In the not-so-distant past, Artificial Intelligence (AI) and Machine Learning (ML) were spoken about as futuristic concepts. But this is no longer the case, their impact is here and now. AI and ML are sometimes used interchangeably, but they represent distinct concepts that play crucial roles in transforming industries. While AI mimics human intelligence to solve complex problems, ML takes it a step further, enabling systems to learn from data and improve over time without constant human oversight.
In the Enterprise Asset Management (EAM) industry, these technologies offer immense potential. From predictive maintenance and optimizing asset performance to automating routine tasks, AI and ML has huge potential to help organizations increase efficiency, reduce downtime, and make more informed decisions.
However, like many emerging technologies, AI in EAM is often misunderstood, leading to myths and misinformation that cloud its true potential. Here we turn to our in-house AI expert, Senior Developer for SAP Solutions, Carlos Jalles, to clear up those misconceptions and provide a clearer picture of how AI is transforming asset management today.
Four Common Myths About AI
Despite the growing adoption of AI, several misconceptions still persist.
Myth #1: EAM is Too Complex for AI
One common myth is that AI is too complicated to fit into the intricate processes of EAM. While AI can indeed involve advanced algorithms and ML models, today’s AI solutions are designed with user-friendliness in mind.
One effective starting point is capturing and automating repeatable, well-established processes, like tracking commonly assigned resources for routine maintenance tasks. As AI automates these simpler workflows, it can provide valuable insights and performance metrics, which in turn pave the way for automating more complex workflows over time.
“Today, with SaaS solutions and pre-trained models, companies can implement AI and ML in a much more affordable and scalable manner,” Jalles points out. “It’s possible to start small, with specific automations and predictions, and expand as results come in.”
Modern AI tools are increasingly being tailored to address industry-specific challenges, making them accessible even to non-experts. These tools handle complexity on the backend, allowing EAM teams to focus on acting on insights rather than managing intricate technology.
Myth #2: AI Only Works if Your Have Vast Quantities of Historical Data
Another common misconception is that AI can only deliver value if you have years of historical data at your disposal. While it’s true that data plays a critical role in the effectiveness of AI, an organization doesn’t need decades of historical information to see results. In fact, many AI solutions leverage smaller, real-time datasets, using algorithms that continuously learn and adapt to new inputs.
The key is improving data capture techniques as early as possible. Simple, tangible changes—like moving entirely off paper for field work and using offline-capable solutions, such as Blueworx—can make a significant difference in data quality and accessibility. By switching to digital data capture in real time, EAM companies can generate actionable insights quickly and build from there.
This dynamic approach allows EAM companies to start small and scale over time. Additionally, AI can integrate data from multiple sources, like IoT sensors or existing ERP systems, helping to fill any gaps in historical data. In short, AI doesn’t require a massive data library to start making an impact.
Myth #3: AI is Just a Buzzword and not Ready for Real-World Use Yet
While AI might sound like the latest industry buzzword, it’s far from just hype—AI applications are already proving their worth in the real world…with tremendous results. In the EAM space, AI is being used for predictive maintenance, asset performance management, and optimizing workflows. Leading companies in asset-intensive industries are already leveraging AI-driven insights to reduce downtime, improve equipment reliability, and enhance operational efficiency.
At S4A, we’ve had our eye on the AI revolution for quite some time and are continually looking for ways to leverage this innovation within our existing and future software solutions.
Myth #4: AI Can’t be Used in Legacy Systems
A common concern for many organizations is whether AI can be integrated into existing legacy systems. Contrary to this belief, AI technologies are increasingly designed to integrate seamlessly with older infrastructure.
“There is a perception that it’s necessary for organizations to modernize their entire system – for example, migrating to SAP S/4HANA – before using AI, but that’s really not the case,” Jalles shares. “Although SAP S/4HANA offers better AI support due to its modern architecture, it is still possible to begin integrating AI solutions into legacy systems with appropriate integration and well-defined data interfaces.”
At S4A, we leverage a development stack that integrates AI capabilities on top of existing EAM systems, enabling gradual modernization without the need for a full-scale transformation. These AI tools can analyze and enhance legacy data, turning it into actionable insights that extend the usefulness and efficiency of current setups. By enhancing—rather than replacing—current systems, AI can unlock new value from legacy infrastructure and support long-term improvements.
Taking That First Step Towards AI Adoption
Adopting AI doesn’t have to be daunting. Start by identifying specific use cases where AI could deliver quick wins—such as predictive maintenance or asset optimization. From there, evaluate AI solutions that can integrate with your existing systems and provide scalability over time. Consider running a proof of concept that can help demonstrate the value of AI on a smaller scale, building confidence before broader deployment.
Most importantly, recognize that AI adoption is a journey, not a one-time transformation. Wherever you are in your AI journey—whether your organization is ready to make the leap or you’re simply in the early exploratory stages—S4A is here to help. Our suite of EAM solutions, built using Neptune, includes AI and ML capabilities within our development roadmaps, ensuring your business can evolve with the latest innovations. Let us guide you towards smarter, more efficient asset management.
