A few years ago, the workflow of a lighting designer was divided into clear stages: exploring lighting solutions through sketches, performing photometric calculations in DIALux, and creating visualizations in Photoshop or 3ds Max. Today, the industry is undergoing a profound transformation. Neural networks generate photorealistic visualizations of lighting concepts in seconds, while specialized AI assistants promise to automate the creation of specifications and bills of quantities. Light is transforming from a passive element into a fluid digital medium.
While some predict the complete automation of the profession, practicing experts are raising the alarm. At industry panel discussions—including major global congresses like the IALD—crucial questions are being asked more frequently: Can algorithms truly understand the physics of light particles (photons)? Will the heavy reliance on neural networks lead to a loss of cultural context and accumulated expertise? And what is the real energy cost of machine-generated design? Let’s delve into how AI is transforming the lighting designer’s workflow today, identify when it becomes indispensable, and explore why the final say still belongs to a human.

1. GENERATIVE DESIGN: INSTANT VISUALIZATION
Today, the primary touchpoint between lighting designers and AI is the concept development stage. The capabilities of AI in visual content creation have fundamentally transformed the speed at which stunning imagery is produced. Instead of tedious scrolling through Pinterest for references or waiting hours for renders, designers can now leverage text prompts. Within seconds, artificial intelligence generates images complete with light and shadow, color temperature gradients, or distinct lighting hierarchies for building facades and entire architectural complexes.
There are already successful global examples of applying algorithm-driven technology in the lighting field. Its use is particularly vital when dealing with complex geometry and algorithmic structures. A prime case in point is the Harry Potter Theatre project in Hamburg by Studio De Schutter (https://studiodeschutter.com/projectsblog/harry-potter-theatre). To create a light installation composed of 3000 pendant luminaires inside a listed historic building with strict static load constraints, the designers utilized a parametric workflow combining Rhino and Grasshopper. The algorithm calculated the ideal grid spacing, the exact lengths for dozens of different pendant types, and the optimal weight distribution in just seconds. Doing this manually would have taken weeks or even months.

2. TECHNICAL ROUTINE: SEAMLESS AUTOMATION
Another area where AI proves highly suitable is the automation of routine tasks. Modern AI platforms—such as Alya, an AI intelligence platform for professional lighting designers—are beginning to integrate into design studio workflows, drastically reducing the time required to transition from an architectural concept to final project documentation.
Algorithms can automatically analyze drawings and space types, sum up the lighting equipment load for each zone, and cross-reference it with international lighting standards including IES, CIBSE, EN, and DIN. They calculate energy consumption limits and even autonomously suggest alternative fixtures from the library. These AI tools independently count luminaires, fill in part numbers, and list technical parameters. A dedicated module helps generate a comprehensive Bill of Quantities (BOQ) and equipment specifications in seconds, detailing fixture counts, wattages, color temperatures (CCT), and color rendering indexes (CRI). For instance, the Dialux Prep tool helps automatically convert flat drawings into basic 3D models, matches them with photometric IES files, and generates step-by-step instructions for exporting into DIALux.
Additionally, there are AI-powered plugins for BIM and 3D modeling—tools that embed directly into familiar software (Revit, DIALux, Rhino) to accelerate visualization. The AI-driven plugin Veras AI by EvolveLab can read the actual geometry of walls and openings directly from DIALux evo or Revit. Instead of hours spent rendering a scene, it applies textures in seconds, calculates realistic light distribution, and generates facade concepts and interior scenarios while strictly preserving the building’s architectural proportions. Meanwhile, the Archlior AI Lighting Studio (https://archlior.com/ai-lighting ) is specifically designed to demonstrate how different scenarios change the atmosphere. You simply upload a photo of a space, and the AI instantly switches lighting styles, color temperatures, beam directions, and fixture types. These capabilities allow professionals to quickly assemble a high-quality project brief without any manual rendering.

3. CHALLENGES: FANTASIES AND PHYSICAL INCONSISTENCIES
However, the excitement surrounding the speed and capabilities of AI quickly fades when a project transitions from the imagery stage to actual implementation. Experts highlight three key problems that AI is currently unable to solve:
• Disregard for the Laws of Physics: Generative neural networks produce spectacular visual effects, but they are completely “blind” to photometrics. AI cannot work with actual manufacturers’ IES files and does not comprehend luminous intensity distribution curves (LIDC). It merely places beautiful “spots” of light on an image. During the technical development phase in DIALux, it inevitably becomes clear that the generated “masterpiece” physically cannot achieve the required illuminance levels, luminance parameters, shielding angles, light distribution, or other professional engineering requirements. Consequently, the beautiful picture remains entirely detached from reality and defies the laws of physics.
• Incompatibility with the Physical World: Research published on ResearchGate indicates that when designers attempt to recreate AI-generated fixtures in the physical world, they hit a technological dead end. The algorithms fail to consider crucial factors such as thermal management (heat dissipation), material thickness, or the constraints of manufacturing machinery and production equipment.
• The Threat of Homogenization (Sameness): If the entire industry begins using the same neural networks with similar prompts, global architecture risks losing its uniqueness and authenticity. As is well known, AI trains on existing imagery (previously created by humans or other AI), meaning it merely compiles what has been accumulated in the past. True progress, however, requires more than just recycled data. It demands a deep understanding of local cultural context, global trends, and current challenges. It also relies on intuition, the ability to fantasize, to shock, and even to make mistakes—all qualities that belong uniquely to a human being.

4. POTENTIAL THREAT: THE ENVIRONMENTAL FOOTPRINT
The irony of modern lighting design lies in the fact that for decades, the industry has fought for sustainability and reduced energy consumption through the adoption of LEDs, control systems, and smart solutions. However, the operation of AI itself—designed to optimize these very processes—leaves a massive carbon footprint. According to US expert reports, by 2028, data centers serving AI requests will consume up to 7–12% of the country’s total electricity (by comparison, this figure stood at around 4% in 2023). Training a single large language model consumes megawatts of energy, which is forcing the professional community to introduce guidelines for the responsible use of AI (such as optimizing the number of generations and avoiding unnecessary server overloads).
Consequently, with more widespread and active use of AI, overall energy consumption will only increase rather than decrease. This, in turn, will require more and more fuel to generate electricity in the necessary volumes. It turns out that instead of being solved, the problem is seemingly being exacerbated.

THE “HUMAN-IN-THE-LOOP” CONCEPT
The rapid advancement of technology inevitably raises an existential question: will artificial intelligence replace humans in lighting design? The expert community’s response is unambiguous: no.
The industry is transitioning toward the “Human-in-the-loop” (HITL) model. HITL is an approach to designing automation and artificial intelligence systems where a human serves as an essential link—approving, monitoring, or correcting the machine’s actions. The machine performs routine, large-scale computations, but the final decision or the resolution of complex edge cases remains the responsibility of the human.
In this paradigm, AI plays the role of an incredibly fast but completely blind executor. An algorithm can generate a hundred lighting scenario variants in a minute or automatically calculate cable lengths for thousands of fixtures. However, the machine is incapable of understanding the beauty of the final object, feeling its emotional impact, tapping into memories, or judging the appropriateness of a particular solution. Under current conditions of AI adoption, the lighting designer’s role is shifting away from routine work, manual fixture selection, and layout toward the role of an operator, critic, and ultimate decision-maker. The task is set by a human, the AI proposes solutions, and then these proposals are analyzed by a human and evaluated through the prism of the laws of physics, building codes, and aesthetics.
The winners in this new digital reality will not be those who attempt to compete with AI in speed, nor those who ignore it entirely. The future of lighting design belongs to professionals who can unite their artistic vision and technical expertise with the computational power of algorithms. While AI is unlikely to fully replace a lighting designer, a lighting designer who utilizes artificial intelligence will undoubtedly replace one who does not.