What’s next for EAM? Get ready for an AI-inspired future

By Berend Booms, Head of Enterprise Asset Management Insights at IFS Ultimo

More and more organisations in every sector are investing in artificial intelligence (AI) technologies that will enable them to automate and transform their operations. Generative AI (GenAI) is the latest innovation to drive the current wave of AI adoption and, according to Gartner, by 2027 more than 50 percent of the GenAI tools enterprises deploy will be utilised for a specific industry task or business function.

In 2024, the global AI market jumped beyond $184 billion – a growth of nearly 50 billion US dollars compared to 2023 as businesses put AI to work to deliver new business value. Top use cases include chatbots and voice assistants, IT operations management, process automation, financial reporting and analysis, and scheduling optimisation to name but a few. However, according to tech industry analysts, AI is now set to revolutionise how industrial businesses boost productivity and performance while reducing operational risk.

The big challenge for today’s industrial firms is understanding how to best unlock this potential and maximise value from their AI investments from Day One. That’s because the integration of AI in Enterprise Asset Management (EAM) platforms is set to radically enhance how organisations operating in industrial sectors such as manufacturing, logistics and food production improve reliability and streamline their operations.

Let’s look at how these firms can expect to derive measurable benefits and value from their AI-infused EAM investments.

A world of opportunity

Indications are that AI will help break the productivity impasse that hampers many industrial organisations from achieving measurable gains and bedding in sustainable growth. For example, deploying AI and automation technologies to address workforce scarcity issues and reduce the amount of time that workers spend on mundane repetitive tasks. But that’s not all.

Research by McKinsey has found that following the successful implementation of AI technologies, companies were able to cut logistics costs by 15 percent, achieve a 35 percent improvement in inventory management and realise a 65 percent increase in service level agreements met. Meanwhile, AI-supported maintenance is set to maximise operational productivity by using machine learning to predict and prevent equipment failures. AI enabled predictive maintenance can extend asset lifespans by 20-40 percent.

By leveraging AI, future EAM toolsets will become more intuitive, accessible and predictive. All of which will deliver a comprehensive and data rich overview of asset productivity, uptime and cost alongside new and highly coordinated maintenance management capabilities.

Be that as it may, AI will not leapfrog organisations’ journey towards predictive maintenance instantaneously. Significant investments are imperative for many of the new gen AI functionalities – an expenditure many organisations are not able to commit to. Organisations starting out on their AI journey should seek out ways to generate incremental gains fast while retailing complete control over their AI implementation and outcomes.

With reactive maintenance being the reality of the situation for most industrial organisations, companies should instead focus their attention on EAM solutions capable of realising fast time to value without any additional investment requirements. The good news is that AI-infused EAM solutions are now making it easy to pursue a step-by-step approach to improving how organisations manage and maintain their physical assets.

Elevating the effectiveness of reactive maintenance

For many industrial organisations, predictive maintenance is a long-term goal rather than a short-term reality. That said, today’s AI-enhanced EAM solutions enable industrial firms to significantly reduce the time they currently spend undertaking reactive maintenance. By realising these benefits today, they will be able to start working towards their future predictive maintenance vision.

For example, with the average cost of downtime in manufacturing often exceeding $100K an hour, maintenance and operational leaders know that any reduction in Mean Time to Repair (MTTR) translates into increased asset productivity and thousands of dollars in downtime saved. However, 80 percent of MTTR is typically wastage that arises from poor inter-team communications and a lack of detailed failure reports that make the diagnosis process more time-consuming and cumbersome.

Traditional asset failure reports often contain too few details, which frustrates the efforts of technicians tasked with pinpointing issues. Yet the use of AI embedded in the latest iterations of EAM software can reduce diagnosis time significantly, with every percent reduction in MTTR equating to cost savings of thousands of dollars. This is on top of the boost in productivity for everyone involved in the maintenance process.

For organisations this means they can utilise AI functionality to not only improve fault reporting but enable better inter-team collaboration and more hands-on ‘wrench time’ for engineers. All of which helps to maximise the productivity of highly skilled employees, so they can increase asset availability in a more streamlined and efficient way.

Frontline workers spend most their day working with critical assets. They know these assets inside and out. They know what the assets smell like, what they sound like and what they are supposed to look like. Any irregularities will not pass them by. Capturing these sensory observations in their failure reports will greatly expedite the MTTR on any incident raised. But that’s not the only real-world benefit that’s up for grabs.

Harnessing the power of data

Besides slashing MTTR times, today’s AI-powered EAM tools enable organisations to elevate their data management capabilities from good to great.

Alongside automating data collection and delivering access to better quality data that adds value instantly, these systems provide powerful reporting and dashboarding tools that enable users to make sense of and operationalise data. When combined with machine learning, this will significantly enhance the error detection and prevention capabilities of organisations looking to move the dial where preventative and predictive maintenance is concerned.

Similarly, these systems make it possible to accurately capture every change, move and repair in a truly user-friendly way. For example, using AI-powered tools to take photo-based meter readings, autogenerate image captions and auto-translate multilingual data.

Working towards an AI-inspired EAM future

Today’s industrial firms want to overcome the multiple challenges that can get in the way of improving uptime, optimising maintenance schedules and elevating asset performance. Today’s AI-powered EAM systems are capable of analysing asset information and suggesting diagnostic and mitigation actions that enhance troubleshooting processes and reduce unplanned interruptions.

One thing is for sure: AI will take EAM to the next level and here at IFS Ultimo we’re focused on the addition of AI features that make it easy for organisations to incrementally apply AI to real-world use cases and generate measurable value – at a pace that works best for them.