Beyond RPA: Why Intelligent Process Automation Is the Automation Architecture Enterprises Need Now

Robotic process automation delivered real value to the enterprises that adopted it early. Rules-based, repetitive, high-volume tasks that followed predictable patterns were good candidates, and automating them produced measurable time savings and error reduction that justified the investment. For a certain category of process, RPA remains a valid tool.

The problem is that most enterprises discovered quickly that the category of process where RPA performs reliably is significantly narrower than the category of process they actually needed to automate. The processes in which documents are used as a source for information, where the information is not structured. The processes where the majority of cases are routine, but a significant number warrant judgment, are typically approval chains. The processes that have to go through multiple systems and be contextually confirmed at various stages of the process. None of these can be easily mapped to a rule-based approach that traditional RPA relies on, nor can they be mapped to a rule-based approach without making them brittle – brittle ones break when the real-world variation they were not intended to handle steps in.

Intelligent process automation solutions were built for exactly this gap. They incorporate AI, machine learning, and robotic process automation into a single architecture and move beyond the relatively simple workflows requiring only judgment calls and exception handling that make up the bulk of enterprise process automation opportunities. The outcome is an automation architecture that is more than just what RPA could do and more of a transformation in the way of operations that most RPA programs were able to deliver.

Quick Summary

• Conventional RPA is only suitable for basic, rule-based processes and is ineffective when it comes to variation and judgment, which are needed in most business processes.
• Intelligent process automation solutions integrate AI, machine learning, and RPA to automate higher-order processes and documents that are complex, exception-filled, and cannot be automated via traditional automation.
• The high-volume workflows with judgment calls that are most changed by intelligent process automation are in finance, operations, legal, H,  and compliance – where most skilled staff time is spent today.
• Businesses that design intelligent process automation solutions with a cybersecurity-first approach ensure the security of the sensitive data they use in their automated processes and attain the expected benefits of automation.

Where Traditional RPA Hits Its Ceiling

To grasp the concept of intelligent process automation solutions and what they are looking for, businesses have to recognize exactly where the conventional RPA stops.

Unstructured and Semi-Structured Data

Traditional RPA operates with structured data and can only process data in predictable formats. It can read a field from a database, or read a value from a spreadsheet that is always the same row and column, or read a form that has all the inputs in the same places, every time. It can’t reliably read and interpret unstructured or semi-structured data like: dozens of invoices in various formats, contracts that phrase the data in different languages based on the counterparty, emails that contain data that needs to be interpreted, not extracted.

Most business document workflows deal with just this type of data. An AP function that’s exposed to hundreds of vendors that send hundreds of formats of invoices. A contract management process that should support and accept agreements from counterparties, who use their own contracts. A compliance monitoring process that involves reading and interpreting regulatory communications that are not in a fixed format.

In response to this change, traditional RPA either fails on encountering a format it was not trained on or needs to have an exception queue that manually forwards a substantial percentage of documents that need people’s intervention, negating the effectiveness of the automation it was designed to achieve.

Multi-Step Processes With Conditional Logic

Enterprise processes are seldom straightforward linear processes. They are conditional paths – where the next action to be taken depends on the content or outcome of the previous action. An online loan application procedure that asks for paperwork based on the borrower’s profile. A dollar category-based procurement approval workflow with an escalation level that is dependent on both the dollar amount and the vendor category. A compliance review process that varies the depth of investigation based on a risk score assigned to the item under review.

Simple conditional logic can be achieved within explicitly defined rules and is something that Traditional RPA can accommodate. It is incapable of the contextual judgment required when some of the factors available for decision-making are not explicit in structured fields but contained in unstructured content, as is typical of many situations.

Exception Management

In any large-scale process, there will be some variation from the norm that will need judgment as opposed to rules to handle. In traditional RPA, exceptions are passed to human queues, resulting in every exception being a manual task that traditional RPA may have reduced. This exception routing can offset a lot of the efficiency boost automation was intended to provide for processes that have a high exception rate.

AI-powered solutions for exception management distinguish between exceptions that need human judgment and those that can be handled with augmented automated processing. Intelligent process automation solutions tackle exception management. This helps to reduce the exception rate and the human review pile with only cases that truly need human attention.

What Intelligent Process Automation Solutions Add to the Equation

There are three key capabilities that are required to overcome these limitations, and that is how intelligent process automation solutions enhance the rule-based automation architecture of traditional RPA.

AI-Powered Document Understanding

The efficiency of machine learning models trained with enterprise document types to extract, classify, and interpret information in unstructured and semi-structured documents can reach near-human accuracy for the document types that are part of their training. An intelligent process automation solution for accounts payable can capture the line items, amounts, vendor data, and payment terms on even the most unstructured of invoices. A contract management solution can help determine important clauses and obligations, as well as risk factors in agreements signed by multiple counterparties, without having to have a set template for all contracts.T his document’s understanding capability augments the scope of processes where automation can reliably be used, adding to those that make up the bulk of enterprise processes automation opportunities, and that traditional RPA has struggled with due to high exception rates.

Contextual Decision-Making Through Machine Learning

In contrast, machine learning models integrated into intelligent process automation solutions can make decisions to classify and route the process input based on the entire context of the content in the process, not just the structured information included in a few fields. A machine learning-powered loan underwriting workflow can be used to prioritize loan applications to the right review level and take into account the complete risk profile of the application, not just the fields that can be incorporated into a set of predefined rules. Rather than relying on fixed rules to determine compliance level and create investigation queues, a compliance monitoring workflow can use the AI to assess the risk level of each component.

These contextual choices are not intended to replace human judgment in those contexts where it is needed. These are enhancements that draw the appropriate products to the human mind and at the proper level of hierarchy; enhancements that make the human judgement used in a process more effective, not the removal of human judgement from the process.

Intelligent Exception Handling

Unlike traditional RPA, which fails to differentiate between exception cases that can be addressed through automated processing and those that need human intervention, intelligent process automation solutions ease the exception queue pressure, making traditional automation more efficient. If the exception differs from the norm in a predictable manner, an AI model to detect and solve such deviation can be created and used to handle it without sending it into a human queue. What makes the exceptions go to the human review queue is that they are truly new to the AI model, and therefore don’t fit the expected patterns.

This intelligent process automation solution will learn from the incoming exceptions encountered by the AI over time, and as the exceptions are processed by human reviewers, the AI will learn and be able to handle more exceptions independently over time. The capability of the automation evolves over time, not at the level it was deployed.

The Enterprise Processes Most Transformed by Intelligent Process Automation

The workflows where intelligent process automation solutions make the most impact on operations are those that are high volume, document-intensive, have conditional logic, and have meaningful exception rates, all of which are weak spots for traditional RPA.

Finance and Accounts Payable

Systems that process a wide variety of invoices that come from different vendors, that require the review of an invoice against a complex set of policy rules, that take data from multiple source systems to perform a financial close, and that perform financial reconciliation across data systems with varying formats are all excellent applications for intelligent process automation. Intelligent process automation solutions’ document understanding and exception handling capabilities help to overcome the variation that makes these processes costly and prone to errors in the manual or traditional RPA approach.

Legal and Contract Management

Processes such as contract review, obligation extraction, classification of clauses, and counterparty risk assessment are document-intensive processes where intelligent process automation solutions yield substantial efficiency gains compared to manual processes and traditional automation solutions. Legal teams using intelligent process automation for contract management consistently report that the time needed for reviewing typical contracts is significantly reduced, allowing legal teams to shift their focus to the complex negotiations and judgment-driven elements that they are best suited to handle.

HR and Employee Operations

Intelligent process automation can more effectively manage the conditional logic and exception handling that are part of some HR workflows with high document volume, such as candidate screening, onboarding document processing, policy acknowledgment tracking, benefit enrollment processing, and compliance documentation management, which are more easily managed by intelligent automation than rule-based RPA.

Compliance and Regulatory Reporting

Compliance workflows within regulated industries are subject to certain accuracy, completeness, and auditability requirements, ts making the benefits of intelligent process automation solutions even more important. Efficiency and consistency are key when it comes to running high-volume compliance processes, while maintaining the audit trail that compliance functions are looking for, or, without exception, handling records that make it easy to see what requires human review and attention are also documented.

Why Cybersecurity Architecture Matters in Intelligent Process Automation

The processes most suitable for intelligent process automation solutions are the processes that contain the most sensitive data within enterprise environments. The workflows to which intelligent process automation is applied carry information about financial records, personal information, legal agreements, health data, and regulatory paperwork.

This implies that intelligent process automation solutions are not simply tools to operate. They are data processing systems that are at the center of enterprise information flows, and they are as important as the functionality they provide. They are data processing systems that are at the heart of enterprise information flows, and the security architecture that they have is as important as the functionality that they provide. An automated solution that introduces data security issues or compliance standards violations isn’t a solution. It is a risk.

Intelligent process automation solutions based on a cybersecurity-first architecture integrate access controls, encryption, audit logging,g and compliance framework alignment into the automation infrastructure as an integral part of the solution, not something tacked on to it after the automation is put in place. If a business is subject to SOC 2, ISO 27001, HIPAA, PCI DSS, GD, PR or DORA compliance standards, then the embedded compliance architecture is not a possibility. It is the level of performance that the automation should meet from the get-go.

How Mindcore Technologies Delivers Intelligent Process Automation Solutions

Mindcore Technologies delivers intelligent process automation solutions built on more than 30 years of enterprise IT and process automation experience and the cybersecurity-first implementation methodology of a Global Top 250 MSSP. Under the leadership of Matt Rosenthal, CEO of Mindcore Technologies, the company combines AI, machine learning, and robotic process automation to automate the complex, document-intensive, exception-prone enterprise workflows that traditional RPA cannot reliably handle.

Mindcore’s smart process automation solutions are created for the enterprise environments that demand automation to perform at scale, adhere to tight regulatory requirements,nts and have the security architecture that the critical data that traverses automated processes requires. They take a more holistic approach, seeing cybersecurity and compliance as integral elements of the enterprise’s architecture, not an afterthought, and provide automation programs that work well from the outset in regulated enterprises.

Conclusion

Traditional RPA provided automation in a very limited range of process types. Intelligent process automation solutions expand that band to include the complex workflows that are document-heavy, judgment-adjacent, and make up most of the opportunity in enterprise process automation, which RPA programs could never be able to fully meet.

It is those enterprises with the automation architecture built around intelligent process automation solutions, not around the limitations of traditional RPA, who are developing operational capacity that accrues over time – the more operational data that is fed into the AI models that are part of the architecture, the more they learn and the more things they can do without human intervention.

Having more than 30 years of experience in enterprise automation and cybersecurity, the process of creating that architecture is structured, well supported, and based on the realities of operating in an enterprise environment.

About the Author

Matt Rosenthal is the CEO and President of Mindcore Technologies, a full-service IT consulting and cybersecurity firm serving businesses across New Jersey, Florida, Maryland, South Carolina, Louisiana, Texas, and nationwide.

With more than 30 years of experience in enterprise IT operations, process automation, and AI implementation strategy, Matt has helped large organizations build automation programs that deliver on their operational and financial objectives while maintaining the security and compliance standards that enterprise environments require. He holds an MBA in Technology Management, is a certified Project Management Professional (PMP), and is the host of Digging In, a weekly podcast on success in business, life, and health.