Digital Innovation and Transformation

Artificial Intelligence (AI) in Real Estate Development and Facilities

Artificial intelligence is creating new opportunities across the real estate lifecycle, from development feasibility and investment analysis to building operations, maintenance, energy management, tenant experience, and portfolio decision-making. The challenge for real estate and facilities professionals is determining where AI can deliver measurable value and how it can be integrated with existing property data, building systems, and management processes.

5 days Classroom or live online English

Smart Horizon Certified - Global Leadership Consultants

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Download the full standard outline: why attend, methodology, objectives, audience, competencies, the daily outline and current plans.

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Why attend

From knowledge to real-world impact.

This Artificial Intelligence (AI) in Real Estate Development and Facilities course provides a practical business and operational perspective on applying AI, machine learning, generative AI, predictive analytics, computer vision, digital twins, and intelligent building technologies. Participants explore how AI can support site and development analysis, property portfolio management, predictive maintenance, space utilization, energy optimization, facilities management, and tenant services while addressing data quality, privacy, cybersecurity, governance, and implementation risks.

Workplace challenges

Professional Problems This Course Is Designed to Solve

The programme explores practical approaches to the following challenges. Results depend on how the learning is applied in your organisation.

Unclear priorities for AI investment across property development and facilities.
Fragmented building data that limits maintenance and energy decisions.
Difficulty evaluating property AI use cases, risks and implementation readiness.

Course methodology

How you will learn.

The programme combines instructor-led sessions, demonstrations, real estate and facilities case studies, AI-assisted analysis exercises, building operations scenarios, group workshops, and implementation planning. Participants examine practical applications across the property lifecycle and develop an AI use-case portfolio based on business value, data requirements, operational feasibility, risk, and measurable outcomes.

Course objectives

What you'll take away.

By the end of this course, participants will be able to:

Understand the role of AI across real estate development, property management, and facilities management
Identify high-value AI applications across the real estate and building lifecycle
Apply AI-assisted analysis to development feasibility and property decision-making
Understand predictive maintenance and intelligent asset management applications
Use occupancy and space data to support workplace and portfolio optimization
Evaluate AI opportunities for building energy and operational efficiency
Understand the role of IoT, building management systems, digital twins, and computer vision in intelligent facilities
Apply generative AI to facilities, property, and management workflows
Evaluate data, privacy, cybersecurity, governance, and implementation risks
Develop an actionable AI roadmap for real estate and facilities operations

Target audience

Who should attend.

This course is suitable for:

Real estate development managers
Property and asset managers
Facilities managers
Real estate investment professionals
Building operations managers
Property portfolio managers
Workplace and space management professionals
Project and development managers
Digital transformation and smart building professionals
Executives responsible for property and facilities strategies

Target competencies

Capabilities you will build.

Participants will develop competencies in:

AI in real estate
AI-enabled facilities management
Real estate analytics
Predictive maintenance
Smart building technologies
Space and occupancy analytics
Property data management
Digital twins and IoT
Generative AI for property operations
AI implementation and governance

Course outline

Your learning journey.

A focused, practical outline with time to explore, apply and reflect.

Day 1AI Applications Across the Real Estate and Facilities Lifecycle

09:00–09:45Understanding artificial intelligence, machine learning, generative AI, predictive analytics, and computer vision in a property context
09:45–10:30Mapping AI opportunities across real estate development, investment, asset management, property management, and facilities operations
10:30–11:00Identifying data sources including property systems, maintenance records, BMS, IoT sensors, occupancy systems, financial data, and external market information
11:00–11:30Coffee break
11:30–11:45Identifying data sources including property systems, maintenance records, BMS, IoT sensors, occupancy systems, financial data, and external market information (continued)
11:45–12:30Understanding how AI converts property and operational data into predictions, recommendations, automation, and decision support
12:30–13:15Evaluating AI use cases according to business value, data availability, implementation complexity, operational impact, and risk
13:15–14:00Practical exercise: Building an AI opportunity map for a real estate portfolio or facilities management operation

Day 2AI for Real Estate Development, Investment and Portfolio Decisions

09:00–09:45Applying AI-assisted research to market analysis, location assessment, property demand, and development opportunities
09:45–10:30Using predictive analytics and scenario modelling to support feasibility studies and development planning
10:30–11:00Applying AI to property portfolio analysis, asset performance monitoring, and investment decision support
11:00–11:30Coffee break
11:30–11:45Applying AI to property portfolio analysis, asset performance monitoring, and investment decision support (continued)
11:45–12:30Using generative AI to analyze development documents, reports, proposals, contracts, and project information
12:30–13:15Exploring AI applications in construction progress monitoring, project risk identification, cost information, and development reporting
13:15–14:00Case study: Evaluating a real estate development opportunity using AI-supported market, financial, operational, and risk information

Day 3Predictive Maintenance and Intelligent Facilities Management

09:00–09:45Moving from reactive and preventive maintenance toward predictive and condition-based maintenance strategies
09:45–10:30Using equipment history, sensor data, alarms, work orders, and failure patterns to identify maintenance requirements
10:30–11:00Understanding AI applications for HVAC, electrical systems, lifts, pumps, and other critical building assets
11:00–11:30Coffee break
11:30–11:45Understanding AI applications for HVAC, electrical systems, lifts, pumps, and other critical building assets (continued)
11:45–12:30Connecting AI analytics with Computerized Maintenance Management Systems (CMMS) and facilities management workflows
12:30–13:15Applying generative AI to maintenance records, work-order summaries, troubleshooting knowledge, and facilities documentation
13:15–14:00Practical workshop: Designing an AI-enabled predictive maintenance workflow for critical building equipment

Day 4Smart Buildings, Energy Optimization and Occupant Experience

09:00–09:45Integrating AI with Building Management Systems (BMS), IoT devices, smart meters, sensors, and connected building infrastructure
09:45–10:30Using occupancy analytics to improve space utilization, workplace planning, cleaning schedules, and building services
10:30–11:00Applying AI to energy consumption analysis, HVAC optimization, demand patterns, and building performance improvement
11:00–11:30Coffee break
11:30–11:45Applying AI to energy consumption analysis, HVAC optimization, demand patterns, and building performance improvement (continued)
11:45–12:30Understanding digital twins and their role in monitoring, simulation, asset information, and facilities decision-making
12:30–13:15Exploring computer vision and intelligent monitoring applications while considering privacy and appropriate controls
13:15–14:00Case study: Developing an AI-supported smart building strategy for energy efficiency, space utilization, and occupant experience

Day 5AI Governance and Implementation for Real Estate and Facilities

09:00–09:45Assessing data quality, interoperability, system integration, and information ownership before implementing AI solutions
09:45–10:30Managing privacy, cybersecurity, access controls, sensitive building information, and third-party technology risks
10:30–11:00Evaluating AI vendors and solutions based on business requirements, integration capability, scalability, transparency, and total cost
11:00–11:30Coffee break
11:30–11:45Evaluating AI vendors and solutions based on business requirements, integration capability, scalability, transparency, and total cost (continued)
11:45–12:30Establishing human oversight and validation for AI-generated predictions, recommendations, and operational decisions
12:30–13:15Measuring AI performance through maintenance cost, downtime, energy consumption, space utilization, service response, tenant experience, and return on investment
13:15–14:00Capstone exercise: Developing an AI Implementation Roadmap for Real Estate Development and Facilities covering priority use cases, required data, technology integration, governance, KPIs, and expected business value

The timings provided are estimates and may vary depending on the number of participants and the discussions taking place during the sessions. They are indicative only and do not constitute a binding commitment.

Dates & venues

Find a session that works for you.

Confirmed dates accept registration requests. Tentative dates are enquiry only.

Good to know

A few things to know.

How is the course delivered?

Our classroom programmes combine facilitated discussion, case studies, exercises and reflection. The precise format will be confirmed before enrolment.

Can you deliver this programme for our team?

Yes, you can enquire about a tailored programme. Share your goals, group size and preferred dates with our learning team.

Will I receive a certificate?

Participants who complete the programme receive a Smart Horizon certificate of attendance. Smart Horizon programmes are not externally accredited and do not award a professional qualification, licence or designation. Where a course title or outline refers to a professional certification (for example, AACE International's Certified Cost Professional), the programme supports learning and preparation only; the credential itself is awarded solely by the relevant professional body under its own application and examination requirements.

What should I confirm before booking travel?

Please confirm the session, exact venue, accessibility arrangements, fees and any visa requirements with the team before arranging travel. Tentative dates are not confirmed sessions.

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