Which Industries Are Being Transformed by AI-Generated Images. And How

Generative artificial intelligence (AI) has only recently entered our lives, yet it has already significantly altered the visual landscape of our world and how we perceive it. Not long ago, we laughed at how early versions of Midjourney generated humans with the wrong number of limbs. Today, stock photo businesses are struggling to survive, losing ground to solutions like Stable Diffusion and other generative tools.

So how is the world changing under the influence of AI-generated visuals, and which business sectors are being transformed? What ethical challenges arise in this process? Alexandrа Pastukhova, Product manager at SNRG (Product: Facelab: Face, Body, Hair Editor), shares her insights.

Trends in visual generative AI

When analyzing the development of AI for visual content, several key trends emerge.

First and foremost is the shift toward video. As computing power becomes more accessible and algorithms improve, more resources are being invested in the creation and editing of video content. This trend opens up new opportunities in marketing, entertainment, communications, beauty, and fashion. It’s only expected to grow stronger.

As for still images, the trend is toward hyper-realism. Developers aim to generate pictures indistinguishable from photographs, especially when creating human pictures. And there has been progress — faces now look much more realistic than they did just a year or two ago.

A third key trend is the emergence of specialized models. We’re seeing more purpose-built solutions designed to handle specific tasks: lighting correction, color grading, makeup transfer, and other niche editing needs. This kind of specialization allows for higher quality results in focused applications.

Over the past year, we’ve seen several technological breakthroughs: improved facial generation and expression rendering, models capable of producing short video clips with realistic movement physics, and stylization tools that preserve a person’s individuality. All of this points to a near future where AI-generated visuals will continue to grow in number but we might finally stop noticing them, as they become indistinguishably real.

How AI Is transforming the Fashion and Beauty Industry

The fashion and beauty industry was among the first to embrace AI’s capabilities.

Virtual try-ons are now widely used by major fashion brands, and their popularity is expected to keep growing.

Big brands are already offering virtual fitting rooms for clothes and accessories, makeup selection tools based on individual facial features, hairstyle and hair color simulations, and skin analysis technology with care recommendations.

The most promising direction lies in personalization, not just in “literal” try-ons, but in the intelligent selection of optimal products and solutions for each user. This is where visual generation tools merge with advances in language models.

Interestingly, these tools not only improve user experience, but also help reduce product returns, make the decision-making process more conscious, and lessen the environmental impact of consumer behavior.

Ethical Considerations in AI Technology

Use At the same time, important ethical questions arise. For instance, these technologies allow virtually anyone -including children — to generate images, including inappropriate content. This presents new challenges for parents and digital literacy education.

Another concern involves psychological well-being. AI tools are reshaping beauty standards and affecting how users perceive themselves. On social media, we increasingly see influencers who never post unedited photos. These digital enhancements become part of their online identity, and being without them causes discomfort.

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But it’s not just the content creators who are affected. Their audiences are too: constant exposure to idealized images can lead to unrealistic expectations about personal appearance. These AI-generated, flawless visuals are far from what we see in everyday life. The bigger the gap between ideal and real appearance, the more frustration users may feel when trying to reach that same “perfect” look in real life.

Another critical aspect is the growing crisis of trust and potential for manipulation. AI makes it easy for anyone to generate artificial videos and images involving real people, raising questions about the credibility of visual information. We have yet to develop effective mechanisms to neutralize these threats.

Technical Limitations: Why Businesses Can’t Fully Rely on AI Yet

Despite tremendous progress, we still can’t fully rely on generative outputs. They continue to show characteristic flaws: strange shape distortions, unnatural color transitions, or visible stitching between image fragments. In videos, these artifacts become even more apparent — watching a single object over time may reveal it behaving unnaturally, ignoring physical laws. This is especially noticeable in hand movements, facial expressions, or the folds in clothing.

More subtle flaws also persist. Eyes often appear blurry or geometrically distorted. Face and body proportions can be off. Shadows don’t always align with light sources. Numbers, text, and clocks may show odd glitches — clear signs of AI involvement.

In partial edits, blending between the real image and the generated one is often visible. You might notice sharp edges, color mismatches, or unnatural blur at the transition zones.

From a technical standpoint, today’s generative models still face specific limitations. Common issues include malformed fingers, teeth, and ears. Eyes frequently look unnatural. Facial and body geometry may be off. Shadow rendering is inconsistent. Numbers, clocks, and text can reveal obvious artifacts.

Another common issue is artifacting in the stitched areas during partial edits. However, as models evolve, the frequency and visibility of these issues are decreasing. Each new generation of algorithms shows significant progress in overcoming existing limitations.

What’s Next?

AI for image and video processing has come a long way in recent years.

Current trends point toward further development in video generation, increased realism, and tool specialization for niche use cases. At the same time, ethical usage, content verification, and the impact on users’ self-perception remain crucial aspects of this evolution.

In the next 2–3 years, we’re likely to see significant improvements in how motion is rendered in videos, reduced demand for computing power, deeper integration between language and visual models, and more advanced personalized recommendation systems based on visual analysis.

The potential for these technologies is immense: from entertainment and commerce to medicine and education. AI is already changing the rules of the game in fashion and beauty, offering new opportunities for brands and consumers alike. Machine learning and computer vision will continue to improve, leading to even deeper integration of these technologies into our daily lives.