probably not as drastic as people think—we’ve been through three other technological revolutions before this. We’re good at evolving.

Even technocrats like Elon Musk, Tesla founder and former backer of OpenAI, admit that machines can’t completely replace human capabilities.


Embrace the change. A future with automation can only succeed with humans and machines working in tandem.