Day One

• Unpacking lessons from assessing and applying computational tools in data center design, focusing on matching complexity with our team’s capacity
• Accelerating planning and improving efficiency in complex projects, such as data centers, using computational design tools
• Achieving faster option generation, clearer evaluation of alternatives, and more consistent decision-making for large-scale projects

• Establishing clear success metrics, such as usage rates, time savings and user feedback, to help quantify the impact of new tools
• Building a scalable evaluation framework for assessing tool performance across different teams and projects to ensure informed decisions for scale-up
• Taking lessons learned from previous adoption successes and failures to enhance your tool selection framework in the future

A prime chance to make the most of in-person networking, and forge new connections as new companies enter and existing ones broaden their presence within the AEC design space. Designed to maximize your introduction to numerous new individuals and serve as a catalyst for ongoing discussions during the summit.

• Re-organizing your internal resources and team to demonstrate readiness and reduce perceived risk in automation investment before your proposition
• Framing design automation in conversations with leadership as a strategic enabler of profitability, innovation and talent retention to overcome scepticism and align proposals with executive goals
• Unpacking insights from firms that have navigated leadership pushback, including step-by-step proposal methods and tactics for sustaining momentum post-adoption to inform your own strategies

• Positioning new design automation tools as an empowering resource to foster psychological safety and an openness to learn
• Exploring the formulation and implementation of successful learning models to ensure the effective engagement and upskilling of your team
• Identifying how to collect and evaluate user insights to refine your training approach and demonstrate impact to leadership

• Examining how choices in project kick-off influence the long-term adoption of computational tools
• Identifying key practices that support evolution of design outputs to ensure maximum alignment with original design intentions
• Exploring methods to validate and evolve adoption to avoid loss of learnings to one-off implementations
• Balancing automation with engineering judgement to ensure quality and reliability whilst understanding limitations

• Sharing the ‘what’ and ‘why’ of AI adoption to create an overall corporate AI growth plan
• Being pragmatic about adoption, policy and usage to create a learning and sharing environment
• Creating oversight and feedback loops to continually evolve, evaluate, and maintain your usage, standards and goals
• It’s not about a ‘silver bullet’: finding incremental gains with AI versus one overall solution

• Evaluating how real-world site and context data acquisition informs early feasibility and design strategy across diverse housing typologies
• Envisioning computational workflows that support rapid, yield-driven iteration to maximize value for clients in mixed-use and housing developments
• Balancing design team performance and placemaking to ensure computational design leads to vibrant, liveable housing environments

• Evaluating the evolution of what is important to sustainability teams and clients in design optimization, and when these decisions are made in the overall design workflow, to inform tool capabilities
• Envisioning the full landscape of processes and tools, from traditional modelling to emerging AI and automation, being developed to support these decisions
• Mapping how we have combined a build versus buy approach into our solution and united technology and sustainability teams to ensure maximum adoption of tools to inform project decision-making
• Unpacking the evolution of tools and optimization in relation to the changing landscape of regulations, code compliance, and third-party certifications