[Hiring] AI Product Owner @Morgan Sindall Construction

Role Description

We are looking for an experienced and talented AI Product Owner to assist the Technology & Transformation function. You will own the lifecycle and value delivery of artificial intelligence products and services, from discovery and prioritisation through development, deployment, adoption, continuous improvement and retirement. You will translate business opportunities into clear product outcomes, roadmaps and prioritised backlogs. Lead cross-functional delivery while ensuring that AI solutions are useful, secure, ethical, compliant, supportable and aligned to organisational strategy.

This position is office/home based, with flexibility for regular remote working.

About the role:

• Product strategy and lifecycle
  • Define and communicate product vision, intended outcomes, target users, value measures and strategic roadmap.
  • Own the product through discovery, development, launch, operation, improvement and retirement, balancing value, feasibility, risk and total cost of ownership.
  • Identify where new AI products or changes to existing products can create value, improve services or address operational challenges.
  • Prioritise investment and delivery across the product or product family, making transparent decisions based on evidence and agreed objectives.
• Discovery, requirements and backlog
  • Lead discovery with users and stakeholders to define the problem before selecting a technology or model.
  • Translate business needs into outcomes, epics, user stories, non-functional requirements and clear acceptance criteria.
  • Own and order the backlog; lead refinement, planning and review activities; maintain sufficient detail for effective delivery without constraining technical design.
  • Define minimum viable products, experiments and release increments that enable rapid learning and controlled scaling.
• AI, data and technical readiness
  • Work with data, architecture, security, engineering and subject-matter specialists to assess solution options, constraints and dependencies.
  • Work with the Data Governance Lead and data owners to ensure data availability, quality, provenance, permissions and lifecycle requirements are understood and addressed.
  • Understand the practical implications of model choice, prompts, retrieval, integrations, APIs, automation and human oversight sufficiently to make informed product decisions.
  • Ensure solutions have clear operational ownership, support arrangements, documentation, version control and change management.
• Responsible AI and governance
  • Embed privacy, security, fairness, transparency, accessibility, safety and accountability throughout discovery, design, delivery and operation.
  • Coordinate proportionate risk, legal, ethical, data protection, security and architecture reviews, ensuring decisions and controls are documented.
  • Define appropriate human oversight, escalation, auditability and user guidance, particularly where outputs could materially affect people, customers or business decisions.
  • Monitor emerging risks, regulatory obligations and organisational standards, adapting the product and its controls where required.
• Delivery and stakeholder leadership
  • Lead multidisciplinary teams and suppliers towards agreed outcomes, creating clarity on priorities, decisions, roles and dependencies.
  • Build trusted relationships with senior stakeholders, users and governance bodies; explain AI opportunities, limitations and trade-offs in clear business language.
  • Manage delivery risks, issues, assumptions, dependencies, budgets and changes within the relevant governance framework.
  • Promote collaboration, constructive challenge, learning and continuous improvement across product and delivery communities.
• Adoption, performance and value
  • Define measurable success criteria covering user outcomes, adoption, service performance, quality, cost, benefits and risk.
  • Use qualitative and quantitative evidence to assess performance, including user feedback and relevant model measures such as accuracy, reliability, drift or unsupported outputs.
  • Plan communications, training, onboarding and business change needed for adoption and responsible use.
  • Recommend continuation, scaling, redesign or retirement based on evidence, realised value and ongoing risk.

Qualifications

• Substantial experience in product ownership, product management, business analysis or technology-enabled change, including responsibility for prioritisation and outcomes.
• Demonstrable experience delivering AI, machine learning, advanced analytics, automation or data-led products into operational use.
• Practical understanding of AI/ML concepts, generative AI, large language models, prompt and retrieval approaches, data pipelines, APIs, integrations, model limitations and lifecycle risks.
• Strong knowledge of agile and iterative delivery methods, product discovery, backlog management, user-centred design and benefits realisation.
• Ability to work effectively with technical specialists while communicating complex concepts, uncertainty and trade-offs to non-technical and senior audiences.
• Knowledge of privacy, security, accessibility, data governance and responsible AI considerations relevant to digital product delivery.

Requirements

• Product ownership, product management or agile certification, for example PSPO, CSPO or equivalent experience.
• Relevant degree or equivalent professional experience in technology, business, data, engineering or a related discipline.
• Experience with Microsoft Copilot, Copilot Studio, Azure AI, Azure OpenAI or comparable enterprise AI platforms.
• Knowledge of the construction or built-environment sector and the operational context in which AI products will be adopted.

Benefits

• Our culture is inclusive, supportive and team focused.
• We take pride in longstanding and committed relationships with our supply chain partners.
• We celebrate our diverse teams who make Morgan Sindall Construction the inclusive, collaborative and rewarding place it is to work.