Intelligent processes are created through the automation of repeatable, operational decisions by embedding business intelligence (BI) into those processes. Yet use of BI tools to create intelligent processes is far from standard in most organizations today, where operational processes are typically disconnected from analytic processes.

Predetermined business rules and logic governing analytic processes don’t adapt automatically, either, as the business changes or to apply more tailored logic to each process instance. The result: rigid, policy-based approaches that don’t treat different processes’ instances in a relevant, personalized or responsive way.

This is far from optimal and the effects are significant. Customers feel the organization doesn’t value their business and that service is poor. Products are replenished based on assumptions, leading to stock outs, loss of revenue and customer frustration. Processes are fixed, even when demand is fluctuating wildly, leading to revenue not being maximized. Customers churn due to unresponsive organizations that fail to react when poor service is delivered.

These are just a few examples of sub-optimal business processes and their consequences—lost revenues, reduced competitiveness and missed opportunities. It’s not hard to find additional examples in almost any business environment.

Intelligent Processes Defined

Intelligent processes are relevant, personalized and responsive. To accomplish this, they need to draw upon both real-time and historic data, evaluate the current in the context of the historic, and then trigger other processes.

Process steps must be relevant to the context of the specific process instance being executed. Since businesses are continuously changing, in practice this means that the process needs to be able to call on real-time data so the latest status can be used. This may include process-state data, such as a real-time measure of supply and demand, or a predicted value, such as a delivery date for a shipment of goods. This data needs to be the completely up to date or “latest state.”

By definition this is a real-time need; the data must be immediately accessible and available to any service that needs it. This effectively eliminates traditional BI approaches from consideration for use in SOA environments because older tools rely on querying historic data in data warehouses.

To complement real-time data and put it into the proper context, processes also need to be aware of the history related to the customer, product or supplier involved in order to be able to personalize the process. This historical data helps the BI service make real-time decisions about the best way to treat a particular customer or product.