=Paper=
{{Paper
|id=Vol-3758/paper-09
|storemode=property
|title=Conversational AI for Framed Autonomy in AI-augmented Business Process Management
|pdfUrl=https://ceur-ws.org/Vol-3758/paper-09.pdf
|volume=Vol-3758
|authors=Angelo Casciani
|dblpUrl=https://dblp.org/rec/conf/bpm/Casciani24
}}
==Conversational AI for Framed Autonomy in AI-augmented Business Process Management==
https://ceur-ws.org/Vol-3758/paper-09.pdf
Conversational AI for Framed Autonomy in
AI-augmented Business Process Management
Angelo Casciani1
1
Department of Computer, Control, and Management Engineering - Sapienza University of Rome, Italy
Abstract
AI-augmented Business Process Management Systems (ABPMSs) represent an emerging category of
process-aware information systems driven by AI technology. These systems autonomously manage
the execution flow of business processes (BPs) within predefined frames, encompassing procedural
and declarative specifications, which may sometimes conflict. Despite operating autonomously within
these boundaries, ABPMSs require dynamic conversations with human agents. These conversations
not only respond to user queries but also initiate discussions to inform them of BP progression and
provide recommendations for performance improvement. This research proposal aims to leverage
Conversational AI to support the ABPMS’s framed autonomy, functioning as a Decision Support System
(DSS). This involves explaining system’s choices and suggesting actions when constraints are violated.
This technique enables intelligent, context-aware interactions with ABPMSs, fostering user trust. Our
findings indicate that Conversational AI has the potential to significantly enhance the interpretability
and usability of ABPMSs, thereby facilitating improved decision-making and process optimization.
Keywords
AI-augmented Business Process Management, Conversational AI, LLM, Decision Support
1. Context and Motivation
In the era of Industry 4.0, the increased availability of event data tracing the execution of
Business Processes (BPs), combined with recent advancements in Artificial Intelligence (AI),
is laying the ground for a new generation of AI-augmented Business Process Management
Systems (ABPMSs), capable of autonomously unfolding and adapting the BP execution flows. A
recent research manifesto [1] defines the vision for ABPMSs, extending the traditional BPMS
lifecycle in two ways. Firstly, traditional lifecycle phases (e.g., modeling, analysis, execution,
monitoring) are iteratively enhanced with AI capabilities. Secondly, the lifecycle includes
additional AI-dependent tasks, including adaptation, explanation, and continuous improvement.
A crucial aspect of this transition is the evolution of BP modeling to the broader concept of
process framing, which involves establishing multiple constraints including procedural rules,
best practices, and norms to guide the BP’s execution [2]. In this regards, an ABPMS is expected
to exhibit framed autonomy, i.e., making independent decisions about the execution within
the boundaries of the established frame. Unlike conventional BPMSs where framed autonomy
Proceedings of the Best BPM Dissertation Award, Doctoral Consortium, and Demonstrations & Resources Forum co-located
with 22nd International Conference on Business Process Management (BPM 2024), Krakow, Poland, September 1st to 6th,
2024.
Envelope-Open casciani@diag.uniroma1.it (A. Casciani)
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rigidly adheres to the prescriptive interpretation and enactment of a predefined BP model,
ABPMSs must simultaneously take into account multiple (potentially conflicting) constraints
regulating strict and/or flexible aspects of a BP execution in a procedural or declarative way.
Among these constraints, there could be a partial BP execution that needs to be completed,
violating the frame with the minimum cost.
Regarding autonomy within the frame, an ABPMS can proactively reframe itself as it acquires
new knowledge during adaptation, improvement, or explanation phases. Alternatively, it can
be restricted by the designer, determining which parts of the frame it can modify independently
and which require human intervention. Furthermore, the ABPMS’s autonomy depends on its
knowledge of the environment: the more information it has about the process’ context and
constraints, the more it can make informed decisions and act accordingly. Besides, the ABPMS
should ask its human agent for additional information whenever needed.
Indeed, despite the autonomous nature of ABPMSs, human involvement remains essential
[3], particularly for maintaining trust and a sense of control within the workforce, a known
barrier to the adoption of automated technologies in Information Systems [4]. To address these
concerns while minimizing human effort, an ABPMS should be conversationally actionable,
engaging with users through natural language when the constraints of the frame cannot be met.
Effective communication with humans is crucial for the ABPMS not only for responding to
queries but also for initiating conversations about the BP progression, making recommendations
for interventions, and discussing with them the benefits and drawbacks of actions.
This research proposal explores how Conversational AI can support the framed autonomy of
ABPMSs. The conversational interface should function as a Decision Support System (DSS),
providing explanations for the ABPMS’s choices and suggestions when constraints are violated.
Additionally, Conversational AI can facilitate what-if BP analysis, explaining the implications
of violating particular constraints and helping to find the optimal trade-off that minimizes
violation costs while allowing the process to progress.
Differently from traditional user interfaces (UIs) for DSSs, a Conversational AI-based interface
in ABPMSs enables intelligent and insightful interactions with users. To better illustrate this, we
examine a healthcare scenario involving the treatment of hip fractures [5]. The frame includes
procedural clinical guidelines and declarative constraints representing basic medical knowledge.
During the process enactment, violations of these constraints may occur, and the ABPMS must
find the near-optimal frame, namely the right trade-off for such violations, through intelligent
interactions with clinicians to discuss with them the nature and severity of the violations.
For instance, if a patient has a high body temperature, he cannot immediately undergo
surgery but an X-ray check must be performed. In this case, the ABPMS should converse with
the doctor to determine whether to book an X-ray check. However, the doctor, leveraging his
expertise, may decide to proceed with the surgery to prevent further damage to the patient’s hip
mobility in cases of extremely severe fractures. Another example involves the patient showing
post-operative delirium. The ABPMS notifies the doctor, who requests a what-if analysis to
explore the consequences of delaying physiotherapy. The ABPMS might explain that delaying
physiotherapy could slow down recovery but help prevent complications from delirium, whereas
starting physiotherapy could worsen the delirium. Hence, the doctor might choose to violate the
conventional guideline of initiating physiotherapy within 24 hours, opting to wait an additional
12 hours to monitor and reassess the patient’s condition before making a final decision.
This healthcare scenario exemplifies how Conversational AI can enable the effective collabo-
ration between the ABPMS and the doctors in a trustworthy manner to manage violations of
the initial constraints by dynamically aligning the execution to the novel frame. Thus, building
on the previous considerations, the following research questions (RQs) can be drawn:
RQ1: What conversational techniques have been developed to enable actionable conversations?
RQ2: How can Conversational AI support framed autonomy in ABPMSs?
RQ3: How does Conversational AI impact human trust and influence the adoption of ABPMSs?
2. Related Work
In recent years, there has been significant interest in DSSs due to their ability to aid humans in
making high-quality decisions based on domain-specific information [6]. Since the foundational
work in [7], various studies have focused on developing recommendation services to guide
decision-making during process execution by suggesting possible next steps. Other research
has employed Markov Decision Processes to determine optimal decisions based on probability
distributions of all potential ones [8, 9]. Novel advancements in AI models have fostered the
evolution of DSSs, allowing for the prediction of decisions and explanation of factors influencing
them [10, 11]. Additionally, emerging Large Language Models (LLMs) technologies have been
explored to improve DSSs by transforming business questions and hypotheses written in natural
language into executable specifications that generate relevant reports for stakeholders [12, 13].
A related area to this research proposal is What-if analysis, which involves building digital
process twins of the BP (i.e., simulation models) to evaluate the outcomes of their execution based
on the specified constraints [14]. Conversational approaches for assisting this analysis have
focused on enabling the specification of what-if simulation scenarios through natural language,
facilitating the comparison of the BP performance against a standard reference [15, 16].
Moreover, Prescriptive process monitoring focuses on optimizing BPs by recommending inter-
ventions in real-time to prevent negative outcomes or address poorly performing cases [17] and
explored the combination of conversational methodologies with recommendation systems [18].
Unlike the existing literature, this research proposal aims to leverage of Conversational
AI to assist the framed autonomy of ABPMSs by providing a holistic approach that includes
process-aware decision support, monitoring, and what-if analysis, thereby enhancing their
functionality and trustworthiness.
3. Methodology
In this section, we examine in detail the RQs and explain how conversational AI can address them.
We present the results achieved, highlight open challenges, and propose potential solutions.
3.1. RQ1: What conversational techniques have been developed to enable
actionable conversations?
RQ1 requires an extensive and structured review of the state-of-the-art approaches facilitating
actionable conversations to back framed autonomy in ABPMSs. Thus, we developed a survey
Table 1
Research challenges identified in [19].
BPM Area Identifier Research Challenge
Descriptive Process Analytics RC1 Unambiguous BP Discovery
RC2 BP model semantics explanations
RC3 From natural language to BP and vice versa
RC4 Conversational interfaces for PM
Predictive Process Analytics RC5 Conversational what-if analysis
RC6 Explainable predictive process monitoring
Prescriptive Process Optimization RC7 LLM-driven process redesign
RC8 Multi-disciplinary integration for prescriptive process monitoring
Augmented Process Execution RC9 Trustworthy conversational corrections
RC10 Conversational RPA
RC11 Cognitive automation
[19] applying a rigorous and reproducible search protocol across recognized academic databases
inspired by [20], and we categorized the findings following the BPM taxonomy drawn in [12].
In Descriptive Process Analytics, Natural Language Processing and neural architectures proved
effective in extracting process models from natural language descriptions [21], and expressing
BP models in natural language [22]. Conversational interfaces can also enhance understanding
and accessibility of process mining findings [23]. Predictive Process Analytics employed con-
versational interfaces for what-if analysis of digital process twins [15] and predictive process
monitoring [24]. In Prescriptive Process Optimization, the focus was on supporting automated
process optimization [25] and prescriptive process monitoring, providing real-time recommen-
dations through natural language [18]. Finally, Augmented Process Execution used conversational
agents for smooth interactions [26] and to assist Robotic Process Automation [27].
As a result of this first research effort, we identified the challenges reported in Table 1, which
were crucial in defining the problems that this proposal addresses for achieving ABPMSs framed
autonomy and in determining the techniques that could be employed to tackle these issues.
3.2. RQ2: How can Conversational AI support framed autonomy in ABPMSs?
RQ2 explores the application of Conversational AI techniques to support framed autonomy in
ABPMSs. Building on the previous example, Conversational AI in the ABPMS vision should
function as a DSS, providing real-time decision support, interactive what-if scenario evaluation,
and proactive process adjustments based on monitoring information. It should detect exogenous
actions violating the frame and converse with human agents to reframe accordingly, adhering
to a human-in-the-loop approach where the final decision rests with the human [28].
A promising solution involves leveraging LLMs [29], which can create conversational inter-
faces that interpret and respond to natural language queries, thereby democratizing access to
complex technologies to a broader audience. We have already experimented with integrating
LLMs. In [30], we implemented a conversational process-aware DSS using LLMs for answering
BP-related questions, combining fine-tuning to incorporate domain-specific knowledge and
Retrieval-Augmented Generation to embed the contextual knowledge for grounded answers.
Additionally, we explored conversational Process Mining analysis over object-centric event logs
following the OCEL 2.0 format, combining LLMs with a preliminary preprocessing step for
extracting relevant information from the log.
Future work includes extending these techniques to fully leverage what-if analysis for antic-
ipating the consequences of frame violations in ABPMSs. For instance, the integration with
prescriptive process monitoring can provide more accurate what-if scenarios, improving the
quality of natural language responses. Moreover, LLMs suffer from poor explainability and
hallucinations [31]: we posit that integrating Symbolic AI reasoners can enhance the reliability
and transparency of the conversational DSS, making its reasoning more human-understandable.
Another avenue involves implementing techniques for continuous process monitoring that
utilize real-time data streams to detect frame deviations and proactively propose adjustments.
3.3. RQ3: How does Conversational AI impact human trust and influence the
adoption of ABPMSs?
RQ3 investigates how Conversational AI can boost human trust in ABPMSs, thereby encouraging
their wider adoption. Indeed, it plays a crucial role in framed autonomy since, instead of merely
presenting actions to select for the human agent as in traditional UIs, it supports decision-
making intelligently by providing useful insights into the choices. Nevertheless, the ABPMS’s
decisions in sensitive contexts, e.g., in our healthcare example, might not be trusted by patients
who fear for their health and safety. Thus, the human agent (e.g., a doctor) remains essential
for trust. Additionally, users of ABPMSs themselves may not trust the system’s decisions.
For this reason, in [32], inspired by established principles for trustworthy AI, we explored
factors that foster human trust in these systems. We developed and validated a classification
framework to assess the trustworthiness of ABPMSs, linking specific trust principles to each step
of the system. Adopting this framework can lead to the implementation of more transparent,
reliable, and accountable ABPMSs, capable of handling modern BP complexities.
Future research will address the lack of standardized metrics for evaluating the trustworthiness
of ABPMSs and the impact Conversational AI has on them. A potential solution is to develop
comprehensive metrics that include both qualitative and quantitative aspects, such as user
satisfaction surveys, adherence to ethical guidelines, and performance benchmarks. Moreover,
users may distrust ABPMS decisions if they do not understand the reasoning behind them.
Implementing explainable AI to provide clear and understandable explanations for decisions is
crucial, along with user-friendly interfaces that visualize decision pathways. Furthermore, the
dynamics of human-AI collaboration in decision-making processes, particularly in high-stakes
environments, are not well understood. Hence, we plan to conduct empirical studies to explore
human and conversational DSS interactions in various scenarios. Insights from these studies
will inform the design of interaction protocols with the ABPMS to improve efficiency and trust.
4. Outlook
Our research proposal investigates the application of Conversational AI to support framed
autonomy in ABPMSs. This research endeavor will improve decision support, what-if analysis,
and real-time monitoring within ABPMSs. Indeed, our initial results show a promising boost in
the system’s ability to provide intelligent, context-aware answers and foster human trust.
Thus, enhancing the framed autonomy and trustworthiness of ABPMSs, we hope to lay the
ground for their broader adoption and more effective application across various industries.
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