About the Role
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. This 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.
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Key Accountabilities
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, and digital teams.
- Track milestones, manage interdependencies, remove barriers, and escalate 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.
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.
