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REVIEW 3 major objections 4 minor 24 references

No AI Without PI! Object-Centric Process Mining as the Enabler for Generative, Predictive, and Prescriptive Artificial Intelligence

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper argues that AI for operational processes requires object-centric process mining as its grounding, and that process models and event data are the missing link.

desk verdict A credible programmatic agenda from the OCPM camp; the PI umbrella is useful, but the title's necessity claim outruns the evidence and should be softened or supported. read the letter →

arxiv 2508.00116 v1 pith:B2CXTQD2 submitted 2025-07-31 cs.AI

Wil M.P. van der Aalst This is my paper · ORCID
classification cs.AI
keywords Object-CentricProcessMiningIntelligenceGenerativeAIPredictivePrescriptiveoperationalprocesseseventdatagrounding
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Organizations struggle to make AI work on end-to-end operational processes, even as generative, predictive, and prescriptive AI advance elsewhere. This paper argues that the reason is a missing link: AI models are not grounded in the structure of the process they are supposed to improve. Process data are structured and organization-specific, and processes are dynamic, so the paper proposes Object-Centric Process Mining (OCPM) as the necessary grounding. OCPM records events as they relate to multiple object types and extracts process models from those events, and the paper calls the resulting combination of process-centric techniques Process Intelligence (PI). If the argument holds, organizations should build AI on object-centric event data and process models rather than on generic text or single-event tables.

What carries the argument

The central mechanism is the object-centric event log: a collection of events in which each event can refer to multiple objects of different types, such as an order, a delivery, and an invoice. Object-Centric Process Mining (OCPM) turns these logs into process models and diagnostics that preserve object interactions. This mechanism carries the argument by supplying the structural grounding that the paper claims generic AI lacks; it lets generative, predictive, and prescriptive AI connect their outputs to the real process rather than to a flattened, case-id-based approximation.

What would settle it

A controlled comparison on the same operational event logs—one version represented as an object-centric event log with mined process models, the other flattened into single-case tables—using the same predictive and prescriptive AI methods would settle the claim: if flattened representations match or beat the object-centric ones, the 'missing link' argument loses its empirical support.

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Extended reading notes

Core claim

The paper claims that AI needs to be grounded using Object-Centric Process Mining (OCPM). Process-related data are structured and organization-specific, and processes are highly dynamic, so generic AI that treats data as text or as flat tables cannot reliably diagnose or improve them. OCPM connects the event data to the actual process by recording events in relation to multiple object types and mining process models from those events. The paper introduces the term Process Intelligence (PI) for the amalgamation of process-centric, data-driven techniques that handle many object and event types, and argues that PI is what enables generative, predictive, and prescriptive AI in organizational settings.

Load-bearing premise

The load-bearing premise is that the main reason AI fails in operational settings is the absence of process-structural grounding, so that object-centric process data are the decisive enabler rather than model capability, data quality, or organizational incentives.

Editorial extensions

If this is right

  • Predictive and prescriptive models can exploit the relations among objects in an event log instead of forcing each event into a single case.
  • Generative AI suggestions can be constrained by a mined process model, grounding recommendations in the organization's own process structure.
  • The same object-centric event data can support diagnosis, prediction, and improvement, making Process Intelligence one shared substrate for all three forms of AI.
  • AI projects in process-heavy organizations would start with object-centric event-data capture rather than with model selection.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the paper is right, then adding an object-centric event log and a mined process model as context should improve the behavior of a fixed generative or predictive model on process tasks; that is a direct experimental test the paper does not run.
  • The argument suggests a data-engineering priority: before scaling models, organizations should build event data that records multiple object types, because the claimed bottleneck is structural rather than computational.
  • A public benchmark that compares OCPM-grounded models with flattened single-case baselines on the same event logs would turn the paper's central metaphor into an empirical question.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. This manuscript is a position paper arguing that effective AI for operational processes requires Object-Centric Process Mining (OCPM) as the grounding representation. The authors introduce the term Process Intelligence (PI) to denote the amalgamation of process-centric data-driven techniques, and they discuss how OCPM enables generative, predictive, and prescriptive AI. The paper surveys challenges such as process dynamics, organization-specific structure, and multiple object/event types, and it presents a series of claims and recommendations rather than experimental results.

Significance. If the central claim were established, this paper would provide a valuable framework for AI in process mining, shifting attention from flat event logs to object-centric models. The paper usefully identifies the gap between generic AI and structured process data, and it draws together three AI paradigms under one conceptual umbrella. Its strengths include a clear articulation of the representation problem and a concrete proposal (object-centric event data) as a candidate solution. However, the manuscript does not provide a controlled comparison, quantitative evidence, or a formal argument that OCPM is uniquely necessary, so the significance is currently that of a research manifesto rather than a demonstrated result.

major comments (3)
  1. [Abstract] The abstract asserts that 'AI needs to be grounded using OCPM' and that OCPM is 'the missing link connecting data and processes.' This is a necessity claim, but the paper offers no comparison against flat event logs augmented with object identifiers, knowledge-graph representations, or LLM orchestration over structured data. Without such a comparison or a formal argument ruling out alternatives, the claim is an unsupported causal hypothesis. Please either add empirical or formal support or reframe the claim as a conjecture or research agenda (e.g., 'we argue that OCPM is a strong candidate').
  2. [Section 1 (introductory paragraphs)] The paper defines Process Intelligence (PI) as 'the amalgamation of process-centric data-driven techniques' and then concludes that 'AI requires PI.' If PI is defined as the collection of all process-centric AI techniques, then the conclusion can become true by definition for any process-AI system, without establishing that OCPM specifically is required. Please clarify the logical status of the claim: is it a definitional statement, a design principle, or an empirical claim? Locate this risk in the abstract and the introduction and address it explicitly.
  3. [Full text (unreadable) and Abstract] The full text received for review is heavily corrupted by an encoding error, so I could not verify any section-level evidence. Based on the readable abstract, the manuscript does not appear to include experiments, benchmarks, or case studies. For a journal, the central 'enabler' claim needs at least one detailed worked example or a small empirical demonstration to show how OCPM changes AI outcomes. Please add such evidence or explicitly state that the paper is a perspective piece and move the necessity claim to a hypothesis.
minor comments (4)
  1. [Title] The title 'No AI Without PI!' is catchy but overstates the argument; consider a title like 'Process Intelligence as a Grounding for AI in Operational Processes.'
  2. [Abstract] The abstract uses 'we show' where 'we argue' would be more accurate given the absence of proof; please align the wording with the paper's evidentiary level.
  3. [Throughout] The term PI is later used as an abbreviation for Process Intelligence, but the acronym is also commonly used for other concepts in AI; please define it at first use and avoid ambiguity.
  4. [PDF/encoding] The text I reviewed contains many garbled characters; please ensure the published PDF uses correct encoding so figures and tables are legible.

Circularity Check

1 steps flagged · score 2.0 of 10

Minor definitional circularity: 'AI requires PI' restates the definition of PI; the OCPM 'missing link' claim is asserted, not derived.

  1. self definitional [Abstract, definition of Process Intelligence (PI) and subsequent claim]
    "We use the term Process Intelligence (PI) to refer to the amalgamation of process-centric data-driven techniques able to deal with a variety of object and event types, enabling AI in an organizational context. This paper explains why AI requires PI to improve operational processes..."

    PI is stipulated as the amalgamation of process-centric techniques 'enabling AI in an organizational context,' so the statement that 'AI requires PI' follows by unpacking the definition: AI requires the techniques that are defined as enabling AI. This makes the PI-level necessity claim true by construction rather than by independent evidence. The paper's more specific assertion that OCPM is the 'missing link' is not forced by this definition, so the circularity is limited to the umbrella term PI and does not by itself prove the OCPM-specific thesis.

full rationale

This is a position paper rather than a formal derivation: the readable text contains no equations, fitted parameters, or quantitative predictions that could reduce to their inputs. The main circular element is terminological. PI is defined as process-centric data-driven techniques 'enabling AI in an organizational context,' and the abstract then concludes that 'AI requires PI'; that conclusion is essentially a restatement of the definition. The stronger, load-bearing claim that OCPM specifically is the 'missing link' connecting data and processes is asserted without a controlled comparison against flat event representations, knowledge graphs, or workflow-level LLM orchestration; that absence is an evidence gap and a correctness risk, not a circularity. Self-citation is present in the broad sense that OCPM is the author's framework, but no quoted passage in the readable text makes the argument depend on an unverified self-cited theorem, and there are no fitted values or equations that force the OCPM conclusion. The score is therefore low: a minor definitional circularity in the PI framing, while the central OCPM claim still has independent content.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No free parameters are fitted and no new entities are postulated; the argument depends on three domain assumptions about process data and AI bottlenecks, none of which are empirically established in the abstract.

assumptions (3)
  • domain assumption Operational processes are best represented as object-centric event logs with multiple interacting object types.
    The argument presupposes this modeling framework, foundational to the OCPM school; invoked throughout the abstract's definition of PI.
  • domain assumption The failures of current AI in industrial settings stem primarily from lack of process grounding.
    Underlies the claim that OCPM is the missing link; no empirical evidence in the abstract.
  • domain assumption Process-related data are structured and organization-specific, unlike text, making generic AI insufficient.
    The abstract states this dichotomy; it is a premise for why OCPM is needed.

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Cite this review

Pith. "Pith review of No AI Without PI! Object-Centric Process Mining as the Enabler for Generative, Predictive, and Prescriptive Artificial Intelligence." pith.science (2026). https://pith.science/paper/B2CXTQD2

@misc{pith2026250800116,
  author       = {Pith},
  title        = {Pith review of: No AI Without PI! Object-Centric Process Mining as the Enabler for Generative, Predictive, and Prescriptive Artificial Intelligence},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/B2CXTQD2}},
  note         = {Machine review of arXiv:2508.00116}
}
read the original abstract

The uptake of Artificial Intelligence (AI) impacts the way we work, interact, do business, and conduct research. However, organizations struggle to apply AI successfully in industrial settings where the focus is on end-to-end operational processes. Here, we consider generative, predictive, and prescriptive AI and elaborate on the challenges of diagnosing and improving such processes. We show that AI needs to be grounded using Object-Centric Process Mining (OCPM). Process-related data are structured and organization-specific and, unlike text, processes are often highly dynamic. OCPM is the missing link connecting data and processes and enables different forms of AI. We use the term Process Intelligence (PI) to refer to the amalgamation of process-centric data-driven techniques able to deal with a variety of object and event types, enabling AI in an organizational context. This paper explains why AI requires PI to improve operational processes and highlights opportunities for successfully combining OCPM and generative, predictive, and prescriptive AI.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

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Reviewed August 6, 2026 · model on record in the stance chip above.