AI Process Mining Solutions for Modern Businesses

Companies will generally have an idea that their day to day tasks may not be working in the most productive way, but knowing just where time money and productivity is being wasted is often quite complicated. Workflow can include various departments, various pieces of software, approvers and hand written tasks that cannot necessarily be seen from the outside in.

AI Process Mining Solutions assist businesses by taking a deep insight into just how processes are functioning using data logged by their systems; Artificial Intelligence can then assist by spotting where potential bottlenecks, anomalies, redundancies or improvements could be found in the process, and rather than working on estimations of how a task may be occurring, use real operational data.

What Are AI Process Mining Solutions?

AI Process Mining solutions integrate process mining tools with artificial intelligence and data analysis capabilities. Process mining involves using event data generated by business systems to re-construct and analyze actual processes that occur within an organization, while artificial intelligence can be leveraged to augment this analysis by discovering pattern relationships within the process, identifying irregularities, forecasting potential deviations and providing suggestions on areas in which process optimization may occur.

For instance, if a business is examining the order-to-cash process of its organization, analysis of order data which streams between its various systems such as:sales, inventory, finance,shipping andcustomer service may be used to uncover areas of delay within the workflow of receiving orders and executing them to a completed shipment; as well as any processes of re-work or manual entries being made by employees for particular activities of the process and the reason behind it.

By integrating Artificial Intelligence, a business can see with a lot of transparency of what the true process look like on a large scale.

Why Businesses Need AI Process Mining Solutions

Most businesses keep records of their ‘ideal’ processes, but they often bear little resemblance to the real ones.

The ways your employees take shortcuts, retrace their steps, seek out sign-offs, carry data around between systems and jump between multiple applications.

The issues can remain undetected until they begin to hinder your operations.

An AI Process Mining Solution can be implemented to help your company visualize the:

• Bottlenecks within the workflow
• delays within the process
• Manual/repetitive tasks
• Non-value-add approvals
• Manual transfer of data
• Deviations from defined process
• System limitations
• Compliance risk
• Potential for automation

How AI Process Mining Works

1. Capture Process data.

Process mining works on data created by business applications. These can be ERP’s CRM’s, E-Commerce, Accounts systems, Help Desk, Supply Chains, HR andIT Help Desk applications among many others. They contains details of time stamps, transaction numbers, status changes, approvals etc. Of work in a business.

2. Rebuild the business process.

The collected process data can be used to assemble a picture of the business processes in operation today. It is possible to get the different pathways that have been processed through the business.

3. Reveal bottlenecks in a business process.

The AI will be used to pick up areas where the processes are lagging or being ping-ponged around the business. For instance, should you be taking to many days to getting invoices paid, this may suggest the process is hanging somewhere in the Approval pathway to much etc.

4. Reveal variants in the process.

Most processes do not always follow the most usual paths. AI’s will pinpoint specific non typical processes and help illustrate where they came from.

5. Suggest improvements.

By spotting the delays in the processes and suggesting to use automation,redevelop the process, tie up more systems, introduce new strategies etc.

Key Features of AI Process Mining Solutions

Discovery Of Processes

Machines can identify the way in which workflows operate at a business, by processing transaction based data.

The identification of bottlenecks

Where does work get stuck in processes.

Anomaly Detection

Spot irregular movements in processes, when something out of the ordinary needs investigating.

Process Conformance

Comparison of processes against what business intends them to be.

Predictive Process Analysis

Identifying when specific cases will likely encounter delays and bottlenecks.

Workflow Optimization

Identifying improvements that could be made to processes, after viewing them.

Scope for automation

Identifying and singling out non value adding manual tasks.

Benefits of AI Process Mining Solutions

Enhanced Process Efficiency

Identifying slow points helps organizations find a chance to streamline operations.

Shorter Process Times

businesses may also notice where processes consistently create waits and then figure out how to shorten the wait time.

Reduced Operating Expenses

Eliminating duplicate efforts and inefficient process steps may lead to less wasted operational resources.

Better Resource Allocation

Managers may learn that employee and machine resources are excessively consumed in certain processes.

Enhanced customer experience

Every aspect that influences the end-consumer is tied together in a business process. Slow processes negatively impact customers as much as it affects operations.

Customers want faster order delivery, prompt refunds, quick and efficient technical support; repeat requests and long waits are often due to inefficiencies at the operational level, and hence process enhancement may be essential for satisfying end-consumers.

AI Process Mining Across Different Industries

Banking and Finance

Financial institutions are able to analyze processes such as loan application, customers’ onboarding, payment processing and the whole Compliance work-flow.

Healthcare

Healthcare organizations can use process intelligence to analyze administration, and operations work-flow in an endeavor to detect bottlenecks and spurious process deviations.

Manufacturing

The work-flows for the processes like production, purchase, inventory, maintenance, and supply-chain can be analyzed by manufacturing bodies using process intelligence tools.

E- Commerce

Organizations in on-line business models are capable of analyzing such processes as order processing, returns processing, refunding process, customers order fulfillment and a multitude of customer service processes.

Logistics

Logistics firms can easily analyze processes including shipment processing, warehouse operations, delivery work-flow and exceptional operations workflow.

Insurance

Insurance firms can analyze claims process, under-writing, customer onboarding and policy administration work-flow.

AI Process Mining and Process Automation

When process mining identifies what needs to be improved, automation can be used to execute parts of these improvements.

If the process analysis identifies that the same data has to be transferred from two different applications to each other by employees, there would be an API integration, automated task, or similar mechanism built between the two systems. This application of both the mining and automation concept has made it possible for more achievable business automation.

AI Process Mining vs Traditional Process Analysis

AI Process Mining and Predictive Intelligence

One more capability where the AI is proving to be valuable is in a move beyond historical inspection of processes.

So instead of “where did my process go wrong?”, business will start saying “which of the ongoing cases is going to encounter a failure?’. So, you may want an AI program scanning an active orders that has traits indicative of past delays. Your process mining system can identify this, and you have the chance to intervene to avoid a future delay. Predictive capacity makes process mining’s output no longer a reporting tool, but a proactive, operational system.

Challenges of AI Process Mining

Data Availability

Process mining needs adequate event data. The omission of important steps in the workflow may result in incomplete analysis.

Data Quality

The analysis of processes can be negatively influenced by missing timestamps, duplicate data or events, wrong IDs or wrong status information.

Complex Systems

There may be hundreds of applications in a large enterprise, which makes combining the various event data and their sources more complex.

Change management

The analysis itself can hardly fix a problem. In consequence, people and teams have to adopt changes in their business processes.

Privacy and security issues

The events data sets might include sensitive customer-, employee-, business-, or financial-data. Thus access and appropriate data governance have to be provided.

Responsible AI in Process Mining

Deploy AI-enabled process analysis with thoughtful consideration, especially if a process involves employees or customers. Businesses must understand what data is being analyzed, and how AI-enabled recommendations are applied.

The NIST AI Risk Management Framework provides considerations for organizations to manage the risk of AI. “Human oversight of decisions with substantial operational or customer impact should continue to be critical.”

How to Implement AI Process Mining

Step 1: Select a Process of High Value

Begin with the processes where the current state of inefficiency is producing tangible problems in the organization. Typically the “low-hanging fruit” include processes like: Order processing, on-boarding, invoice processing, claims processing, customer support.

Step 2: Identify Data Sources

Determine which of the enterprise applications store process event data that can be utilized for the analysis.

Step 3: Connect Systems

Create the correct integrations or data feeds required to access the pertinent data sources.

Step 4: Map Current Process Flow

Use process mining technology to observe transactions traveling through the organization.

Step 5: Spot opportunities for improvement

search for parts of the process with bottlenecks, rewrites, extra steps, or odd-looking behavior.

Step 6: Automate where possible

find some automation like an API, a workflow engine, or AI bot, etc.

Step 7: Measure

monitor results likecycle times; failure rates; the amount of manual work; the percent of times the process completes and operating costs;

Measuring AI Process Mining Success

Some examples of measures to determine how process improvement is being conducted:

• Average processing time
• Cycle time of the process
• Amount of manual operations
• Error frequency
• Number of process deviations
• Automation degree
• Customers response time
• Cost per transaction
• Approval cycle time
• Workflow fill rate

Of course, the more relevant metrics depend on which process is examined.

Why Businesses Work With HiveRift

In addition to performing analysis on processes, deploying an AI process mining solution may involve integrating data, writing custom software, building APIs, and using automation, dashboards, the cloud and building an AI.

HiveRift empowers businesses to evaluate software, AI, automation, and digital transformation solutions configured to the actual operational needs of the business. A tailor-made solution, the right one to an organization, allows integrating process intelligence with applications and processes which the employees already use in a daily basis.

Future of AI Process Mining

The link between AI automation and process intelligence is likely to be intensified in the future, with integrated systems that leverage real-time process monitoring, predictive analytics, agents in AI, intelligent automation of workflows, automated detection of abnormalities, generative AI, process simulations, and real-time decision support.

Instead of merely representing how processes perform, these intelligent systems are being developed to enable forecasting the future outcomes and propose action toward those predictions and toward process improvements.

Final Thoughts

AI process mining solutions are key to enable organizations to get insight to their process executions, locate inefficient process paths and reveal options to enhance and automate the processes. While finding a bottleneck process execution is valuable, so is implementing solutions based on these discovered improvements so organizations get their processes in the order and see a measurable impact. Process mining alone may not yield success to any business without an aligned AI automation, integration with data as well as modern software engineering.