AI Will Never Be Smarter Than Your Data
We usually only face problems when something finally breaks. This is the case for most asset management teams as well. The reality, however, is that the problem had already appeared years earlier, but no one noticed—or simply turned a blind eye, knowing that everything still worked.
Again and again we encounter the same problem when a company wants to activate IBM Maximo's AI features, such as failure prediction, anomaly detection, or those maintenance recommendations that can be proudly presented to management. They sincerely believe their data is in order and usable. But when we start using it, it suddenly turns out that it was never really okay.
What is IBM Maximo—and why does it matter now?
IBM Maximo Application Suite is an enterprise asset management platform. In other words, the system in which a factory, an energy company, or a transportation company records, maintains, and operates its physical assets. Machines, equipment, vehicles, infrastructure elements. Everything that can break—and whose downtime costs money.
In recent years, Maximo has far surpassed mere record-keeping. The platform now offers AI-based features, enabling it to predict when an asset will fail, recognize anomalies in operational data, and generate maintenance recommendations—before the problem even becomes visible. This allows reactive firefighting to be replaced by conscious, preventive planning. The raw material for these AI features is data, which is the real challenge in most implementation projects.
The Comfortable Illusion
We have worked with an organization that would have sworn by their data. On paper, the database was perfect: entries going back years, flawless asset hierarchy, thousands of completed work orders that could be immediately fed into the Maximo system. It seemed like a 'perfect' historical foundation from which any algorithm could easily learn what normal operation looks like and immediately alert on deviations. However, when we actually dove into the project, it quickly turned out that most of the data was unusable.
In the failure code field, almost everywhere 'OTHER' appeared, because selecting the exact code would have required three extra clicks. Some assets were duplicated with slightly different names, and 'successful' maintenance was performed on equipment that had been scrapped two years earlier. Instead of structured fields, free-text comments were used, so the same mistake was made by multiple technicians. On top of that, the dates did not indicate when the work was actually done, but rather the day when someone finally got around to closing the ticket.
Of course, none of this was intentional sabotage. Day-to-day operations ran smoothly, so these errors went unnoticed. The data was sufficient for running the company day-to-day, but proved insufficient for training an algorithm.
Why Is AI So Ruthless When It Comes to Data Quality?
We humans are brilliantly able to fill in missing pieces using context. An experienced engineer or planner can see through bad data. When they see an error code, their twenty years of professional experience intuitively filter out where the problem might be.
However, Maximo's AI model cannot do this, because it takes the data literally without any other reference points. If it gets a failure history where most entries are labeled 'OTHER', it will learn that the main characteristic of the assets is that no one knows why they broke. If it gets duplicated assets, it will split one machine's history into two separate profiles, leaving neither with enough information to predict anything. If it gets administrative dates instead of real events, it will learn the rhythm of the engineers' paperwork, not the physical behavior of the equipment.
So, AI does not hide or correct data weaknesses—it amplifies them, and can even generate completely false predictions. This is the well-known GIGO principle (Garbage In, Garbage Out) in IT: if you put in garbage, you get garbage. And the result often looks so professional and convincing that decision-makers tend to believe it. Therefore, the more ambitious our AI goals, the higher we must set the bar for data quality.
What Is Enough for Operations Is Not Enough for Prediction
It is a mistaken conclusion that a huge volume of records accumulated over years automatically means quality, because volume does not equal accuracy. There is a sharp difference between data needed for daily operations and data needed for prediction.
The latter requires a consistent, complete, and structured database whose patterns faithfully reflect reality. A decade of inconsistent, chaotic data is just a much larger pile of problems.
What Does a Data Cleaning Process Actually Look Like?
When it turns out that data quality is inadequate, we need to take a step back to fix it. However, data cleaning is not a one-week project—it is the highest-return investment in the entire AI project.
A predictive model built on a clean database can reduce unexpected downtime by 20–30%—while an AI 'raised' on bad data burns millions in false alarms and unnecessary interventions.
But fixing everything once is not enough to eliminate the problem. Data quality is largely a process and behavioral issue dressed in technology. So we primarily need to change the daily habits that create the disorder.
- Start with an honest assessment! Before expecting immediate results from AI, assess the data to understand the real state.
- Clean and structure! Eliminate duplicates, organize free-text chaos into structured categories, define what 'good' means, and bring past entries closer to that state.
- Improve the source as well, not just the symptom! If error codes are useless because the form is inconvenient, simplify it. If technicians describe faults in free text, give them a structured list that is faster to choose from and easier to interpret later.
- Be consistent! Free-text comments are not the enemy—they just require a different approach. Maximo's modern AI features, such as watsonx integration, can already interpret technicians' text entries. But this requires a minimum of consistency: consistently similar descriptions for the same fault.
- Show colleagues that accurate data entry makes their work easier! If the system uses good data to immediately order the right part for the next repair, they will become interested in precision—they experience it not as paperwork, but as a tool.
- Monitor continuously! Embed quality checks into daily work so that problems surface as quickly as possible.
The Bottom Line
IBM Maximo's AI capabilities are truly revolutionary: the platform can lead a company from reactive firefighting to conscious, forward-looking planning. It does not just record assets, but understands them: it monitors operational patterns, detects small deviations, and alerts before failure occurs.
But there is no magic here. AI cooks with what it gets, and it reflects the received data with ruthless precision. AI will never be smarter than your data. That's why it's worth finding out before launch whether your available data is actually suitable for training the system. If you are not sure how suitable your company's asset and maintenance data is for AI-based prediction, start with a targeted Maximo data quality assessment!
If you would like to learn more about what IBM Maximo Application Suite can do and how it can help your company transition from reactive maintenance to predictive planning, visit our website or contact us.
[Prepared on behalf of SCMax Solutions Kft, an IBM Platinum Partner, paid material.]
