The landscape of business process management (BPM) is undergoing a seismic shift with the advent of generative AI. As experts in process management grapple with ambitious goals, they must also confront the innate fear of redundancy that comes with the technological advancements of Gen AI and the possibilities it brings. This article explores how professionals can leverage Gen AI for innovative use cases, ensuring that process improvement continues to thrive in this new era.
Contents
Why BPM Pros Are More Valuable—But Only If They Adapt
There is a significant increase in business interest and market growth in BPM, driven by the need for organisational excellence and reduced operational costs. The BPM market is currently valued at $15.4 billion and is projected to reach $65.8 billion by 2032, with 74% of businesses showing increased interest in its adoption.
- BPM is crucial for driving efficiency and agility by streamlining complex processes, optimising workflows, and enabling data-based decisions. This helps businesses meet their goals, adapt to dynamic markets, and accelerate time to market.
- BPM is essential for successful digital transformation initiatives by providing the pathway to business process digitalisation and enabling the integration of digital technologies into all aspects of a business. A mature BPM capability is critical for rapid automation and optimisation.
- BPM facilitates automation and cost reduction by automating parts of processes, reducing manual effort and errors, and freeing up resources to work on more value-adding tasks. Many IT leaders believe automation saves significant time, and companies using BPM have seen a reduction in errors.
The irony is hard to ignore: process professionals promote automation, yet often resist automating their own work. As Gen AI spreads, those doing routine documentation, especially early-career professionals, are at real risk of being displaced. Survival means elevation
In the same way, Process improvement experts push an agenda of automation and digitalisation, they too must consider automating and digitialising their work
Challenges of Traditional Process Modelling
Before talking about Gen AI and the future, we must consider the key challenges that BPM experts face today:
- Resistance to change: One key challenge is resistance to change from employees and management who may be hesitant to adopt new processes due to various reasons, such as fear of the unknown or comfort with existing workflows. Overcoming this requires clear communication, employee involvement, and strong buy-in from leadership.
- Technological integrations: Another significant challenge is integrating BPM tools with existing legacy systems, due to differences in technology stacks and data formats. This can lead to data silos and process inefficiencies, hindering the full benefits of BPM. Selecting BPM solutions with robust integration capabilities and involving IT early in the planning process are crucial to address this.
- High implementation costs: There are costs associated with purchasing software, customisation, training, and process redesign, which can be a significant deterrent, especially for SMES. Even without discussing the tech stack, there is a considerable upfront headcount cost associated with interviewing and creating an MVP process diagram.
These challenges create fertile ground for a new paradigm—one where Gen AI doesn’t just accelerate, but redefines process design.
Gen AI Isn’t Just Faster—It’s Smarter
Generative AI is more than a tool for speeding up analysis—it’s a shift in what’s possible for process professionals. Rather than just identifying inefficiencies, Gen AI can generate draft process models, simulate process variations, and propose optimisations based on real-world data and intent, not just templates.
This redefines the role of BPM from descriptive to prescriptive. Instead of spending time documenting “what is,” professionals can now focus on exploring “what could be.” Gen AI enables rapid prototyping of future-state processes, scenario simulations before implementation, and data-driven benchmarking against best-in-class operations.
It allows process experts to:
- Accelerate discovery by parsing conversations or documents into structured process maps.
- Test decisions before costly rollouts through intelligent simulations.
- Scale their impact by delivering high-quality insights without being bottlenecked by manual modeling or documentation work.
Crucially, Gen AI doesn’t replace process professionals—it upgrades them. It automates the grunt work, freeing them to become performance architects, shaping business strategy with a new level of insight and speed. In doing so, it reduces the threat of redundancy by making their expertise more valuable, not less.
In a world where processes must adapt faster than ever, leveraging Gen AI isn’t optional—it’s the new baseline for those aiming to lead, not lag behind.
Case in Point: From Interviews to Diagrams in Minutes
One of the most interesting use cases for process improvement experts is using Gen AI to accelerate the generation of process models. PwC in Switzerland has branded this “BPMN AI“. Here is a pretty quick overview of the concept:
The process is relatively simple:
- Record an online interview with process owners and experts
- Use Gen AI to parse the audio file to text
- Taking this natural language input and some metadata. The large language model breaks down the text into components: actions, actors, and outcomes. It recognises verbs as actions and nouns as subjects or objects. For example, in “Approve the budget,” “Approve” is the action while “budget” is the subject.
- Use a Gen AI solution to analyse and generate a BPMN file
- The model organises these components into a structured format. It defines how each action relates to others, establishing a sequence. This structured output is then translated into a visual diagram that represents the entire process.
- Converting them into clear, actionable diagrams
- Polish the file to a state you’re happy with
How do you implement BPMN AI in a practical sense?
A typical traditional process mapping process might look like this:
The GenAI techniques are intended to produce initial drafts of process models more quickly, usually with the trade-off of not being perfect, fully detailed models.
The idea here is to focus on using AI for draft generation and the expert’s insights for refinement:
Although the techniques outlined above are not designed to produce flawless process models across all levels of abstraction, they offer a rapid way to generate initial drafts. This enables BPM experts and Subject Matter Experts (SMES) to focus their efforts on refining these drafts, benchmarking and generating insights, and ultimately working more efficiently.
- This capability democratizes process design. Professionals without technical expertise can now create complex models effortlessly. They can focus on their core responsibilities without fearing redundancy or being sidelined by technology.
- In essence, Gen AI bridges the gap between complex technical jargon and everyday language. It empowers professionals to actively engage in process improvement, ensuring they remain relevant in an evolving workplace landscape.
From Threat to Tool: Reframing Redundancy
Fear of job loss due to AI is a genuine concern. Many professionals worry about redundancy. To combat this fear, focus on collaboration with AI rather than competition. Embrace AI as a tool that helps you enhance your skills.
- Start by identifying areas where AI can support your work. For example, if you’re in marketing, use AI for data analysis to uncover insights faster. This allows you to focus on creative strategies that machines can’t replicate.
- Invest in continuous learning. Upskill in areas like data literacy or emotional intelligence. These skills are less likely to be automated and will make you indispensable.
- Build a network with others who share your concerns. Discussing challenges and solutions fosters resilience. Remember, the future isn’t about humans versus machines; it’s about humans working alongside machines to achieve more significant outcomes.
By shifting your mindset from fear to opportunity, you position yourself as a valuable asset in an evolving landscape. Embrace the change and lead the way forward.
Conclusions
Embracing generative AI in process improvement is not merely about keeping pace with technology; it’s about leveraging these advancements to enhance human capabilities. While fear of redundancy is valid, the potential for Gen AI to facilitate process model generation, simulation, and intelligent mining offers professionals an opportunity to elevate their roles rather than diminish them. The future is bright for those willing to adapt and evolve.