AI Product Owner

Great British Energy - Nuclear; Warrington; On-site; GBP 70,000 - 100,000; Full time

Job summary

Great British Energy – Nuclear is seeking an AI Product Owner to identify, prioritise and mature AI-enabled use cases that deliver measurable business value. The role links business needs with technical delivery and supplier capabilities, ensuring opportunities become tangible products and services.

The postholder will manage the AI backlog, build business cases, support investment decisions and ensure alignment with strategic objectives, value, and readiness while collaborating with

Qualifications

• Experience delivering AI, data or digital initiatives in complex environments.
• Ability to identify, prioritise and deliver AI/use case projects beyond proof-of-concept.
• Strong understanding of AI governance, risk and ethics in public/regulated sectors.

Responsibilities

• Identify and prioritise AI use cases delivering measurable business value.
• Own and manage the AI backlog and define requirements and acceptance criteria.
• Lead experimentation, pilots and proof-of-concept activities with clear success criteria.
• Monitor benefits realisation and ensure ongoing value delivery for AI investments.
• Promote governance, assurance and responsible AI practices across initiatives.

Skills

AI product management

Stakeholder engagement

Agile delivery

Backlog management

Requirement analysis

Product ownership

Leadership

Education

Degree in Data Science / Computer Science / Engineering

Postgraduate qualification in Data Science / AI

Professional cert in Product Management / Agile

Tools

Microsoft Copilot

Azure AI Services

Azure Machine Learning

OpenAI

Databricks

Job description

Originally named, Great British Nuclear, Great British Energy Nuclear is an arm’s length body of the Department for Energy Security and Net Zero, dedicated to supporting the development and deployment of new nuclear technologies in Great Britain. We play acrucial role in advancing nuclear new build, ensuring the UK’s energy security and achieving net-zero carbon emissions. Great British Energy – Nuclear focuses on fostering innovation, facilitating investment, and coordinating efforts across the nuclear industry to build a resilient and sustainable energy future.

Great British Energy – Nuclear’s first step was to start the technology selection process for Small Modular Reactors (SMRs) in 2023. SMRs can potentially be quicker to deploy and less expensive to build than traditional nuclear power plants because they are smaller, have factory-based modular manufacturing and more flexible deployment options. In June 2025, Great British Energy – Nuclear announced that Rolls-Royce SMR had been selected as the preferred bidder to build the UK’s first SMRs, following a technology selection process that began in 2023.

Great British Energy – Nuclear is aiming to deliver fast, based on a supportive and collaborative culture which values equality and diversity and creates an inclusive workplace. We draw on deep nuclear expertise – our Executive Committee has over 100 years of nuclear experience at home and abroad. GBE-N will unlock billions of pounds of private and public investment from design to operation, helping to get sites ready for development, and working to grow manufacturing capacity and skills capability.

Our activities will be driven by our values, which are:

Trust – We prioritise safety, we act responsibly and with integrity.

Collaboration – We work as a team; we value diversity and expertise.

Challenge – We are curious and courageous in the way we think and act.

Care – We are thoughtful, inclusive and respectful of others.

Drive – We get things done and we make a difference.

If you have a disability and would prefer to apply in a different format or would like us to make reasonable adjustments to enable you to apply or attend an interview, please contact us at [e-mail] and we will talk to you about how we can assist.

Role Description

The AI Product Owner is responsible for identifying, assessing, prioritising and maturing AI-enabled use cases that deliver measurable business value for GBE-N. The role provides the link between business needs, operational challenges, technical delivery and supplier capabilities, ensuring opportunities are translated into deliverable products, pilots and services.

The postholder will own and manage the AI use case backlog, developing business cases, supporting investment decisions, balancing competing priorities and ensuring delivery activity remains aligned to strategic objectives, business value and organisational readiness.

Working closely with stakeholders, SMEs, suppliers and technical teams, the role will define requirements, coordinate delivery, support adoption and track benefits realisation, ensuring AI initiatives progress effectively from concept through to implementation.

As GBE-N develops its AI capability, the role will also help establish and promote effective Product Ownership practices, supporting consistent approaches to prioritisation, requirement definition, value measurement and continuous improvement whilst remaining a hands‑on practitioner responsible for day‑to‑day product ownership and delivery activities.

Key Responsibilities

Use Case Identification & Prioritisation. Develop and maintain the organisation's AI use case pipeline. Facilitate workshops to identify business opportunities, challenges and improvement areas. Own and maintain the AI use case backlog, balancing business value, strategic objectives, delivery constraints, risks and organisational readiness to ensure resources are directed towards the highest-value opportunities.

Product Ownership & Delivery. Define requirements, user stories and acceptance criteria. Act as Product Owner for approved AI products and services. Coordinate delivery activities across internal teams and suppliers. Ensure solutions remain aligned to business objectives. Support operational adoption and change management activities. Help develop GBE-N's Product Ownership capability by promoting effective approaches to backlog management, prioritisation, requirement definition, benefits tracking and stakeholder engagement across AI initiatives.

Experimentation & Innovation. Lead proof‑of‑concept and pilot activities. Define success criteria and evaluation measures. Capture evidence, lessons learned and recommendations. Support decisions regarding progression, scaling or retirement of solutions.

Benefits Realisation. Define expected outcomes and success criteria. Monitor adoption, utilisation and realised benefits. Track value delivery and report progress to stakeholders. Ensure AI investments continue to deliver measurable outcomes.

Governance & Assurance. Ensure AI initiatives comply with organisational policies and governance requirements. Support AI assurance, risk management and ethical review processes. Promote responsible use of AI technologies.

Knowledge, Skills and Experience Required

AI Product Management & Delivery

• Proven experience applying AI technologies to solve real business problems and delivering measurable outcomes through experimentation, pilot deployment and operational implementation.
• Demonstrable experience identifying, prioritising and delivering AI, advanced analytics or data‑driven solutions that have progressed beyond proof‑of‑concept into operational use.
• Demonstrable interest in emerging AI technologies, industry developments and practical application of AI within complex business environments.
• Experience managing a portfolio of digital, data or AI initiatives across multiple business stakeholders.
• Proven ability to define business problems, assess value opportunities and develop compelling business cases for investment.
• Experience operating as a Product Owner or Product Manager within an agile, iterative or product‑based delivery environment.
• Experience establishing or improving product management and backlog management practices within a developing or growing organisation would be advantageous.

AI & Data Expertise

• Strong understanding of AI, Machine Learning, Generative AI and emerging AI technologies.
• Practical experience applying AI techniques to solve business challenges, including experimentation, pilot delivery and operational deployment.
• Understanding of the data, metadata and information management foundations required to support successful AI adoption.
• Familiarity with AI development lifecycles including problem definition, data preparation, model development, evaluation, deployment and monitoring.
• Ability to assess the feasibility, risks and limitations of AI solutions and make informed investment recommendations.

Stakeholder & Delivery Leadership

• Strong stakeholder engagement, facilitation and influencing skills.
• Ability to communicate technical concepts clearly to non‑technical audiences.
• Experience operating across business, technology and supplier communities.
• Excellent organisational and prioritisation skills with the ability to manage multiple initiatives simultaneously.
• Comfortable operating as a hands‑on practitioner, balancing strategic thinking with detailed delivery management and stakeholder coordination.

Governance & Responsible AI

• Understanding of AI governance, assurance, data protection and ethical considerations associated with AI deployment.
• Experience balancing innovation with governance, security and risk management requirements.
• Hands‑on experience with AI platforms and technologies such as Microsoft Copilot, Azure AI Services, Azure Machine Learning, OpenAI, Databricks or equivalent.
• Experience working with Large Language Models (LLMs), Retrieval Augmented Generation (RAG), Agentic AI or Knowledge Management solutions.
• Experience delivering AI solutions within regulated, safety‑critical or public sector environments.
• Familiarity with Agile, Scrum or product management frameworks.
• Understanding of data architecture, data engineering and enterprise data management principles.
• Knowledge of AI assurance frameworks, model evaluation techniques and responsible AI standards.
• Experience managing supplier‑delivered AI solutions and innovation partnerships.

Qualifications

• Degree‑level qualification (or equivalent professional experience) in a relevant discipline such as Data Science, Computer Science, Engineering, Mathematics, Information Management, Business, Digital Technology or a related field.
• Demonstrable professional experience delivering AI, data and digital transformation initiatives.
• Evidence of continuing professional development in Artificial Intelligence, Data, Product Management or related disciplines.
• Professional certification in Product Management, Agile, Scrum or equivalent delivery methodologies.
• Microsoft, AWS, Google and other recognised certifications in Artificial Intelligence, Data Science, Machine Learning or Cloud technologies.
• Postgraduate qualification in Data Science, Artificial Intelligence, Analytics, Engineering, Technology Management or a related discipline.
• Certification or training in Responsible AI, AI Governance or Data Governance.