McLaren Racing
Lead, AI Transformation (12 month FTC)
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Purpose of the Role
McLaren Racing is evolving its Commercial and Marketing operating model to become more data-driven, automated, and AI-enabled.
This role is responsible for defining and embedding AI across that future operating model.
Working in parallel with:
- Process Engineers (who define how the business should operate)
- Lead, Technology Transformation (who defines the core technology landscape)
the Lead, AI Transformation ensures that AI is designed into processes and systems from the outset, not retrofitted later.
The role focuses on identifying high-impact AI opportunities, shaping use cases, and ensuring practical adoption across workflows, decision-making, and automation.
You define how AI transforms how Commercial & Marketing operate.
You work alongside Process Engineers and Technology Transformation to embed AI into workflows, decisions, and automation.
You turn AI from concept into real, measurable operational advantage.
How This Role Works
- Process Engineers → define future ways of working
- Technology Transformation Lead → defines systems and platforms
- AI Transformation Lead → defines where and how AI drives step-change performance
Together, you will:
- Identify where AI removes manual effort and improves decision quality
- Embed AI into workflows, systems, and data flows
- Ensure AI is practical, scalable, and aligned to real operational needs
Key Responsibilities
AI Use Case Design
AI Strategy and Transformation Leadership
- Define the role of AI within the Commercial & Marketing operating model
- Identify where AI delivers the largest impact (efficiency, insight, automation)
- Shape a clear AI roadmap aligned to business priorities and technology strategy
- Bring a strong external perspective on AI capabilities, trends, and practical application
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AI Opportunity Identification
- Work alongside Process Engineers to analyse end-to-end processes
- Identify opportunities to apply AI across:
- Workflow automation
- Decision support
- Personalisation and customer insight
- Content generation and optimisation
- Prioritise use cases based on feasibility, impact, and readiness
- Translate business and process requirements into clear AI use cases
- Define:
- Input data requirements
- Expected outputs and business value
- Integration into workflows and systems
- Ensure use cases are:
- Practical and deliverable
- Measurable in terms of outcomes
- Aligned to existing and future technology landscape
Embedding AI into the Technology Landscape
- Work closely with the Technology Transformation Lead to:
- Ensure AI is integrated into system and platform design
- Define how AI capabilities interact with core data and application layers
- Ensure alignment with:
- Data platforms and identity layers
- Existing MarTech and workflow tooling
- Avoid point solutions—focus on scalable, integrated AI capability
Delivery & Adoption
- Support translation of AI use cases into delivery initiatives
- Work with delivery teams, vendors, and partners to validate feasibility
- Define adoption approaches, including:
- User engagement and training
- Workflow integration
- Change management considerations
- Ensure AI moves beyond pilots into real operational use
Governance, Risk & Responsible AI
- Ensure AI is implemented responsibly, including:
- Data usage considerations
- Output quality and reliability
- Transparency and trust in AI-driven decisions
- Define guardrails for how AI is used across Commercial & Marketing
- Work with broader McLaren initiatives to align on AI policy and governance
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Stakeholder Engagement
- Engage across Commercial, Marketing, and Technology Teams
- Translate complex AI concepts into clear business outcomes
- Provide structured recommendations to senior stakeholders on:
- Where to invest in AI
- What to prioritise
- What to avoid
Experience & Background
Essential
- Strong experience applying AI/ML, automation, or advanced analytics in business environments
- Background in digital or technology transformation (consulting or in-house)
- Experience translating business problems into data/AI-driven solutions
- Strong understanding of:
- Data platforms and pipelines
- AI/ML capabilities and limitations
- Workflow and system integration
- Experience working in complex, multi-system environments
Desirable
- Experience in marketing, commercial, or customer-facing domains
- Exposure to:
- GenAI tooling
- Personalisation and customer data platforms
- Content automation and optimisation
- Agentic AI
- Experience working alongside process transformation or operating model programmes
Capabilities & Behaviours
- Strong ability to bridge business, technology, and AI
- Commercial mindset—focused on value, not experimentation for its own sake
- Comfortable working in ambiguity and shaping direction
- Strong stakeholder influence across technical and non-technical teams
- Bias for action—focused on embedding AI into real workflows
- Collaborative approach within a multi-disciplinary transformation team
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