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Top 5 process discovery use cases in businesses

July 8, 2022


Enterprises today still rely on the human workforce to extract data from a plethora of documents, which is becoming tedious and time-consuming. Companies thus need to adapt their operating models and enhance processes intelligently to survive the competition by shifting their gears towards automation. One such solution that immediately comes to our mind is Process Discovery.

Process Discovery – What is it, and how does it work?

Process discovery is a machine learning-based tool and technique to define, map, and analyze an organization’s business processes and transforms the dynamics of acquiring an accurate and comprehensive mapping of business processes.

These machine learning-based solutions facilitate identifying business processes and capture process variability through logical reasoning, besides providing recommendations for automation. Consequently, planned workflows can be expedited and made more cost-effective. Also, an inverted level view drives the Business Process Analysis (BPA) when automated business process discovery steps into the scene.

Automated Process Discovery tools assemble data and change this into structured, usable datasets for diagnosis, either by AI or experts. User repetitive procedures are contextually arranged into events, and an analytical process model is devised. The bots operate passively in the background, collating multi-app data to monitor employee activity. Advanced machine learning algorithms analyze this, creating automation workflows for later interpretation by automation tools. This forms the basis of intelligent process discovery.

Digital strategies rely on process discovery’s prompt, efficient & dependable nature to promote an ideal working environment. Subsequent tasks can then also be automated. This curbs manual effort for scalable process identification and paving all variations plus exceptions. It also creates a robust automation blueprint, driving transparency & eradicating subjectivity. Now errors & drawbacks can be dodged with more competent exception handling.

Benefits of business process discovery

Enhanced quality and performance: Process discovery can accurately prioritize & update process workflows with deep learning & process optimization. In addition, it gives a clear picture by automatically identifying, analyzing, and determining tasks and priorities for automatable processes with any human labor.

Visibility: With process discovery, visibility of ownership for business processes is lifted, mitigating risk with better access control, and thus companies can find new inroads and future automation opportunities.

Lesser risks: Risks can be reduced considerably if fewer people are provided access to process information.

Cost efficiency: Process Discovery application avoids needless recurrences or other inefficiencies as it works without a human workforce; hence, the costs get reduced significantly.

Increased scalability: With the insights engendered via Process Discovery, companies can make intelligent choices on which processes to automate next. It unlocks further growth potential and performance with marginal use of resources and time.

Top 5 use cases for process discovery are:

Telecommunications: Process discovery in the telecom industry helps identify the workflow processes to be automated. Mostly, these include interactions with onboard customers by updating them with the latest service options. It also steers them through other processes and fulfills customer service needs, such as updating contact information and addresses.

Accounting: Legacy systems can be time-consuming, tedious, and error-prone. Process discovery cuts down the cycle time by finding ways of automating the process of handling payrolls, balance sheets, filling tax forms, expenses, and making sure that invoices are paid on time. Process discovery solutions can analyze turnaround time to guarantee that resources are used optimally and make up-to-date decisions to improve each process. Whether you’re looking to automate invoicing or create error-free financial reports, process discovery can help.

Insurance: Claims processing involves the confirmation of required information to verify the authenticity of claim requests. For instance, claims processing is generally data-centric, time and labor-intensive task, susceptible to human errors when handled manually. Therefore, the application of process discovery tools can finish this validation effortlessly.

Finance: Automated business process discovery is used extensively in the financial domain. The biggest use case in this sphere is regulatory compliance besides mandates like ‘Know Your Customer.’ Process discovery, combined with RPA, guarantees that these requirements are completed on a timely basis without any error.

Human Resources: The HR domain is another use case of process discovery. It is particularly effective with processes related to interview invitations, background checks, screening resumes in addition to sending job offers. It also assists in leave requests and health insurance requirements, and many more.

AssistEdge Discover – Realizing the automation potential through process discovery

AssistEdge Discover, an AI-powered solution, helps unlock hidden process insights to accelerate enterprise-wide automation. It gently captures human-machine interactions and leverages AI for actionable process insights.

It cannot be denied that process discovery can do wonders for business operations when implemented. However, the challenge lies in its execution, as many existing conventions are peppered with data blunders, inadequate details & questionable integrity. Therefore, a tool can only churn out decent results depending on the quality of data fed into it in the first place.

So, with good data, one can observe better cost savings & willingness to reinvest. Additionally, with the advancing standards of organizations and their customers, it has become vital to have a well-devised business process discovery approach. Therefore, it is always advisable to consider your business data carefully and refine any questionable assumptions before executing any such solution for analysis.

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