Generative AI is no longer an emerging technology, it is rapidly becoming the infrastructure layer underpinning how people search, decide, communicate and even relate to one another. In this TechBullion interview, Veronique works at a company dedicated to building applications and systems powered by large language models, she also operates at the less visible, but increasingly critical, intersection of AI and brand perception.
Her role reflects a new reality: brands are no longer shaped solely by what they publish, but by how machines interpret, synthesise and present them. As AI tools become embedded in everyday decision-making, ensuring accurate, consistent and strategically aligned representation across search engines and generative platforms is fast becoming a competitive necessity.
Veronique offers a measured yet candid view of an industry moving at extraordinary speed. She points to accessibility and immediate utility as the catalysts behind widespread adoption, while highlighting a deeper shift already underway, the growing humanisation of AI, from productivity tool to digital companion. At the same time, she does not shy away from the tensions: limited control over outputs, evolving regulation, and the ethical grey areas that accompany more personalised and emotionally intelligent systems.
Perhaps most notably, she introduces the concept of “technical branding”, a convergence of SEO, PR and product thinking that reflects how AI now constructs brand narratives from fragmented digital signals. In a market where algorithms increasingly mediate perception, the question is no longer just what a brand says, but what AI understands.
Find out more from this insightful look at a sector still defining itself, one where opportunity is vast, competition is intensifying, and the rules are being rewritten in real time.
Please tell us more about yourself and your role in the generative AI industry?
My name is Veronique, and the company I work for operates in the field of generative AI and is composed of engineers and experts in advanced artificial intelligence. Its mission is to develop applications and infrastructures based on Large Language Models (LLMs).
In addition to working for a company in the field of generative AI, my position consists of optimizing our brands for LLMs and crawlers and ensuring they are represented the way we want them to be on search engines and generative AI tools.
The generative AI space has expanded rapidly over the past two years. From your perspective, what is driving this acceleration?
The ease of use, free access, and value that generative AI brings to users have fostered its rapid, everyday adoption. Generative AI has become an indispensable tool for hundreds of millions of people, much like the smartphone did a few years ago, and with good reason. These tools have completely transformed how people solve problems and address needs that were once difficult to meet.
It’s no surprise that many companies have sprung up in the field of generative AI; the needs are there, demand is constantly growing, and AI is here to stay. The number of players in the industry has increased exponentially, and AI models have been refined in record time due to the growing competition.
I’m thinking of several application areas that could be extremely lucrative for companies specializing in this field, but which have not yet been, or only very minimally, exploited. It’s definitely an industry that hasn’t yet reached its full potential, and we might be surprised by the applications that will appear on the market in the coming years.
Data suggests that generative AI use cases are diversifying rapidly. Which areas do you believe will define the next phase of growth?
According to a Harvard Business Review study, while in 2024 people primarily used AI to generate ideas, conduct specific searches, and have companionship, by 2025 AI’s companion role has become more important, with people using AI to find a confidant, be heard, organize their lives, and find purpose. There is a growing trend toward humanizing AI, seeking its guidance, and recognizing its empathetic side.
I believe that digital comfort will play a very important role in the coming years, as generative AI provides immediate support and responds empathetically and without judgment. It is definitely an application that will be increasingly in demand.
Speaking of AI companions, there seems to be a growing interest in platforms specializing in this industry. What does this trend reveal about user behaviour?
This is one of the AI trends that I believe will experience the strongest growth in the coming years. According to a study by Grand View Research, the AI companion industry is expected to grow by 30% by 2030.
The AI companion differs from the AI agent in that it is more personalized and fulfills a specific need. It doesn’t simply answer questions like an AI agent; it remembers conversations, has a personality, and can even have tastes and preferences. Various AI companion platforms already exist and are experiencing explosive growth in popularity. Among them, I’m thinking of role-playing platforms such as spicychat.ai and gptgirlfriend.online, which can simulate a friendship, a flirtation, or even an immersive world for players, a bit like a “choose your own adventure” book.
Other AI companions could fill a critical need for isolated individuals, those experiencing psychological distress, or those suffering from illnesses like Alzheimer’s, by providing them with daily support and assistance at all times. Although the need is real and an AI companion could be ideally suited to meet it, AI companies will still need a financial incentive to develop such solutions. I believe it will ultimately fall to governments to invest in these products.
One last, and rather controversial, application will likely see growth in the coming years: the deadbot. This is an AI companion modeled after a deceased person, to which one could give their tastes, desires, and personality in order to maintain a relationship with a deceased loved one. This application may be difficult to implement in the short term for legal ethical reasons, but I believe there could be a very real demand for it.
What are the main challenges that organizations face today when entering the AI market?
Companies entering the AI generative market may face several challenges, including lack of control, legislation, ethical questions, and fierce competition.
AI learns on its own; it cannot be 100% controlled. Companies venturing into the field of generative AI will inevitably have to develop moderation systems. No company wants to make headlines because its generative AI tool said something or provoked inappropriate behavior.
Regarding legislation, the development of AI technologies and models is evolving faster than the laws. What might initially seem like a good business concept could quickly be bogged down by the legislative system or ethical issues. I mentioned the phenomenon of deadbots earlier. In theory, it’s an interesting concept that likely meets a market demand. In practice, how can we ensure that the deadbot truly represents a deceased person? Is there a risk of identity theft? Should we be able to create an artificial intelligence that represents a real person, living or dead?
These are all ethical questions that could be raised and that could potentially harm companies that venture into this niche.
Finally, fierce competition remains a challenge, and the search for skilled labor will become the key to success for companies entering this field. AI learns faster than humans.
How should companies approach marketing in such an evolving and technically complex environment?
AI offers many opportunities, including the ability to work more efficiently. It also changes how consumers interact with our brand and purchase our products. Finally, AI is disrupting traditional marketing methods and practices.
On the one hand, marketing includes prediction, performance and content optimization, targeting, segmentation, and results tracking. Several AI tools allow marketing teams to save time, automate, better target audiences, personalize quickly and efficiently, and predict conversions.
Cooperation between marketing, data, and IT teams is paramount. Marketing teams that understand how to optimize their campaigns and track their performance using relevant AI tools will have a significant advantage. Olga Ukrainskaya has done an excellent article on the subject.
Next, marketing teams will need to understand how their consumers interact with their brand. What happens if a consumer uses an AI agent to shop online on your website? Will the AI agent be able to complete the transaction, or will it encounter a pop-up it can’t close? If the AI agent manages to complete the transaction, how will this transaction be tracked?
What was once called user experience is now becoming user/bot experience. And the queries that enabled conversions from generative AI are a black box. Marketing teams will have to accept that they have no choice but to proceed blindly when it comes to the conversion funnel from generative AI.
On the other hand, technical branding is becoming an essential discipline. How does AI interpret our brand? Does it recognize our logo? Is it able to read the text on our packaging in an image? Does it hallucinate when asked what it knows about our brand?
The companies that will come out on top are those that understand their branding as seen by AI and manage to take control of their brand narrative.
Marketing teams need to ask themselves all these questions and adapt quickly. These aren’t trends to watch tomorrow. This is a transformation already underway, one that the most advanced companies have already begun to integrate.
How does branding differ in the context of AI compared to more traditional digital products?
Several challenges have emerged with the rise of AI.
Generative AI can generate a phenomenon known as AI brand drift – or semantic drift. This is a phenomenon where generative artificial intelligence distorts or alters the perception of a brand by describing it inaccurately or erroneously.
One of the best examples to illustrate this phenomenon is a real-life situation experienced by a company, reported on Search Engine Land. The company in question had announced the launch of a fake new product on its Facebook page as an April Fool’s joke. LLMs are machines, and even if they are good at understanding context and emotions, they can have certain shortcomings in understanding unspoken meanings, jokes, and irony. This fake product ended up being mentioned when users inquired about the brand using generative AI. Customers were calling the company because they couldn’t find the product on their website. This situation can create frustration and reminds us that every post about our brand feeds the LLMs.
Also, AI can represent our brand in a positive, negative, or neutral way. AI doesn’t just analyze our site; it synthesizes the entire brand’s digital presence, including controlled messages like press releases, social media posts and our web pages, but also subreddits of disappointed consumers, negative reviews on various platforms, and even our Facebook post made during an April Fool’s joke.
In this context, brand management becomes more complex. We can no longer control brand image as effectively, but we can feed LLMs, react, and influence to counterbalance certain information. We must be careful about everything we say, and even what we don’t say. We must also, and above all, be aware of how AI perceives our brand.
You mentioned technical branding. What does this entail and how does it relate to generative AI?
Technical branding is a convergence between technical SEO, public relations, and brand marketing.
For a long time, SEO and branding evolved on two parallel paths. On one hand, public relations and marketing campaigns served to build a brand image among consumers. On the other hand, technical SEO allows search engines to analyze and to understand a website according to technical rules. These two worlds could be optimized separately.
With the rise of AI, this boundary no longer exists. Machines now interpret a brand much like a human: they understand the context, synthesize information, and even capture certain emotional nuances. At the same time, users are adopting behaviors similar to those of machines: they consume instant answers, AI summaries, personalized recommendations, and so on.
Branding is becoming technical. SEO is becoming experiential. Technical optimization and branding are now inseparable, hence the emergence of technical branding.
