Building a foundation for agility through automation

In the age of AI, there’s a lot of hype and anticipation around how technology can be used to predict or determine the next best action for a variety of business processes, while the current reality is that logic-based rules and systems have been used for some time to run calculations and determine likely outcomes and the best course of action.  

Now we have arrived at the point where logic or AI can not only make decisions or recommendations but also drive their implementation.  

This level of automation could mean a step change in how organizations manage their assets and allows for new levels of agility. However, to take advantage of this opportunity, organizations must build a solid foundation of accurate and complete data.  

Automation and the blue-sky scenario for asset management  

 Imagine there’s a change in your operating environment, or a change to your asset conditions, or there is a change in raw materials or asset duty. Then, based on that, your asset strategies are updated in real time, such that new strategies are deployed when work orders are automatically generated and sent to the right people, and the tasks are executed seamlessly to the right level of quality. Organizations have long aspired to this blue-sky scenario, and technology is no longer the constraint.  

We now have the technology to analyze data, run calculations, compare scenarios and outcomes, and determine the best options. In fact, technology can often perform evaluations and make decisions better than humans. It's then not difficult to extend this and automatically drive updates to an Enterprise Resource Planning (ERP) system to reflect these decisions or changes. It is just a matter of building the right integrations and workflows.  

The challenge in driving end-to-end automation in asset management is ensuring you have accurate, reliable data and there is trust that the algorithm or AI model is making the right decisions.  

Closing the data and trust gap in AI and automation  

So how can organizations build a foundation for greater automation and operational agility? To start with, any data entered into an AI model or rule-based system must be precise, up to date, and reliable. This includes detailed work order content, accurate records of asset maintenance, and clear failure codes.  

Historically, this has been a challenge as individuals and teams have been ambivalent about data collection because there wasn't a direct application driving its use. Now, though, as people see data being used to drive specific outcomes, they are more inclined to participate in gathering and verifying data. This culture shift can be further supported with training and better awareness about the value of complete and accurate data. Fortunately, AI can also be used to interrogate historical data and categorize as required to support contemporary data analysis. 

When it comes to building trust in AI, it is helpful to start slowly and provide a level of human oversight. So, while an AI model may generate a recommendation, someone with experience and context can decide whether that recommendation should be implemented. This person could be onsite or remote and co-located with other decision-makers as part of a Center of Excellence set up to support the business’s broader operations.  

As we progress towards this vision, the combination of AI-driven and rule-based recommendations and human oversight will pave the way for full automation. We are already seeing this in areas like asset health and strategy management where automation takes care of mundane, repetitive, and time-intensive tasks and frees up resources for higher value work.  

The use of intelligent condition monitoring systems in asset health is a great example. Built using proven algorithms and machine learning, these systems can act as a first line of defense and eliminate a large number of false alarms that many operators experience today. So they can use their decades of experience and training on interpreting qualified alerts.  

To take advantage of these opportunities, organizations must mature their processes and data. Learn how you can get started by connecting your systems and data.