Maintenance operational leaders are overwhelmed with disconnected data systems and siloed information, which hinders effective decision-making. But gaining decision intelligence capabilities isn’t as easy as simply connecting everything together.
Organizations need the advanced analytics capabilities—like network graphs and scenario planning tools—to help analysts do the deep data exploration that uncovers all the nuanced information and relationships within the connected data. This is how maintenance leaders get the answers they need to take the right actions to minimize disruptions and optimize performance.
Another barrier to overcome is the lack of trust in AI technologies due to their “black box” nature, which makes it hard to interpret how the system arrives at certain results or recommendations. In order for decision intelligence to be adopted more widely, AI needs to also be accepted by the rest of the organization. By bringing in Explainable AI technology, in particular, maintainers can start to build that trust and speed up analytical and interpretation practices so that companies can get to more accurate answers faster.