Regional Workplace Program Manager

Overall Role

The Regional Workplace Program Manager will lead the AI Fluency and Enablement Program and deliver AI-enabled workplace programs within Real Estate & Workplace (REW). The role combines workplace knowledge, program management and hands-on technical delivery to address operational needs across physical and digital environments.

Working closely with REW stakeholders, the successful candidate will build, test and deploy selected AI applications, automations and integrations using ChatGPT and other development tools. They will lead delivery from use-case definition through implementation and ongoing improvement, coordinating approved specialist support and enabling teams to own and maintain their solutions.

Duties & responsibilities

AI Fluency and Enablement Program management

• Own the program plan, rollout, stakeholder coordination, and ongoing learning rhythm. Align with program sponsors and regional counterparts to reuse proven materials and share builds across other regions.

• Support operational teams to complete Fieldwork, preferably through facilitated sessions using the REW ChatGPT Fluency Field Guide. Coordinate facilitators, learning time, and practical participant support through defined share-out sessions.

• Assess readiness and resolve account, approved tool-access, and training gaps with responsible parties. Agree support coverage and a review cadence with account and operations leaders adapt delivery to local service scope, language, time zone, and team maturity.

• Assess whether participants retain what they have learned and apply it in real workplace use cases. Capture reviewed examples showing the problem, output, and checks use learning feedback and delivery evidence to improve the program.

AI solutions and workplace automation

• Identify operational pain points and personally build selected AI applications, lightweight agents, and automations connecting approved tools such as Slack, service-request systems, dashboards, and internal platforms.

• Take solutions through requirements, prototyping, coding, integration, testing, deployment, documentation, and ongoing improvement. Involve qualified engineering support for complex production work.

• Partner with workplace teams and employees on useful experiences such as workplace assistants, meeting-room interactions, and wayfinding tools. Adapt proven solutions across sites where appropriate.

Hardware and systems integration

• Where an approved use case requires it, integrate software with workplace devices such as sensors, cameras, touch displays, and room-status hardware.

• Troubleshoot software and hardware interfaces with relevant teams and suppliers, including privacy, security, and operational review before deployment.

• Lead additional workplace programs that support the adoption of evolving OpenAI technology, including the evaluation and integration of emerging hardware and robotics where relevant and approved. Partner with responsible technology, security, privacy, safety, and operations teams on readiness and deployment.

Agent and workflow management

• Educate teams on the difference between a one-time prompt, a reusable skill, and an automated workflow. Define inputs, steps, outputs, checks, and stop rules before adding a trigger or agent.

• Coach teams on setting clear limits for delegated tasks, defining agent roles, and managing handoffs. Require approved sources and permissions, representative tests, human review, monitoring, escalation, and recovery proportionate to risk.

Quality and reusable capability

• Maintain an organised library of approved methods, skills, and tools with named owners, documentation, version history, and a feedback route. Reuse proven work where permissions and regional needs allow.

• Review workflow quality and learning gaps, communicate relevant feature changes, and coordinate with technology, data, and security specialists on integrations, source reliability, and production risks.

Program management and outcomes

• Maintain a prioritised roadmap with outcomes, milestones, dependencies, costs, and delivery owners. Communicate progress, unresolved risks, and blockers. Arrange approved specialist support with clear deliverables and acceptance criteria, retaining end-to-end delivery accountability within agreed authority.

• Confirm service-owner acceptance and assign named owners for maintenance, backup arrangements, support, and fallback procedures. Test failure paths before scaling. Track reliability, sustained adoption, and improvements in workplace experience or operational performance.

• Agree measures and baselines with relevant stakeholders. Use existing service records and representative, aggregated team evidence of accepted work, errors, corrections, reuse, and independent capability. Distinguish prototypes from dependable workflows and generated answers from completed physical work.

• Treat participation as an early adoption signal, not delivery evidence. Assess whether teams can define outcomes, test work, and know when to stop or escalate do not use individual prompt counts, logins, or AI usage quotas.

What You Will Need

• Typically 5+ years of relevant experience across software engineering, technical program delivery, automation, or AI enablement, including hands-on AI solution delivery and cross-functional adoption initiatives. Candidates with fewer years and strong evidence of equivalent impact are encouraged to apply.

• Demonstrated ability to build, test, and deploy useful technical solutions or reusable workflows, with clear documentation and evidence of your own contribution from concept through implementation.

• Practical use of AI-assisted coding tools including Codex, and the ability to review, test, and troubleshoot generated code. Proficiency in Python or another modern programming language will be advantageous.

• Working knowledge of APIs, integrations, version control, and software testing, plus product-management fundamentals including requirements, prioritisation, user testing, releases, and maintenance.

• Experience leading programs through planning, delivery, and follow-through, including stakeholder coordination, participant support, and outcome measurement.

• An understanding of workplace operations and employee experience, with the ability to translate user needs into practical requirements and intuitive tools.

• Clear communication, facilitation, and coaching skills, with evidence of helping non-technical colleagues sustain new ways of working after training. Able to support small-group and remote learning and explain technical choices plainly.

• Sound judgment about data quality, access, privacy, reliability, failure modes, human review, and AI limitations including the ability to recognise when engineering, security, or another specialist must take ownership.

• o Hardware integration, front-end development, dashboard design, and multi-site rollout experience would be useful. A relevant degree or equivalent practical experience is welcome.

Working Expectations

• Balance program leadership and participant enablement with hands-on development and technical problem-solving. Build shared ownership and team capability rather than dependence on one person.

• Work closely with regional and account leadership, workplace operations, learning and development, IT, data, security, privacy, engineering, service owners, and approved external partners.

• Follow required approvals and data-handling standards. Keep automation within approved scope, with human review and a practical fallback. Obtain appropriate permission and safeguards for sensitive process recordings.

• Start with capability and access assessment, a few recurring jobs, and learning and support routines. Demonstrate a tested workflow with an owner and acceptance evidence match coverage and resources to service demand before expanding.