Why business analysts shouldn't rely entirely on AI for BPMN modeling and simulation

Artificial intelligence is reshaping both business analysis tools and the skills business analysts need to integrate AI into their daily work. Over the past year, the industry conversation has shifted from whether a tool should adopt AI to how well it uses it: every major vendor now claims to own AI and AI agents, and the differentiation has moved to what they can actually do, rather than whether they exist at all.

Vendors offer AI-powered tools that generate fully structured BPMN diagrams from a text prompt, AI agents that explore and assess workflows, and support automation, simulation, process mining, and process analysis. As in other industries, the AI benefits in process analysis are visible: faster process design and modeling, faster documentation, lower barriers to implementation, and reduced coordination time.

The question that lingers, however, isn't whether AI can be useful in process modeling and simulation. It clearly can. The real question is whether business analysts can afford to rely on it entirely. The practitioner community suggests the answer is no.

This article explores why, drawing on research and experience from the business analysis and AI communities.

What business analysis actually is: the starting point

Before examining AI's impact on business analysis, it's worth recalling what business analysis is. The IIBA Business Analysis Standard defines it as "the practice of enabling change in an enterprise by defining needs and recommending solutions that deliver value to stakeholders within a given context."

That definition contains six core concepts captured in the Business Analysis Core Concept Model (BACCM™): change, need, solution, value, stakeholder, and context. The Standard emphasises that ignoring any of these concepts, or the relationships between them, reduces a model's effectiveness. Business analysis is more of a web of intertwined concepts, and less off a linear output. In effect, BACCM is conceived as a thinking model for effective business analysis, and it implies critical thinking for problem understanding and prioritisation, risk management, and value maximisation. It also requires relationship management and coordination — stakeholder involvement, leadership support, team empowerment — alongside agility in adapting approach and strategy on the fly.

What AI lacks for business analysis

The context

Here lies the structural problem with full AI reliance: AI doesn't know the context. Context — the dynamic background of stakeholder attitudes and behaviours, company culture and history, niche market regulations — cannot be captured in a text prompt. Stakeholder interests, influence, and relationships are social realities no AI can be aware of. And value, whether tangible or intangible, must be interpreted relative to specific stakeholders in a specific situation. Only business analysts can perform this contextual reading and priority interpretation.

If BPMN modeling were only documentation work, you could perhaps rely on AI entirely. But it isn't, it's business analysis work. And business analysis, according to its own professional standard, is irreducibly multi-dimensional and ultimately human.

The mindset

The IIBA Business Analysis Standard furtherly highlights an essential asset AI lacks: mindset. The Standard argues that a practitioner's mindset is as fundamental as their technical skills. Effective business analysis requires critical thinking, intellectual curiosity, a willingness to challenge assumptions and solutions, and continuous questioning of whether a proposed solution truly addresses the underlying need.

As IIBA Senior Advisor Fabrício Laguna puts it,

"AI should be viewed as a component of a broader solution rather than the solution itself. The role of the business analyst is to ensure that technology remains aligned with business objectives and stakeholder needs."

This matters profoundly in process modeling. A skilled BA reviewing a draft BPMN will ask:

• Does this model reflect how things actually work, or how someone thinks they work?
• Are the exception paths realistic?
• Does the model expose the right risks?
• Does this process redesign deliver value?
• How to align all stakeholders around it?

These questions require the contextual judgement, stakeholder empathy, and analytical scepticism that the Standard frames as foundational competencies. They are exactly the competencies AI cannot exercise on your behalf and the ones most likely you’ll lose if you delegate the thinking to an AI and review the output passively.

Why business analysts shouldn't rely entirely on AI

The risk the IIBA community has explicitly flagged in entirely relying on AI is precisely this: passive acceptance. A 2026 IIBA practitioner article warned directly:

"The risk I caution against is passive acceptance of AI output without critical evaluation. That is where quality degrades and where the analyst's professional contribution is genuinely diminished."

The following sections look at the concrete risks of relying on AI entirely.

Reason 1: AI hallucinates, even when generating business process models

A 2025 empirical study by Kourani, Antonov, Berti, and van der Aalst, presented at the GenAI4PM workshop, identifies a phenomenon they call "knowledge-driven hallucination" in automated process modeling.

LLMs are trained on vast datasets that include extensive documentation of standard business processes. When asked to model a process, the AI consults its internalised schemas of what these processes "normally" look like. When the actual process described conflicts with those schemas — because the organisation's real workflow deviates from the standardized pattern — the AI tends to quietly resolve the conflict by reverting to the generic version it "knows."

Even when explicitly prompted to strictly adhere to the provided criteria, no model achieved full fidelity to atypical inputs. We can imply that the processes that most differentiate from textbook examples — the ones where an organisation's real workflow diverges from conventional ones — are precisely the ones AI is most likely to silently misrepresent.

A BPMN diagram that looks professionally designed but has quietly standardized the organisation's actual workflow is worse than no model at all. Not only does it create false confidence but also consumes time in rework to accurately represent the reality.

Reason 2: The structural issues of AI-generated BPMN

Beyond the semantic errors of text-to-BPMN, several studies highlight significant structural issues in LLM-generated BPMN. Real-world open-source experiments, run by German Research Center for Artificial Intelligence (DFKI) and Saarland University, found that models frequently produce BPMN-XML with invalid syntax, struggle to capture all relevant process paths, and, when asked to modify an existing BPMN, often fail to correctly update, remove, or add the right sequence flows.

A further study conducted by DFKI and Saarland university on assessing the BPMN competences of LLM across eleven open-source LLMs states that while overall pragmatic quality was generally high, specific structural infrastructure – such as for models with multiple parallel paths, loops, or limited block structuring – scored lower.

The practical consequence is that an AI-generated BPMN diagram may look solid but be technically broken: invalid, structurally unsound, or missing paths. Catching these structural errors requires a human eye with genuine BPMN competence.

Reason 3: AI skips the dialogue that process modeling requires

Professional process modeling requires a conversation in which the business analyst draws out, challenges, and confirms process knowledge that stakeholders often hold incompletely or inconsistently in order to sort out desired solutions and outcomes. The IIBA's BABOK Guide calls this elicitation and treats it as a core skill for business analysts precisely because getting to the real issue — and thus the right solution — requires empathy, dialogue, and alignment on stakeholder requirements.

The AI entirely skips the dialog. The researchers from DFKI and Saarland University found out too that LLMs did not ask clarifying questions about ambiguous inputs and proceeded with generating output through implicit assumptions rather than flagging ambiguities for clarification. On top of that, output quality degraded significantly for longer process descriptions. When tackling complex processes, participants had to manually chunk their descriptions into fragments, ultimately slowing their work rather than accelerating it.

The absence of clarifying dialogue points to a mismatch between how current AI tools generate process models and how reliable process knowledge is actually discovered. And again, highlights that AI usage is a starting point, not a finishing line.

Reason 4: For simulation, the issues compound

BPMN modeling and BPMN simulation are different activities with different requirements. Simulation requires data inputs: resource availability and cost parameters, task durations, probability weights on gateway paths. AI can generate a structurally plausible BPMN model to feed into a simulation, but it cannot supply these parameters: they must come from organisational data and stakeholder knowledge.

If you accept an AI-generated BPMN and feed it into a simulation tool without verifying that the model accurately represents actual process behaviour – reliable digital twin – the simulation will produce data that are consistent with the model but inconsistent with your actual process. The decisions on the process changes taken upon these simulation outputs are meaningless because they are based on a model that does not reflect your reality.

The IIBA Standard's framing of business analysis as value creation for stakeholders is significant here. Value is not created by producing documentation, it is created by enabling better decisions. A confident-looking simulation built on a hallucinated process model destroys value, even if every individual technical step was performed correctly.

Reason 5: Over-reliance is a documented, measurable risk

The research literature on human–AI collaboration has documented a recurring pattern: people tend to over-rely on AI recommendations even when those recommendations are wrong, and the explanations AI includes do not counteract this behaviour. On the contrary, explanations could increase uncritical acceptance, as users treat their presence as a signal of trustworthiness. A 2025 paper by Ibrahim et al. on over-reliance on LLMs identifies three core long-term risks: cognitive deskilling, high-stakes errors, and governance challenges.

This resonates with advice from practitioners across the business analysis community: AI is a tool that must be used actively and critically, not a service to consume passively. Analysts who routinely use AI and slip into passive usage risk committing severe structural errors in their business analysis.

What the BA community actually thinks

IIBA's 2025 Global State of Business Analysis Report found that 74% of BA professionals say AI positively impacts their careers, a percentage growing by 11% from the previous year. The same report identified a pattern the practitioner community has since expanded on: as AI absorbs more routine and repetitive tasks, human skills — communication, critical thinking, strategic judgement, adaptability — become critically more important. The business analyst’ profession is not being replaced by AI; their daily tasks shifted more toward the judgement-intensive work AI cannot do.

That shift resonates through IIBA's Reframing and Reshaping Business Analysis series. Filip Hendrickx, IIBA Board of Directors member, frames the evolution of business analysts role and pinpoints three directions for professional growth worth investing in: horizontal (into trend exploration and benefits realisation), vertical (into portfolio and strategic decision-making), and transversal (across domains and adjacent roles affected by AI).

In his companion piece, Enabling Confidence [or Trust?], IIBA Senior Advisor Fabrício Laguna states that a business analyst's core differentiators within an AI-driven environment are the confidence derived from knowledge, analytical and strategic thinking, and the ability to enable organizations to make decisions they can truly trust.

The same tone dominated BA & Beyond 2026 in Belgium, which we attended. Sessions focused on how analysts should challenge AI outputs, facilitate conversations, and validate outcomes, with empathy emerging as the main character alongside AI.

The IIBA experts' best practices are echoed in day-to-day discussions on Reddit's r/businessanalysis and r/businessanalyst communities. The overall shared thought is that AI has become genuinely useful for documentation-heavy tasks such as generating initial diagrams, querying documentation, drafting executive summaries, and similar. A participant in a discussion described this as the process where an analyst still does the thinking while the AI handles the writing-up.
Equally clear is what AI doesn't do, that is the actual business analysis: prioritising and defining the real issues, reading between the lines when a stakeholder says one thing but means another, deciding what is worth paying attention to in the data. That is still entirely human.

Conclusion

Both experts and practitioners agree: AI can accelerate drafting BPMN models, reduce documentation overhead, and help less experienced modellers get started.

But as we just saw, AI can’t elicit tacit knowledge, exercise the professional mindset, or substitute for stakeholders’ collaboration. It also can’t exercise empathy — which, as speakers at BA & Beyond highlighted, may become the most important AI-era skill an analyst has.

As we learned so far, the right frame is not "AI or no AI". It is "AI with what safeguards."