Workflow management systems, as originally conceived, tried to impose structure to work, in a way similar to the assembly line. While imposing constraints may be needed because of regulations and compliance, it is important to recognize that much of knowledge work is done in an ad-hoc fashion. Case management is an admission that ad-hoc work exists, and an attempt to adjust to the flexibility of work. Unfortunately, it still requires some explicit steps. With recent changes in the nature of knowledge work, enterprises have increased the adoption of online communication tools such as chat and collaborative documents. Ad-hoc work has become the new norm, and knowledge workers are increasingly utilizing these communication tools to resolve and complete cases. In this work, we argue that business process management solutions need to engage workers in the channels where they already work and outline the challenges of bringing the tools to these environments.
Business process management (BPM) comprises a spectrum of modeling and
management approaches and tools, including workflow management and case
management. Workflow management systems take a control-flow centric view,
based on pre-defined business processes and explicitly defined activities with
ordering constraints, where activities may be automated or assigned to human
knowledge workers [
While case management acknowledges the flexible and ad-hoc nature of knowledge work, implementations of case management solutions still require centralized tracking of the case. Workers are free to carry out the work how they see fit, but need to return to the case management tool to record the results of their work. For example, a nurse may call a patient to check their status the day after getting discharged, but then needs to explicitly login to the case management tool, find the correct case for the patient, select the appropriate Copyright © 2021 for this paper by its authors. Use permitted Creative Commons License Attribution 4.0 International (CC BY 4.0). under
Conventional BPM solution E-mail
CRM sheet … Chat
Phone
CRM
Spread sheet Denotes useful business tasks the worker performs Denotes administrative record-keeping tasks 3 1 E-mail 2
Worker-centric BPM solution
CRM sheet
… Chat
Phone
CRM
Spread sheet 1 Chal enge 1: Record case instances and activities from observing work in each channel 2 Chal enge 2: Proactively engage with workers in the channels where they work 3 Chal enge 3: Enhance process model with uncertainty and mutability activity, and record the patient’s status. As illustrated in Figure 1, the nurse is performing work twice here: once to carry out his task in the channel of his choice (a phone call in this case), and then an administrative step to record the status and results of this task in the BPM tool.
It is not enough to recognize that knowledge work is ad-hoc, unstructured,
and takes place across different channels. We need BPM tooling to reach into
the channels and modalities where people already work, observe what they do,
and record the results in the BPM tool. We call this a worker-centric BPM
solution. Having separate steps to carry out the actual business task and
recordkeeping is time consuming and risks the same problems with rigid workflows:
work gets done “behind the system’s back” [
We recognize that there are situations where knowledge workers need to legitimately interact directly with the BPM tools. For example, they may need to examine the case history to find who worked on a particular activity so they can followup with questions, or they may want a list of pending overdue work items. A secondary argument in this paper is that these interactions with the BPM tools should also happen in the channels where workers work. For example, a conversational assistant would allow a knowledge worker to query for overdue work items through a “more natural” free-form chat interface. The multi-channel interfaces to the BPM tooling is illustrated in Figure 1 as solid arrows from the BPM tool to the workers’ channels. The figure also depicts our vision that direct interactions with the BPM tools should be deprecated in favor of more channelspecific ones.
A worker-centric BPM solution brings several benefits. First, knowledge
workers no longer need to perform the redundant step of recording their work in the
BPM tool after already having performed it in the channel of their choice, such
as a voice call. Second, workers that need to interact with the BPM artifacts
have more choice in the modality. Third, process owners and workers get a more
complete and accurate picture of the end-to-end “as-is” process [
Realizing a worker-centric BPM solution will require a wide variety of research innovations in process modeling, tooling, and algorithms. To this end, we present three sets of broad research challenges. (1) Techniques that automate the record-keeping aspects of a business process across multiple channels and modalities are needed. (2) Solutions to allow multi-channel interactions to the BPM tools via a diverse set of more natural interfaces are required. (3) Business process models need to be extended to take into account uncertainty and errors in automated record-keeping. We discuss these challenges in more detail in Section 2. 2
In this section, we outline some of the research challenges, illustrated in Figure 1, that need to be solved to realize a worker-centric BPM solution. We also reference relevant existing techniques and technologies that offer initial solutions to these challenges that the community can build on. 2.1
Inferring the process instance. It is generally difficult to correlate work to a
process instance [
For example, when a nurse calls a patient, the context includes the phone number of the patient, the identity of the nurse, and the time the phone call was made. Cross-referencing this information with the patient profile, history of hospital visits, and the department where the nurse works can be used to infer that the call is in relation to a procedure that the patient had for which they were recently discharged. Increased adoption of personalized treatment pathways makes this task even more challenging.
Some existing work that address this problem for a simpler case of structured
logs include an Expectation-Maximization approach to estimate a Markov model
from an uncorrelated event log [
Inferring the activity. In case management tools, workers log work by explicitly selecting from a pre-defined list of activities or by creating and naming a new ad-hoc activity. There is no such explicit activity when workers perform their tasks in their native channels of work, such as a chat room.
For example, a radiologist may email her analysis of a patient’s x-ray scans.
An AI model can process the email subject, body, and attachments to classify
the email as completing a pre-defined “Analyze x-ray” activity, as well as extract
metadata, such as the radiologist’s name and whether the diagnosis was negative,
and record all of this as part of the process instance. AI models to classify
documents and extract key-value pairs [
A more challenging problem occurs when workers perform tasks that don’t
fall into the set of pre-defined activities. For example, the radiologist may forward
the x-rays to a senior colleague for help interpreting the scans. It is not clear if
this task should be treated as an unnamed sub-activity of the “Analyze x-ray”
activity, or a new activity altogether. Perhaps the system should intervene and
ask the radiologist to identify this task. Techniques from conversational systems
to infer intent and resolve ambiguity are instructive here [
Eliminating the direct interaction of users with the BPM systems will require
these systems to proactively interact with users in their channels to close the
loop. This translates to channel-specific alerting capabilities to inform users of
any changes in the system, especially those that involve multiple actors that
may change the state of the system (including automation bots that perform
tasks in the process) [
Mutable case history. Due to the challenges in Section 2.1, the system may make mistakes in inferring the case instance or activity of some work. There needs to be a facility for these errors to be corrected manually at a later time by a knowledge worker or a case owner.
It is not clear how the provenance of these mis-classifications should be
recorded. A process improvement specialist likely only wants to see the
corrected case history, while a forensic auditor may be interested in the erroneous
history. A knowledge worker may or may not want to see the errors depending
on whether there were downstream activities that acted on those errors. This is
somewhat related to compensations in workflow management systems [
Another subtlety is that in some cases the system may correct itself as more context becomes available. For example, consider an email thread that the system infers is related to the most recent procedure for a patient. However, a later email may provide additional clues that the thread is actually about a future procedure. In this case, the activity associated with the email thread needs to be removed from one process instance and added to another. Current BPM solutions do not accommodate mutating the history in this way.
The database community has developed solutions on how to update query
results as streaming data arrives late or is corrected in the future [
This will have fundamental changes to how processes are modeled, perhaps
adopting probabilistic Petri nets [
BPM systems have been developed with a top-down mindset, catering first to process owners and administrators that want to impose structure and order on the business processes and knowledge workers. This has resulted in solutions that force workers to perform administrative record-keeping tasks and learn to use unfamiliar tooling outside their comfort zone, and offers a view of the process devoid of any of the real-world uncertainty.
We think it is time for a more worker-centric BPM solution that brings in the latest in AI advancements, including computer vision, speech recognition, and natural language processing, in order to engage knowledge workers in the channels and modalities in which they are already most comfortable and productive. Doing this will also require fundamental extensions to how processes are modeled, including adding notions of uncertainty and mutability, and addressing the ripple effects of such core extensions.
While challenging, we believe that the intersection of BPM and AI can bring about a new form of process management that is more worker friendly, less redundant and more amenable to modernization.