Artificial intelligence is quickly moving beyond image generation and into the everyday workflows of interior designers and architecture firms. But as AI becomes more capable, designers are also confronting questions about accuracy, privacy, intellectual property, creativity, sustainability, and how much trust to place in AI-generated information.

In this episode of I Hear Design, host Robert Nieminen draws on recent interiors+sources reporting to explore how firms are using AI for visualization, research, data analysis, storytelling, knowledge management, and other design tasks. He also examines what industry leaders are learning about the technology’s limitations, why AI literacy involves more than writing better prompts, and how designers can experiment without losing sight of the people and places their work ultimately serves.

The episode includes insights from Gensler, Mattoboard, Spectorgroup, Design Appy, ASID, and other voices examining how artificial intelligence is reshaping design practice.

Key Moments in This Episode

00:00 — AI moves beyond image generation
Robert Nieminen sets the stage for how artificial intelligence has evolved from an experimental visualization tool into something design firms are using for research, programming, data analysis, specifications, presentations, and client communication.

02:07 — From curiosity to real-world experimentation
Early interiors+sources coverage shows how firms such as Spectorgroup and SGA began testing dozens of AI applications, with the technology increasingly serving as a “copilot” that helps designers explore more possibilities in less time.

03:54 — Creativity, trust, and the designer’s judgment
Research from Mattoboard reveals designers’ mixed feelings about AI’s creative potential, including concerns about homogeneity and losing control of the design process. Nieminen examines why professional judgment becomes more important as AI moves into higher-stakes applications.

06:35 — Building smarter AI skills
AI education needs to extend beyond learning how to write prompts. Nieminen explores why training should focus on real design tasks, including visualization, research, privacy, intellectual property, and learning to critique AI-generated work for errors and weak ideas.

08:23 — Using AI to make sense of design data
Drawing on conversations with Gensler leaders, Nieminen looks at how AI can help designers navigate growing volumes of workplace utilization, employee, demographic, and building-performance data while still relying on human context to determine what those patterns mean.

10:07 — AI as a storytelling and communication tool
Gensler’s experiments with AI-generated personas and “day-in-the-life” scenarios demonstrate how the technology could help clients better understand proposed spaces and experiences before they are built.

11:14 — Privacy, copyright, and firm policies
As designers upload presentations, plans, client information, and proprietary material to AI platforms, firms face increasingly important questions about data privacy, ownership, authorship, and which tools are appropriate for professional work.

13:21 — Avoiding generic design in an AI era
Generative AI’s tendency to draw from familiar visual patterns raises concerns about sameness. Nieminen considers why cultural context, regional influences, material knowledge, personal observation, and a distinctive point of view may become even more valuable.

14:59 — AI meets the other forces reshaping design
Artificial intelligence is developing alongside major changes in hybrid work, climate resilience, demographics, wellness, energy performance, and client expectations—expanding the range of knowledge designers are increasingly expected to bring to their work.

16:28 — The sustainability question behind AI
ASID’s 2026 trends research highlights the growing energy demands associated with AI and data infrastructure, creating another consideration for designers working to reduce the environmental impact of buildings and materials.

17:37 — Where firms should start with AI
Rather than waiting for a perfect AI strategy, Nieminen suggests purposeful experimentation: identify practical use cases, establish rules around confidential information, understand where the technology is unreliable, and apply the same professional standards to AI-generated work as any other design output.

19:08 — What ultimately matters in AI-enabled design
As AI becomes embedded in everyday practice, the most successful designers may not be those who use it the most, but those who understand where it actually improves their work. Nieminen closes with Gensler’s reminder that, regardless of the technology involved, people are ultimately the ones who use the spaces designers create.