Head of Strategic Programme Management Office; SPMO
Listed on 2026-09-01
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Management
Change Management, AI Business & Operations
Head of Strategic Programme Management Office (SPMO) Gerrards Cross
Do you want to help shape software that affects thousands of lives?
Who are we?
We are ranked as the UK’s #1 construction specific software player and our mission is simple; to provide market leading end-to-end software solutions to the construction and construction like industries across the entire build life cycle.
If you are looking to build an exceptional career with an award-winning company you’ve come to the right place. Our teams are based in the UK, Europe, and India, working on products that are used on a global scale. We have a clear and defined road map to deliver over the next 3 years, which is centred around a large-scale digital transformation as well as continuing our growth and expansion.
We embrace diversity and equality and want our employees to be comfortable bringing their whole selves to work. We are committed to building a team with a variety of backgrounds, skills and views. Creating a culture of Equality isn’t just the right thing to do, it improves every aspect of our business.
PurposeThe Head of the Strategic Programme Management Office (SPMO) is accountable for enabling, governing, and scaling Causeway’s transition to an AI-native operating model, whilst maintaining delivery excellence and strategic accountability.
This is simultaneously a delivery governance role and a transformation leadership role. The SPMO ensures that every mechanism, process, and decision point serves two purposes: it must drive better outcomes and accelerate the shift to how we work as an AI-native organisation. The postholder will challenge governance that merely adds friction, and design frameworks that embed AI-first thinking into how Causeway defines, prioritises, and executes strategic work.
The guiding principle remains: if governance does not influence an outcome, we do not need it. But in an AI native environment, that principle takes on new meaning. Governance must enable rapid experimentation, support teams operating with AI copilots and agents, and manage the human change required to work differently at hypergrowth.
Key Responsibilities Strategic Leadership & Governance- Define and own the SPMO operating model, ensuring governance is outcome-focused, proportionate, and agile – and explicitly designed to support AI-native delivery patterns.
- Champion outcome-focused governance, regularly challenging and removing process that adds friction without value, especially as teams operate at increased velocity with AI tools.
- Act as a trusted adviser to the Executive and senior leadership on portfolio health, risks, priorities, and dependencies, with particular attention to transformation and change readiness in an AI-first context.
- Provide clear, accurate, and timely representation of the current state of the portfolio to inform strategic decision-making, using leading indicators of project health relevant to AI-augmented teams.
- Establish and maintain a consolidated view of all in-flight projects, including status, risk, resource, and financial position, with visibility into how AI tools are changing team velocity and resource requirements.
- Design reporting frameworks that surface learning velocity, experimentation cadence, and model/product health metrics – not just traditional stage gates and burn‑down charts.
- Lead regular portfolio reviews and governance forums, ensuring decisions are tracked and actions are executed, with governance models that scale as the organisation grows.
- Own and continuously improve the SPMO front door process to reflect AI-native delivery patterns: rapid small experiments, learning gates (not just kill decisions), and proportionate governance based on project uncertainty.
- Define and manage SLAs for project approvals and governance sign‑off that support rather than inhibit the velocity benefits of AI-augmented teams.
- Work with teams to establish success metrics for AI-native projects, including AI-specific risks (bias, hallucinations, model drift, data quality) and governance frameworks to manage them.
- Design and implement…
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