Manager, Technology Planning & Governance

The AI Transformation Manager leads the refinery AI transformation agenda by identifying, prioritizing, orchestrating, and embedding AI and data-driven initiatives that deliver measurable business value. The role acts as the bridge between business functions and technical teams, ensuring that AI opportunities are converted into practical, scalable, and value-generating solutions.

The position drives the transformation pipeline across operations, maintenance, engineering, reliability, planning, energy, supply chain, Inspection and support functions by maintaining a robust use case portfolio, aligning stakeholders, enforcing delivery governance, and supporting adoption and scale-up.

A. AI Transformation Strategy & Roadmap

• Lead the refinery AI transformation agenda in alignment with business priorities and enterprise digital strategy.
• Translate strategic objectives into a structured AI transformation roadmap and prioritized portfolio.
• Identify high-value opportunities for AI, analytics, automation, and intelligent decision support across refinery functions.
• Support the transition from isolated pilots into scalable deployments with repeatable value delivery.

B. Use Case Identification & Prioritization

• Engage business stakeholders to identify pain points, opportunities, and transformation themes suitable for AI deployment.
• Evaluate opportunities using value, feasibility, data readiness, implementation effort, and risk criteria.
• Develop concept notes, business cases, initiative charters, KPIs, and expected-benefit logic for shortlisted opportunities.

C. Cross-Functional Delivery Leadership

• Orchestrate end-to-end delivery of AI initiatives in coordination with operations, maintenance, engineering, Digital teams.
• Track milestones, manage interdependencies, remove barriers, and elevate issues when required.
• Ensure initiatives progress through idea assessment, design, pilot, deployment, adoption and scale-up.

D. Value Delivery & Performance Management

• Define expected business outcomes and benefit-realization logic for each initiative.
• Monitor progress against value targets such as throughput, yield, energy, reliability, maintenance cost avoidance, HSE improvement, labor productivity, or margin uplift.
• Provide clear leadership reporting on initiative health, delivered value, forecast value, and implementation status.

E. Change Management & Adoption

• Drive stakeholder readiness and business alignment for AI-enabled changes in workflows, decisions, and operating models.
• Coordinate communication, awareness, training, and adoption actions required for sustained use.
• Help embed AI solutions into day-to-day operating and management routines rather than leaving them as stand-alone tools.

F. Governance, Standards & Risk Management

• Apply required AI governance, risk, compliance, and control processes for AI initiatives.
• Coordinate with cybersecurity, data governance, architecture, legal, procurement, and HSE stakeholders as needed.
• Support responsible, safe, and business-aligned use of AI in refinery environments.

G. External Collaboration & Vendor Management

• Support development of scopes of work, technical and business requirements, proposal evaluation, and vendor delivery monitoring.
• Ensure external partners remain aligned with refinery needs, data constraints, operational realities, and value expectations.

Internal Interfaces

• Refinery operations; maintenance and reliability; process engineering / technical services; planning and economics; energy management; HSE; IT / OT / cybersecurity; finance; procurement; and leadership forums.

External Interfaces

• Refinery operations; maintenance and reliability; process engineering / technical services; planning and economics;…