My washing machine is labelled AI Washing.
What does that even mean?
From what I understood, AI Washing means the machine can sense water level, laundry weight, timer, and a few other things. But… weren’t those features already available in most automatic washing machines?
Then why the AI tag?
Well, this is the latest marketing trend - sticking the “AI” label on everything, from basic tools to ultra-sophisticated machines.
Does it mean AI always improves performance or efficiency?
It depends. In many cases, some features are best when kept simple. While AI can be extremely useful, there are still plenty of areas that don’t need it. Let me explain why we at Design Intelligence haven’t fully adopted an “AI-first” model for our work.
AI & Business Growth
AI has proven to be a catalyst for many industries. Generative AI, in particular, has made processes faster, more efficient, and less resource-heavy. It has transformed coding, content creation, data entry, research, and analysis. With continuous updates and growing specialization, AI tools are becoming more relevant and necessary for business operations and growth.
From my personal experience: I used to write for B2C agencies where I used AI-generated content (always with my own input). Many of those blogs still rank well on Google.
Later, another employer asked me to write listicles for B2B clients using ChatGPT. Those turned out to be some of my best-performing pieces. So yes, AI has directly benefited my career and the companies I’ve worked for.
I’ve also seen developers use AI for coding and code reviews, becoming much more productive. In marketing and business analysis, AI has been game-changing. Companies no longer need to hire for basic, repetitive tasks - AI handles much of that.
Then Why Not Use It?
Here’s where it gets interesting. Design Intelligence works in a very niche space. At a minimum, even a marketing person like me needs to understand civil engineering and software engineering (or I wouldn’t able to type a word here). Our engineers are, of course, far more specialized.
From a Marketer’s Perspective
When writing about our services, I need at least a working knowledge of both civil and software engineering. Our clients are also highly specialized, mostly in the AEC (Architecture, Engineering, Construction) sector - so they expect what we talk about their projects, should reflect strict standards (like Eurocode compliance) and their own business uniqueness.
Out of habit, I tried using AI tools for structure and ideas. Believe me - the results were completely off. The generated content was far from our actual work and couldn’t be approved. AI simply doesn’t have the depth or domain-specific knowledge for our sector.
Now, instead of AI, I rely on talking to our engineers, studying relevant references, and researching industry material. That way, the writing reflects true understanding, not generic filler.
From a Developer’s Perspective
AI works great for about 90% of tasks, but that remaining 10% requires deep expertise and a human touch. Think of it like a pyramid: AI can handle the large base, but the critical tip must be driven by human specialists.
There are also risks in over-relying on AI. For example, the recent Tea app breach was reportedly linked to AI-generated code combined with weak testing.
In our own projects, we’ve found that AI sometimes generates overly complex code. Our philosophy is to simplify problems, but if AI creates complicated solutions, our engineers end up wasting time debugging and simplifying those codes.
Take on of our latest Py-Engineering project TBS-SVA Gutter Tool, which involves deep knowledge of Open Channel Flow (fluid mechanics). AI tools consistently “hallucinated,” even confidently presenting wrong answers on simple topics. For such specialized work, we’ve gone back to textbooks and rely entirely on our engineers’ expertise.
So yes, AI is powerful, but in our field, it often misleads more than it helps.
So There’s Absolutely No Use of AI?
Of course not. We do use AI, but only for supporting tasks. For example, I use it to review content tone or compare writing styles (like I’m doing now). It’s also helpful for simplifying concepts or brainstorming.
But when it comes to producing unique, technical, and highly customized work, AI falls short. Either the tools lack specialized knowledge, or they hallucinate under complexity.
For us, personalized, expert-driven orientation and development are essential. That’s what sets us apart in a competitive market.
At the End of Discussion
AI is evolving rapidly. Someday, it may be capable of supporting specialized fields like ours. We even dream of creating AR and VR tools for our sector, and AI could play a big role in shaping that future.
But until AI can truly deliver quality, unique content and simplify (rather than complicate) our engineering projects, we prefer to stay problem-first, not AI-first.
When the tools catch up to our standards, we’ll be glad to call ourselves AI-driven. Until then, we’re focused on building solutions that work.