More than half of industrial firms (57%, according to Verdantix’s 2026 Global Corporate Survey) plan to increase investment in decarbonization this year, up from 46% just twelve months ago. Energy management software is on the buy list for 53% of industrial organizations. Boardrooms are setting targets. Sustainability reports are getting thicker. Energy Management ambitions are genuine and growing.
And yet, the foundational infrastructure required to make any of it work is missing from most facilities.
Verdantix reports that only 6% of large corporates have extensively deployed smart meters and submeters. The other 94% are managing their energy programs on monthly utility bills, manual reads, and data that arrives weeks after the moment it describes.
This is the “94% problem.” It rarely surfaces in discussions about energy transformation because it isn’t exciting. Interval meters don’t generate headlines. Data infrastructure isn’t the best party conversation topic. But it belies every energy initiative that stalls, every AI investment that underperforms, and every decarbonization target that turns out to be harder to hit than the press release implied.
At facilities genuinely committed to managing energy better, the barrier is almost always simple measurement.
When your primary window into facility energy consumption is a monthly utility bill, you’re making decisions about multimillion-dollar systems based on data that’s already a month old, aggregated past the point of usefulness, and incapable of telling you what happened, let alone why. You know how much energy your facility used last month. You have no idea which compressor ran at full load all night because a setpoint was never adjusted from its commissioning default. You can’t see that your refrigeration system has been running colder than necessary since the last maintenance cycle. You have a consumption total, stripped of the context that would let you act on it.
The Verdantix survey puts a sharp edge on this: 1 in 4 corporate leaders report that data quality and accuracy issues arise “very frequently” when managing decarbonization information. One in four is practically every other management conversation. It’s the dominant experience, dressed up as an exception.
We live in a world where the quality of your data determines what’s possible in the future. 76% of industrial firms say poor or incomplete data is slowing their AI analytics projects. This matters because those same firms are counting on predictive analytics (cited by 90% as among their most impactful technologies) to drive operational improvements. They’re building the intelligence layer on top of a data foundation that isn’t ready yet.
Here’s the counterintuitive part: many companies are already spending more on energy management software. 53% of industrial firms plan to increase EMS spending in the next twelve months. That’s a real commitment.
But EMS software is only as good as the data it receives. If the underlying measurement infrastructure is monthly bills and manual entry, adding a software layer doesn’t fix the problem. It gives the problem a better interface.
Perhaps that’s why 85% of firms implementing industrial AI analytics projects struggle to measure ROI, and 82% cite insufficient in-house expertise as a barrier. And nearly a third of respondents remarkably still use pen and paper or spreadsheets for core operational activities like asset maintenance and capital allocation. These are companies increasing their software budgets while their operational data infrastructure remains a generation behind.
What typically happens when energy software gets deployed on inadequate metering infrastructure: the system produces dashboards. The dashboards show monthly totals with slightly better formatting than the utility bill. Alerts are configured but rarely actionable because the data resolution isn’t fine enough to diagnose a root cause. After twelve months, the platform generates an aggregate consumption trend. The one question that actually matters goes unanswered: what is this specific piece of equipment doing right now, and is that what it should be doing?
Interval metering at the asset and subsystem level changes the category of question you can ask.
Monthly billing data tells you what you spent. Fifteen-minute interval data tells you how your facility actually operates: which systems cycle when, which loads appear at off-hours, which setpoints have drifted from their targets, and which pieces of equipment are running when they shouldn’t be.
Across the facilities we work with, this resolution of data separates programs that find and capture savings from programs that identify opportunities and then watch them age in a spreadsheet. The implementation gap is almost always a visibility problem underneath. When you can see a system’s actual operating pattern in 15-minute increments, the case for fixing it stops being an audit finding and becomes something you can point to on a screen and track to resolution.
Consider what becomes visible at that resolution: a cooling system maintaining setpoints 5°F colder than the process requires, not because anyone decided that, but because the setpoints were established at commissioning and never revisited. A compressed air system where a compressor has been running in a pressure bleed-down configuration for over a year because the primary unit failed quietly and the workaround became the default. Refrigeration running on a schedule built for a production cycle that changed three years ago.
None of these show up in monthly consumption data. All of them become obvious and fixable when you can see the actual operating pattern.
One of the clearest disparities in the Verdantix data is the gap between where industrial firms want to be on data infrastructure and where they are. Less than a fifth have fully centralized all on-site industrial and enterprise data into a consistent, analyzable format. 46% use a mix of on-premises systems that push data to a centralized cloud. The rest are working with even more fragmented arrangements.
Firms are looking to scale AI quickly, and that ambition requires unifying operational data. But centralization doesn’t solve fragmentation if what’s being centralized is low-resolution data from systems that were never metered at the asset level. Customers wary of vendor lock-in want to ensure they own their data and can trust it; transparency and data portability matter as much as platform capability.
We help facilities build the measurement infrastructure that makes the software investment worthwhile, starting with understanding what’s actually running, what it’s consuming, and where actual performance diverges from expected performance. That measurement layer is what makes the analytics layer, the reporting layer, and the decarbonization program function as intended rather than as a well-funded symbol of good intentions.
The facilities that consistently find and capture energy savings can see their operations at the resolution required to make decisions. They’re not waiting for the monthly bill to understand whether last week’s process change had the intended effect. They’re not relying on operator memory to explain why consumption spiked on Thursday. They have good data, and it’s actionable.
Good data doesn’t require the most sophisticated technology platform. It requires the right measurement foundation underneath whatever platform you’re using. And it requires treating that foundation as the prerequisite, not the afterthought.
You need good data before you need good analytics. You need good meters before you need good software. This sequencing isn’t a limitation; it’s what makes the investment in everything that follows actually pay off. When facilities skip this step, they find out the hard way, after publishing a decarbonization target that the data infrastructure can’t support.
57% of industrial firms are increasing decarbonization investment in 2026. 38% have identified reducing energy consumption at the asset and plant level as a high priority. Both signals point in the right direction. The programs that deliver on them are the ones that build data quality into the foundation first.
The 94% have some real work ahead. We help them start with the measurements that make everything else possible and the expertise to follow through.
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