Why \u201cProcess\u201d Precedes \u201cAutomation\u201d in AI for Process Automation<\/h2>\n
Considering end-to-end automation while taking a process-first approach might sound counterintuitive, especially when you\u2019re under pressure to launch automation initiatives quickly.<\/p>\n
However, think of it like building wooden chairs:<\/p>\n
In both cases, your materials and method will drive the future-state process design.<\/p>\n For instance, your source lumber for the chair might be dimensional, surfaced, or rough cut. You need to know which type of lumber you will be using because each type requires different levels of processing.<\/strong><\/p>\n Likewise, your source data for a process automation project might come from a database, website, PDF, or other unstructured source. But you can\u2019t build the process until you know what that source data is.<\/p>\n Let\u2019s continue our example: The tools for building the chair may be a bandsaw to easily and quickly make curved cuts, a jigsaw that\u2019s less stable than a bandsaw but easier to use, or a time-consuming and labor-intensive handsaw. Similarly, your business might have:<\/p>\n Because you have so many ways to automate inexpensively, you need to consider those options first in every step in your processes. After all, not every process should be automated.<\/p>\n A good, practical rule for when to automate and when to stay manual<\/a> is to reserve manual work for uniquely human processing, such as complex problem-solving or creative projects, approvals, one-off tasks, processes requiring empathetic or nuanced customer interactions, processes that frequently change or don\u2019t have clear rules, and team collaboration.<\/p>\n On the other hand, automate repetitive, high-volume processes and those that are time-sensitive, involve data-driven decisions based on large volumes of data, or have well-defined inputs, outputs, and rules. Otherwise, you\u2019re wasting your valuable employees \u2014 and time and money.<\/p>\n After you\u2019ve identified the materials and methods that will shape your automations, and made informed decisions about which processes to automate and which to set aside for manual processing, you can start to think about how these components will work together to achieve operational excellence.<\/strong><\/p>\n Later in this blog post, we\u2019ll identify six essential process automation catalysts that will help you achieve that operational goal. But first, let\u2019s take a closer look at the role that existing and emerging AI technologies play in process automation.<\/p>\n AI-powered automation<\/a> for processes is the culmination of a technology evolution that began with RPA.<\/p>\n RPA, software programmed to automate repetitive tasks with bots, is a big cost reducer and productivity gainer. Case in point: In mortgage processing, an RPA can review forms to vet the accuracy of the initial data, resulting in a faster quality check process, less fraud, and more bandwidth to handle more mortgage applications.<\/p>\n Intelligent automation, which combines machine learning and process automation, came next. It goes beyond RPA by adding capabilities like document processing, task and process mining, and processing unstructured information, such as emails.<\/p>\n In real life, this might be used by a healthcare organization that wants to introduce a multistep process for verifying patient eligibility. Intelligent automation can compare multiple sources \u2014 websites, spreadsheets, patient rosters, audio recordings, and more \u2014 to identify who is eligible and who has changed their status.<\/p>\n The next step in this evolution is AI-powered automation.<\/strong> It adds large language models (LLMs) and generative AI, or agents, to the mix. AI-powered automation uses its cognitive-aware and context-aware functionality to help teams integrate enterprise applications and complex workflows so they can employ the LLMs or agents to learn, think, and process actions.<\/p>\n For instance, the process for credentialing a hospital\u2019s healthcare providers shows how this can work. Hospitals usually need months to manually verify that a physician is qualified. Since AI-powered automation can automate application reviews and determine if physicians meet all necessary criteria, even while accounting for variables such as non-U.S. training, it can cut process time in half<\/a> \u2014 at least \u2014 and get qualified providers on board much sooner.<\/p>\n As a result of AI for process automation\u2019s transformational potential, AI-driven process automation is growing significantly.<\/p>\n A survey by Forbes Advisor in 2024<\/a> found more than half of businesses were using AI tools to enhance operational efficiency. Another study put AI adoption in 2024<\/a> at 74 percent and projected it to grow to 94 percent by 2029. At the same time, RPA adoption should increase from 31 percent to 51 percent over the same period.<\/p>\n However, as we\u2019ve noted, not everything can be automated. Even for automated processes, human engineers, technicians, programmers, and monitors will still be needed.<\/p>\n Now, let\u2019s closely examine the six catalysts for using AI for process automation.<\/p>\n Process design should include automation from the start by considering data quality and structure, the systems and applications involved, the availability of no or low-code platforms, and the requirements for human oversight.<\/p>\n If you design a process without automation or AI in mind, you risk creating processes that require manual steps that could have been automated upfront. Industrial automation offers a model for meticulously planning workflows, resource allocation, and system integration.<\/strong><\/p>\n For example, a major automotive manufacturer demonstrated this approach. They cut production time by 20 percent and costs by 15 percent through exhaustively analyzing existing workflows and cross-functional collaboration before implementation.<\/p>\n AI contributes significantly to process improvement in other industries, too. In pharmaceuticals, generative AI-powered synthetic data helps develop data-rich processes while reducing waste and improving quality control, particularly in product defect detection.<\/p>\n \u201cFor example, you may streamline paperwork, automate certain tasks, or introduce a mentorship program,\u201d says Vince De Benedetto, the founder and CEO of Automated Dreams, a business process consulting firm that develops custom automation solutions to help companies accomplish sustainable growth. \u201cYou can add details with each new step, such as who is responsible for what and any specific instructions or requirements.\u201d<\/p>\n Traditional automation includes custom code built within an enterprise resource planning (ERP) platform to match business requirements. But custom code is difficult to manage because you need to stay current with the ERP<\/a> updates. This might make custom code obsolete after a few patches and upgrades. Hence, customization needs to be outside these types of systems.<\/p>\n Best-of-breed systems are one customized alternative to traditional ERPs. These niche systems focus on specific functionalities or industries. For example, customer relationship management (CRM) systems might be dedicated strictly to Salesforce automation.<\/p>\n Another even more effective option to traditional ERPs may be a standardized product. Business requirements change often, so creating automation solutions must be easier and more flexible for future adaptation.<\/strong> Low-code or no-code systems don\u2019t need the complex customization or configuration that ERP solutions usually require.<\/p>\n Along these lines, BPMS systems<\/a> offer a low-code platform that\u2019s easy and flexible. BPMS can optimize business processes in use cases such as customer orders, employee onboarding, invoice processing, budget tracking and management, project and workflow tracking, business performance monitoring, and management operations involving accounts, claims, compliance, inventory, and documents.<\/p>\n Ready-made ERPs are well-suited for companies that have uncomplicated operational structures or need very little customization. Off-the-shelf ERPs work in industries such as:<\/p>\n AI has made its presence known in transforming ERPs, too. In fact, AI is now the rule rather than the exception.<\/p>\n \u201cIn 2025, the distinction will no longer be whether an ERP system has AI features, but rather than sophistication and specialization of its AI implementations,\u201d said Christina Morrison, an expert in supporting and simplifying web-based business research into ERP software.<\/p>\n The marriage of AI and ERP lets ERP platforms learn from vast datasets, automate complex workflows, improve forecasting precision, deploy virtual assistants, and enhance performance analytics. Manufacturing industries<\/a> are Exhibit A in this regard: AI-powered functionalities now include decision-making in quality control, production scheduling, and inspection.<\/p>\n AI agents are also surfacing in the ERP world. They have the potential to monitor inventory, coordinate with suppliers, examine budgets, and alter schedules \u2014 all while following established business rules.<\/p>\n Long-term labor shortages<\/a> make process automation essential rather than optional. According to a HowToRobot survey<\/a>: \u201c80 percent of manufacturing automation projects aimed to move employees from manual tasks to value-added work.\u201d<\/p>\n The new normal ought to be building bots to process your new vendor contracts instead of hiring two people for the job. That will make your workers happier to be doing more meaningful jobs: 89 percent of employees say they have greater job satisfaction because of automation.<\/p>\n With labor in shorter supply, enterprise automation (EA) manifestations such as AI-powered chatbots<\/a> can lessen the need for a larger customer service team by simultaneously handling numerous customer questions.<\/strong><\/p>\n Enterprise automation<\/a> \u2014 which automates all the gaps in between core applications \u2014 should be a top priority for every business. Real-life use cases include:<\/p>\n In short, AI is substantially redefining enterprise automation. AI agents are disrupting enterprise data management by working across the entire data landscape to autonomously integrate datasets, eliminate information confusion, and operate data pipelines. Google\u2019s BigQuery<\/a> went in this direction by integrating Google Gemini into its platform to break down data silos and enhance management processes.<\/p>\n Moreover, AI agents are shifting data team responsibilities<\/a> from executing repetitive workflows to supervising AI system outputs and recasting them for specific business purposes. Analysts and engineers who formerly spent so much time on these humdrum tasks now can divert their energies to more creative, difference-making roles.<\/p>\n Custom automation within IT is a heavily controlled, cumbersome process that often subordinates your business\u2019s small automation to major company initiatives. IT will likely never automate your manual, rules-based, structured-data business process that takes eight hours every week if it\u2019s in their queue below tasks needed to keep the lights on.<\/p>\n The good news is that with the tools we\u2019ve been discussing, you can automate processes through the RPA center of excellence<\/a> (CoE) model or with a citizen developer on your team. An RPA CoE should have an intake form for inputting the details of your process so the CoE team can automate it. That will give your team eight more hours every week to spend on qualitative analysis and other high-value tasks.<\/p>\n For example, Woolworths Group, Ltd.<\/a> \u2014 an Australian retail company with multiple subsidiaries \u2014 employed a no-code workflow automation platform that enabled their in-house legal team to develop self-service options offering legal advice to all Woolworths Group stakeholders within their existing workflows.<\/p>\n Without relying on technical skills or any IT resources, the no-code tool automated the company\u2019s intake and triage and devised additional solutions, including:<\/strong><\/p>\n There\u2019s even a role for AI in non-IT automation.<\/p>\n AI algorithms analyze huge datasets to detect patterns, trends, and discrepancies, enabling systems to learn, adapt, and make decisions in complex, ever-changing circumstances. In this way, AI and non-IT automation complete tasks optimizing manufacturing processes, forecasting equipment failures, and helping healthcare professionals diagnose conditions, plan treatments, and discover new drugs.<\/p>\n Mapping out current- or future-state processes should include a denotation technique to clearly identify where the opportunities lie. You might not be able to immediately implement the automation, but having it in the design and automation backlog will mean it\u2019s only a matter of time.<\/p>\n The generic workflow below denotes the automation opportunities with blue stars. While reviewing several processes, this easily identifiable notation can quickly give your team a clear view of overall automation value:<\/p>\n A regional bank turned to business process model and notation<\/a> (BPMN) to create a streamlined, automated loan application process. The bank mapped out every process step, moving from the original application to the credit check and then on to underwriting and funding. It also identified all the roles and responsibilities for every stakeholder department and employee, along with all pertinent documents and data.<\/strong><\/p>\n The BPMN-based workflow management system automated routine tasks, such as replacing manual credit score monitoring with automatic retrievals of credit reports from third-party vendors. That one switch saved time, cut down on mistakes, and gave the bank the most current information to enlighten its loan decisions. The decisive metric? The bank is now processing loan applications up to 50 percent faster.<\/p>\n The challenge with BPMN mapping has been the time it requires, especially for companies with complex workflows. Fortunately, AI-powered BPMN<\/a> tools are now making life easier with automated diagrams that capture process details from structured (ERP, CRM, PIM) and unstructured (emails, collaboration boards) data. They provide real-time data updates, speed up decision-making, achieve greater compliance, and yield continuous process improvements with very little manual support.<\/p>\n Top-performing organizations have used operational excellence<\/a> (OpEx) or continuous improvement<\/a> (CI) CoEs for years. These CoEs work closely with the business and have built a backlog of process opportunities to reengineer. Often, they are tied to strategic plans and prioritize their projects based on C-suite direction.<\/p>\n On the other hand, most RPA or automation CoEs currently exist within IT. The backlog is often full of low-value, tactical tasks like automating the cleanup of a weekly report in Excel. There is little alignment with the business or anything strategic.<\/p>\n Ideally, the automation CoE\u2019s backlog closely aligns with strategic initiatives, and the backlog reflects that. It should sit on the business side and be integrated into the OpEx CoE. Since most organizations that struggle with their automation initiatives don\u2019t have the necessary commitment from the business, aligning it with the OpEx CoE would help solve that.<\/strong><\/p>\n Effective RPA CoEs create the metrics that demonstrate their process automation initiatives\u2019 success. That was the experience of a large global telecommunication provider that engaged Microsoft Power Automate in place of their current RPA tool to cost-effectively produce superior bots.<\/p>\n The problem was that the company had to recode 15,000 bots either manually or through consulting and outsourced services that would be costly, time-consuming, and highly error-prone.<\/p>\n Subsequently, the company turned to an intelligent automation solutions provider to ingest and convert all the process flows from the RPA tool into digital blueprints so the company could analyze them within the existing program. The dashboards and analytics applied in this way discovered highly expensive processes that were either redundant or poor quality. Retiring those old bots saved the company $5 million in replatforming and licensing fees.<\/p>\n The telecom\u2019s solutions partner deployed a model that created a common language between different RPA formats and learned that the remaining bots mapped at a rate of 70 percent into the Microsoft Power Automate format. In other words, just 30 percent of those processes would require human intervention or recording to operate effectively within the new tool.<\/p>\n An automation CoE also helped a large U.S.-based bank that had been plagued by up-and-down manual transactions processing, including processing inbound and outbound ACHs, file transfers, payments, and partner data. The error rate was high, employee morale was low, and people were spending an average of 20 percent overtime every week.<\/p>\n In this case, generative AI enabled agentic process automation (APA) systems to dynamically learn and adapt business processes that reflect changing business conditions, including workflows with judgment, decision-making, and creative complexity. The bank\u2019s generative AI APA went beyond the rigid structural, data- and workflow-handling limitations of RPA to yield benefits such as:<\/p>\n Another real-world use case can be found in insurance claims processing. RPA can\u2019t make immediate decisions about the validity of a claim, but APA controls the entire claims process from extracting data to determining validity. Because APA completes real-time workflow adjustments, it can detect patterns such as potential fraud or mismatches to make claims decisions more quickly and accurately.<\/p>\n As you can see, process automation and AI, when done well, can positively affect your bottom line as well as quality, customer experience and employee satisfaction. When you design processes with automation in mind, you give them the priority attention they deserve while getting good results faster and more consistently.<\/strong><\/p>\n The six process automation themes we have explored in this blog post have one primary dovetailing objective: transforming how operational excellence happens. However challenging, achieving operational excellence is possible. Embracing the strategies and tools described in these six approaches to automation is how to do it.<\/p>\n \n ![\"Centric](\"https:\/\/centricconsulting.com\/wp-content\/uploads\/2025\/04\/AI-for-Process-Automation-Graphic-2.png\")
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When to Automate and When to Stay Manual<\/h2>\n
What Exactly Is AI for Process Automation?<\/h2>\n
6 Essential Process Automation Catalysts<\/h2>\n
1. Design With Automation in Mind for More Long-Lived, Sustainable Processes<\/h3>\n
2. Identify a Tool to Manage Your Custom Automations<\/h3>\n
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3. Enterprise Automation Is an Imperative With a Continued Labor Shortage<\/h3>\n
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4. Explore Alternatives to Relying on IT for Automation<\/h3>\n
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5. Use Automation Notation to Improve Process Mapping<\/h3>\n
<\/a><\/p>\n6. Closely Tie Your Automation CoEs to the Business<\/h3>\n
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It All Comes Back to Automation Excellence<\/h2>\n