Transforming Design and Documentation from Concept to Construction

2 AIA Continuing Education credits provided by AIA Detroit upon course approval

RSVP: https://DABUG_August2026.eventbrite.com

DESCRIPTION:

The AEC industry is undergoing a monumental shift. AI is no longer a futuristic concept or a technology relegated to generating isolated visualizations; it is actively reshaping the entire project lifecycle. This comprehensive session bridges the gap between two major frontiers of AI in architecture: creative design exploration and automated technical documentation.

In the first half of this presentation, we explore the world of AI-driven design. Moving beyond rendering, we will demonstrate how generative AI unlocks new workflows—transforming 2D sketches into 3D concepts, enabling design persistence across multiple angles, and streamlining multiple design concepts.

In the second half, we transition to the production phase, demonstrating how AI is disrupting the highly repetitive drafting workflow. We will dive into how AI can help automate tedious documentation tasks like view creation, dimensioning, and sheet packing. Finally, we will look ahead to a new cloud-based ecosystem where machine learning trains AI on your firm’s unique graphic standards, setting the stage for truly autonomous, agentic documentation. This session provides an end-to-end roadmap for building a fully integrated, AI-powered practice.

Light food and beverages provided!

AGENDA:

Session 1: AI-Driven Design Exploration — From Concept to Form

(Tools referenced: Veras, Morphis)

Description:

The AEC industry is moving beyond static renderings into a new era of generative, iterative design. This session explores how AI tools like Veras and Morphis are transforming early-stage architectural workflows by turning 2D sketches into explorable 3D concepts. Attendees will see how generative AI supports design persistence across multiple viewpoints, rapid population of interior spaces, and automated lighting layout exploration. The session also covers how AI-generated walkthroughs let designers communicate concepts to clients faster and more compellingly than traditional methods. By the end, participants will understand how to fold generative AI into the earliest phases of the design process without sacrificing creative control.

Learning Objectives:

• Understand the paradigm shift from traditional, linear rendering workflows to iterative, generative AI-driven design within the architectural process.

• Apply generative AI techniques to convert 2D sketches into explorable 3D design concepts, accelerating early-stage massing and form studies.

• Evaluate the practical application of AI for early-stage design tasks, including automatic space population, rapid lighting design layouts, and creating AI-generated walkthroughs.

• Assess methods for maintaining design persistence across multiple angles and iterations, ensuring generative outputs stay coherent as a design concept evolves.

 

Session 2: AI-Powered Documentation — Automating Concept to Construction

(Tool referenced: Glyph)

Description:

As AI matures in the design space, a parallel revolution is underway in construction documentation. This session shifts focus to the production phase, examining how AI tools like Glyph are disrupting the repetitive drafting tasks that have long consumed architectural staff hours. Attendees will learn how automation can handle tedious but essential work such as view creation, dimensioning, tagging, and sheet packing. The session then looks ahead to an emerging cloud-based ecosystem in which machine learning is trained directly on a firm’s own graphic standards, enabling increasingly autonomous, agentic documentation. Participants will leave with a forward-looking strategy for building a fully AI-integrated documentation pipeline.

Learning Objectives:

• Identify specific opportunities to eliminate manual drafting bottlenecks and reduce overhead by automating repetitive construction drawing tasks (dimensioning, tagging, sheet setup) using AI.

• Formulate a future-focused strategy for leveraging cloud-based machine learning and agentic AI to train automated systems on proprietary firm standards, paving the way for self-driving drawing sets.

• Examine how AI-driven documentation tools integrate with existing BIM/Revit workflows while maintaining data integrity and QA checkpoints.

• Distinguish the human oversight responsibilities required when deploying agentic AI on construction documents, balancing automation speed with professional accountability.