Adapt, restore, and function better
Without human intervention, a self-healing process detects errors, exceptions, and deviations in its defined path. And its survival instinct helps it adapt and restore itself to a better-functioning state.
A self-healing process is:
- Designed to restore and repair with little or no external help for course correction
- Embedded with intelligent metrics setting the ideal and permissible process parameters
- Guided by a continuous monitoring and detection mechanism to report and correct any deviation from expected behavior
There are two types of self-healing capabilities that instinctive finance organizations are embedding into their processes:
1. Reactive healing: Triage and treatment
This type of healing is in widespread use. In the past, finance teams incurred the manual effort of triaging a problem in a process to diagnose and resolve it. But today, we have process discovery and mining tools. This integrated automation capability goes deep into the depths of dark data to surface issues. And it drives corrective actions from its insights at superhuman speeds. And while the machine makes light work of this labor-intensive task, humans are then free to focus on managing the issues and their impact. An example of a reactive self-healing process is a process discovery tool that can identify at what stage invoices are delayed. It automatically sends active alerts to humans to manage and rectify exceptions, issue approvals, and escalate as necessary.
2. Predictive and preventive healing: A more human process
AI technology is changing the paradigm. Organizations can now create a visualization model of process scenarios and outcomes. And data-driven intelligent workflows automatically sense, analyze, and remediate issues before or just after they occur.
One example of a predictive healing system is an AI-led control tower that takes exceptions to zero. The machine's algorithms account for established relationships and routine issues, such as time differences between a transaction and its recording date. And as it filters exceptions through its intelligent approval engine, it recognizes and responds accordingly, automating approvals and flagging for validation with the relevant teams.