From Prompt Engineering to Brand Authenticity: A Complete Blueprint for AI Adoption in Fashion Marketing, Operations, and Creative Strategy

A brand positioning document that once took a week to develop now gets completed during a 30-minute train ride. A graphic designer who produced 10 visuals in an hour now produces 100. A photorealistic product image that required a full studio shoot appears overnight, ready for the website by morning.

These are not projections. They are operational realities at GQ Group Thailand, where artificial intelligence has moved from experiment to infrastructure across every department in the company. And they illustrate something the fashion industry is only beginning to understand: AI's most transformative impact is not on the content it generates. It is on the speed, scale, and economics of creative work itself.

The Good to Great Podcast, hosted by Timea Hejja and Tamara Hejja, explored this transformation with George Hartel, Chief Commercial and Development Officer at GQ Group Thailand. With over 20 years of experience transforming brands and integrating technology-driven strategies across Asia, Hartel has led GQ Group's AI adoption from early experimentation to full operational integration. His perspective is neither evangelical nor skeptical. It is practical, shaped by the daily reality of running AI systems inside a commercial fashion business where the stakes are measured in revenue, brand equity, and competitive position.

What emerged was not a conversation about AI's theoretical potential. It was a detailed account of what happens when a fashion company commits to AI fully, what works, what breaks, what changes permanently, and what remains irreplaceably human.

01. The Impact of AI on Brand Storytelling

GQ Group's AI integration is notable for its breadth. This is not a company that uses AI in 1 department while the rest of the organization operates traditionally. AI touches design, marketing, content creation, packaging development, forecasting, HR communications, and consumer insight synthesis. The result is a compound effect where gains in 1 area amplify gains in others.

"The benefits of AI have really touched every part of the company. If you haven't started using AI yet, you really, really should," Hartel stated.

The impact on content creation is particularly instructive. GQ Group specializes in repeatable, high-quality apparel categories, products like premium white shirts and men's cooling underwear where the value proposition is consistency, fabric performance, and fit rather than seasonal novelty. This product profile turns out to be ideally suited for AI-driven content development, because the brand can train models on a stable set of consumer insights, design parameters, and brand voice attributes that do not shift dramatically between seasons.

By running consumer insights through AI systems, GQ Group produces content that aligns with brand identity while introducing a counterintuitive advantage: reduced bias.

"When you run those same insights through an AI system, in many cases, it's less biased than our own people," Hartel noted.

This observation deserves scrutiny. Human creative teams carry accumulated assumptions about what works, preferences shaped by personal taste, recency bias, and internal politics. AI systems, when properly trained on consumer data rather than internal opinions, can surface patterns and possibilities that human teams overlook or unconsciously filter out. The result is not that AI replaces creative judgment. It is that AI provides a broader, less filtered starting point from which human judgment can then select, refine, and elevate.

02. Overcoming Challenges in AI Adoption

Hartel was candid about the limitations. AI's effectiveness in fashion correlates directly with the repeatability of the product category. For brands operating in fast fashion, where the entire value proposition depends on constant novelty and unique designs, AI faces a structural challenge.

"When you have lots and lots of unique designs, it gets harder and harder for utilizing the AI tools as things become more and more unique," he explained.

This is an important qualification that AI enthusiasm in the fashion press frequently omits. Training effective AI models requires substantial volumes of consistent data. A brand producing 10,000 unique SKUs per season cannot train models with the same precision as a brand refining 50 core products over multiple years. GQ Group's advantage is not just that it adopted AI early. It is that its product strategy aligns with AI's current strengths.

The second challenge is subtler and more consequential for the long term: the relationship between AI capability and human creativity. Hartel describes this as an "interception" between technology and human learning, a point where the 2 systems meet and must be deliberately managed rather than allowed to drift into either full automation or defensive resistance.

The brands that will extract the most value from AI are not those that automate the most aggressively. They are those that design the collaboration between human and machine most thoughtfully, identifying which creative decisions benefit from AI speed and pattern recognition and which require human intuition, cultural sensitivity, and the ability to make aesthetic judgments that no training data can fully encode.

03. Evolving Creative Professions

The question Hartel encounters most frequently in public forums is whether AI will eliminate creative jobs. He addressed it directly, drawing from his experience presenting at a CMO panel at the British Chamber of Commerce.

"Maybe a graphic designer before could put out 10 graphics in an hour a day. Now they can do 100."

His analogy to the transition from paper-based mathematics to calculators is well chosen. Calculators did not eliminate mathematicians. They eliminated the tedious computation that consumed most of a mathematician's time, freeing them for higher-order work. AI is doing the same for creative professionals, absorbing the repetitive production tasks that occupied the bulk of a designer's day and liberating time for conceptual thinking, strategic decisions, and the irreplaceable human skill of recognizing what Hartel calls "aha moments," those unexpected outputs that an AI generates but only a human can identify as brilliant.

At GQ Group, AI adoption has not led to redundancies. It has led to productivity expansion. The same team produces dramatically more output, explores more creative directions, and iterates faster. The roles have not been eliminated. They have been restructured around higher-value activities.

But Hartel's reassurance comes with an implicit warning. Creative professionals who define their value by production volume, by how many graphics they can make or how many copy variations they can write, are competing directly with a technology that will always be faster and cheaper. Professionals who define their value by judgment, by the ability to curate AI output, spot the exceptional amid the adequate, and make decisions that require cultural context and emotional intelligence, are positioned for a career that AI enhances rather than threatens.

The critical new skill is prompt engineering: the ability to craft precise, nuanced inputs that guide AI toward useful outputs. This is not a technical skill in the traditional sense. It is a communication skill, the ability to articulate creative intent with enough specificity that a machine can act on it. Designers who master this skill multiply their creative capacity. Those who dismiss it as beneath them will find their relevance narrowing.

04. Maintaining Brand Voice and Authenticity

The most common objection to AI-generated content in fashion is that it feels generic. Hartel acknowledged this concern and then systematically dismantled it.

"It's quite straightforward to start getting the AI to really understand your brand voice," he said.

The process mirrors traditional brand development workflows: you feed the system existing content that exemplifies the brand voice, competitor analysis that defines the positioning landscape, visual references that establish aesthetic parameters, and specific instructions about tone, vocabulary, and emotional register. The AI then produces output within those constraints. The difference is not quality. It is speed.

Hartel described completing a full brand positioning or marketing plan during a 30-minute train commute, work that previously required days of meetings, drafts, and revisions. The output is not a rough draft that needs extensive human rework. It is a polished document that reflects the brand's established voice because the AI has been properly trained on what that voice sounds like.

The key insight is that brand voice is not magic. It is pattern. It is consistent choices about vocabulary, sentence structure, emotional tone, and visual language. These patterns are precisely what AI excels at learning and reproducing. Brands that complain about generic AI output have typically failed to invest in the training process. They are feeding the machine generic inputs and being surprised by generic outputs.

The brands that will maintain authentic voices in an AI-driven landscape are those that have done the foundational work of defining their voice with enough precision that it can be taught, not just to new employees, but to machines. That definition process itself is valuable, because it forces brands to articulate what makes them distinctive in concrete, operational terms rather than vague aspirational language.

05. Building Emotional Connections

Can machine-generated content create genuine emotional resonance with consumers? Hartel's answer is nuanced and more honest than most commentary on this question.

"There's an element of making sure that the words that are coming out are authentic," he cautioned.

His approach at GQ Group is explicitly hybrid. AI does not generate final consumer-facing content autonomously. It refines, polishes, and scales content that originates from human intent. An email drafted by a team member gets smoothed and sharpened by AI. Feedback that needs to convey empathy gets refined through AI prompts calibrated for emotional tone. Marketing copy generated at scale gets reviewed by humans who ensure it carries the specific warmth, humor, or authority that the brand requires.

The danger Hartel identifies is the shortcut: teams that copy AI output directly into consumer communications without human review. This produces content that is technically competent but emotionally hollow, text that reads correctly but does not feel like it was written by anyone in particular. Consumers detect this absence, often without being able to articulate what is missing. What is missing is intent, the sense that a specific human being chose these words for a specific reason.

The brands that build lasting emotional connections with consumers will be those that use AI to amplify human intent rather than replace it. The AI handles volume, speed, and consistency. The human provides purpose, personality, and the cultural sensitivity that determines whether a message resonates or falls flat.

06. Ethical Concerns and the Coming Authenticity Crisis

Hartel's most forward-looking observations concerned a risk that most fashion brands have not yet confronted: the weaponization of AI-generated content against brands themselves.

"What I really think is going to get harder and harder for brands is the ethics around other people impersonating your brand," he warned.

As AI video generation becomes increasingly photorealistic, the barrier to creating convincing fake brand content is collapsing. A bad actor can produce a video that appears to be an official brand advertisement, featuring products that do not exist, claims that were never made, or messaging that contradicts the brand's values. In markets across Asia where counterfeit goods are already prevalent, this capability adds a digital dimension to an existing problem with potentially devastating reputational consequences.

Hartel predicts that platforms will introduce authentication mechanisms, similar to verified social media accounts, that certify whether video content genuinely originates from the brand it claims to represent. But until those systems mature, brands face an asymmetric threat: creating authentic content costs time, money, and creative effort. Faking it costs almost nothing.

The broader ethical landscape extends beyond impersonation. Consumers, particularly Gen Z, are developing expectations about how AI is used in the content they consume. They want to know whether an image was AI-generated. They want assurance that AI systems were trained on data obtained ethically. They want transparency about where human creativity ends and machine output begins. Brands that treat these concerns as irrelevant will discover that trust, once lost over AI ethics, is extraordinarily difficult to rebuild.

07. Future Innovations: Video, Agents, and Hyper-Personalization

Hartel identified 3 AI-driven developments that he believes will reshape fashion marketing within the next 2 to 3 years.

AI video production is the most immediately impactful. The ability to produce high-quality video content at speed and scale transforms how brands engage consumers across platforms. Video has already become the dominant content format on social media, and brands that can produce it rapidly, at professional quality, without the cost structure of traditional production, gain a decisive advantage in the attention economy.

AI agents, software systems that autonomously execute repetitive workflows, represent the next frontier of operational efficiency. "How brands are using agents to help automate tasks and automate repetitive activities, I think this will be quite popular," Hartel predicted. These agents do not replace employees. They absorb the low-value administrative work that currently consumes a disproportionate share of creative and strategic professionals' time.

Hyper-personalization is where AI's consumer-facing impact becomes most visible. GQ Group is already experimenting with delivering different website experiences based on consumer profiles, showing 1 version to a corporate executive and another to a university student browsing the same product category. This mirrors the algorithmic personalization that social media platforms have normalized, applied to the branded retail environment.

Hartel acknowledged the tension inherent in personalization. Some consumers find it intrusive. Others find it useful. His position is pragmatic: personalized content is more relevant and effective than generic content, and consumers will increasingly expect it. But he also believes that personal human connection will remain essential. AI personalizes at scale. Humans connect at depth. The brands that thrive will do both.

08. The Culture of Adoption

Perhaps the most practically valuable insight from the entire conversation was not about technology. It was about organizational culture.

GQ Group's AI success did not come from hiring a team of AI specialists or purchasing enterprise software. It came from creating a culture where experimentation is expected, where employees are encouraged to try new tools and share what works, and where the organizational response to failure is learning rather than punishment.

"This morning I can come in and the visuals could be as good as they could go on the website," Hartel described, illustrating how overnight AI-generated product imagery has become a routine part of the design workflow.

The company operates on what Hartel calls a "no rules, just use it" philosophy toward AI adoption. Unlike organizations that impose lengthy approval processes, legal reviews, and compliance checkpoints before any new tool can be deployed, GQ Group trusts its teams to experiment and iterate. The result is a "flywheel effect" where each successful experiment inspires others, and the organization's collective AI capability compounds over time.

For fashion companies watching from the sidelines, this cultural point is more important than any specific tool or technique. AI adoption is not primarily a technology challenge. It is a leadership challenge. The companies that move fastest are not those with the biggest technology budgets. They are those whose leaders create permission to experiment, tolerance for imperfect early results, and channels for sharing what works across the organization.

Hartel's advice to hesitant brands is characteristically direct: "Get started. The faster you start in on it, the more you learn, the better you get at being a prompt engineer."

The gap between companies that have embraced AI and those that have not is widening daily. Every month of delay is not neutral time. It is time during which competitors are building capabilities, training models, refining workflows, and developing the institutional knowledge that turns AI from a novelty into an advantage. The cost of starting imperfectly is trivial. The cost of not starting at all is compounding.