---
title: "From Process Mining to Augmented Process Execution"
type: source
tags: [process-mining, augmented-execution, analytics-pyramid, prescriptive-analytics, predictive-analytics, conversational-process-management]
authors: [Chapela-Campa David; Dumas Marlon]
year: 2023
venue: "Software and Systems Modeling 22(6): 1977–1986 (Expert Voice)"
doi: "10.1007/s10270-023-01132-2"
kind: paper
raw_path: "raw/Process Frameworks & BPM/From process mining to augmented process execution.pdf"
key_claims:
  - Data-driven BPM has evolved as a four-layer pyramid - descriptive process analytics, predictive process analytics, prescriptive process optimization, and augmented process execution.
  - Each layer builds on the one below; moving up the pyramid decreases the need for human interaction and increases the degree of autonomy of the system.
  - Augmented process execution is an emerging, nascent top layer where the system autonomously manages and optimizes the process within frames set by managers; it maps onto levels 3 to 5 of van der Aalst's six levels of autonomous process execution.
  - Descriptive analytics comprises automated process discovery, conformance checking, performance mining, and variant analysis - mature since the 2000s / early 2010s.
  - Predictive analytics splits into what-if digital process twins (tactical, macro-level) and predictive process monitoring (operational, case-level).
  - Prescriptive optimization splits into automated process optimization (tactical, design-time recommendations) and prescriptive process monitoring (operational, runtime recommendations).
  - Augmented execution comes in two variants - autonomic (system acts inside the frame, escalates outside) and fully autonomous (system may modify the frame given business-goal alignment).
  - Conversational process management is orthogonal to the pyramid - LLM-based conversational interfaces support tactical and operational use-cases across all four layers, translating natural-language questions into queries, predictions, prescriptions, or frame specifications.
  - Three open challenges: ensuring augmented systems do not produce actions that technically obey the frame but yield undesirable business outcomes; composability under frequent process change; designing guardrails for the black-box unpredictability of LLM-driven conversational systems.
created: 2026-04-13
updated: 2026-04-21
sources: []
---

# From Process Mining to Augmented Process Execution — Chapela-Campa & Dumas 2023

Short *Expert Voice* article (10 pages) in *Software and Systems Modeling* that positions the trajectory from [[methods/process-mining-basics|process mining]] to AI-augmented execution as a four-level **analytics pyramid**. Co-authored with [[entities/marlon-dumas]] by his postdoc David Chapela-Campa at the [[entities/university-of-tartu|University of Tartu]] Information Systems group.

## Summary

Business process management has traditionally relied on manual data collection and analysis, leading to slow and narrow decisions. Over the past two decades, data-driven techniques have emerged to automate activities across the [[concepts/bpm-lifecycle|BPM lifecycle]]. The authors synthesise this progression as a **four-layer pyramid**:

```
           [ Augmented Process Execution ]       (operational)
         [ Prescriptive Process Optimization ]
       [    Predictive Process Analytics     ]
     [    Descriptive Process Analytics        ] (tactical)
```

Each layer builds on the ones below. The left–right axis of the pyramid splits each layer into tactical use-cases (weeks-to-months horizon, aimed at managers) and operational use-cases (day-to-day, per-running-case). The chronology of research in data-driven BPM follows this pyramid from bottom to top.

**Descriptive Process Analytics** — the mature base. Automated process discovery, [[concepts/conformance-checking|conformance checking]], performance mining, and variant analysis, typically applied to event logs extracted from ERP/CRM/IoT systems. Consolidated in the 2000s and early 2010s.

**Predictive Process Analytics** — two sub-families. *What-if Digital Process Twins (DPTs)* build simulation models from event logs (a more honest term is *digital process shadow* since the model does not affect the real process) to answer tactical what-if questions. *[[concepts/predictive-process-monitoring|Predictive Process Monitoring (PPM)]]* operates at the case level: predict outcome, [[concepts/remaining-time-prediction|remaining time]], or [[concepts/next-activity-prediction|next activity]] for a running case. A footnote observes that maintaining model performance post-deployment under [[concepts/concept-drift|concept drift]] is a persistent unsolved challenge.

**Prescriptive Process Optimization** — predictions become recommendations. *Automated process optimization* produces design-time redesign suggestions (e.g., resource re-allocation to relieve a bottleneck) using search-based algorithms + DPTs. *[[concepts/prescriptive-process-monitoring|Prescriptive Process Monitoring (PrPM)]]* recommends runtime actions per case, e.g., sending a second loan offer to prevent rejection. Case-level techniques dominate; macro-level PrPM (recommendations across many cases) is identified as a gap.

**Augmented Process Execution** — the nascent top. Where prescriptive systems recommend to humans who execute, augmented systems *execute the process themselves* while humans assist/supervise. Roles invert: the system drives, humans intervene at the frame boundary. Augmented systems are of two types:
- **Autonomic** — the system acts inside the frame; when a situation out-of-frame or high-uncertainty is detected, it escalates to a human and records the human's decision for future autonomous handling.
- **Fully autonomous** — the system has complete control and may even change the frame, provided the change advances the business goals. Humans are supervisors.

The authors explicitly align these with **levels 3–5 of van der Aalst's six levels of autonomous process execution management** (APEM, arXiv:2204.11328, 2022) — level 0 maps to predictive analytics, levels 1–2 to prescriptive, levels 3–5 to augmented.

**Conversational Process Management** is introduced in a separate final section as an *orthogonal dimension*: LLM-based conversational systems translate natural-language user questions into executable analytical, predictive, prescriptive, or frame-specification actions at any level of the pyramid. This is the first explicit articulation in this wiki's corpus of [[concepts/conversational-actionability|conversational actionability]] at BPM-analytics granularity — a precursor of what the [[sources/2026-calvanese-agentic-bpm-manifesto|APM Manifesto]] later elevates to a first-class APM agent capability.

## Key claims

- **The four-layer pyramid** — descriptive → predictive → prescriptive → augmented — is the organising schema for data-driven BPM research.
- **Autonomy grows monotonically up the pyramid.** Predictive = level 0 (assist only). Prescriptive = levels 1–2 (actively assist). Augmented = levels 3–5 (system drives; human intervenes at the frame).
- **Two sub-families per layer**, split by scope (tactical / macro vs operational / per-case). Researchers tend to work in one quadrant at a time.
- **Augmented execution is nascent.** Few deployed systems; challenge of reliable short-term impact evaluation across many cases.
- **Macro-level PrPM is a gap.** Existing techniques recommend for a single case; cross-case recommendations (e.g., *"too many resources on task A, too few on task B, redeploy"*) are open.
- **Conversational systems are orthogonal to the pyramid,** not a new layer. Their role is translation, not new analytical capability.
- **Risk: frame-compliant, goal-divergent action.** A key open challenge is ensuring augmented systems do not technically obey the frame while producing undesirable business outcomes — essentially a specification-gaming problem for BPM frames.
- **Composability under frequent change** is identified as a safe-transition requirement for hyper-automated BPM.

## Framing distinctions introduced

- **Digital Process Twin vs Digital Process Shadow.** Since changes to a simulation model do not affect the real process, *shadow* is the arguably more accurate term — an uncommon (and useful) qualification in a field that uses *twin* loosely.
- **Tactical vs operational use-cases** inside each pyramid layer. Makes explicit that every layer contains both design-time and runtime instances.
- **Autonomic vs fully autonomous augmented systems.** Autonomic systems escalate; fully autonomous systems may modify the frame. This maps onto [[concepts/abps-autonomy-levels|autonomy levels]] and anticipates the *meta-framing* distinction from ABPMS.
- **Augmented execution ≠ prescriptive optimization.** The role inversion — human assists the system rather than the reverse — is definitional, not a matter of degree.

## Positioning vs related work in this wiki

- **Complement to [[sources/2023-dumas-ai-augmented-bpms|AI-Augmented BPMS (Dumas et al. 2023)]].** ABPMS is the system/architecture-side manifesto; this paper is the analytics/capability-side synthesis published the same year by one of the ABPMS co-authors. The two are explicitly pointed to by the APM Manifesto as complementary lenses on the same transition.
- **Complement to [[sources/2026-calvanese-agentic-bpm-manifesto|APM Manifesto (2026)]].** APM takes the agent-centric, management-oriented lens; Chapela-Campa & Dumas take the intelligence/analytics lens. The four-layer pyramid is the data-driven backdrop against which the APM capability stack (framed autonomy, explainability, conversational actionability, self-modification) is enacted.
- **Extension of [[sources/2021-dumas-process-mining-2-from-insights-to-action|Dumas 2021 — Process Mining 2.0]].** The "insights → action" thesis is precisely the descriptive → predictive → prescriptive progression, extended here by one more layer (augmented) and orthogonally by conversational interfaces.
- **Extension of [[sources/2011-vanderaalst-process-mining-book|Van der Aalst Process Mining (2011/2016)]].** The book's [[concepts/operational-support|operational support]] (detect / predict / recommend) seeds layers 1–3 of this pyramid; this paper extends the ladder with a fourth rung (augmented) above *recommend*.

## Connections

**Concepts:** [[concepts/process-mining-spectrum]] · [[concepts/operational-support]] · [[concepts/predictive-process-monitoring]] · [[concepts/prescriptive-process-monitoring]] · [[concepts/conformance-checking]] · [[concepts/process-discovery]] · [[concepts/business-process-simulation]] · [[concepts/conversational-actionability]] · [[concepts/framed-autonomy]] · [[concepts/abps-autonomy-levels]] · [[concepts/concept-drift]] · [[concepts/agentic-bpm]]

**Methods:** [[methods/process-mining-basics]]

**Authors (entities):** [[entities/marlon-dumas]] · [[entities/david-chapela-campa]] · [[entities/university-of-tartu]]

**Related sources:** [[sources/2023-dumas-ai-augmented-bpms]] · [[sources/2026-calvanese-agentic-bpm-manifesto]] · [[sources/2021-dumas-process-mining-2-from-insights-to-action]] · [[sources/2024-kampik-large-process-models]] · [[sources/2022-kubrak-prescriptive-ppm-slr]] · [[sources/2011-vanderaalst-process-mining-book]] · [[sources/2012-vanderaalst-process-mining-manifesto]] · [[sources/2026-dumas-agentic-bpms-pyramid]]

## Cited by
- [[sources/2026-calvanese-agentic-bpm-manifesto]] (ref [80]) — complementary analytics-side viewpoint.
- [[sources/2026-dumas-agentic-bpms-pyramid]] (ref [8]) — the 2026 keynote explicitly adapts this paper's four-level pyramid into the [[concepts/agentic-bpm-pyramid]], replacing the apex *Augmented Process Execution* layer with *Agentic BPM*.