Supporting Digital Banking by Choosing the Right EA Solution (Special Report)

Companies that develop or support products in the field need to transition from reactive to predictive maintenance and service. Such a transition is possible using ubiquitous connectivity and predictive analytics on machine data. Here’s how.

Although edge architectures differ fundamentally from conventional cloud and on-premises environments, edge isn’t an alternative to traditional infrastructure strategies as much as it is an augmentation of them.

The relationships banks have with their customers can last for decades, but too many banks fail to use the data they have to provide personalized digital banking services. 

Every transaction, order, customer interaction, and internal operation depends on decisions. At a time when businesses are under pressure to improve efficiencies, cut costs, and be more responsive, automating decisions becomes essential. While many businesses have picked off the low-hanging fruit when automating, most need to add more. Specifically, businesses want to automate complex decisions, involving multiple steps, multiple data sources and many rules.

Running AI experiments as ventures, such as distinct, startup-like entities with their own mandates, teams, and freedom to imagine, is one of the few mechanisms that gives a scaled company a real shot at genuinely new thinking.

Organizations that treat AI as a standalone initiative see incremental results. Those that treat it as a data architecture transformation, however, unlock sustained performance improvements.

Drone programs have already proven their value as a data collection tool across construction, agriculture, energy, and infrastructure. The next frontier is making that data work harder.

For AI to work in production, models are not enough. Systems need access to current, external information that can be used directly in decision-making.