The Dashboard Paradox

The true scarce commodity of the near future will be human attention.

Satya Nadella wrote that in Hit Refresh. Not long after, MIT economist David Autor put it even more plainly:

Data is plentiful. Attention is scarce, and we’ll never get more of it.

They were talking about the digital economy in general. But if you work in renewable energy asset management, this hits closer to home than most.

Dashboards were built to solve information overload.

Raw SCADA data was impossible to act on, so the industry visualised it. Charts, trend lines, KPI tiles, colour-coded status panels across screens showing the status of an entire wind portfolio. That felt like progress, and in many ways it was.

Except it wasn’t the end of the problem. It was a repackaging of it.

Dashboards were designed to bring clarity and reduce cognitive strain. And they did make data easier to access. But that’s precisely where the paradox kicks in. When data was hard to reach, people were selective about when they looked and what they looked for. Friction was a natural filter. Once dashboards removed that friction, the expectation shifted: now you should be checking, regularly, thoroughly, across everything. Easier access didn’t reduce the analytical burden. It expanded it. The tool that was supposed to free up your attention ended up consuming more of it.

This is the Dashboard Paradox: the prescribed cure became the new source of the disease.

Renewables make this worse than almost any other asset class.

A renewable portfolio is not one machine to watch. It’s dozens, often hundreds, each with its own performance, availability, and maintenance needs. None of them can be ignored, and none of them deserves less attention than the others.

Dashboards don’t change this reality. They just line up all assets on the same screen and ask you to find the problems yourself. It’s not a smarter way to manage assets. It’s a haystack with better graphics.

Digitising asset management was absolutely the right move.

Connecting turbines to SCADA, pulling data into platforms, and building visualisation layers were all necessary steps, and the industry was right to take them. But somewhere along the way, it stopped too soon.

Because after all that investment in data infrastructure, the fundamental question remains the same: who is watching? The answer is still a human being, with limited hours, finite focus, and an attention supply that simply cannot be scaled.

The problem with dashboards isn’t the dashboards themselves. It’s that they still depend on you to do the analysis.

You open the platform, scan the charts, and look for anomalies. You try to remember what normal looks like for WTG 34, benchmark it with WTG 35. You check yesterday’s numbers against last week’s and wonder if that dip in WTG 12’s performance is worth escalating or just a wind condition artefact. What about the recent temperature rise in WTG 5’s non-drive-end bearing? Should that alert you, or is it within normal range?

This isn’t decision-making. It’s treasure hunting. And sometimes there’s no treasure at all. Nothing is wrong, and you’ve just spent an hour confirming that.

Meanwhile, rack 47 of the storage system has been showing 82% efficiency at 80-100% state of charge (SOC) against 88% at 30-70% SOC for six days, possibly indicating lithium plating. No BMS alarm triggered. Just a quiet piece of evidence in the numbers, buried somewhere in a dashboard. Waiting. Whether you’ll find it is another question.

Machines don’t have an attention problem.

They don’t get tired, they don’t skip Tuesdays, and they don’t lose focus somewhere between WTG 43, inverter 14, and rack 22. A machine can watch thousands of assets simultaneously, every minute of every day, without ever questioning whether the effort is worth it.

This is precisely what machines should be doing: the searching, the monitoring, the pattern recognition across every data point, every turbine, every hour. The human role in this equation should be exactly one thing: acting on problems that have already been found.

This is what Kavaken does.

Kavaken’s machine learning models run continuously across your portfolio. When something needs your attention, they flag it. When nothing does, they stay quiet. And that silence is part of the product. The absence of a notification is Kavaken telling you that nothing requires you right now, so your focus can go somewhere more valuable.

When Kavaken does flag something, you’re not arriving at a blank screen trying to figure out where to look. You arrive at a clearly identified issue with the relevant data already surfaced. You know what you’re looking at, you know why it matters, and the needle has already been pulled from the haystack. What’s left is the part that actually requires human judgment: deciding what to do about it. And to support that decision, Kavaken surfaces recommendations for each case, drawing from more than 1,500 field-verified cases.

Even the follow-through gets lighter.

Say Kavaken flags a fault that needs OEM action. Normally that means drafting an email, describing the issue, pulling the supporting data, and attaching reports. Another block of your attention spent on logistics rather than the problem itself. On the flag page, there’s a ‘Send Email’ button. Press it, and KEN, Kavaken’s AI agent, drafts a clear, detailed message with the full flag report attached, ready to go to the OEM. You review it, send it, and move on. Your attention stayed where it belongs: on the decision, not the admin.

In the attention economy, attention is the only resource that genuinely doesn’t scale.

You can hire more people, buy more compute, and add more screens to the control room. None of it gives you more attention per expert.

The only rational strategy, then, is to spend that attention where it creates the most value. Not on searching for problems, but on understanding what a fault means for the next six months of generation, deciding whether to push for an earlier maintenance window, or explaining to the board why availability held up despite a difficult quarter. That’s where expert judgment is irreplaceable.

The watching, the scanning, the sifting through hundreds or thousands of turbines worth of data: that’s a machine’s job, and it always should have been.