Centralize Existing Data

Pull data from every system it currently lives in — SCADA historian, Maximo, GIS, ILI vendors, financial systems, and the dozens of spreadsheets that fill the gaps between them. Stand up a centralized data warehouse where it all lands in a common schema.

Why this comes first: You can't analyze data that's scattered across 12 systems and 40 spreadsheets. Centralizing it is the prerequisite for everything else on this roadmap.

Clean Historical Data — With the Teams

Data cleaning isn't an IT task — it requires the people who created the data. Sit down with integrity engineers, operations, finance, and land teams to review what's there, flag what's wrong, and fill in what's missing. They know which spreadsheets are authoritative and which are stale copies.

Why this comes first: Cleaning data in a vacuum produces clean-looking garbage. The domain experts know the difference between a data error and a real anomaly.

Build Ingest Pipelines for Future Data

Centralized data is only useful if it stays current. Build automated ETL pipelines from every source system so new data flows into the warehouse without manual intervention. Every new dig report, alarm event, ILI run, and MoC change lands in the central system as it happens.

Why this comes first: A data warehouse that stops getting updated becomes another stale source. Automation ensures the foundation stays reliable.

Work with Groups on Pain Points

Meet with each operational group — integrity, operations, construction, finance, land — and ask what's broken. What decisions are they making without the data they need? What reports take hours to build? What information exists somewhere but never reaches them? These conversations shape the priority of everything that follows.

Why this comes first: Technology that doesn't solve real problems doesn't get adopted. The teams closest to the work define the roadmap — not the other way around.

AI Literacy Training — By Role

Start training employees on LLM-based tools — not a generic seminar, but role-specific sessions. An integrity engineer needs to understand how AI can assist with ILI analysis. An operator needs to understand confidence scoring and decision support. A project manager needs to know how to query data conversationally. Meet people where they are.

Why this comes first: AI adoption fails when the people who need it don't understand it. Training early means the workforce is ready when the tools arrive.

Find the Connections

With data centralized, relationships that were invisible in silos start to appear. Correlate ILI results with operating conditions. Connect alarm patterns to integrity events. Link dig history to financial outcomes. These connections are where the real value lives — and they've been hiding in plain sight across disconnected systems.

Right Data, Right People, Right Time

Identify where critical information exists but isn't reaching the people who need it. Build dashboards, alerts, and automated reports that surface the truths buried in the data — to the specific roles that need to act on them. An integrity engineer shouldn't have to ask operations for SCADA context. A construction lead shouldn't have to chase finance for budget status.

Alarm Rationalization

With alarm data flowing into the central system, run a systematic review and alignment — especially critical after acquisitions when two alarm philosophies collide. Root cause analysis for recurring alarms, priority discipline, performance monitoring per CSA Z662.

GIS Integration — Spatial Intelligence

All integrity data georeferenced on the pipeline centerline. ILI features, dig results, repair history, and operating exceedances visible as map layers. Field crews and engineers see the full picture on one map instead of cross-referencing spreadsheets.

Deeper Role-Specific LLM Training

Advance from AI literacy to hands-on competency. Integrity engineers training models on ILI data. Operators using AI-assisted anomaly detection in simulation environments. Finance teams querying operational data conversationally. Each role learns to use AI as a tool specific to their work — not a generic chatbot, but a purpose-built assistant.

Predictive Modeling of Pipeline Failure Points

Machine learning-driven corrosion growth modeling using historical ILI comparisons, operating conditions, soil data, and repair history. Risk scoring that combines probability, consequence, and real-time conditions to predict where the next failure is most likely — before it happens.

ML-Assisted ILI Feature Detection

CNN-based corrosion feature detection on inspection imagery. Multiple models classify anomaly types — metal loss, dents, cracking — reducing manual review time and improving consistency across high-volume dig seasons.

In-House AI Leak Detection

Machine learning layer alongside existing CPM systems. CNN and RNN architectures trained on your own SCADA data — pattern recognition across multiple variables, confidence scoring, operator decision support. Built internally so the data and the competitive advantage stay in-house.

"When we engage third-party vendors, we essentially hand over our valuable data, enabling them to build algorithms that they can then market to our competitors. The true value lies in the data itself."

Integrated Risk Platform

All data streams — integrity, operations, alarm, financial, GIS — feeding a unified risk model. Dynamic risk scoring that updates in real time as conditions change. The system that ties every earlier phase together into a single view of pipeline health.