Rising energy demand is forcing integrity teams to do more with the same (or fewer) resources.
Electricity demand, aging infrastructure, and expanding system complexity are increasing the scope and pressure on integrity programs, without a proportional increase in staff or time.
Most integrity automation fails because data isn’t ready—not because the engineering is wrong.
Fragmented, inconsistent, and unstructured integrity data makes automation brittle, undermines trust in results, and limits the effectiveness of ML and AI. Automation amplifies data problems if the foundation isn’t solid.
Unified integrity data is the prerequisite for scalable automation, ML, and AI.
When inspection, pipe, repair, and assessment data are validated, aligned, and structured to reflect how engineers work, automation becomes predictable, defensible, and scalable—unlocking faster assessments, better decisions, and truly predictive integrity programs.
As we enter a new year, the energy industry continues to scale and evolve, increasing both the scope and complexity of integrity work. (And what an exciting time it is!)
According to a recent report, “U.S. electricity demand is expected to grow by 25% by 2030 and by 78% by 2050, compared to 2023.”
“In a sudden shift from nearly two decades of low U.S. load growth, Americans are now demanding more electricity. The rapid growth of data centers to support AI technology, along with a wave of new manufacturing and oil and gas production, is causing a surge in industrial electricity demand
Global energy demand continues to rise, infrastructure is aging, and operators are being asked to deliver more capacity, reliability, and safety, often with the same or fewer resources.
The industry response is familiar: big data, AI, machine learning, agentic AI... insert the latest buzzword here.
But when you talk to pipeline integrity engineers, the people doing the work, the conversation sounds very different.
They aren’t asking for buzzwords. They’re asking practical questions:
We need faster assessments, less manual data prep, and fewer one-off workflows that only one person understands. Yet for many integrity programs, “automation” has been more frustrating than transformative.
Why?
Most integrity automation fails not because the logic is wrong, but because the data isn’t ready.
When it comes to machine learning and AI, automation on steroids, this problem only becomes more pronounced.
Automation amplifies inconsistency if the foundation isn’t solid.
For automation (from automated analyses to ML & AI models) to work, integrity data must be structured, related, and aligned in a way that reflects how pipelines are assessed and managed.
As discussed in Irth’s recent webinar on utilizing AI in Pipeline Integrity:
“You can’t just wave your wand, throw your [unstructured] data into AI and hope for the best.”
Across the industry, integrity teams are being asked to do more with constrained resources:
Automation can help. In practice, it often disappoints.
Scripts break. Models require constant babysitting. Assessment results need manual QA/QC before work can even begin. Instead of accelerating engineering, automation becomes fragile—and engineers lose confidence in it.
The root cause isn’t a lack of engineering rigor.
It’s fragmented, unstructured data.
Integrity data rarely lives in one place or one format. Most integrity teams are working with:
In these environments, automation doesn’t eliminate effort; it amplifies inconsistency.
Every script and workflow must account for exceptions, edge cases, and missing context. Over time, automation becomes brittle, tightly coupled to specific datasets, people, or assets.
The more fragmented the data, the more fragile the automation.
As discussed in Irth’s recent webinar on utilizing AI in Pipeline Integrity:
“That standardization process is fundamental to pretty much everything you would want to do downstream.
“Regularization and putting everything in a database and standardizing definitions are incredibly important for being able to quickly iterate on machine learning models.”
Do you want to utilize machine learning and AI more this year? The first step is validating, integrating, and aligning your data. Data Unification.
Unified integrity data does not mean dumping everything into a repository and hoping insights emerge.
It means integrating and aligning inspection, pipe, repair, anomaly, and assessment data into a consistent, governed structure that reflects how integrity engineers work.
When integrity data is unified, automation becomes possible in very concrete ways:
The list goes on.
Once data is unified, automation becomes predictable instead of brittle.
With unified data, the conversation changes. Scalable automation becomes feasible. Applying ML and AI to integrity programs stops being a pipe dream and becomes a practical tool that engineers can rely on.
Pipeline integrity teams can then focus on:
That shift is only possible once your integrity data is unified rather than a collection of siloed databases and fragile workflows.
The difference is subtle, but critical: Unified data supports automation at scale. ML and AI models become possible. Integrity decisions become better. Pipelines perform more safely.