Project Lead, AI Transformation & Enablement

New York

Headquartered in New York with a nationally-scaled platform, RXR is a 450+ person, vertically integrated investment manager with expertise in a wide array of value creation activities, including acquisitions, asset and portfolio management, property operations, development, construction, leasing, and technological innovation. RXR is an active investor in real estate credit, rental housing, commercial property, and property technology through value-added and opportunistic investment strategies.

Project Lead, AI Transformation & Enablement is a cornerstone role within RXR’s AI-First transformation, embedding AI into the daily fabric of how our teams operate. Acting as translator, architect, and orchestrator, this person partners with business units to assess current processes, identifies opportunities for automation and augmentation, builds AI solutions that deliver measurable value, and monitors ongoing adoption, operationalization, and value measurement.

This role requires proficiency with enterprise AI platforms (such as ChatGPT and other no/low-code automation tools), the ability to design and prototype human-centered AI workflows, expertise in structured problem framing, prompt design, and responsible AI practices, and experience in transformative change management with non-technical stakeholders.

• DISCOVER:
  Workflow Assessment & Opportunity Discovery
  • Conduct structured workflow audits with key business unit stakeholders using a problem-first canvas, mapping current-state processes to identify inefficiencies, decision points, and human-in-the-loop needs.
  • Translate ambiguous business objectives into clear problem statements, system requirements, and success metrics (such as opportunity size, ROI, and level of effort).
  • Maintain an intake pipeline of AI opportunities; evaluate and prioritize using a standardized scoring framework (impact, effort, risk, adoption readiness).
• DESIGN: AI Solution Design & Prototyping
  • Define criteria for measuring the success of prioritized use cases (both pilot and post-deployment).
  • Design AI-first future-state processes for these prioritized use cases and validate with key business unit stakeholders. Determine ability to build these processes using no-code/low-code workflows (e.g., ChatGPT Enterprise paired with MCP servers) or if custom code built by the software & data engineering team is required.
  • For no-code/low-code prioritized use cases, build prototype use cases by developing structured prompts and scenario tests (clearly defining context, inputs, outputs, constraints) to validate via proof-of-concept. For custom code prioritized use cases, work with software & data engineering team to design specifications.
  • Pressure-test and iteratively refine pilots into final deliverables with business leads to ensure reliability, accuracy, and "last-mile" integration into daily operations.
• DELIVER:
  Value Realization & Performance Measurement
  • Measure post-deployment impact through a clear ROI methodology, partnering with finance and business leads to quantify time savings, cost avoidance, risk reduction, or efficiency gains.
  • Identify trends, gaps, and issues in results to continuously improve how AI workflows operate.
  • Report regularly to the AI Advisory Council on performance, adoption, and value creation.
• DRIVE:
  Organizational Change Management & Enablement
  • Act as an AI evangelist, leading live demos, workshops, and user testing sessions to help teams understand how, why, and when to use AI.
  • Identify and mentor departmental AI Champions as part of RXR’s hub-and-spoke AI model.
  • Develop and maintain process documentation, prompt libraries, and training content as part of RXR’s AI Acumen Program.
  • Collect user feedback and adoption data to ensure all efforts are optimized.
  • Ensure alignment with RXR’s standards of Responsible AI and Governance.

• CROSS-CUTTING
  • AI Fluency: Strong understanding of AI platforms (e.g., ChatGPT), prompt engineering, and a broad and diverse background of experimentation with automation tools.
  • Responsible AI Judgment: Awareness of fairness, risk, and governance implications in AI deployment.
• DISCOVERY
  • Human-Centered…