Manager, Technology Planning & Governance

ADNOC Group; Abu Dhabi; On-site; AED 600,000 - 900,000; Full time

Job summary

ADNOC Group is seeking an AI Transformation Manager to drive the refinery AI agenda, identifying and prioritizing AI opportunities, and embedding scalable, value-generating solutions across operations and engineering. You will bridge business and technical teams, align stakeholders, and govern delivery through a robust use-case portfolio.

The role champions adoption, oversees the transformation pipeline, and ensures measurable improvements in throughput, reliability, and energy efficiency while

Qualifications

• Bachelor’s degree in a relevant engineering or data discipline.
• 8–12 years of experience in refinery/oil & gas or industrial transformation environments.
• Experience in digital transformation and AI use-case delivery.
• Strong stakeholder engagement and business case development.
• Knowledge of OT/IT governance and data governance.

Responsibilities

• Lead AI transformation strategy and roadmap for refinery.
• Orchestrate end-to-end delivery of AI initiatives with operations, maintenance, and digital teams.
• Define KPIs and expected-benefit logic for shortlisted opportunities.
• Track milestones and manage interdependencies to ensure value delivery.
• Coordinate governance, risk, and change-management activities for AI deployments.

Skills

Stakeholder management

Cross-functional leadership

Change management

Value realization tracking

Education

Bachelor’s degree in Chemical Engineering / Data Science / Computer Science / Industrial Engineering

Master’s degree or transformation/AI certification desirable

Tools

ERP systems

CMMS

Planning tools

Historians

Job description

Job Purpose

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.

Job Purpose

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.

Key Accountabilities

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

Role Interfaces and Delivery Context

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; energy management; HSE; IT / OT / cybersecurity; finance; procurement; and leadership forums.

Typical Initiative Themes

• Process optimization, AI-enabled decision support, predictive analytics, operator assistance, energy optimization, inspection / robotics, anomaly detection, and value-tracking programs

Qualifications And Experience

Minimum Qualifications

• Bachelor’s degree in Chemical engineering, Data Science, Computer Science, Industrial Engineering, or a related discipline.
• Preferred background in chemical, process, instrumentation, electrical, mechanical, or industrial engineering.
• Master’s degree, transformation certification, project-management certification, or AI / analytics certification is advantageous.

Experience Requirements

• Typically 8–12 years of relevant experience in refinery, oil and gas, petrochemicals, energy, or industrial transformation environments.
• Demonstrated experience in digital transformation, AI or analytics use case delivery, operations improvement, or technology implementation in industrial settings.
• Strong experience in stakeholder engagement, business case development, cross-functional coordination, and value realization tracking.

Technical Knowledge

• Working knowledge of refinery operations, value levers, and performance drivers.
• Understanding of AI, analytics, machine learning, and industrial digital use cases from ideation through scale-up.
• Awareness of business case methods, benefit tracking, change management, data governance, and OT / IT deployment considerations.
• Familiarity with historians, APC, ERP, CMMS, planning tools, and performance reporting systems is preferred.