AI in BPM: Where Should You Begin?

As AI in BPM continues to gain relevance, organisations are increasingly asking: Which stage of the BPM lifecycle will benefit most from AI adoption? Should you begin with process discovery, process mapping, analysis or improvement? Or should AI be introduced across the entire BPM lifecycle at once?

With organisations seeing the benefits of AI in accelerating BPM activities, there can be a temptation to use AI wherever possible. However, introducing AI across multiple activities simultaneously may not necessarily deliver the desired outcomes.

Recent guidance from Australia’s National AI Centre also suggests taking a focused approach to early AI planning. It recommends beginning with a contained process where there is enough volume, a recognisable pain point and information that AI can potentially process, rather than attempting to tackle everything at once.

In this guide, we look at the key questions you need to answer before introducing AI into your BPM initiative.

3 Practical Questions Before You Start AI in BPM

1. Where Is Your Team Spending the Most Manual Effort?

Start by looking at the activities consuming significant amounts of your BPM team’s time. For instance, is your team spending a lot of time gathering existing process information, interviewing SMEs, documenting processes and drawing and then reviewing process maps. Or is your team spending time in analysing processes and identifying improvement opportunities. Some of these activities require considerable manual effort that AI may be able to accelerate.

This helps identify where AI can take on the heavy lifting, allowing BPM professionals and SMEs to focus their time where their expertise adds more value.

2. Do You Already Have Information AI Can Work With?

Is your business knowledge spread across different sources? Most organisations have information scattered across sources, such as SOPs, policy and procedure documents, spreadsheets, existing process maps and flowcharts, audio and video recordings and other business documents.

Traditionally, BPM teams may need to locate this information, interpret it, speak with SMEs and manually translate what they learn into structured process documentation.

AI creates an opportunity to accelerate part of this work by interpreting existing business information and turning it into structured outputs. That makes the availability and quality of existing information an important consideration when deciding where to start.

Can You Measure the Before and After?

Your first AI use case should ideally be one where you can clearly demonstrate the difference AI makes.

Start with an activity where you already understand the time and effort involved today. This gives you a baseline against which you can assess the impact of introducing AI.

Depending on the activity, you might measure:

• Time taken to complete the work
• Analyst hours required
• SME involvement
• Number of review cycles
• Volume of work completed
• Time from starting the activity to an approved output

The objective is to determine whether AI actually changed the way the work gets done. Being able to demonstrate this before-and-after difference can also make it easier to build confidence in AI and decide where to expand its use next.

Why Process Mapping Is a Strong Starting Point for AI

If there is one BPM activity that brings many of these characteristics together, it is process documentation and process mapping. Picture a traditional process mapping cycle. A process analyst identifies the process and gathers whatever information already exists. They speak with the SME, conduct interviews or workshops, document the process, create the process map, send it back for validation, identify gaps, make changes and eventually move it through approval.

Multiply that effort across hundreds or thousands of processes, and it quickly adds up. Instead of beginning with a blank page, an AI agent can interpret existing process information and use it to generate the foundation of a process map. Where information isn’t already available, SMEs can provide the missing knowledge to the AI agent at a time convenient to them, reducing the dependency on lengthy process mapping interviews.

AI-assisted process mapping can turn text and existing documentation into BPMN-compliant process maps, but the resulting maps still require human validation. This is why process mapping represents such a practical entry point: AI takes on much of the manual documentation work, while people retain control over the outcome.

Where Process Mapping Ticks the Boxes as an AI Starting Point

There are several areas where process mapping ticks the boxes:

High Manual Effort

Traditional process mapping involves significant effort across information gathering, SME interviews, map creation, review and validation. AI can take on much of this manual heavy lifting.

Availability of Existing Information

Organisations rarely start completely from zero. Even when formal process maps don’t exist, there may be SOPs, spreadsheets, procedures, diagrams and other sources containing valuable process knowledge.

AI can help interpret and structure this information rather than requiring an analyst to manually rebuild everything from scratch.

Clearly Defined Output

The desired result is clear: a structured process map that represents how the process works. Also, defining modelling standards such as BPMN provide additional structure around the expected output.

Easy to Demonstrate Impact

The impact of AI on process mapping can be relatively easy to measure. Organisations can compare factors such as time taken to create a process map, analyst effort, SME involvement and the volume of processes documented. Comparing against this metrics makes it easier to assess whether AI is genuinely accelerating the work rather than simply adding another technology to the BPM stack.

Addressing the Myth Around SMEs

One common misconception is that AI-powered process mapping eliminates the need for subject matter experts. That’s not the case.

AI can interpret what has already been documented, but documented processes don’t always tell the complete story.

There may be:

• Workarounds that have never been documented
• Exceptions known only by experienced employees
• Differences between the documented and actual process
• Business context needed to understand why a particular step exists
• Recent changes that haven’t yet made it into the documentation

SMEs remain essential for providing this context. What changes is how their time is used. Instead of spending hours explaining information that already exists elsewhere, SMEs can focus on filling knowledge gaps, validating the AI-generated process and highlighting what the documentation doesn’t show.

Next Steps After Process Mapping

Once organisations become comfortable using AI for documentation and process mapping, they can begin expanding its role. Process analysis is a natural next area to explore, as AI can help significantly compress the timeline to achieve specific objectives.

Instead of manually looking for optimisation opportunities, AI can automatically suggest areas for:

Reducing cycle time by looking for delays, bottlenecks and waiting time.

Finding automation opportunities by identifying repetitive, rules-based or high-frequency activities that make suitable candidates.

Improving standardisation by helping identify process variations and areas where greater consistency may be possible.

Quick Checklist: Getting Started with AI in BPM

From AI Experimentation to AI-First BPM

The goal of your first AI use case in BPM should be to demonstrate that AI can improve a clearly defined part. Activities that previously required significant manual effort, such as interpreting existing process information, creating initial process documentation and analysing processes, can increasingly be supported by AI.

That allows BPM professionals to spend more of their time on the activities that still require human expertise: understanding business context, validating outputs, engaging stakeholders, redesigning processes and implementing change.

This is also the approach behind PRIME BPM’s AI Agents, which are designed to support BPM teams across activities including process mapping and analysis. Rather than removing people from the BPM lifecycle, AI agents can take on much of the manual heavy lifting so teams can focus on validating, improving and implementing processes.

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