Overview

In construction and engineering, the value of Artificial Intelligence comes from solving specific problems using existing data and integrating carefully into established workflows.

This 2-day intensive course provides clarity on how to apply AI in environments where conditions are constrained by safety requirements, fragmented data, and complex projects.

The curriculum focuses on systematic thinking for AI application where mistakes are costly and trust is paramount. Delegates do not need to be engineers of AI, but will learn how these systems behave, how to evaluate them, and how to deploy them responsibly. The goal is to enable decisions about AI that are grounded in evidence, practicality, and sound engineering judgment.

Why Should an Individual Attend?

• Identify Real Value: Move beyond abstract discussions to focus on what actually works and where AI adds measurable value in construction and engineering.
• Evaluate System Behaviour: Understand how AI systems behave to better evaluate their effectiveness in asset-intensive environments.
• Implement Responsibly: Learn practical frameworks for implementing AI while navigating real-world constraints like safety and human judgment.
• Strategic Road mapping: Gain the skills to select the right first use cases and build a structured AI adoption roadmap for your projects.

Outcomes

On completion of this course, the delegate will be able to:

• Foundational Clarity: Explain what AI really is, and what it is not, within the context of the engineering sector.
• Lifecycle Application: Identify value-add opportunities for AI across design, planning, construction, safety, and asset management.
• Data Strategy: Assess data readiness and the realities of using existing data for AI-driven insights.
• Governance & Leadership: Apply principles of risk, ethics, and governance while managing workforce impact and change.

Program Outline

Module 1: Foundations of AI in Engineering

• Demystifying AI: Reality vs. Hype in the construction sector.
• Identifying where AI adds value in construction and engineering.
• Overview of current AI tools and platforms relevant to the industry.

Module 2: AI Across the Lifecycle & Assets

• AI applications in design and strategic planning.
• Integrating AI into active construction phases and safety protocols.
• Leveraging AI for long-term asset management and maintenance.

Module 3: Data Readiness and Realities

• Assessing data quality and addressing fragmented data issues.
• Using "the data you already have" to solve specific engineering problems.
• Practical frameworks for data integration into existing workflows.

Module 4: Implementation and ROI

• Frameworks for implementing AI in real-world projects.
• Strategic selection: How to pick the right first use case.
• Defining and measuring ROI and success metrics.

Module 5: Governance, Ethics, and Leadership

• Managing AI-specific risks, ethics, and governance in engineering.
• Workforce impact: Managing change and the human-AI interface.
• Building a sustainable AI adoption roadmap.

Who Should Attend?

• Engineers & Architects: Technical professionals looking to enhance design and project delivery through AI.
• Project Managers & Planners: Those seeking to optimise scheduling, safety, and site operations.
• Executives & Business Leaders: Decision-makers responsible for organisational strategy and technology investment.
• Asset & Facilities Managers: Professionals focused on long-term asset performance and predictive maintenance.
• Transformation & Innovation Teams: Change agents tasked with deploying AI responsibly in engineering environments.
• Engineering & Construction Professionals: Those needing to identify and implement AI responsibly within project environments.
• Project Leads & Managers: Individuals responsible for making decisions grounded in evidence and engineering judgment.
• Asset Managers: Professionals managing asset-intensive environments looking for data-driven efficiencies.
• Strategic Leaders: Anyone responsible for building an AI adoption roadmap or managing organisational change.