Architectural presentation boards have always been a strange hybrid. They are part drawing set, part magazine spread and part argument. A good board has to show a building convincingly, explain how it works and persuade a jury, a client or a planning officer, often in the space of a single A1 sheet. For most practices, producing them is one of the most time-consuming parts of any competition or early-stage submission.
AI image models have already found a place in the earliest stages of design, where studios use them to explore mood and massing. The latest generation is starting to move closer to the presentation board itself, because it is finally addressing three things that used to make generated images unusable for serious architectural communication: legible text, believable three-dimensional form and controllable light.
For a long time, the giveaway of a generated image was its writing. Shop signs, wayfinding panels, room labels and facade lettering came out as convincing nonsense. Any image destined for a board with real signage had to be retouched by hand, which removed most of the time saved.
Newer models handle short text far more reliably. A retail ground floor can carry the actual tenant names proposed in the brief; a wayfinding study can show the real signage hierarchy; a cultural building can display the name it will open under. For presentation boards, where every element is read as a statement of intent, this matters more than any improvement in resolution.
The second change is in how models handle volume. Earlier outputs often looked like paintings of buildings: atmospheric, but structurally vague, with ambiguous depth and inconsistent perspective. Current models are noticeably better at producing imagery with coherent volumes, plausible materials and consistent vanishing points, which makes them usable for perspective vignettes, sectional perspectives and contextual views.
They are still not modelling tools, and they do not understand structure. But for the vignettes that sit around the main drawings on a board, showing a courtyard at dusk, a stair hall in winter light or a roof terrace in use, they now produce images that read as architecture rather than as illustration.
The most important workflow change is the ability to guide generation with reference images. Feeding the model a grey-shaded view from the practice’s own 3D model, a site photograph or a material palette keeps the output tethered to the actual project. Without references, models drift towards a generic contemporary aesthetic. With them, the image describes your building in your context.
Studios using these tools well typically follow a simple rule: every generated image on a board must start from project geometry or a real site photograph, never from a blank prompt.
Practices comparing image models for presentation work tend to evaluate four things: text rendering, handling of people and portraits in scenes, three-dimensional coherence and the quality of reference-guided output. Microsoft’s latest model is a useful example of where the field is heading, with an emphasis on stronger text rendering, portraits, 3D imagery and reference-guided creation; studios that want to integrate it into their own visualisation pipeline can review the capabilities of the MAI-Image-2.6 API alongside other current image models before committing to a workflow.
Integration through an API matters for larger practices because board production involves many images generated from many views, named and versioned per option. Doing that one image at a time inside a chat interface does not scale beyond a single designer.
Consider a small practice preparing a two-board competition entry in a week. The plans, sections and axonometric are produced conventionally. For the four supporting vignettes, the team exports grey-shaded views from the model at the agreed camera positions, photographs the site at the right time of day, and assembles a short palette of materials. Each vignette is generated from its own view and the site photograph, with the material palette as a second reference, and the brief’s real signage text written into the prompt.
The team generates a dozen versions of each view, selects the one that best matches the design intent and then refines it by hand in image-editing software, correcting any detail that implies something the design does not contain. What used to consume two or three days of rendering for a small office becomes an afternoon of generation and a morning of careful editing, leaving more time for the drawings and the argument that actually win competitions.
Do not let images imply decisions you have not made. A generated view can show a cladding detail or a structural span that the design team has not resolved. Juries and clients read images as commitments.
Label studies as studies. Images used for exploration should be clearly identified as indicative, especially in planning contexts where visual material may be scrutinised.
Keep the main drawings conventional. Plans, sections and elevations remain the technical core of a board. Generated imagery supports them; it does not replace them.
The presentation board is not going to be generated in one click, and it should not be. What is changing is the cost of the supporting imagery around the drawings: the vignettes, contextual views and atmospheric scenes that used to require days of rendering. With legible text, better volume and reference-guided control, AI image models are becoming a credible tool for that layer of architectural communication, provided practices keep them anchored to their own geometry and honest about what has and has not been decided.
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