Delivery Manager

Role Purpose

Own end-to-end delivery of the internal AI portfolio and the OneAI platform as the single front door for safe AI across the business. Accountable for prioritisation and predictable execution across platform capabilities and AI solutions, ensuring quality, operational readiness, measurable value, and adoption in partnership with Enablement and Governance.

Key Responsibilities

1. Portfolio Ownership and Prioritisation

• Own and maintain two connected backlogs:

  • OneAI platform and OneAI Labs, including publishing workflows, templates, governance enablement, telemetry, and UX

  • AI solutions delivery, including business use cases, automation, and integrations

• Drive prioritisation trade-offs between platform roadmap and solution delivery, escalating and aligning through the AI Council when required

• Maintain a single, transparent portfolio roadmap with clear milestones, dependencies, and outcomes

2. Delivery Leadership and Execution

• Lead delivery across multiple pods and streams, ensuring clear scope, timelines, and value-focused increments

• Establish and run delivery operating rhythms including planning, backlog refinement, sprint execution, demos, retrospectives, and release cadence

• Manage dependencies across platform, infrastructure, security, data, and enablement workstreams

• Ensure each solution is packaged for scale, with reusability via OneAI templates, patterns, and documentation

3. Platform and Operations Leadership

• Own OneAI product outcomes including usability, adoption, publishing throughput, and effectiveness as the single front door

• Ensure platform engineering follows enterprise standards including architecture, CI/CD, observability, and identity and permissions

• Run OneAI as a service, including support model, incident processes, release communications, and platform health reporting

• Ensure solutions and agents are publishable and discoverable through OneAI and Labs with appropriate guardrails

4. Quality, Safety, and Release Readiness

• Ensure initiatives meet release criteria including testing, evaluation, documentation, monitoring, runbooks, privacy and security controls, and rollout plans

• Partner with Responsible AI and Quality teams to embed quality gates, evaluations, regression testing, and safety reviews into the lifecycle

• Drive operational learnings back into delivery and platform practices through post-mortems, prevention backlog, and reliability improvements

5. Team and Capability Leadership

• Build, lead, and coach cross-functional teams including AI engineers, data scientists, solution architects, automation specialists, and platform engineers

• Line manage delivery and platform roles including hiring, performance management, and development

• Define clear ownership boundaries and handoffs across pods, platform, and federated partners

6. Stakeholder Management and Value Realisation

• Act as the primary delivery and platform point of contact for in-flight work with business and IT stakeholders

• Translate objectives into deliverable increments with success metrics and measurable outcomes

• Track value and adoption outcomes in partnership with Enablement, including training, communications, change management, and champion activation

Success Measures (KPIs)

Delivery Performance

• Delivery predictability including on-time milestones and sprint goal achievement

• Reduced unplanned work

• Time to value from intake to first usable release and production

Quality and Operational Readiness

• Defect leakage, rollback frequency, evaluation pass rates, incident rate

• Monitoring coverage, runbook completeness, supportability, and handover quality

OneAI Platform Outcomes

• Adoption metrics such as monthly active users and repeat usage

• Publishing throughput including time to publish and number of reusable templates, apps, and agents

• Platform health including availability, performance, incident trends, and cost signals

Stakeholder Outcomes

• Stakeholder satisfaction across business and IT

• Measurable value, savings, and reuse rates

Qualifications

Required Experience and Skills

• Proven leadership delivering software, data, or AI programmes across multiple parallel streams

• Strong agile delivery capability including backlog ownership, prioritisation, execution cadence, and dependency management

• Experience running production services with operational accountability such as SLOs, incident management, and release discipline

• Ability to work within architecture, security, privacy, and governance constraints while maintaining delivery speed

• Strong product judgement for platform trade-offs including roadmap decisions, usability, adoption, and service model

• Excellent communication skills including executive reporting, risk management, and stakeholder alignment

Nice to Have

• Experience delivering GenAI solutions, MLOps, or AI-enabled automation in enterprise environments

• Familiarity with Microsoft ecosystem including Azure, M365, Teams, and Copilot, and related integration patterns

• Experience implementing AI evaluation, safety, and compliance gates at scale

Working Style

• Pragmatic and outcome-driven with the ability to balance speed, reliability, and risk controls

• Comfortable operating in ambiguity, creating structure, and building repeatable delivery and platform playbooks

• Strong coach and people leader capable of scaling teams and improving delivery maturity