The creative instability of hallucination is no longer a flaw to be eliminated, but a force to be harnessed. Responsible deployment will depend on knowing when to correct it, when to contain it, and when to let it run wild.
As enterprises rush to implement generative AI across operations, design, and customer engagement, the phenomenon of hallucination is rapidly moving from an academic curiosity to a pressing commercial concern. It is a term that evokes instability, error, and misinformation, but those interpretations fail to grasp its significance. According to the NTT DATA white paper All Hallucinations Are Not Bad: Acknowledging Gen AI’s Constraints and Benefits, hallucination is not just a malfunction in large language models (LLMs). It is the very mechanism by which these systems innovate, simulate, and propose new ideas.
That realisation marks a critical turning point for senior executives responsible for AI deployment in industrial and manufacturing settings. Hallucination is not just about falsehoods. It is about the system’s ability to generate plausible yet unverified content by extrapolating patterns from vast training datasets. Whether that capability becomes a liability or an asset depends entirely on how it is managed, contextualised, and deployed.
At the core of this creative volatility lies the architecture of generative AI itself. Technologies such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and autoregressive models are designed to do more than recall facts. They are structured to generate new content that mirrors, extends, or transforms their input data. This is not reproduction but reimagination, enabled by the emergence of attention-based transformers that can track long-range dependencies and simulate human-like reasoning. The result is a machine that not only learns to think like us but also learns to imagine with us.
Designing for unpredictability
In manufacturing, where precision is prized and safety non-negotiable, the notion of embracing hallucination may seem counterintuitive. Yet the NTT DATA report highlights the growing recognition that not all hallucinations are harmful. In fact, under controlled conditions, they can catalyse breakthrough thinking.
Consider the role of synthetic data. In data-sparse environments, rare failure modes, edge-case safety scenarios, or simulations of new materials, hallucinatory generation provides a means to supplement incomplete datasets. When paired with robust human oversight and domain expertise, these synthetic inputs can improve the accuracy and resilience of downstream models.
Similarly, in product development, early-stage concept generation increasingly relies on AI’s ability to explore unconventional possibilities. By allowing generative systems to extrapolate beyond historical patterns, organisations can uncover design options or functional ideas that might not emerge through traditional engineering logic. Hallucination, in this context, becomes a tool for structured creativity.
The risk, of course, is that such creativity bleeds into operational decision-making without proper constraints. An AI-generated materials recommendation may appear optimised on paper but fail to account for real-world tolerances, compliance requirements, or environmental impact. Left unchecked, hallucinations can introduce unseen weaknesses that only surface after deployment. In manufacturing, that latency can be catastrophic.
Imagination with a governance framework
To reconcile that risk, NTT DATA outlines a set of mitigation strategies designed to filter, contextualise, and evaluate hallucinated outputs. These range from foundational practices such as prompt refinement and the use of diverse, high-quality datasets, to more advanced implementations like Retrieval-Augmented Generation (RAG), which grounds AI outputs in verifiable source material.
Human oversight remains central to this approach. Rather than aiming for total elimination, organisations must accept that hallucination is a structural feature of generative models and plan accordingly. This involves embedding validation workflows, introducing ‘human-in-the-loop’ review for high-risk outputs, and maintaining transparency regarding the provenance and status of generated content.
In practice, this requires a cultural shift. Hallucination cannot be treated as a binary, fact or fiction, correct or incorrect. It must be evaluated on a continuum of usefulness, accuracy, and risk. For industrial leaders, this means training teams not only to detect and flag hallucinated outputs but also to recognise when they might offer valuable, if imperfect, insight.
The ethics of simulation at scale
The consequences of unchecked hallucination go beyond technical inaccuracy. The NTT DATA paper addresses the ethical and societal implications of LLM-generated content that appears plausible but embeds historical bias, unfounded claims, or stereotypes drawn from skewed training data. These outputs can be especially problematic in manufacturing contexts that intersect with health and safety, environmental sustainability, or consumer well-being.
Bias hallucination, where the model mirrors or amplifies prejudices present in training data, presents a real threat to organisational values and regulatory compliance. In environments governed by ethical standards, such as sustainability certifications, labour practices, or anti-discrimination frameworks, the risk of generating content that subtly deviates from those principles cannot be ignored.
Moreover, hallucinations that simulate expert advice or technical documentation without verification raise concerns about misinformation and liability. When language models generate outputs that resemble authoritative technical manuals or compliance checklists but lack a foundation in validated knowledge, the result is not just misleading. It is potentially dangerous.
Mitigating these risks requires more than filters. It demands governance. Enterprises must implement accountability structures that include transparent labelling of AI-generated content, rigorous tracking of data sources, bias mitigation procedures, and cross-functional oversight. The goal is not to constrain creativity but to channel it within clearly defined operational boundaries.
A creative engine with an industrial edge
Manufacturing leaders are already familiar with the idea of variation within control limits. In many ways, hallucination fits the same paradigm. It introduces controlled instability that, when framed and validated correctly, can enhance rather than erode value.
From advanced materials research to digital twin simulation, generative hallucination is emerging in real-world use cases. By feeding AI models with prompts to explore the properties of hypothetical alloys or to simulate future supply chain disruptions, organisations can test scenarios that have no historical precedent. In doing so, they are not misusing hallucination, they are designing for it.
The challenge is to know where that design ends and delivery begins. In the production environment, there is no room for hallucinated safety procedures or imaginary maintenance schedules. The same models that generate imaginative concepts upstream must be grounded and verified downstream. That transition, from conceptual play to operational execution, will be where many manufacturing businesses succeed or fail in their adoption of generative AI.
Where trust meets transformation
NTT DATA concludes that hallucination is not a problem to be fixed but a force to be understood. For enterprise leaders, the question is no longer whether hallucinations exist; the question is whether they can be effectively managed. It is how they are detected, evaluated, and used. Responsible innovation will depend not only on better models but on better methods for integrating those models into systems of human judgment, domain knowledge, and operational discipline.
This is not a footnote in the AI story. It is its turning point. The capacity to hallucinate is what enables AI to transcend automation and enter the realm of imagination. And imagination, if given the proper infrastructure, governance, and intention, is what will separate the digital factories of tomorrow from the legacy systems of today.
Manufacturers now face a choice: resist the generative spark or learn to harness it. In the coming years, that decision will define not only how AI is used, but what it is trusted to do.
