by Brian Power, Head of Engineering, EMR Integrated Solutions

There is a lot of noise around artificial intelligence right now. Every sector is feeling the pressure to adopt it, move fast and demonstrate results. Critical infrastructure is no different. But when you are working with water treatment plants, power networks, flood monitoring systems, and renewable energy assets, moving fast without thinking carefully is exactly the kind of approach that creates serious problems.

At EMR, we have been building and maintaining operational technology for over 45 years. Working with critical national infrastructure providers, it’s this history that shapes the way we think about AI. It makes us far more deliberate than the average technology conversation might suggest.

Controlled Autonomy

Problem-Led Approach

Human in the Loop

Engineering Judgement

OUR APPROACH

Problems first. Tools second.

The starting point for how we approach AI is the problems we already know we have. Not the capabilities of a new platform, not a vendor’s feature list. We look at the real pain points that experienced engineers encounter, and then ask what AI can actually do to address them.

One concrete example is service inbox management. When a team is handling a high volume of technical alerts and fault notifications simultaneously, important signals can get lost. An AI agent trained on historical service data and recommended actions can triage that inbox, surface the most critical issues and suggest next steps. The engineer still makes the call. The agent just makes sure nothing slips through.

That distinction matters. We are not looking to hand over complete autonomy to a system. We are looking to support the people who have the knowledge and the judgement to make the right decision.

PRINCIPLE

The engineer stays in the loop

This is probably the clearest principle we work to, and it applies across every sector we serve. A water utility needs to know that no AI system is adjusting pump settings or issuing operational commands without a qualified engineer reviewing and approving that action. A power transmission operator needs the same assurance about network switching decisions. The same logic applies in renewable energy and transport.

The value AI brings is in pattern recognition across large volumes of data, the kind of analysis that would otherwise either take too long or simply never happen.

We have enormous amounts of data coming from loggers and telemetry assets across our customer base. Much of it sits unanalysed. AI can surface what that data is telling us; it can point to where an issue is most likely to develop next, or where flood risk is building based on sensor readings and weather data. What it cannot do is replace the engineer who decides what to do with that information.

“If you do not have deep engineering judgement and critical thinking, you can go in completely the wrong direction with AI output. The tool is only as good as the person interpreting it.”

Brian Power, Head of Engineering, EMR Integrated Solutions

CONTEXT

45 years of context is an asset

There is something that experienced organisations in CNI bring to AI adoption that newer entrants simply cannot replicate: a repository of real-world knowledge about what goes wrong, what patterns recur, and what decisions have been made under pressure over decades.

That accumulated knowledge is what makes AI genuinely useful rather than just technically impressive. When our senior engineers look at the output from a predictive model, they are not reading it cold. They are reading it with the context of hundreds of similar situations. That is what allows them to validate the recommendation, challenge it if it does not sit right, or act on it with confidence.

We start with the problems that experience has taught us to look for. AI helps us get to the answers faster, or find answers that we would not have reached without it. The direction of travel is always from the problem, not from the technology.

45

Years of OT and infrastructure experience

6

Countries across Ireland, UK and Europe

4

Critical infrastructure sectors served

CUSTOMER RELATIONSHIPS

Building trust step by step

Our customers in the utility and energy sectors are rightly cautious about AI. Critical infrastructure is not a space where you experiment casually. The organisations responsible for water supply, flood defence, power distribution, and transport networks carry enormous obligations to the public.

The way we approach this with customers is step by step. We show them how a solution is structured, where the human checkpoints are, and what level of control the AI system does and does not have. We start with lower-risk, high-value applications before moving to anything more ambitious. The goal is to build confidence in the process, not to land a big promise and deliver it later.

An AI agent that communicates directly with a customer and makes operational recommendations without an engineer in the chain is not something we would implement. The engineer is always the point of contact. The AI is a guide for that engineer, not a replacement.

Water Utilities

Power Transmission

Renewable Energy

Transport & Maritime

Cybersecurity is part of the picture

Any discussion of AI in OT and CNI environments has to include cybersecurity. As AI agents become more capable, the question of how much system access they have becomes critical. The same security principles that govern how we design SCADA and telemetry systems apply to AI integrations. Limiting agent permissions, documenting decision chains, and maintaining clear audit trails are built-in requirements.

LOOKING AHEAD

What the next few years look like

Now
Service Triage
AI agents triaging fault queues and surfacing critical alerts, with engineers retaining sign-off on all actions.
Near Term
Predictive Maintenance
Pattern recognition across telemetry and logger data, flagging likely failures before they occur.
Further Ahead
Flood Prediction
Combining sensor data with external sources like weather feeds, particularly for water and transport customers.

We are building our own AI agents, starting with straightforward, well-defined use cases where we can demonstrate value clearly. Service triage is one early priority. Predictive maintenance, using the pattern recognition capability of AI across telemetry and logger data, is another. Flood prediction, combining sensor data with external sources like weather feeds, is an area where we can bring genuine additional value to customers, particularly in the water and transport sectors.

The pace of that progression will be determined by results, not ambition. We will upskill our engineers, invest in data literacy alongside technical expertise, and continue to approach each new application by asking what problem it solves rather than what the technology can do.

What’s the takeaway?

AI is a tool. A powerful one, when applied thoughtfully and responsibly, and backed by sound engineering judgement. After 45 years of working in environments where the consequences of getting things wrong are serious, we know that the best tools are the ones that make skilled people more effective, not the ones that try to work around them or replace them.

EMR Integrated Solutions delivers SCADA, telemetry, OT cybersecurity, and communications solutions for critical infrastructure across Ireland, the UK, and Europe. To find out more about our approach, contact us below.

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Controlled Autonomy

Problem-Led Approach

Human in the Loop

Engineering Judgement

Problems first. Tools second.

The engineer stays in the loop

45 years of context is an asset

45

6

4

Building trust step by step

Water Utilities

Power Transmission

Renewable Energy

Transport & Maritime

Cybersecurity is part of the picture

What the next few years look like

Now

Service Triage

Near Term

Predictive Maintenance

Further Ahead

Flood Prediction

What’s the takeaway?

Latest News

Our Contact Details

EMR Business Terms