Enterprises worldwide are experiencing a digital transformation. They’re adopting automation for the enrichment of business processes. AI-based process discovery tools guide them in choosing significant practices to automate and maximize its value. They can record heaps of human interactions and give real-time inputs.
What is Process Discovery?
Process Discovery is defined as a set of tools and techniques used to identify, document, and analyze an organization’s processes. This analysis proves to be a starting point for process improvements. It identifies problem areas to be addressed by Business Process Management (BPM).
Process Discovery is also known as Business Process Discovery.
Process discovery shows how processes are executed in an enterprise. It observes and collects digital footprints left behind by people that could’ve been missed by employees on scrutiny. It is an advanced approach to collect both visible and invisible processes.
Process discovery tools are based on machine-learning. They help identify business processes, record deviations using ML algorithms, and make suggestions for automation. They can help design automation workflows, mapping, planning, and implementation faster and more competently.
Automated Business Process Discovery (ABPD)
Automated Business Process Discovery (ABPD) is a fully mechanized business process. The data collected from the audit is sorted and analyzed to identify process models. It examines existing data thoroughly. So, ABPD can give businesses a clear idea of how they will perform in various probable situations.
ABPD collects unstructured data acquiring information from applications used in business processes. This comprises event logs, databases, audit trails, and transactions. It uses this data to identify patterns and build process definitions and models. An exhaustive analysis is possible because the volume of data collected and examined is extremely huge. It is possible to identify basic causes of problems, deviations from processes, and distribution of process events because of ABPD.
Compared to completing tasks in conventional ways, building a business process model using ABPD comparatively takes less time and cost to accomplish the same tasks. However, ABPD cannot capture human activities that are non-automated. So, it is used in combination with other techniques to fulfill the goals.
Data collection is a lengthy and resource-intensive step. Analyzing data and drawing conclusions from it manually takes a lot of time. Meanwhile, the opportunities might pass by. Since it is practically impossible for an individual to manage the entire process alone, it is often allocated to different departments or operations. Thus, it lacks an overall view. Analysts can’t have complete information. It is also prone to human error due to lack of knowledge or complete data. ABPD resolves all these challenges.
To summarize, the advantages of ABPD include faster process discovery, quick and cost-efficient, detailed and interactive analysis, and good forecasts.
Benefits of Process Discovery
Process Discovery provides organizations with a fast, reliable, and cost-effective way of identifying ideal processes for Robotic Process Automation (RPA) and additional automation tasks.
Process Discovery can perform large-scale process identification and mapping process variations with minimal possible manual effort. It brings transparency, eliminates human bias and probable errors, and creates an effective automation blueprint through smarter exception handling. Some of its benefits are:
1. Improved Quality and Performance:
Process Discovery automatically identifies, analyses, and determines tasks and priorities for automatable processes. This provides more accuracy and deeper understanding, ensures up-to-date process workflows, and enhanced process optimization without any presumption or human bias.
2. Visibility:
Process Discovery ensures ownership visibility for specific and overall process steps across the organization. Enterprises can define new pathways and future automation opportunities effortlessly with mapping.
3. Fewer Risks:
Adapting automation means giving fewer people access to business process information. It shrinks down chances of risks.
4. Cost Efficiency:
Process maps help us understand whether or not the suggested changes will add value to the business. Process discovery avoids unnecessary repetition and inefficiencies. Thus, with less human resources and more automation costs go down significantly.
5. Improved Scalability:
With insights generated through Process discovery, organizations can make smart decisions; for example, which processes to automate next. RPA implementation requires data analysis but it takes substantial time if done manually.
6. Competitive Advantage:
Gartner says that over 80% of companies still resort to manual process mapping. Switching to process discovery would prove to be a competitive advantage for the organization with reduced costs and notably quicker ROI realization.
Why do enterprises need process discovery?
Process discovery records and analyzes recurring user actions. It creates RPA bots to automatically perform those actions later. That’s how Process Discovery leads to process automation.
The traditional manual documentation processes were slow, ineffective, and costly. Process discovery simplifies this:
- It allows business users to record their activities.
- Process analysts can collect and review records to identify future automation opportunities.
- RPA managers can auto-generate bots to finalize and utilize information.
Smart process discovery tools identify and prioritize automation opportunities easily. They observe the activities of multiple users first. Then they employ pattern recognition technology to identify processes and their repetitive steps. This provides new opportunities.
Managers who perform or oversee end-to-end processes may not have a complete view of overall performance. They may miss minute details crucial for finding automation opportunities.
RPA developers know automation technology very well but they’re no process experts either. Process analysts have a general idea of improving process information but not the order to apply that knowledge effectively.
Process discovery solutions allow these groups to work together as a team. They can help each other overcome difficulties optimally.
As complex processes are mapped, inventory is used by the system and by whom and how is known. This information is important to meet the technological needs of a business. Process discovery identifies recurring problems and focuses on specific areas of improvement. Hence enterprises need to evolve themselves with process discovery.
Conclusion
Process Discovery helps in the automation of processes and undertaking RPA initiatives. It also unlocks hidden knowledge that can be optimized for digitalization.
AI-powered cognitive process discovery helps untangle an enterprise’s business processes and realize future work processes. Hence, process discovery plays a crucial role in assisting the digital transformation in any organization.
