Learn the tools for harnessing AI | Agnieszka Wilk

Agnieszka Wilk_Decorilla — Agnieszka Wilk

AI‘s presence is being felt everywhere, and interior design is no exception. Paradoxically, while adoption rises, so does apprehension. AI tools are often perceived as a threat to job security, and the best way to overcome that anxiety is through awareness. Tools can never replace talent, and tomorrow’s best-equipped designers are the ones who have honed both their ‘human’ and AI skills.

Here are the key lessons designers need to know in the age of AI.

Train smart, not hard

Learning new skills, especially technical ones, can be incredibly overwhelming, even for the seasoned designer. With the right blueprint, upskilling can be clear-cut and straightforward.

Start by pinpointing the workflows where AI is needed the most. Knowing where these tools will drive the most impact and value helps identify the most relevant skills to focus on. It’s also a great way to set expectations around skills development, so designers understand the direction their training will take.

These workflows might vary between design boutiques and companies, but there are overarching areas where AI creates impact:

Faster development times of design proofs.
Predicting project needs, like types of material, as well as project timelines.
Quickly connecting clients with designers based on their personal tastes.
Summarizing insights and feedback so everything is recorded and accessible for future reference.

There’s a clear pattern here: AI works best in straightforward tasks, usually where data processing is involved. These tools don’t have a client-facing role but rather help with the behind-the-scenes legwork, like administrative tasks. It’s up to the designers to ensure these outputs align with their clients’ needs and preferences, while strengthening the overall customer experience.

Learn by doing

One of the most common upskilling mistakes is to keep things purely theoretical. Designers need to be exposed to how these tools work in real life situations. If they’re not familiar with applying AI to workflows, there’s a higher risk of mistakes and fallouts that could harm reputation and client relationships.

A practical approach to training also nurtures confidence and clarity among designers so that they can become empowered custodians of AI in the design process. How might this pan out in AI training? An interior designer is assigned a brief and asked to prompt an AI tool for a 3D rendering accordingly. However, the brief is vague (as is often the case with design briefs), which could cause misalignment in what the AI’s informed with and what it produces.

In this instance, the designer needs to be able to translate that vague brief into a detailed and actionable prompt for the AI-powered rendering tool. They could draw upon anecdotal information, like a tendency to prefer warm neutrals and medium wood tones in a farmhouse kitchen — details that otherwise might not have been mentioned in a brief simply asking for a ‘farmhouse kitchen.’

Other team members or a more senior designer can get involved with this training initiative, assessing the AI’s output according to the brief and the trainee’s prompt. This collaborative approach is an incredibly effective way of building confidence in AI upskilling.

Iteration means impact

AI training can’t be ticked off in a one-off crash course. These tools are constantly evolving, and designers’ skills need to grow alongside them. Micro courses are an excellent way to ensure designers are constantly honing their skills in relevant areas.

Each micro course should have a distinct skills focus. Keeping these courses bite-sized makes it much easier for teams to quickly yet thoroughly familiarize themselves with new features or know-how. This also means that designers aren’t sinking months or even years into long-term courses that may be outdated by the time they finish them.

Benchmark for better performance

You wouldn’t train for a marathon without benchmarking your performance, and the same rule applies to AI upskilling. Additionally, when individuals don’t have clear goals they’re working towards, they can easily become demotivated.

Here are some examples of commonly used KPIs to measure progress:

Slashed timelines between receiving the brief and producing the initial design proof.
Fewer iterations and amendments of designs.
Faster project turnaround time.
Prompt proficiency.

Here’s another area where looping in a ‘coach’ for less familiar designers can hugely transform the training process. More experienced designers well-versed in using AI in their projects can mentor and assess trainees’ progress.

The human eye is irreplaceable

AI can create stunningly realistic renderings, but it ultimately isn’t able to contextualize designs in the real world. An algorithm can’t predict how people will live in a space, nor fully grasp real-life factors like the emotional and psychological impact of color and lighting.

No matter how many workflows AI is embedded in, nor how many renderings it produces, the trained human eye must always be the final judge. We’re at an interesting intersection of artificial intelligence and art, but the teams behind the tools have got to be empowered to oversee and guide them.

Before diving into the technical skills to focus on, it’s imperative that designers also refine their soft skills that will ultimately make or break how successful AI deployment strategies are. Emotional intelligence, cultural nuance, lifestyle preferences, and empathy are just some of the skills that no algorithm will ever be able to replicate.

Designers who also master these skills will truly shine in the age of AI. They’re fully in control of the creative and computational sides of the design process, acting as custodians who bring their clients’ visions to life.

Agnieszka Wilk is the CEO of Decorilla.