A match made in heaven!
Process Mining in the era of GenAI
What would the confluence of two rapidly emerging technologies look like? How would it have played out in California in the 1850s - when the gold mining ecosystem met the rail road technology? That is what we are set to explore in the next few paragraphs, where we will study the intersection of process mining , which is growing at a 50% CAGR and generative AI, for which I will refrain from even making any guesses on the growth numbers. There are a lot of questions and a broad spectrum of views - ranging from they are mutually exclusive to Gen AI will subsume process mining. My view is that together they complement each other. In fact, Gen AI will be a force multiplier to what process mining aspires to do.
Gen AI forms another stack or layer between the core process mining algorithms and the end user. This layer empowers the user with many possibilities that would have otherwise been complex or cumbersome. Across my reading and experimentation, I listed several of them and I was looking for a way to group and simplify the myriad use-cases. I came across McKinsey's article on the impact of GenAI on operations, which grouped the capabilities into four categories viz., concision, creative content, customer engagement and coding. In the next few paragraphs, I will start with an explain of what each category is and the process mining use-cases that it could unlock.
Concision
Concision refers to capability of Gen AI to interpret large corpuses of unstructured data to identify and summarize relevant answers in the service and analysis contexts. In other words, help you get to the crux of what's relevant and important.
The first concision use-case is about assisting the process analyst in generating knowledge models. Process knowledge is supposedly contained in the SOPs and the manuals. But in reality, much more nuances are present as tacit knowledge with the people close to the process. That is why, during process discovery, Gemba visits are super helpful. Its just a fancy Japanese term for being in "the actual place" and watching the operators and the process in action. This is helpful because it gives you an uncensored insights into workings of the process. In the services context, it could mean observing the operators and having detailed discussions and interviews. When these interviews are fed to the Gen AI systems, the BPMN or DMN model can be autogenerated. Using these generated models, we could execute multiple process mining use cases like quality control, process conformance, auditing etc.,
The next use case is about surfacing out the insights that need attention. This could translate to many possibilities like,
Sentiment analysis could be performed on the in-flight cases and, in conjunction, with other process parameters like wait/cycle time, reworks, number of touches - corrective actions could be initiated if needed
One could build an RAG(Retrieval augmented generation) systems and automatically surface out insights of importance based on the context. Different from a querying or prompting approach, this is a complete push model to filtering out and consuming relevant insights
The RAG could be extended to give prescriptive insights based what could unfold and how it could impact given the context of the process? An example of this would be to alert when there could be a potential SLA miss for a customer with a penalty clause attached in contract
The final use case in concision is about using the GenAI to do root cause analysis. It essentially involves studying the impact of multiple factors on the KPIs of interest. While many vendors have implemented this feature using data science algorithms, Gen AI could bring in external data that is relevant to the context of process and augment the list of factors.
Creative content
Next comes, the capability of generating 'Creative Content'. It implies rapid tailoring of complex and structured documents to specific needs and contexts. Simply put, its about creating or helping co-create solutions relevant to the context.
To start with, a very straightforward use case, is about documentation of process, SOPs etc., from the technical specifications like BPMN, DMN and control charts.
The next use case in 'creative content' is about gen AI partnering with process designer in devising a solution. In one of the interviews Marlon Dumas, the founder of Apromore mentions that, the process improvement life cycle there is "big chasm" from identifying the bottleneck or the root cause to devising a solution and feels Gen-AI could play a vital role there. He also mentions that in the pilot implementation, the Six Sigma practitioners found that Gen AI suggested solutions that they would otherwise not have thought of.
Many at times, in an improvement or transformation journey, it is important to incorporate the domain expertise to meet the compliance norms or to prevent reinventing the wheel. While an experienced process engineer in the domain could bring in those design patterns, a Gen AI system could analyze the processes and suggest alternatives. Also in the process design, they could also help in the risk analysis of future state.
Customer engagement
This is the most popular Gen AI use case and it refers to the capability of building copilots that can guide customers through their personalized journeys in the realm of customer engagement. More popularly known as the democratization of process mining journey.
One straightforward use case, is in using a copilot for querying. Instead of writing complex SQLs / PQLs, the user can prompt their queries, which the Gen AI can convert to query language and return the results. This can be extended to creating complete dashboards with prompts. Many vendors have already implemented this features in different forms.
The next use case, is about making simulations more conversational. Once the bottlenecks are identified and addressed through solutions, the next logical step is to have a view of the future state. It could be having a view on new through throughputs, cycle times, SLAs, resourcing etc., Simulating multiple scenarios using the digital twin helps the process designer in answering those questions. Given that the nature of 'what - if' analyses is inherently conversational, having a co-pilot do that would be a great enabler.
Another interesting use case in this category is in the process story telling. To contextualize what each user needs to know from the process. Depending on the role of the user, the Gen AI could highlight aspects that they might interested in. For example, senior business user might be interested to know the broad KPIs and their trends. A department head might want to understand the KPIs, trends and causation factors in depth for her department. A process engineer might need to know the inefficiencies like long cycle times of segment, loops and reworks in the process. Like wise, someone looking for tech, automation and AI opportunities to refine the process will look for different things. Currently these requirements are partly catered by building different dashboards or views, but Gen AI can definitely do a much better job in contextualizing the content. Gen AI along with AI can be a powerful navigator.
Coding and software
The final capability of Gen AI is in 'coding and software' - the capability that enables the user to co-create new software and migrate legacy systems at scale and speed. There were not many process mining use cases in the segment - but I did come across use-cases where entire user stories and software could be generated from the BPMNs and DMNs. Well, why not? With the AGI not far-away, I think we should aspire to reach 'process autonomization'. Systems that automatically detect and act on compliance and performance gaps. And 'self-heal' too!
You might have noticed that this is not an exhaustive or a mutually exclusive set of use-cases. There are overlaps between them and I was more interested in laying of the different broad possibilities that could unfold. Undoubtedly, like in any other discipline, Gen AI would have a tremendous impact on process management. The technologies, both rapidly evolving, would complement each other and transform the way processes of tomorrow are designed, improved and maintained.
What are the ones that you find are most impactful to your industry?