Alberta Industry AI Roadmap for 2026

Across Alberta, industrial AI programs should be structured around operational outcomes, not isolated pilots. Practical programs align use cases, data readiness, governance, and rollout sequencing from the start.

Where Industrial AI Usually Fails

AI work breaks down when it is treated as a model project instead of an operating change. Plants need source data with context, clear ownership for outputs, a way for operators to challenge recommendations, and integration boundaries that keep advisory workflows separate from control execution.

Priority Use Cases

• Operations copilots for troubleshooting and shift turnover.
• Predictive maintenance on critical constrained assets.
• Quality prediction and vision inspection for high-scrap processes.

Data Readiness Checklist

• Enterprise asset model linking controls, maintenance, and production data.
• Baseline KPIs by plant and process unit before any model launch.
• Data quality ownership assigned by function, not by project team.

System Integration Pattern

• Adopt a hub-and-spoke AI architecture across sites.
• Standardize model monitoring, alerting, and retraining criteria.
• Package reusable templates for SCADA, historian, and CMMS integration.

Deployment Deliverables

• A scoped use-case definition that names the decision, user role, source systems, and acceptable failure behavior.
• A data readiness review covering historian quality, event context, asset hierarchy, permissions, and retention limits.
• A validation plan that starts with read-only or advisory output before any operational workflow depends on the model.
• A support model for drift review, user feedback, access control, audit logs, and post-incident learning.

Governance and OT Safety

• Create an AI steering group with operations and cybersecurity leads.
• Tier use cases by consequence and required assurance level.
• Publish quarterly control reviews and remediation actions.

KPIs to Track

• Portfolio ROI by use-case class
• Pilot-to-production conversion rate
• Operational incidents linked to AI output
• Model uptime and drift stability

30-60-90 Day Plan

• Day 1-30: choose portfolio use cases and define governance model.
• Day 31-60: launch 2-3 pilots with shared architecture standards.
• Day 61-90: scale proven patterns to additional sites.

Related Service Paths