While automation has transformed many business processes, organizations still struggle with manual, inefficient workflows that their automation initiatives may have missed. AI process mapping offers a powerful solution for identifying, analyzing and improving these remaining pain points, and it enables organizations to achieve comprehensive business process improvement through data-driven insights and strategic optimization.
In brief:
- AI process mapping identifies inefficiencies that initial automation efforts miss by using data-driven analysis to create visuals of workflows.
- AI process mapping continuously updates with new data, so it can quickly identify bottlenecks and recommend specific process improvements.
- Implementation should target high-impact processes first, involve all stakeholders, and emphasize how AI enhances human skills rather than replacing them.
While automation has already revolutionized many business processes, organizations continue to struggle with manual, inefficient workflows that their initial automation initiatives may have missed. AI process mapping is a powerful solution to identify, analyze and improve these remaining pain points. It helps you streamline business process improvement through data-driven insights and strategic optimization.
Traditional business process documentation methods — like flowcharts and swim lane diagrams — are manual, time-consuming, and static, relying on limited data sets. By contrast, AI process mapping uses machine learning (ML) and natural language processing (NLP) to create dynamic, visual representations of business processes that continuously update with new data inputs.
This approach offers real-time, objective analysis that quickly identifies bottlenecks, suggests improvements, and scales across complex organizational structures.
To implement AI process mapping, you need to carefully consider the right processes to target, selection of appropriate AI tools, and use effective change management strategies. By starting with high-impact, high-volume processes, you can demonstrate early wins while building momentum for broader adoption. For successful implementation, involve stakeholders and communicate benefits to end users throughout the rollout.
In this blog post, we’ll dive deep into the pain points of partially automated businesses, discuss how AI can identify process improvements, and show a preview of what these benefits can look like for your business.
What Is AI Process Mapping?
As I mentioned above, AI process mapping is a technique that uses machine learning and natural language processing to create visual representations of business processes, typically in the form of flowcharts or workflow diagrams.
This technique documents the individual steps or details of a business process to help you identify the sequence, decision points, and interactions. It’s especially helpful for complex business processes, because it can turn routine documents into intuitive workflows.
What Are the Benefits of AI Process Mapping?
Many companies are turning to AI process mapping thanks to its data-driven, dynamic nature and scalable process. AI-powered process mapping tools can help organizations identify and improve processes that weren’t addressed in their initial automation efforts.
AI process mapping is:
- Real-time and data-driven
- Fast
- Scalable
- Excellent at identifying bottlenecks and inefficiencies
- Able to offer prescriptive recommendations
- Objective and unbiased
AI Process Mapping vs. Traditional Business Process Improvement Methods
Traditional business process improvement methodologies involve setting up workshops and interviews with the process owners and relevant stakeholders to understand the current state, brainstorm the future state, and document both. Information is gathered using tools such as flowcharts, swimlane diagrams, value stream mapping, Gantt charts, and SIPOC diagrams.
These dynamic, time-tested in-person workshops are valuable because they help stakeholders understand the current state and envision the future state. They reduce the need to rely on human memory and can be shared across the organization to increase understanding and get buy-in for necessary process changes.
However, the documentation required can be tedious and may result in limited, small data sets.
That’s where AI can help.
AI can reduce that burden even further with its ability to help document the current and future state quickly while making some initial recommendations for improvements.
Because of these capabilities, AI can make the task of process mapping easier, but AI is not ready to take over the complex, nuanced human reasoning that goes into documenting a complex process or designing a new process. Early experiments for using AI to map processes have yielded rough results — the basics are there, but they’re not in an easy-to-use format.
Still, my Centric Consulting colleagues, including automation architect Nick Rahn, are excited about the possibilities.
“Let’s say you’re documenting the process of a customer relationship management (CRM) update by mapping how contacts turn into leads and how leads turn into opportunities. That’s very linear and simple,” Rahn says.
“But with AI, you could get significantly more bleeding-edge. For example, you could have an AI agent conduct an interview with a stakeholder for mapping a process, then provide feedback, recommendations, and even best practices,” Rahn says. “That would be a much more dynamic, complex workflow.”
Especially as businesses move faster and more complex business processes are constantly shifting and evolving, traditional business process documentation methods can’t keep up with the speed, accuracy, and capability of AI-driven process maps. What humans could do in weeks, AI mapping could do in days — or even hours. Businesses are moving and changing faster than ever, and AI process mapping continually updates with new data or inputs.
How AI Process Mapping Could Work
When the capability is fully matured, AI process mapping will use smart algorithms to turn your data into visualizations and documentation. Here’s what that would look like step-by-step:
- Step 1. Feed transcripts of process owner interviews into AI to help. Create a process map. These interviews can be conducted by humans or AI agents. However, consider the complexity of the process and the comfort level of the process owner before using an AI agent for an interview.
- Step 2. Collect data from all relevant sources and prep it for analysis. This might mean data hygiene clean-up, organization, or transformation into standard naming conventions.
- Step 3. Put the AI algorithm to work by modeling a process for using its pattern recognition and natural language processing capabilities to analyze the data and interviews. In this way, AI can help uncover trends, patterns and decision points.
- Step 4. Once a first pass is completed, use AI algorithms or employees to input new data or make changes to correct errors, update the algorithm, or suggest opportunities for improvement. The AI algorithm continually updates the process map with real-time insights.
Now that you understand the basic steps of AI process mapping, let’s discuss what it looks like in action.
AI-Powered Business Process Improvement Methods in Action
AI business process improvement works to recommend better data-driven improvements, optimize efficiency, and enhance human decision-making capabilities. It doesn’t replace human engineers, continuous improvement managers, or business operations experts.
Instead, it simply enables your team to automate processes and contribute strategic insights and recommendations at every step. That removes a lot of costs from the data collection and analysis process while generating recommendations more quickly. It allows highly skilled employees to do more valuable work with the time they previously devoted to tasks such as recording information manually.
AI business process mapping has already significantly reduced costs and improved productivity around the world in finance, manufacturing, healthcare, retail, telecommunications, logistics, e-commerce, and even government agencies.
When designing future state, AI can be a good friend in opening up the world of improving processes. Let’s take a look at a few real-world examples of how AI helped improve a process.
- Stax.ai, a financial company, used AI to automate manual processes and transform a massive volume of trust accounting data in minutes.
- BMW optimized its industrial planning process and supply chains with generative AI.
Now, let’s explore some more in-depth examples.
AI for Data Transformation: A Healthcare Example
The healthcare industry accounts for 35 percent of the world’s total data volume and generates massive amounts of medical imaging, patient documentation, histories, records, test results, and more. This data is highly unstructured and requires a process to move it into a consumable format. A healthcare organization might have worked with an external vendor to move this patient documentation, but AI can be built into the process to quickly move this data into a data warehouse.
However, since AI models aren’t perfect, there may still be errors in converting the raw, unstructured data. But you could still use AI to find anomalies in the conversion process and check the transferring of medical imaging results to the correct patients. AI could also reduce costs associated with the time needed to make less-than-perfect decisions by analyzing thousands of different patient records to suggest specific optimizations, such as rerouting document approvals to deliver medical results quicker.
AI Process Mapping: A Finance Example
In terms of AI process mapping, consider a large bank with outdated processes and very little documentation. The bank want to cut costs and grow customer satisfaction by improving their processes, but they don’t have the staff to evaluate their entire organization to understand current state and then design future state.
To address the challenge, the bank could launch an AI process mapping initiative that helps process users work with an AI agent to easily document their current state and get recommendations for future state.
Of course, you’d still want to engage with process experts to validate the future state design before implementing changes, but AI mapping and the AI agents can move you closer to the goal much more quickly.
How to Choose an AI Process Mapping Tool
Carefully consider budget, security, technical constraints, and use cases before selecting an AI process mapping tool. With these new technologies, various companies often achieve different levels of success and practical application in actual results. Explore multiple tools and consider getting recommendations from colleagues and peers within your industry.
For example, UiPath has years of experience in the process automation space, specifically in banking, healthcare, insurance, manufacturing, and the public sector.
“UiPath has also evolved significantly over just the past few years, making it even more valuable for tasks like process mapping,” says Tracy Dixon, Centric’s operational modernization partnership lead and a UiPath Most Valuable Professional for six consecutive years.
“AI has played a big role in that evolution,” Dixon says. “AI has allowed UiPath to be less rigid in its requirements, which allows it to handle more complex, long-running processes and provide more opportunities for the kind of human-in-the-loop functionality you need to map processes. UiPath’s recently added ‘healing agent’ capabilities add additional value.”
But whichever tool you use, AI has great potential for helping with process mapping. Next, let’s see how you can implement it in your organization.
Implementing AI Process Mapping for Manual Process Transformation
Not every process is automatically a perfect fit for AI process mapping, because many processes today rely on highly complex combinations of touchpoints, swim lanes, multiple systems and more. However, Rahn has hope for other AI applications. For example, it could easily generate traditional standard operating procedures (SOP) documents or training materials.
To identify prime candidates for AI-driven improvement, look at processes that only involve one or two systems, functions or decisions. Simpler processes will help avoid human bottlenecks, datasets that are too large for human pattern recognition, or human decision-making breakdowns caused by fatigue or skill gaps.
Change Management and Adoption Strategies for AI Process Mapping
When it comes to change management and adoption strategies for AI, ensure that you involve all stakeholders from the start and consider genuine end-user feedback. Provide ongoing training and support while communicating the benefits to end users.
Emphasize how their day-to-day roles will get easier, and they’ll be able to focus on higher-level tasks instead of mundane details. Track success by paying close attention to user feedback, measure efficiency gains, error reduction, and any productivity improvements or cost savings.
With a clear understanding of the definition, benefits and best practices for implementing AI process mapping, you can take confident, concrete steps toward fixing the non-automated processes that fall through the cracks.
Get Started With AI Process Mapping
While it might feel overwhelming, AI process mapping is attainable, scalable and beneficial. Also, it’s not a threat to humans. Instead, it enhances their intelligence. Human workers still provide valuable context and insights, while AI handles a lot of the heavy, manual labor.
If you want to streamline and further enhance your processes while solving stubborn pain points, AI process mapping helps businesses optimize their existing automation investments.
To get started, identify a high-impact process such as patient documentation or client intake. Then, gather internal stakeholders and select the right AI process mapping tool. Work closely as a team to connect different data sources and let the AI algorithm run discovery and data analysis.
As more businesses implement AI and automation, these new technologies will become fundamental tools for businesses to flourish within the next decade. Expect benefits like cost savings, process improvements, increased efficiency and smarter decision-making with AI process mapping.
Consider working with an expert business process management firm to begin using AI and human intelligence.
We helped our long-term client, a thriving tech company, combine RPA and AI – hyperautomation – to achieve no-touch, end-to-end automation.
If you would like to learn more about how automation platforms and technologies can assist your organization, our Operational Modernization experts can help. Let’s talk
