AI is about much more than algorithms, databases and machines – the people powering AI are just as important and play a key role in ensuring predictive maintenance works, sticks and really does transform a businesses bottom line.

Embarking on your predictive maintenance journey as a business can be a daunting task. There are many decisions to make, for instance do you build your AI solution or partner with a software as a service company. Another is around building your team, and the critical roles required.

It’s easy to assume that if predictive maintenance is powered by artificial intelligence, it must be all about the machines – teams of developers building powerful algorithms to predict the future, optimise processes and digitally transform our world.

And it's absolutely the case that, done the right way, AI has immense power to shift the needle, transform a business's bottom line and drive never before seen return-on-investment.

But what can't be overlooked is the important role that humans play in enabling, adapting and utilising artificial intelligence for predictive maintenance. If 'no man is an island' then no machine is either.

Even if you have decided to utilise a no-code AI platform like OPUS, you still need an internal team that will be responsible for cleaning and maintaining data, formulating the problem statement and models for the AI platform, and then interpreting the results so that management can make informed business critical decisions.

So, how do you go about building this dream predictive maintenance team?

What staff do you need? And can you up skill existing staff?

We believe that the best approach is to build a lean predictive maintenance team and empower them with a powerful AI platform to predict future asset deviations and failures. This will greatly reduce the time it takes to implement predictive maintenance programs and see tangible benefits to your business.

Another benefit in using an AI predictive maintenance platform is that they will already have many of the tech roles that you would have read about – AI Software Developers, AI Researchers, System Architects, Data Engineer, User experience designers and Project Managers. Allowing you to build a more agile, streamlined team.

Building a predictive maintenance team to enable your industrial AI journey is a key element in making sure it actually succeeds.

We've put together the top five players you need to be recruiting, rewarding and working with to make sure your predictive maintenance play leads to optimal asset performance and increased uptime.


Every project needs a champion – one person to sing it’s praises, rustle up support and keep pushing even when obstacles appear. The business sponsor is the person trying to solve a legitimate business problem that can be tangibly impacted by the use of innovate new technology like AI. As the ‘problem owner’ they will be invested in seeing that it succeeds and will act as the project director. If an AI solution is part of the initiative, then it’s imperative that the sponsor is a big supporter of new technology, as this will go a long way to helping effectively navigate the machinations of an AI solution.


The role of the Data Scientist is critical to the team – and probably why LinkedIn ranked the job as the most sought after of 2019. With massive amounts of big data being produced by industrial machines, the role of the Data Scientist is to formulate the problem statement in a way that is digestible for machine learning algorithms. The other key part of this role is the ability to relay important information back to the broader team and to share and validate findings. With a broad background in technology and a strong aptitude for maths, this team member is the link between the mathematics and the business.

Note, that if using a no-code AI platform to assist your predictive maintenance team, it will be possible for subject matter experts and engineers to frame their own problem statements and build predictive maintenance models on their own, without relying on a data scientist. 


If Data Scientist’s have a direct line into how the algorithms work, then Business Analyst’s are the translators between the data nerds and the rest of the business! A good Business Analyst performs data cleaning & organizing and finds business relevant trends in data. By creating relatable and easily understandable visualizations, their role is the important one of communicating AI results to the broader business. As the interpreter of AI results and insights, this person often works across a variety of business functions – from marketing to finance to product – to ensure that data is being used in the right way by the right people.


Getting the right data feeding through in the right way is a critical component of a functioning AI system. The role of the Data Engineer is to ensure that high quality data is entering the system – this is achieved through the definition and implementation of complex data integrations, which this role is responsible for. It’s a highly complex role that requires the integration of many varied systems, data types (structured and unstructured) and databases (SQL and NoSQL).


The Asset Managers, technical maintenance team and operators are the link between working assets and the systems supporting their uptime. The operator is the end user that will take the actionable insights provided by an AI platform and use those predictions and optimisations to inform maintenance schedules, planned shutdowns and manage spares inventory. With working knowledge of the assets being monitored by AI technology, they can provide a valuable insight to the rest of the predictive maintenance team.


You may find your business already has the necessary staff to commence a predictive maintenance program, and you just need to gather them from different parts of the business. If so, great! If not, we hope this article has helped you visualise your predictive maintenance dream team.

Once you have your team established we recommend selecting a high value problem statement to commence your predictive maintenance program with. Following this, start mapping out your processes and once you have successfully implemented you can consider rolling it out to your entire enterprise.

As you do that, consider partnering with a no-code AI platform like VROC which provides your predictive maintenance team with a scalable AI solution. AI platforms provide internal subject matter experts with actionable insights to help assist with business decisions.

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