Program Manager

The AI Program Manager is accountable for providing holistic visibility, planning discipline and Program execution control across AI initiatives within the CIO portfolio and across cross-delivery initiatives where AI capabilities, platforms, data, security, architecture, operations or business change are required. The role translates AI strategic intent into a practical, sequenced and governed Program roadmap, ensuring AI initiatives have clear scope, outcomes, ownership, delivery route, dependencies, risks, decision points, readiness criteria and measurable value. The AI Program Manager works closely with the Head of Delivery, Associate Director of Architecture, Strategy & Delivery, AI & Innovation Hub, CIO direction leads, CTO Product Engineering, Chief Strategy Officer stakeholders, IS Security, Architecture, Technology Service & Operations, Performance Intelligence and business stakeholders to ensure AI Programs are visible, controlled and aligned to enterprise priorities. The role owns the delivery governance and stakeholder coordination for AI Programs. It does not replace AI product ownership, model ownership, architecture authority, security approval, data governance or final AI strategy ownership; instead, it ensures these accountable owners are connected through a practical delivery model with clear decisions, evidence and follow-through. The role champions AI-first ways of working while maintaining human judgement, ownership and final decision-making. It ensures AI delivery practices are fast, transparent, governed, secure-by-design, value-oriented and scalable across CEE markets.

• Translate AI strategic priorities into a clear, sequenced and measurable AI Program roadmap across CIO and relevant cross-delivery initiatives.
• Work with the Head of Delivery, Associate Director of Architecture, Strategy & Delivery, AI & Innovation Hub Director and CIO to align AI initiatives to CIO strategy, business value, technical feasibility, funding visibility and delivery capacity.
• Ensure AI initiatives have defined outcomes, accountable owners, delivery route, roadmap milestones, value hypothesis, readiness criteria and governance checkpoints.
• Maintain an integrated view of AI strategy execution, including active initiatives, candidate initiatives, experiments, scale opportunities, dependencies, risks, decisions and benefits.
• Surface gaps, duplication, priority conflicts, capacity constraints or governance issues that could prevent AI strategy from moving into controlled delivery.
• Own delivery governance for AI Programs and AI-related initiatives, ensuring execution is structured, transparent and aligned to agreed CIO and enterprise priorities.
• Ensure each AI Program has clear scope, sponsor, product or business owner, delivery owner, architecture owner, security owner, data owner, milestones, RAID, dependency map, reporting cadence and closure criteria.
• Maintain delivery control across AI Programs by ensuring plans, risks, assumptions, issues, dependencies, decisions and actions are current and actively managed.
• Challenge unclear scope, weak ownership, unrealistic benefits, missing controls, unmanaged dependencies, unclear operating model or poor delivery discipline.
• Escalate delivery blockers, governance exceptions, unresolved decisions, risk acceptance needs or cross-direction conflicts before they materially affect outcomes.
• Maintain a single, trusted view of AI initiatives across CIO and relevant cross-functional portfolios, covering status, health, value, risk, dependencies, funding implications, governance stage and delivery confidence.
• Provide decision-grade reporting for the Head of Delivery, Associate Director of Architecture, Strategy & Delivery, CIO leadership and relevant governance forums.
• Ensure AI initiatives are represented consistently in portfolio tooling and reporting mechanisms, including initiative taxonomy, ownership, status, milestones, RAID and benefits information.
• Create visibility of AI experiments, pilots, scaling candidates and productionised solutions so leadership can distinguish between exploration, delivery and run-state obligations.
• Use portfolio insight to support prioritisation, sequencing, escalation and investment conversations.
• Coordinate the formal intake and qualification of AI ideas, use cases and initiatives, ensuring minimum information is captured before delivery commitments are made.
• Ensure intake considers strategic fit, business value, user impact, data availability, security and compliance implications, architecture fit, operational readiness, funding needs and delivery capacity.
• Work with AI & Innovation Hub, Architecture, IS Security, Performance Intelligence, TSO, CTO and CSO stakeholders to shape AI initiatives into deliverable options.
• Help separate experimentation, pilot, scaled delivery and operational change so governance is proportionate to risk, value and complexity.
• Support prioritisation discussions with clear evidence, options, assumptions, dependencies and trade-offs.
• Own stakeholder coordination for AI Programs across CIO, CTO, Chief Strategy Officer direction, AI & Innovation Hub, IS Security, Architecture, Technology Service & Operations, Performance Intelligence, Finance and business stakeholders.
• Establish stakeholder maps, governance forums, communication routes, decision logs and escalation paths for material AI initiatives.
• Ensure stakeholders have transparent visibility of Program progress, risks, dependencies, controls, decisions required, value assumptions and readiness impacts.
• Manage expectations proactively where scope, timing, funding, data readiness, security requirements, architecture decisions, vendor dependencies or business adoption plans change.
• Ensure escalations include clear context, impact, options, recommendation, decision owner and required timing.
• Ensure AI Programs are routed through appropriate architecture governance, security review, privacy and compliance checks, data readiness assessment and operational readiness planning.
• Coordinate with Architecture and IS Security so secure-by-design, resilient-by-design, scalable-by-design and operable-by-design principles are considered early in AI delivery.
• Track architecture decisions, security actions, compliance evidence, risk acceptance items, data dependencies, service transition requirements and support model readiness.
• Ensure AI solutions have clear ownership for monitoring, support, access management, incident response, change control, performance tracking and lifecycle management where applicable.
• Escalate unresolved architecture, security, data or service readiness issues that could block or weaken AI delivery outcomes.
• Coordinate the pathway from AI idea or proof of concept to pilot, scaled delivery and supportable operational solution where value is proven.
• Ensure scaling decisions are supported by evidence of value, feasibility, risk, cost, adoption readiness, operational impact and ownership.
• Define and track expected benefits, adoption indicators, operational improvements, quality metrics and realised outcomes for AI Programs.
• Coordinate post-implementation reviews and lessons learned to improve future AI delivery, governance and adoption practices.
• Ensure AI initiatives are closed or transitioned with clear ownership, documentation, support model, benefit tracking and follow-up actions.
• Use AI to improve Program planning, reporting, action tracking, meeting summaries, RAID analysis, dependency mapping, stakeholder communication and knowledge management.
• Build reusable AI-enabled templates, prompts and ways of working for AI Program governance, while ensuring outputs are checked, contextualised and owned by people.
• Challenge manual, repetitive or low-value Program activities through an AI-first improvement lens.
• Promote responsible AI use across Program routines: AI improves speed and quality, while judgement, ownership and final decisions remain with people.
• Share practical improvements, lessons learned and reusable patterns with ASD, Delivery Forum and relevant AI governance communities.

• Strong experience in Program management, transformation delivery, technology delivery, PMO governance or complex cross-functional delivery roles (required).
• Experience managing AI, automation, data, digital platform, cloud, security, product technology or technology transformation initiatives (required).
• Strong understanding of Program governance, roadmap planning, delivery lifecycle management, milestone planning, RAID, dependency management, reporting and escalation practices (required).
• Ability to translate strategic intent into practical delivery plans, decision points, ownership structures, measurable outcomes and governance cadences (required).
• Experience working across senior business, technology, security, architecture, operations, product, data, finance and executive stakeholders (required).
• Experience operating in matrix environments where multiple teams, sponsors, functional owners and delivery directions contribute to shared outcomes (required).
• Good understanding of AI delivery considerations, including experimentation, data readiness, security/privacy, operating model, adoption, benefits and scaling challenges (required).
• Ability to challenge unclear scope, weak ownership, unsupported benefits, unmanaged risk and poor delivery discipline in a constructive and professional way (required).
• Strong communication skills, with the ability to explain AI Program status, risks, options and decisions required to both senior leaders and delivery teams (required).
• Experience with portfolio, delivery or collaboration tooling such as Aha, Jira, Confluence, MS Project, Smartsheet or equivalent (required).
• Experience in gaming, betting, fintech, digital platforms, regulated technology or multi-market environments (preferred).
• Experience supporting AI governance, responsible AI adoption, AI tooling rollout, AI operating model implementation or AI transformation Programs (preferred).
• Experience coordinating proof-of-concept, pilot and scale-up lifecycles for AI, automation, analytics or platform capabilities (preferred).
• Experience working with architecture review, security review, privacy/compliance review, data governance and operational readiness gates (preferred).
• Experience with cloud, vendor management, procurement dependencies or technology cost visibility for AI or platform initiatives (preferred).
• Experience supporting executive reporting, quarterly planning, portfolio prioritisation, intake models, Lean Canvas or similar initiative qualification approaches (preferred).
• Experience using AI to improve Program management routines, reporting, action tracking, knowledge management or stakeholder communication (preferred).

• 30 days annual leave as standard.
• Excellent parental leave policy.
• Flexible working opportunities.
• Pension benefits.

Flutter is the world’s leading online sports betting and gaming company, operating some of the most innovative, diverse and distinctive brands in the sector such as FanDuel, Sky Betting and Sky Gaming, Paddy Power, PokerStars, Betfair, Sportsbet, Tombola, Adjarabet, Sisal, Snai, Betnacional, Junglee Games and MaxBet. We have an unparalleled portfolio of world-class brands, global scale and challenger mindset, through which we excite and entertain our customers, in a safe and sustainable way. Using our collective power, the Flutter Edge, we aim to disrupt our sector, learning from the past to create a better future for our customers, colleagues and communities.

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