Lifting the coordination burden: how AI turns operational data into commercial intelligence

AI is transforming how asset-intensive organisations manage their operations – not just by reducing the coordination burden on engineering teams, but by turning operational data into the commercial decision intelligence that drives performance.

Energy and infrastructure businesses are being squeezed from every direction. They are managing growing fleets of increasingly diverse assets – wind, solar, storage, grid infrastructure – with workforces that aren't growing at the same pace. Markets are moving faster too: negative power pricing events, once rare, are becoming routine in renewables-heavy grids, and the window to make a good dispatch or curtailment decision can be minutes wide. Meanwhile, margin pressure is relentless. Operational inefficiency is no longer something organisations can quietly absorb. The coordination burden sitting at the heart of most asset-intensive organisations has a direct cost.

The hidden cost of coordination

The coordination burden is easy to underestimate because it doesn’t show up as a line item. But its effects are visible everywhere: slower response to emerging risks or operational issues, decisions affecting uptime, safety and overall asset performance made with incomplete information and teams that spend more time chasing data than acting on it.

The underlying problem is structural. Most asset-intensive organisations run on a tangle of IT and OT systems, each holding a partial view. Workflows don't connect, so handoffs are manual. Teams work from different versions of the truth. The problem isn't a lack of insight – it's that insight rarely translates into timely action.

The breakthrough: AI as a coordination layer

The solution lies in using AI not just as an analytics tool, but to manage the coordination layer itself. For example, an agentic AI system¹ can detect early signs of equipment wear, draw on historical maintenance data, alert the right engineers and propose a prioritised maintenance schedule – automatically and in time to make a difference. The coordination that previously required multiple people, multiple systems and significant manual effort happens in the background.

The commercial impact becomes clear when that coordination layer extends beyond operational systems into the commercial sphere. Consider a battery storage asset operating in a market with frequent negative pricing events. An AI coordination layer can simultaneously monitor state of charge, degradation data and real-time market signals – and translate that into a recommended dispatch decision that maximises revenue while protecting asset life.

Or take a regulated network business. AI that connects asset health data with regulatory reporting requirements can surface the maintenance investment decisions most likely to support an acceptable return on their regulated asset base.

In both cases, the value isn't just faster decisions – it's decisions made with a richer, more complete picture of what drives commercial performance. This is what 'decision intelligence' looks like in practice: operational insight and commercial context arriving together, in time to matter.

Freeing up engineering capacity to create value

None of this is about replacing engineers. It’s about giving them time back. When AI handles the coordination layer – connecting data, triggering workflows, routing information to the right people – engineers can focus on the decisions that genuinely need their judgement.

Take the example of an anomalous vibration signature on a critical tower, flagged by an overhead line monitoring system. Historically, this triggers a manual process: the operations engineer is notified, pulls the asset history, contacts the inspection team to check when it was last surveyed, reviews the maintenance backlog, assesses contractor availability and then makes a scheduling decision – typically over several days across multiple conversations.

With AI as a coordination layer, that sequence runs automatically. The anomaly triggers a check against the asset's full condition history, maintenance record and remaining life estimate. If the risk profile crosses a threshold, the relevant engineer receives a single alert with context already assembled – the trend, the history, the recommended action and the earliest contractor slot. The decision still belongs to the engineer. The coordination has already happened.

Where to begin: picking the right use cases

Not all decisions are equal and neither are the opportunities for AI to help. A useful starting point is to map your own operations across three tiers of decision type.

The first tier covers decisions that can be checked by machines. The answer is right or wrong, and a system can verify it. So, SCADA threshold breaches, BMS fault codes, warranty expiry tracking, scheduled maintenance due dates. AI already handles this well – most organisations would call it automation. It is valuable, but it is also the entry point, not the edge.

The second tier is where the real opportunity lies. These are decisions that require human expertise to validate but where AI can do significant heavy lifting first. Think root cause analysis on repeat faults, reviewing a commissioning plan against OEM specifications, interpreting degradation trends, validating contractor scope. AI doesn't replace engineering judgement here – it arrives with the groundwork already done, so that judgement is applied to a sharper, better-prepared question. For most organisations, this tier represents the best near-term return on AI investment.

The third tier is judgement dependent. Decisions such as when to extend a degraded asset versus taking a planned outage, how to allocate capital across a portfolio or how much operational risk to carry in volatile markets. AI can inform these decisions – surfacing relevant data, running scenarios – but the call itself belongs to experienced leaders. That's appropriate. The goal is not to automate judgement but to make sure it is never operating in an information vacuum.

A worthwhile exercise is mapping your own operations across these three tiers before committing to a use case. It quickly reveals where coordination pain is highest, where AI can add value fastest and where human expertise needs to remain firmly in the loop.

From data to coordinated action

The future of asset-intensive operations isn’t just about smarter machines or more sensors. It’s about operations where insight reliably leads to action – where the gap between knowing something and doing something about it is measured in minutes rather than days. That’s what AI embedded properly across the asset lifecycle makes possible.

The organisations that move deliberately now – identifying the right use cases, sequencing capability building and avoiding the trap of scattered AI experimentation – will compound those gains over time. Those that don't will find the gap increasingly difficult to close.

Getting there requires a partner willing to be honest: about where AI will genuinely help, where it won't and what needs fixing before any of it works. That means disciplined judgement rather than technology enthusiasm – identifying real decision opportunities, prioritising practical interventions and avoiding wasted spend. Good decision intelligence starts with strategic triage, not a technology roadmap.

Reframing the asset value chain

With AI as a coordination layer, it makes sense to look at the asset value chain differently. A clean, connected framework might look like this:

1. data and IT/OT foundations – reliable, structured and connected data sources
2. operational analytics – predictive and descriptive analysis to highlight patterns and risks
3. AI-driven insight – interpretation of structured and unstructured data into actionable knowledge
4. workflow orchestration – linking insights to action and automating coordination tasks
5. decision support – delivering recommendations where decisions are made
6. change and adoption – ensuring teams embrace AI and adapt processes
7. commercial value – faster, smarter decisions that improve reliability, reduce downtime and enhance overall performance.

This framework shows AI supports the shift from data-driven insight to coordinated action that drives real outcomes.

^{[1] Find out more about the different types of AI in our first blog in this series.}