From Retiring to Retained: How TwinMind Helps Preserving Corporate Knowledge
17
October
2025
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A venture concept from Mehdi Alaoui, Ananda Arjun Sharma, Paola D’Incecco and Yassin Raklami (Team 11).
Try to picture the following scenario: a senior engineer in her 60s, bearing more than 40 years of on-the-job experience, shuts down her work laptop for the last time. The moment she walks out the door on her last day, together with a gift from her colleagues, she brings problem solving skills and deep institutional memory home with her.
What you just read perfectly exemplifies the “brain drain” crisis the European economy is silently facing; Italy alone is projected to need millions of new workers just to replace retirees by 2030 (Segreti, 2024). Actually, every single seasoned expert who is about to retire in the next future is taking invaluable, tacit knowledge with them. Concurrently, skills are becoming obsolete even more rapidly: 39% of them are estimated to be outdated only within the next 5 years (World Economic Forum, 2025). Such combination becomes an existential threat for knowledge-intensive sectors like healthcare or manufacturing; and rising training costs and economic pressures simply exacerbate the situation (Freifeld, 2024). Hence, the question is: how can organisations retain critical expertise while continuously up-skill their workforce in a scalable, yet cost-effective way?
Here is where TwinMind comes into play: a startup proposing a disruptive solution for a whole paradigm shift. TwinMind is designed as a Generative AI-powered platform to transform retiring experts into perpetual digital mentors. The concept is powerful: to systematically capture the tacit knowledge of senior employees through interviews and scenario walkthroughs. Such proprietary data is then used to build a dynamic, AI-powered “Digital Twin” of the expert. This twin doesn’t just store information; it actively engages newer employees in tailored and repeatable simulations of complex workplace situations, ranging from technical procedures to delicate leadership conversations.
The value proposition is clear: TwinMind offers scalable, personalised training to reduce the workload of human mentors, accelerate time-to-productivity, and, most importantly, safeguard companies’ tacit expertise. Its SaaS model makes all of this accessible, offering tiered subscriptions to enterprises where preserving critical expertise is a top priority.
Ultimately, TwinMind is not just a tool, nor does it just create an static database; it builds an ever-evolving legacy. And thanks to it, immortalising tacit corporate knowledge into a renewable digital asset is finally possible.
Explore our vision and experience TwinMind prototype for yourself here: [link].
No ratings yet.Source: Shahi, S. (2024, May 1). How to balance human Creativity and Generative AI in product Marketing. Innovation & Tech Today. https://innotechtoday.com/how-to-balance-human-creativity-and-generative-ai-in-product-marketing/
Just like you, as a student in 2025, it is probably not surprising that I use genAI tools, such as ChatGPT, almost every day. What has been almost stigmatized in university settings in late 2022, now is an open discussion and a core part of learning and student life. Personally, I use ChatGPT regularly for school work which, I believe, is pretty similar to what my fellow students do. Just to mention a few instances: brainstorming for projects, cleaning up messy notes into structured study summaries, getting quick explanations for terms I don’t fully get, and even asking it to “grade” my drafts against the rubric so I know what to fix. One of my favourite practice, tho, is to feed the lecture notes to ChatGPT and ask for a 10-15 minute podcast of the key takeaways and learning goals. I listen to this during my walk to school and feel like I’ve revised without sitting behind my laptop. It’s fast, helpful, and honestly makes me feel on top of things when I’m at the lecture.
I also use GenAI outside of school. For example, when my parents visited me, I asked it to plan a simple Netherlands tourist route for city trips so they could see the highlights without me stressing over the details. Another instance is that on random weeknights, I’ll throw in three or four ingredients I have at home and get a quick dinner idea that actually works. This saves me a lot of time and money usually, and I also get to try new recipes. However, after our guest lecture on GenAI tools, it really hit me that there are tons of other options out there, but I keep defaulting to ChatGPT. It has really became my comfort zone when it comes to using AI.
But here are some contradictory feelings. I’ve started noticing a reflex: the moment I hit a tough problem, I want to ask the bot before I even try. When I do that too often, my brain feels a bit “sleepy”. Like I’m outsourcing or giving away the hard, creative part. Funnily enough, when I otherwise force myself to work without it, a different feeling kicks in: what if I’m falling behind because everyone else is using AI and shipping better, faster? That AI “FOMO” is real, especially when our standards are also rising with the AI-powered solutions. Anyone else feel that?
As a last thought, sometimes I think about kids, the next generation, who’ll never remember school without GenAI. Lucky them? Maybe. I’m half jealous of the time they’ll save and half protective of the slow, messy struggle that taught me to think. What do we want their first reflex to be: ask or attempt?
How AI Can Turn Ideas Into Organized Maps
10
October
2025
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We often use mind maps to organize our thoughts, connect ideas, and visualize complex topics in a simple but structured way. They’re especially useful for brainstorming, note-taking or preparing projects. However, it can take time to create a detailed mind map. Especially when we have to summarize long articles, videos or research papers. That’s where AI tools like Mapify.so come in. A tool like Mapify makes it easier and faster to create these clear mind maps automatically, helping us save time.
After signing up for Mapify.so, the platform asks how you plan to use it: for work, education or personal projects. It then gives a short introduction about its features. Some key features include:
Free on-page summarization
Suppport for 30+ languages
Works with Youtube videos, PDFs, documents, research papers, blog posts, social media posts and even emails
You can ask questions and the tool will research and brainstorm ideas for you
There are two AI models: Instant (faster, credit-efficient) and Powerful (more detailed, higher quality)
Option to generate mind maps step by step for more control or all at once for quick results
And lastly a wide selection of templates for brainstorming, outlining, project planning, analysis and more
For my test, I uploaded a Youtube video essay called “Drowning in Entertainment: The Age of Distraction.” Within minutes, Mapify analyzed the 33-minute video and turned it into a color-coded mind map that summarized the entire discussion clearly. Each subtopic had its own color, making the structure easy to follow and visually appealing. I could also change the format (logic chart, tree chart, timeline or standard mind map) and present it as slides. One of the most impressive features was being able to view the transcript of the video and even chat with the AI tool about the content.
Essentially, I was able to understand a 33-minute video in under 5 minutes, without even watching it. When finished, I could export or share the mind map and even tag it for better organization in my account.
Besides the free plan (which gives you 10 credits), Mapify offers 3 paid options:
Basic Plan: €5.99/month
Pro Plan: €11.99/month
Unlimited: €17.99/month
You can receive a 40% discount if you take a year plan. The basic plan excludes audio summarization and image uploads, while the pro plan adds text-to-image generation and more file types. Personally, I’d choose the pro plan, as it offers the best balance between price and functionality. The unlimited plan mainly includes early access and premium perks that I don’t find essential.
Overall, Mapify.so is a powerful, time-saving AI tool for students, professionals and anyone who wants to learn efficiently. However, I did face some navigation challenges. Improving the user interface with a clearer navigation bar or dashboard could make the experience smoother. Despite these small issues, Mapify.so stands out as an efficient AI tool for summarizing and visualizing information. It’s an excellent example of how generative AI can make productivity more effective.
Mapify: AI Mind Map Summarizer. (n.d.-b). Mapify. https://mapify.so/
My Personal Experience using GenAI
10
October
2025
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As a Master’s student who also works part-time as a software engineer, I’ve been relying more and more on generative AI tools in my daily life. At first I treated them as a novelty, but over time they’ve become a multipurpose tool for both my academic and professional life. Still, GenAI is not a magical tool that solves everything, its limitations have become increasingly clear to me over time.
One of the earliest ways I used GenAI was for search. Instead of digging through ten different Google results, I could ask a direct question and get a straight answer. This saved time when I was researching for papers, looking up a software library, or just wanting an answer to random questions I had (as one does). However, I was cautioned against trusting the answers blindly. I quickly experienced first hand that sometimes the AI gives outdated information or confidently states something incorrect. So I do additional research, depending on how high the stakes are.
Another major use case for me is summarizing. During my studies I often have to digest long articles, papers, or lecture notes. Letting the AI condense 20 pages into a quick summary was a game changer. Of course I still have to do more in-depth reading when I need to fully understand an argument, but it gives me a head start and helps me prioritize what to focus on.
GenAI is quite good at brainstorming and drafting too. Whether for a group project in class or when sketching ideas for a feature at work, it provided prompts and perspectives I wouldn’t have come up with myself. The downside is that its creativity can be surface-level, models often just regurgitate variations of ideas that were in their training data. So if I want to come up with something truly novel, I try to think of it myself and then use GenAI for “validation”.
In terms of drafting, I’ve used it to outline essays, emails, and even software documentation for my work. It’s great for overcoming writer’s block and speeding up the initial phase. Still, if I don’t rewrite and refine the draft myself, it’s easy to see that it was generated by AI because it sounds generic.
In my work, I use GenAI mostly for boilerplate code and bug explanations. It saves me time on repetitive tasks. But in complex systems, its contextual capabilities fall short. I’ve had it produce code that looked correct but had subtle flaws, not in syntax but how it used other functions in the codebase. Also, at times it has difficulty adhering to the design philosophy and stylistic choices of larger projects.
Finally, I’ve even used GenAI for language learning (currently Dutch). It’s particularly good for practicing small conversations and checking grammar. That said, I heard from a couple Dutch friends that it sometimes uses phrases that feel unnatural to native speakers. However, for now its Dutch is definitely better than mine, so I will continue using it for learning.
In sum, I already use GenAI for a variety of tasks, and I’m sure I will continue to discover new ways it can be useful. Have you tried any of the use cases I mentioned? What was your experience? I’m curious to hear.
From Print(“Hello”) to Data Analysis: My Thesis with AI-Assisted Coding
9
October
2025
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When I started my thesis, I barely remembered how Python worked. I knew what a dataset was and how to print a line or write a simple loop, but that was about it. The idea of building an entire data-science workflow seemed far beyond what I could do on my own. Yet, a few months later, I had written a full pipeline to analyze hybrid work patterns using behavioral logs, location data, and daily surveys. What made that possible was Generative AI.
ChatGPT quickly became my silent collaborator. Whenever I got stuck, I simply described what I needed: filtering AWT data by time, merging JSON files by date, or running a Mann-Whitney U-test. Within seconds, it generated structured and readable code that actually worked. It helped me clean and merge datasets, calculate metrics like active work time and task switches, and even combine GPS data with behavioral data to label each day as home or office. Suddenly, something that felt completely out of reach became manageable.
Of course, the process was not perfect. I often had to debug the AI’s mistakes, rewrite lines of code, and verify that the logic fit my data. Sometimes ChatGPT used outdated Pandas functions or made assumptions that didn’t make sense. But those moments taught me more than any tutorial could. I started to understand not just what the code was doing but why it worked that way.
Looking back, Generative AI didn’t write my thesis for me; it expanded what I was capable of. It turned Python from something intimidating into a tool I could actually use. For me, that is the real power of AI. It doesn’t make you less of a coder; it makes you more confident to learn, experiment, and create things you once thought were impossible.
How Generative AI Became My Job-Search Partner.
9
October
2025
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I first started using GenAI tools for internship search during my bachelor’s studies, mainly to save time when preparing applications. What began as a small shortcut quickly turned into something much more valuable. Over time, I learned to use AI not only for writing but for analysing vacancies, identifying missing skills, and tailoring my applications to what employers were really looking for. I often asked it to highlight key competencies and keywords that could make my CV or motivation letter stand out. Combined with insights from HR professionals and career influencers I follow online, I developed a method that feels strategic and personalised, where AI acts as a sparring partner rather than a content generator.
GenAI also became part of my preparation for interviews and assessments. I’ve used it to simulate common behavioural and technical interview questions, structure my answers, and gain feedback on how clearly my reasoning comes across. When preparing for the TestGorilla assessment, I applied the same study habits I used for university exams by asking ChatGPT to create logical reasoning and situational judgment exercises and to explain how recruiters might evaluate responses. It made me feel more confident, structured, and aware of my performance.
Looking back, I’d say GenAI has become a real career companion by helping me work smarter, reflect more deeply, and stay organised throughout the job-search process. However, this experience changed how I view AI in the recruitment process. While it saves time and helps formulate ideas clearly, it doesn’t remove the need to put effort into each application. The outcome still depends on how well I can express my own story, experiences, and motivation. AI can refine my words, but it can’t replace authenticity or individuality. In the end, standing out still comes from the human touch behind the screen. Do you think AI will ever fully understand what makes a candidate truly unique?
The Timing of GenAI is the Best Thing to Happen to IT/BIM Graduates
3
October
2025
No ratings yet.Krea generated image
Generative AI (GenAI) looks ready to challenge and dominate the labour market, but potentially only the part of the market that graduates don’t need to do anymore that deteriorate their wellbeing. One of the main reasons for AI adoption is the “need to reduce costs and automate key processes”, and GenAI has greatly addressed the bottleneck in product development, sales activities, prototyping, media creation, and many other fields of work (Bashynska et al., 2023). However, fear and insecurity can arise when the work activities known to graduates are being replaced by GenAI tools.
In building the sales infrastructure of online businesses I use GenAI very often. The more I use it the more I realise how much I overlooked the true value of being able to relate to people. Szufang Chuang’s study on the impact of robots and AI on human employee skills goes further by defining the key skillsets as social skillset and decision-making skillset (Chuang, 2024). The key in both skillsets is ‘nuance’. The ability to recognise the nuance of a context and make a high level decision is only going to be in more need to complement GenAI tools. Thus, recent graduates are in the best position to design the jobs of the future that haven’t been created yet. GenAI as a support tool is creating the demand for a labour force that is experienced with GenAI, has technical training on IT tools, and is aware of higher level business activities (Soni, 2023). It is no longer enough to be a pure technician or just a manager. a.k.a. An IT or BIM graduate
There are entire industries that are rapidly shifting to employing graduates with both technical and social skillsets, but no one knows what these jobs will truly look like. The answer is not in this blog post, but in the decisions of the next cohort of graduates. It is an opportunity to shape the nature of work to reduce burnouts, to reduce financial insecurities, and to simply advocate for the wellbeing of people.
References:
Bashynska, I., Prokopenko, O., & Sala, D. (2023). Managing human capital with AI: Synergy of talent and technology. Zeszyty Naukowe Wyższej Szkoły Finansów i Prawa w Bielsku-Białej, 27(3), 39-45.
Chuang, S. (2024). Indispensable skills for human employees in the age of robots and AI. European Journal of Training and Development, 48(1/2), 179-195.
Soni, V. (2023). Impact of generative AI on small and medium enterprises’ revenue growth: the moderating role of human, technological, and market factors. Reviews of Contemporary Business Analytics, 6(1), 133-153.
An Ocean of Insecurity: My Experience with GenAI
28
September
2025
5/5 (1)
“The Birds Don’t Sing, They Screech In Pain”
Dear reader,
I invite you to read a collection of my thoughts and meditations, all relating to my own use of GenAI. The tone of this article is definitely different from my previous one, and I apologise in advance for that. With all that being said, I still hope that some of you may relate to what is written here today.
Foundations of Fear
I would be lying if I said that the past few years were not a complete nightmare for me. My lifelong aspirations of being a creative had never felt so threatened.
First it was the rise of image generators like Midjourney, which generate images while being trained on millions of artists’ stolen works (Goetze, 2024). It was an injustice which I had witnessed firsthand. I was scared, and it felt like something that I wanted to do for years was suddenly taken away from me.
But hey, maybe it would only be visual arts right? They would surely never come for music and video…
It was a truly naïve moment for me, as later other programs would arise that would be able to generate both music and video. Now did I particularly like or find merit in what was generated? No absolutely not, most of the music made on programs like Suno sounded abhorrent. Videos made by Stable Diffusion lacked any of the vision which someone like Denis Villeneuve could have. But that was my opinion, the general public seemed to think otherwise.
In any case, I was not too happy with the emergence of GenAI.
A Puppet on A String
Because of the views that I had previously held, it would come to no-one’s surprise that when I actually seriously had to use GenAI I was practically forced to.
I remember that day very clearly. It was during my second year at Erasmus in my BA bachelor. We had a course on Entrepreneurship, and had to use these resources to help us make a business. It seemed innocent enough, right? But I couldn’t help but feel horrible with every prompt I was typing.
I will be the first to say that when it comes to group work, I have no intention of pulling my group down because of my disdain towards GenAI. I understand that many students use it, and I will not push back. These are just the values that I hold.
And so, I fell into the trap that many students do: I kept on using ChatGPT, DeepSeek etc. I used it to summarise my articles, but never really to brainstorm on my own. Sometimes, I used it to see what grade I would get for an assignment, though the accuracy varied. In the Digital Business course that we followed in year 3, we had to write an entire Essay with AI.
I’ll be the first to say that I did not enjoy the process and I find that AI cannot write in the same way that I do. Even when I had fed the AI with essays and other writings of mine in the past it just really couldn’t compare. I do not know if I was just lucky or uncritical, but I do know that my grade for the essay that I wrote myself was higher than the AI-written one.
Still, I often ask myself if we are entering an era where critical cognitive skills are being eroded due to the overreliance on AI (Zhai et al., 2024). How are we going to move forward when we are unable to detect misinformation and just accept everything that a machine gives us?
Moreover, how am I supposed to not feel guilt for using such a technology? It is not only actively consuming major amounts of energy, but also causing me and my peers to have a harder time in the future job market due to entry-level positions declining (Jockims, 2025).
A Deal with The Devil
For a time, I became quite apathetic to it all as a bachelor’s in business tends to do that to you. So I decided to use GenAI for personal reasons too.
My first experience with this was when I used an AI beauty app to get rid of some acne on my forehead. My partner wanted to post a picture of me in a cat café on their story, but there was some visible acne on my forehead. I then had the “brilliant” idea to use an app to get rid of the Acne, and hey it worked. We were both happy, I got to look good, and they got to post.
I then tried to incorporate GenAI into my writing as my apathy had reached the point of “If you can’t beat them, join them”.
I wrote down lines, and tried to continue sharing ideas with ChatGPT. But still something was missing.
It wasn’t really the story that I wanted to tell. The story I wanted to tell was a lot softer, and more human. It was laced with quiet moments and thoughtful conversations about characters living in a Cyberpunk world. (Ironic I know)
What ChatGPT gave me was…closer to a Marvel movie or a rip-off of Blade Runner. It was instant gratification, and a story with no substance. Why would it be one? It was a story that no human had bothered to write before. Just an amalgamation of the average.
Don’t Let AI Steal Your Daydream
I obviously do not know all of you, but I do urge you to think more critically about your GenAI use and the impact you have by using it.
I know for myself that by using it, I am actively contributing to injustice. Every prompt and sentence will make the models better and with the massive network effects that platforms like ChatGPT have experienced, this trend will continue.
To be able to forgive myself, I first had to admit that what I did wasn’t aligned with my values.
Not all is lost though, as the section’s title suggests we should still be hopeful. When it comes to art, humans still tend to prefer human made art, when they know that something is made by AI according to Millet et al. (2023). They later also say that preserving art is important as it is one of the last beacons of human uniqueness.
I feel like this sentiment extends beyond just art though. All of your ideas are worth something and is part of what makes you human. I have also noticed that in the age of hyper-polished, well, ,everything (movies, music & artwork). I’ve become more drawn to the rawness and imperfection which can be found in a lot of older works. I remember not being able to listen to In Utero by Nirvana for a long time, but now I find myself appreciating the album’s rough edges.
I do not intend to say that I have a moral high ground. In fact, I am also extremely flawed. All of the times that I used GenAI on my own accord was to cope with some form of insecurity that I had. My appearance. my writing ability and even my grades. It was an instant fix for a problem, but it did not fix the underlying issues.
As a subtle form of rebellion, I decided to teach myself guitar. Yes, the process is hard but also gratifying. If I ever want to get on stage, I’ll have to work for it. There’s no instant fix. But that’s the thing, you can’t instantly become Kurt Cobain. It takes hours, days, years of hard work. And you know what? I find that to be beautiful.
I hope that we can take back some form of power. That we can live in a world where we are allowed to have and chase our daydreams. A world where our ideas do not serve as a means for profit to some megacorporation. I hope that I made you think about how our actions are impacting the people around us. I ask you not to be a revolutionary, but I do ask you to contribute to a world that is fairer towards all.
To you, dear reader, I ask the following questions: Do you think that I am overreacting or do you harbour similar feelings? Did your fears around GenAI cause you to change major life plans you had? (I know that it caused me to choose this master!) And finally, are you willing to sacrifice the instant gratification of AI in order to preserve our sense of being human?
References: Goetze, T. S. (2024). AI Art is Theft: Labour, Extraction, and Exploitation: Or, On the Dangers of Stochastic Pollocks. 2022 ACM Conference On Fairness, Accountability, And Transparency, 89, 186–196. https://doi.org/10.1145/3630106.3658898
Jockims, T. L. (2025, 7 september). AI is not just ending entry-level jobs. It’s the end of the career ladder as we know it. CNBC. https://www.cnbc.com/2025/09/07/ai-entry-level-jobs-hiring-careers.html
Millet, K., Buehler, F., Du, G., & Kokkoris, M. D. (2023). Defending humankind: Anthropocentric bias in the appreciation of AI art. Computers in Human Behavior, 143, 107707. https://doi.org/10.1016/j.chb.2023.107707
Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: a systematic review. Smart Learning Environments, 11(1). https://doi.org/10.1186/s40561-024-00316-7
Though I didn’t use it, I find these ones important too, they deal with the environmental aspects: De Vries, A. (2023). The growing energy footprint of artificial intelligence. Joule, 7(10), 2191–2194. https://doi.org/10.1016/j.joule.2023.09.004
Shukla, N. (2025, 19 augustus). Generative AI Is Exhausting the Power Grid. Earth.Org. https://earth.org/generative-ai-is-exhausting-the-power-grid/
Author: Ian Parabirsing
A lover of music, good coffee and cats. I'm a MSC student at RSM studying Business Information Management. In my blog posts I'll be attempting to write about how technology impacts the consumers and society at large.
View all posts by Ian Parabirsing
Can Blockchain Protect Creators in the Age of GenAI?
19
September
2025
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Generative AI is revolutionizing creativity, but it also exposes deep cracks in how ownership and intellectual property (IP) are managed. Most models are trained on vast datasets scraped from the internet, raising questions about whether creators gave consent, and how they can be recognized or compensated when their work is reused (Balan et al., 2023).
One promising answer lies in blockchain whose immutable, decentralized ledger, can record provenance, encode usage rights, and automate payments. The DECORAIT project shows how this could work: it allows creators to opt in or opt out of AI training, embeds provenance metadata through the Coalition for Content Provenance and Authenticity, and uses smart contracts to distribute rewards whenever a creator’s content contributes to a synthetic output (Balan et al., 2023).
Blockchain can underpin decentralized data marketplaces, allowing creators to monetize AI training data directly, while NFTs serve as tamper-proof certificates of authenticity and enable secondary royalties (Telles, 2025). Consulting leaders argue that blockchain may become the first mainstream safeguard against GenAI-driven IP risks. Encoding corporate knowledge assets (such as contracts, white papers, and presentations) into NFTs with embedded permissions, organizations could control how AI systems access and reuse their data (KPMG, 2023). Such mechanisms not only create new revenue models and reduce reliance on intermediaries but could also lower the risk of costly lawsuits, like the Getty Images case against Stability AI over alleged copyright misuse.
While challenges like blockchain scalability or regulatory uncertainty remain, the direction is clear. As GenAI blurs the boundary between human and machine creativity, blockchain provides a foundation for consent, attribution, and compensation which ensures that creators remain empowered in the AI economy.
Balan, K., Black, A., Jenni, S., Gilbert, A., Parsons, A., & Collomosse, J. (2023, September 25). DECORAIT — DECentralized Opt-in/out Registry for AI Training. arXiv.org. https://arxiv.org/abs/2309.14400v1
Comparing AR and VR in Beauty Industry: the L’Oréal Case
19
September
2025
5/5 (1)
In the age of digital transformation, many companies started to implement Generative AI in their strategy and business models. Terms such as AR or VR have become the new “trend” and competitiveness. One example is L’Oréal, one of the world’s leading companies, which have recently implemented AR and VR technologies in their business.
Even if these terms are frequently heard, the difference between AR and VR will be explained for clearness and in order to facilitate the reading. Augmented Reality (AR) starts with physical things and shows digitally what is physically present, through a smart device, using the data stored in the digital twin cloud (Porter, M., & Heppelman, J., 2025). Virtual Reality (VR) creates a fully new virtual experience, by using computer-generated images and a headset. You directly step into another reality, experiencing sensory and visual engagement (Coursera, 2025).
So, how did L’Oreal implement these new digital technologies?
As the leading company in beauty industry, L’Oréal owns a portfolio of thirty-two diverse and complementary brands (Mechdyne, 2017), with generated sales of over forty-three billion euros in 2024 (L’Oréal Finance, 2024). L’Oréal products are present in multiple physical distribution channels, such as mass market, department stores, pharmacies, hair salons, as well as in the e-commerce channel. (Mechdyne, 2017).
Few years ago, L’Oréal, in collaboration with Google Cloud and Capgemini, implemented products’ digital twins, scannable via a QR code found on the physical item (Capgemini, 2022). With this GenAI implementation, L’Oréal aims to improve customers relationship and loyalty by improving its transparency, offering personal education and guidance, and increase brand trust (Capgemini, 2022). Moreover, this creates better customer experience and adds value to the company by boosting the company’s digital business model and giving them competitive advantage.
By scanning the QR code, different product features appear, namely ingredients, formula, and sourcing (Capgemini, 2022). Moreover, customers have access to much more information regarding personalised professional tips (Capgemini, 2022). Furthermore, L’Oréal, in collaboration with NVIDIA and Accenture, recently founded the first AI-powered multi-brand marketplace focused on beauty products, Noli.com (Martin, 2025). Using AR features, the customer can scan their face, and the platform creates the so-called “BeautyDNA” using a large amount of skin data and analyses on product formula (Martin, 2025). Following this, it suggests the perfect combination of products, personalised by each customer, additionally offering different options and their associated benefits, so that the customer can choose which product decides to buy (Martin, 2025). Moreover, the user has the possibility to directly iteratively discuss with the app, and to access information about the ingredients and their chemical formulas (Martin, 2025). In addition, a tutorial of how to apply the products, the possibility of directly purchasing from the app, and customer reviews are available. (Martin, 2025)
Internally, L’Oréal incorporated VR implementation in their Labs from Paris and New York. This allows to project future store concepts, evaluate packaging, and create store layouts before the physical development begins. Furthermore, L’Oréal offers this feature to their retail partners as well, to get a better idea of the products and shelves before the implementation starts (Mechdyne, 2017).
What is next?
L’Oréal’s last year initiative, CreAITech, an AI-powered beauty content lab, has already been implemented on La Roche-Posay and Kérastase to generate content and beauty images (Dominguez, 2024). The platform is used for marketing purposes, image creation, product launches acceleration, time, and production costs savings, and it is expected to develop further in the upcoming years (Doolan, 2025; Dominguez, 2024).
What do you think about this digital implementation within the beauty sector, and the emerging opportunities? What could be some limitation that you think about?
