AI in industry is a different game from the AI everyone talks about in social media, chatbots or employment. It runs in manufacturing plants, pharma production, oil and gas, utilities, and other high‑stakes environments where safety, uptime and strict regulation are non‑negotiable. This article looks at those specifics and shows what both developers and users should reasonably expect – and demand – in documentation, contracts, and everyday practice.
In advisory work with companies deploying AI in regulated industrial environments, I see the same issues surface repeatedly.
Practical example from regulated industry:
In a regulated pharma production environment, an AI system flags a batch as non-compliant and triggers disposal. When the decision is later reviewed as part of a deviation and audit process, the rationale cannot be reconstructed: the model’s decision logic is opaque, documentation is incomplete, and no clear human review step was defined. The issue is not whether the AI was right. The issue is that compliance requires the ability to explain, assess, and, if necessary, correct the decision.
Bias AI in Industrial AI
When “Fairness” Misses the Point
Industrial AI bias looks very different in practice than what’s discussed in general AI regulations and ethics codes. While fairness in consumer systems centers on gender, ethnicity, or other human characteristics, industrial AI risks come from things like unbalanced or incomplete process data, models that push one KPI (like throughput) at the expense of safety or reliability or ignoring rare but critical scenarios in real operations.
For Developers
What matters in practice is to name these domain‑specific biases early, document them clearly, and explain them to customers in your contracts and manuals. Design with these biases in mind from the start and then be as transparent as you reasonably can without giving away trade secrets. Treat that transparency as a way to build trust and reduce your liability, not as a threat.
For Customers
While regulations usually talk about “fairness,” buyers in industry should actually be insisting on clarity: What kinds of errors has the vendor considered? What trade-offs are built into the system? Expect these specifics to be covered transparently.
Asking for “zero bias” isn’t rational – even though it still happens in practice but demanding that biases and limitations are made explicit and relevant to your business is completely reasonable.
Transparency That Counts
Docs for Industrial AI
Contract partners, customers and auditors often expect clear documentation of system features, limitations, decision logic and update history, not just a generic “AI inside” label. In high‑risk areas such as pharma or critical infrastructure, this kind of documentation is essential to prove legal compliance and to show that AI does not undermine safety or existing controls.
For Developers
Provide clear, maintained model/system cards and logs describing all system updates, changes, and performance in diverse scenarios.
Be prepared for audits but recognize there are practical limits: detailed internal code may sometimes be out of scope for general customers but should be ready for regulators if required.
For Developers
A reasonable expectation is access to solid, high‑level technical documentation and transparency materials that relate directly to your system and use case.
Asking, however, for full, unrestricted access to proprietary source code or internal IP is usually neither realistic nor necessary for safety – unless this is explicitly negotiated for very specific, highly regulated environments.
Training AI on Customer
Data: Where to Draw the Line
Using operational and customer data for ongoing model improvement is attractive but ethically, legally, and commercially sensitive. Industrial clients increasingly demand to know – and control – whether their data is used for model training, especially with strategic or sensitive operations. Modern industrial contracts often reflect this, either strictly forbidding default training on client data or requiring explicit, documented opt-in.
For Developers
A practical approach is transparency plus value: explain that training on client data may improve their results, stability or safety margins. Use safeguards and be specific about them – for example, training on‑premise, strong access controls, aggregation, or federated learning so raw plant data does not leave their environment – and build in clear options for opt‑in, opt‑out, or tightly scoped use in contracts instead of hiding training in generic terms and conditions.
For Customers
You should insist on clear information and contract terms about how your data is used for training, including the option to opt in or at least opt out, require strong aggregation and privacy safeguards if applicable.
In some high‑security environments, it is reasonable to say that none of your data can be used for training – but this may also limit how much the system can improve over time.
In many other cases, it makes sense to allow training on a carefully defined set of less sensitive, well‑prepared data, combined with solid technical measures from the vendor to reduce privacy and security risks. You can also benefit from model improvements without putting your data at additional risk.
When AI Must Answer “Why”
Explainability and Real Human Oversight
Industrial buyers and end users want to understand how and why an AI system makes decisions, especially when safety, quality, or regulatory compliance are at stake. “Black box” answers don’t work in this context. Models need to be explainable in practice and interpretable in the language of specific process and equipment, not just in abstract technical terms.
For Developers
If you build these systems, design them so they provide clear, domain-relevant explanations and decision paths, and make sure people can intervene – pause, override, or roll back – when it matters.
In complex, time‑critical environments, “human in the loop” can easily become an illusion if the person overseeing the system has no real chance to see what is happening or to react in time. This is exactly what you need to think through and test upfront: what a human can realistically see, understand and decide under real operating conditions.
Done well, this is not just an ethical requirement; it also reduces your liability and makes your system more acceptable for serious industrial use.
For Customers
If you are on the buying side, expect explainability that fits your teams – operators, engineers, compliance, safety – not just generic model descriptions. You probably won’t get a full, detailed explanation for every single micro‑decision in real time, but you should insist that major events, alarms, and critical decisions are auditable, interpretable, and reconstructable afterwards.
By Ewa Wojnarska-Krajewska
Ewa Wojnarska-Krajewska 