Blog

The design world has always been about blending creativity with technology, but in the last few years, artificial intelligence has shifted that balance in ways we couldn’t have imagined a decade ago. From generating entire layouts in seconds to offering instant market research, AI is no longer a futuristic add-on — it’s part of a designer’s daily toolkit. But what does this mean for the role of designers today and in the years ahead? We sat down with Iana Kudriavtseva, designer at YTC, to talk about the reality and future of design in the AI era. - Iana, from your perspective, how did AI change design in the last couple of years?

In today’s market, user experience isn’t a “nice to have” — it’s a deciding factor in whether people stay loyal to your brand or switch to your competitors. And the expectations are higher than ever. Customers want speed, personalization, and seamless interaction across every channel. This is where AI moves from buzzword to business advantage. It doesn’t just automate tasks — it transforms how people feel when they interact with your product or service. And a better experience usually means better retention, higher spend, and stronger brand advocacy. Let’s break down three key ways AI can take your user experience to the next level.

A futuristic concept reserved for labs and sci-fi plots - that's how we imagined AI short time ago. Now? It’s here — and it’s actively reshaping how businesses operate, make decisions, and deliver value. From predictive analytics and recommendation engines to intelligent automation and generative tools, AI is unlocking new opportunities across industries. But turning an AI idea into a working product isn’t magic — it’s strategy, architecture, and execution. Whether you're a founder, product owner, or innovation lead, understanding how to approach AI software development will help you move with confidence and clarity. Here’s a deep, practical guide to building AI software that actually works for your business, your users, and the future.

Learn how generative AI boosts productivity, enhances code quality, and accelerates software innovation.

Building a data lake might sound like a task for large tech corporations or data scientists in lab coats. However, in reality, it’s a practical and strategic move for any business seeking to harness the full potential of machine learning. If you're gathering data and considering how to utilize it for informed decisions, automation, or AI-powered services, you need a data lake. At YTC, we help businesses transform their scattered data into structured, accessible, and scalable systems. In this article, we’ll walk you through what a data lake is, why it's essential for machine learning, and how to build one the right way.

The rapid evolution of artificial intelligence (AI) has led to growing concerns among software engineers about their job security. Will AI replace human developers? Or will it serve as a powerful tool to enhance productivity? The truth lies somewhere in between - kind of like a badly written function that still somehow works. AI is already making significant strides in software development. Tools like GitHub Copilot, ChatGPT for coding assistance, and various AI-driven testing frameworks help automate repetitive tasks, reduce human errors, and improve efficiency. According to a report by McKinsey, AI-powered development tools can boost productivity by up to 40%. That’s right - AI is here to do the grunt work, so you can focus on the important things, like debugging that same function for the 15th time. However, software engineering is much more than writing lines of code. It involves problem-solving, architecture design, creative thinking, and an understanding of human needs - areas where AI still stares blankly, like a junior dev on their first day.