The Impact of GenAI from the Lens of Bain, Google and L’Oréal at Elaia
Extracted from a panel moderated by Sébastien Lefebvre featuring speakers: Cyrille Vincey (Partner, Bain Consulting), Nico Gaviola (Director Data Analytics and Sales, Google), and Jean-Paul Paoli (GenAI Business Transformation Director, L’Oréal).
As deep science is a part of Elaia’s DNA, we look to invest in generative AI across the whole stack, all the way from the latest applications to the new fundamental LLMs. While we continue to sharpen our perspective on GenAI, we recently brought together three intersecting and complementary experts to the Elaia offices to create a conversation around how different companies are embracing genAI. From foundational development to providing strategies for integration and business workflow enhancement, Cyrille (Bain), Nico (Google) and Jean-Paul (L’Oréal) discuss the topic on everyone’s minds.
For these companies, Machine Learning (ML), a subset of the broader term of artificial intelligence (AI), have been part of their corporate conversations for several years, even decades. As generative AI has swept the tech industry in the next wave of AI technology, clear foundation LLM models have emerged (think ChatGPT, Mistral, Midjourney, Copilot…). As Cyrille Vincey (Bain) points out, “We are witnessing a new form of race across all industries, on two fronts: race of differentiation by cost, and innovation on customer experience.” For consumers looking to take advantage of GenAI technologies now, companies such as Google are offering tiered approaches to easily integrate GenAI applications.
Nico Gaviola, Director Data Analytics and Sales at Google, said, “While Google is still developing LLMs at the foundational level”, says Nico, “the opportunities to integrate GenAI technologies at lower entry points such as APIs and 3rd party chatbots is becoming easier, especially for companies who are unsure where to begin.” This audience-centric approach, from professionals to consumers and enterprise users, gives consumers different entry points to play and integrate GenAI applications. The barriers to entry have never been lower for organizations to adopt this new technology.
“We are witnessing a new form of race across all industries, on two fronts: race of differentiation by cost, and innovation on customer experience.” — Cyrille Vincey (Bain)
For companies such as Bain, Google and L’Oréal, each is facing the big question — how do I take advantage of this new wave to not only develop internal operational efficiencies but to also best suit my core client base? Unlike Web3, GenAI’s technologies, while new, can be more easily integrated into existing functions. As our helpful graphic points out below, not all companies have to be GenAI disruptors. It’s now a question of how to integrate GenAI with the most meaningful application(s).
Image courtesy of Google
For Cyrille Vincey, Partner at Bain, he is seeing many companies come to Bain with exactly this kind of strategic question. He shared the example of iconic brand, Coca-Cola, whose CEO decided to adopt a unique three-year approach to take advantage of the current wave and completely reinvent each of the company’s functions using GenAI. For example, using GenAI, they created a content factory within the marketing function to generate Coca Cola’s online ads, with the idea to streamline support functions across the firm.
Although all companies are now facing the question of how to best integrate GenAI into their organizations and offerings, it’s not always about complete reinvention. Having a distinct vision of the differentiation that GenAI can offer your brand, comes from “taking a scientific approach to testing, discussing technological integrations and developing a clear risk and responsibility framework”, says Jean-Paul Paoli, GenAI Business Transformation Director at L’Oréal. The tradeoff between using AI for value creation and functional optimisation should always be weighed, especially with brands that have a strong DNA and loyal customers.
Testing smaller backend optimization processes can be a good way to trial bottom-up approaches to implementing GenAI technologies, such as Carrefour’s past purchasing optimization replays using LLMs. Although GenAI represents a ‘new’ challenge, AI has been around for some time and companies can build upon their existing knowledge of the opportunities and challenges offered by AI to design a considered roadmap for implementing GenAI functionalities.