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Rail’s assurance regime isn’t a barrier to AI adoption. It’s the foundation responsible AI deployment needs. Dr Mike Rustell CEng MICE, Founder and Chief Executive Officer at Inframatic Engineering Limited explains more
Your safety culture is an AI advantage – start using it
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ail’s safety case culture is routinely cited as a barrier to AI adoption. Too regulated, too slow, and too much
paperwork. It’s an argument that surfaces repeatedly across the industry with engineers genuinely enthusiastic about AI but who see their sector’s governance architecture as something to work around rather than work with. I think it’s exactly backwards. Rail’s Common Safety Method and the CENELEC standards require organisations to identify hazards, assess their severity and likelihood, define proportionate controls, and document the evidence that those decisions are defensible. That framework was designed for a world where experienced engineers make judgement calls under uncertainty, where the consequences of getting things wrong can be severe, and where accountability must be clearly traceable. That description applies to AI-assisted decisionmaking with uncomfortable precision. Sectors without this discipline are writing AI ethics policies from scratch, inventing review processes, arguing about accountability structures they don’t yet have. Rail already has the bones of this.
The analogy to assurance practice Consider how checking already works. A checking engineer reviewing a structural design doesn’t verify every calculation from first principles. They apply professional judgement – checking critical load paths, interrogating assumptions, sampling detailed calculations. The framework defines what level of checking is proportionate to the consequence of failure. AI maps onto this logic, though the mechanics are different and that difference matters. Automation complacency is a real risk when reviewing AI-generated outputs – evidence from aviation and radiology suggests that reviewers scrutinising algorithmically generated work tend toward lower critical engagement over time. The question for directors isn’t just whether to have human review, but how to design it so it remains genuinely critical rather than becoming a formal step that absorbs liability without catching errors. The structural principle – matching assurance rigour to consequence of failure – transfers directly. An AI system retrieving and cross-referencing technical requirements to support an engineer’s
systems are non-deterministic – the same query can produce different responses, and models update in ways that can change behaviour without obvious signals. This sits uncomfortably inside assurance regimes built on repeatability, and guidance from the Rail Safety and Standards Board and the Office of Rail and Road on AI in safety-critical applications is still developing. This is solvable, but it requires deliberate design: managing AI as a versioned system, with change control, regression testing, and documented behaviour baselines. There is also a data infrastructure question that governance readiness alone doesn’t answer. Having the right frameworks means little if engineering records are fragmented or locked in legacy systems. Governance maturity and data accessibility need to be developed together. decision is analogous to a graduate preparing a design check package: useful, time-saving, reviewed before it carries weight. The conceptual leap to governing AI within your existing assurance structures is far smaller than building governance from nothing.
A concrete illustration Take a large infrastructure project with hundreds of interface requirements distributed across civil, systems, signalling, and operational documentation. Requirements conflicts – the civil package assumes one clearance envelope, the systems package assumes another – are the kind of error that is costly to find late. AI can be configured to retrieve and crossreference these requirements systematically, flagging potential conflicts for an engineer to assess. Every flag traces back to the specific document and clause. The engineer makes the judgement call; the AI handles the information processing. This is what verification by design looks like. The AI reasons over your verified specifications, not its own training data. Its outputs are auditable in the same way that any engineering decision in a safety case is auditable, not because someone reviewed it after the fact but because the trail was built into how the system works.
What needs to be solved Let’s be clear about what this framing doesn’t resolve. CSM and CENELEC were designed around repeatable, deterministic processes. Current AI
March 2026
Navigating a fast-moving market These challenges – non-determinism, version control, data infrastructure – are solvable, but they require genuine depth in both AI and engineering. And this is where a practical question emerges for anyone looking to adopt AI in rail: the current generation of development tools has lowered the barrier to building a convincing prototype so dramatically that it has become genuinely difficult to distinguish surface capability from deep capability. A polished demo is no longer evidence of either. That doesn’t mean directors should be sceptical of AI: the opportunity is real, and the organisations engaging with it now will be better positioned than those waiting. But it does mean that the same evidence-based discipline rail applies to engineering decisions is worth applying to supplier evaluation. Look for depth that can be demonstrated, not just described. The market is young enough that long track records in rail AI don’t exist yet. But the underlying expertise, the kind that takes years to develop and can’t be assembled over a weekend, is either there or it isn’t. Rail’s safety case culture is an asset here too. The same mindset that requires proportionate, evidencebased control of risk can help organisations navigate a market where confidence is easy to project and capability is harder to verify. That’s not a reason to slow down. It’s a reason to engage – carefully, proportionately, and with the same rigour the industry applies to everything else that matters. www.inframatic.ai/